International Journal of Intelligent Engineering and Systems

13

Authors: M. Mageshwari, R. Naresh

Cloud computing (CC) is a pivotal technology in the entire universe. Cloud platforms allow management applications or administrators with privileged accounts to remotely do privileged activities for particular tasks like eliminating virtual hosts. If private accounts can be leaked and perform a dangerous confidential activity, severe security issues are performed in cloud environments. To sort out these issues, researchers focused on auditing the behaviours of private users. But it was found to be tough to audit fine-grained privileged behaviours automatically for the graphical operating system. Also, it is difficult to prevent users from avoiding the audit mechanism or preventing hackers from attacking the audit method. Therefore, this study presents a new improved sunflower optimization algorithm based encryption with public auditing scheme (ISFOAE-PAS) in the secure cloud environment. The major intention of the ISFOAE-PAS technique lies in the accomplishment of data security, integrity, and auditing in the cloud environment. In addition, the ISFOAE-PAS technique designs a new secret image encryption using the homomorphic encryption (SIE-HE) technique to encrypt the data. Moreover, the ISFO algorithm is derived for optimally choosing the keys related to the SIE-HE technique. Furthermore, the public auditing scheme is designed to improve security in the cloud environment. Finally, the DDoS attack detection process is performed by a deep convolutional autoencoder (DCAE). For demonstrating the greater efficiency of the ISFOAE-PAS technique, a series of simulations were involved. The experimental values inferred that the ISFOAE-PAS technique reaches maximum accuracy of 99.42%.

35

Authors: T. Kumar, R. Ponnusamy

Classification of medical X-Ray image (XRI) is a task where machine learning (ML) approaches are utilized to categorize XRIs into various categories on the basis of some criteria. The core objective is to automate the task of evaluating XRIs and enhance the efficiency and accuracy of diagnosis. Current advancements in deep learning (DL) have given rise to better performance in various medical image analysis processes. Chest radiographs, the frequently performed radiological exam, are specifically significant modalities for which various applications were studied. The issue of publicly available, multiple, large chest X-Ray datasets in recent times has increased investigation. Therefore, this article presents a new barnacles mating optimizer with DL based medical image classification model (BMODL-MICM) for XRIs. The BMODL-MICM technique uses wiener filtering (WF) as a preprocessing step to eradicate the noise. Besides, the ShuffleNetv2 feature extractor is involved to create a collection of feature vectors. Moreover, the BMODL-MICM technique uses the BMO algorithm for the hyperparameter tuning procedure. At last, elman neural network (ENN) technique is applied for XRIs classification. The investigational assessment of the BMODL-MICM approach on the benchmark XRI dataset reported its effectual outcome of 99.67% over the other methods.

60

Authors: Badari Narayan Vijayeendra Srinivasa Murthy, Siddappa Madappa

The growth of data is an ongoing phenomenon, driven by the expansion of cloud networks, the rapid development of computer systems, and the proliferation of highly beneficial applications. To defend networks from various attacks, such as denial-of-service (DoS), SlowHTTPTest, Hulk, Slowloris, and LOIC-type DDoS attacks, the area of intrusion detection has grown significantly. Traditional intrusion detection systems are combined with a variety of security methods to provide security. However, such a system has limitations when it comes to effectively analyzing vast amounts of data. To detect the vast amount of different intrusion attacks and to select the optimum features, an improved marine predators algorithm (IMPA) is combined with a swarm optimization algorithm named particle swarm optimization (PSO) is proposed. The experiments are conducted using the open-source available dataset referred to as the CSE-CIC-IDS2018 dataset. Here, the classification is done using the Bi-LSTM classifier, an improved version of the LSTM classifier. The experimental results showed that the performance of the proposed IMPAPSO-Bi-LSTM model has achieved high accuracy of 99.40%, recall of 98.20% and f1-score of 98.62% compared to the existing techniques such as recurrent convolutional neural network (R-CNN) and federated intrusion detection system integrated with the blockchain (FIDChain).

85

Authors: Snehal Bankatrao Shinde, Kankipati Lahari, Keerthika Chowdary Garimella, Vicharapu Sowmya Sree, Nileshchandra K Pikle, Girish S Bhavekar, Pradnya Borkar, Sagarkumar Badhiye, Mukesh Raghuwanshi

Cardiovascular disease is any illness that makes the heart work less well. Researchers are working on making smart systems that can correctly detect heart conditions from electronic health data using machine learning algorithms. This is because heart conditions can be very serious. In this study, data from patients and key clinical factors are used to identify cardiovascular disease using machine learning. The main goal of the suggested model is to improve the accuracy and reliability of predicting cardiac disease by focusing on parameter tuning, ensemble methods, and recursive feature removal approaches. Our methods for making predictions included logistic regression, decision trees, K-nearest neighbour (KNN), support vector machine (SVM), naive bayes (NB) machine learning (ML) approaches, ensemble technique approaches, and artificial neural networks (ANN) with stress on regularisation. Compared to the other ways, it was found that using a KNN model gave the most accurate results for the model. A number of factors, such as accuracy, precision, memory, and F1-score, were used to judge the models. The KNN model is the most accurate, at 97.8%.

113

Authors: Gomathi R, Logeswari S, Jothimani S, Sangeethaa SN, Arul Sangeetha S, LathaJothi V

Due to its impartiality in emotion identification, micro-expression can be used in emotional computing. Deep learning has proved successful at recognizing nuanced facial emotions. When the stakes are high, micro-expressions (MEs) reveal people's true sentiments. Early micro-expression recognition (MER) methods use traditional traits. MEs are delicate, rapid facial movements, making them harder to measure and annotate than macro-expressions. Recent deep learning (DL) methodologies aim to improve ME and MER performance. Micro-expression in small, localized areas of the face and a lack of large databases now hinder emotional facial movement recognition. To meet these issues, this study suggests a unique attention mechanism dubbed micro-attention that operates in tandem with residual networks called micro expression fusion network (MEFNet). Enhance the ME model by incorporating attention mechanisms to focus on the most informative regions of the face. This can help improve the accuracy of spatial and temporal data extraction, especially during subtle micro-expressions. Extend the two-stream CNN model to incorporate additional modalities, such as audio or textual cues, in addition to the eye and mouth regions. This multi-modal fusion will enable a more comprehensive understanding of emotions and increase the system's robustness to variations in facial expressions. The MEFNET achieved a specificity of 99.2%, sensitivity of 99.5%, and accuracy of 100% on the CAS(ME)2 dataset, and a specificity of 99.6%, sensitivity of 99.3%, and accuracy of 100% on the SMIC dataset. Rendering to the experimental results, the suggested framework compares favourably to the state-of-the-art methods.

134

Authors: Madhu Belur Gopala Gowda, Naveen Kumar Boraiah, Varun Eshappa, GopalaKrishna Chandrashekara

Epilepsy is a neurological disorder that is distinguished by the presence of seizures, which are caused by abnormal brain activity. On average, about 60% of epilepsy cases are due to focal seizures, which affect a specified part of the cerebrum. Epileptic seizures can be cured at the initial stage, but untreated seizure for a long time cause severe effect and sometimes leads to death. Therefore, accurate classification of the epileptic Electroencephalogram (EEG) signal is an essential step in the diagnosis of epilepsy, aiding physicians in making the diagnosis. This research presents an effective classification of epileptic EEG signals using the improved atomic search optimization (IASO) algorithm and the random search strategy (RSS). The IASO is used to select the appropriate features but has a high probability to enhance the convergence rate. However, due to a lack of population variety and exploratory capabilities and this convergence rate may decrease in some circumstances. As a result, the RSS is offered to enhance the solution and improvise the exploration capabilities. The raw data of EEG signals are obtained from TUH, BONN, and BERN datasets. The raw data is pre-processed using an adaptive filtering method. The IASO is utilized in selecting the relevant features which ease the process of classification using LSTM. The results show that IASO attained better classification accuracy of 98.40% for TUH dataset which is comparatively lower than the existing Deep network model with 97%.

158

Authors: Hendri Darmawan, Mike Yuliana, Moch. Zen Samsono Hadi

Identifying pests and diseases is vital for rice farming, but the lack of experts often limits it. We propose a novel method that uses deep metric learning and k-NN classifier to classify pests and diseases in paddy plants. We experimented using different distance metrics, latent dimensions, base models, and optimizers to train the neural model that produces latent representation from images. Then, we used k-NN retrieval approach to find and label similar images from the latent space. During the inference phase, we manipulated the original image using various transformations and fused them with its latent representation to enhance the quality of latent space. The results show that our method surpasses the conventional deep learning classification (softmax classifier). Specifically, our method achieved maximum accuracy of 0.920 on ResNet-50 and 0.878 on ResNet-152. In comparison, the softmax classifier only achieved maximum accuracy of 0.752 on the same modeling scheme. Our method can produce more discriminative and robust data representations for classification tasks. Moreover, latent fusion from input augmentation during inference can also improve accuracy up to 19.2%. We also deployed the best model from our experiment on serverless cloud computing, allowing users to use the platform for prediction and monitoring through GIS.

185

Authors: Endi Rizal Ferdiansyah, Adian Fatchur Rochim, Wahyul Amien Syafei

The rapid growth of mobile internet usage, particularly in LTE networks, poses challenges for mobile operators in maintaining quality of service (QoS) and optimizing network design planning. Accurate traffic volume forecasting is crucial for network planning and effective resource allocation. This study aims to propose an enhancement to the accuracy of the holt's winter multiplicative seasonal (HWMS) method by applying the rolling forecast technique to achieve better forecasting results. Using a public dataset of 56 cells, the evaluation based on mean absolute percentage error (MAPE) reveals the following sequence of prediction errors: HWMS & rolling forecast (20.47%), HWMS only (30.06%), FbProphet (30.45%), and auto regressive integrated moving average (ARIMA) (31.52%). The cells are categorized as "Good" (59%), "Reasonable" (39%), and "Poor" (2%). The results indicate that the proposed method achieves a significantly higher percentage of cells in the "Good" category, with a 45% difference compared to HWMS without rolling forecast, which only achieved 14%. Moreover, the proposed method outperforms ARIMA by 50% and FbProphet by 37% in the same category. Furthermore, when applied to real data from a telecommunications company in Indonesia, the proposed method identified 11 cells that require solutions out of a total of 100 cells. In comparison, the ARIMA method identified 3 cells, FbProphet identified 12 cells, and HWMS without Rolling Forecast identified 9 cells. Thus, the company can provide solutions for the identified 11 cells without the need for excessive investment while still maintaining revenue potential.

213

Authors: Diana Purwitasari, Abid Famasya Abdillah, Safitri Juanita, I Ketut Eddy Purnama, Mauridhi Hery Purnomo

More people have gotten used to online health consultations (OHC) because of the COVID-19 pandemic to reassure themselves of their health conditions or seek other treatment options. The OHC system could use named entity recognition (NER), specifically for health-related texts called biomedical NER (BioNER), to filter text entities from posting history to ease users' finding information. The terms of named entities (NEs) could be related to human anatomy that have some inconvenience or terms to find out any symptoms of the disease. However, OHC posts, especially user questions, are often non-formal sentences and even long sentences or have incorrect medical terms since the users are most likely non-trained medical professionals, which may lead to out-of-vocabulary (OOV) problems. Although long short-term memory (LSTM) architecture is known for its advantage in modeling sequential data like text, and even with the bidirectional version of BiLSTM, it has some difficulties handling those long sentences. A transformer model could overcome the problems. Another problem concerns fewer annotated data in low-resource language OHC texts despite data abundance in the corpus crawling from the OHC platform. To augment data training, our process includes a self-training approach as semi-supervised learning in data preparation to improve a BioNER model. In preparation for our BioNER model, this work observes and makes a comparison on the embedding step, whether stacked embedding of BiLSTM-based or fine-tuning of transformer-based and defines filtering pseudo-labels to reduce noise from self-training. Although the empirical experiments utilized OHC texts in Indonesian as a case of low-resource language texts because of our familiarity, the procedures in this work apply to Latin alphabet-based languages. We also observed other biomedical NER model creation and topic modelling for verifying the extracted entities from the resulted BioNER model to validate the procedures. The results indicate that our framework, which includes preparing data from raw texts into labelled data using self-training, with a confidence threshold of 0.85, to create the BioNER model, can give F1 scores of 0.732 and 0.838 for BiLSTM-based and transformer-based models.

237

Authors: Mayar M. Moawad, Magda M. Madbouly, Shawkat K. Guirguis

The internet of things (IoT) has been industrializing in a variety of real-world implementations to improve human life. As IoT becomes more industrialized, a huge portion of data is being generated by various devices. Edge, Fog, and Cloud security is a topical issue associated with data storage, managing, or processing systems. Once an attack occurs, it has irreversible and disastrous effects on the development of IoT. Therefore, many security systems have been proposed and implemented for the sake of system security. So, this study consists of three steps based on deep learning, and blockchain for IoT (DLBIoT) architecture security. BoT-IoT and TON-IoT datasets are utilized to demonstrate the DLBIoT, also, the performance was measured using a variety of metrics. Results indicate that the proposed architecture is more efficient than other systems. DLBIoT architecture reaches an approximately 5 % increase for F1-Score compared to DT, NB, RF, and TP2SF. Also, the proposed architecture has simultaneously displayed an accuracy of 98.9% that increases steadily as the Learning Rate increases. Additionally, DLBIoT architecture may be utilized for real-time IoT applications.

261

Authors: Samer Saeed Issa, Sinan Q. Salih, Yasir Dawood Salman, Faris Hasan Taha

The detection rate of network intrusion detection systems mainly depends on relevant features; however, the selection of attributes or features is considered an issue in NP-hard problems. It is an important step in machine learning and pattern recognition. The major aim of feature selection is to determine the feature subset from the current/existing features that will enhance the learning performance of the algorithms, in terms of accuracy and learning time. This paper proposes a new hybrid filter-wrapper feature selection method that can be used in classification problems. The information gain ratio algorithm (GR) represents the filter feature selection approach, and the black hole algorithm (BHA) represents the wrapper feature selection approach. The comparative analysis of network intrusion detection methods focuses on accuracy and false positive rate. GBA shines with exceptional results: achieving 96.96% accuracy and a mere 0.89% false positive rate. This success can be traced to GBA's improved initialization via the GR technique, which effectively removes irrelevant features. By assigning these features almost zero weights, GBA hones its ability to accurately spot intrusions while drastically reducing false alarms. These standout outcomes underline GBA's superiority over other methods, showcasing its potential as a reliable solution for bolstering network security.

286

Authors: C. Saranya Jothi, D. Umanandhini

Hepatitis is a disease originating from a viral bacterium that can be avoided by the earlier identification of the disease and also with the proper classification. The proposed methodology is split into two phases: pre-processing technique and classification method. The pre-processing methods are incorporated to improve the accuracy and reduce the complexity of data. In the first phase, the pre-processing method such as (i) First, label encoder with one hot encoder (LE-OHE), is adopted to convert all the categorical data into a numerical column (ii) Then, Fuzzy logic with may fly (FL-MF) is developed to fill the missing values and (iii) Finally, inter-quartile range (IQR) technique is used to detect the outliers. In the second phase, the classification method is refined by following three steps (i) A correlation-based decision tree (CDT) is constructed to interpret the data as simple, (ii) A reduced error pruning tree (REPT) technique is employed to split the data using grid search algorithm and (iii) The different combination of REPT is given as input to the ensemble stacking method that enhances the predictive performance. According to the results, our proposed method outperforms with an average accuracy rate of 96.1%, and time complexity in both datasets (hepatitis and indian liver patient record (ILPR)) when compared with the existing algorithms such as min max scalar- support vector machine (MMS-SVM), mean imputation-random forest (MI-RF), gradient boosting (GB), random forest (RF), ensemble-stacking (ES), extreme gradient boosting (XGBoost), and radial basis function-extreme learning machine (RBF-ELM). Moreover, the proposed method also reduces the mean absolute error (MAE) error rate.

312

Authors: Sheela Sadashiviah, Nataraj Kanathur Ramaswamy

In vehicular networks, efficient communication between vehicles and infrastructure relies on the ergodic capacities of V2I links. Meanwhile, the crucial transmission of urgent information, collision avoidance, and improved safety hinges on the ergodic capacities of V2V links. Within this context, the present research endeavors to optimize fundamental parameters within a communication system. The ultimate goal is to achieve peak performance by employing the GSA-BPSO (Gravitational Search Algorithm and Binary Particle Swarm Optimization) optimized neural network approach. The primary objective entails maximizing a weighted sum encompassing three critical components. These components encompass the ergodic capacities of Vehicle-to-Infrastructure (V2I) links, Vehicle-to-Vehicle (V2V) links, and the latency requirements tied to V2V links. The study introduces a time delay threshold for V2V data transmission and leverages the GSA-BPSO optimized neural network to optimize key parameters. This enhances system capacity without compromising link communication. Result analysis, specifically for varying time intervals (0.2 ms to 1.2 ms), reveals insights. The Kuhn-Munkres model exhibits the lowest throughput consistently, implying limitations in handling power variations efficiently. The NN model surpasses Kuhn-Munkres but lags behind the GSA-BPSO optimized NN model. Longer time intervals lead to decreased throughput for all models, indicating interference and channel variations. The optimized NN model maintains consistent performance across time intervals, achieving superior throughput under fixed power. The GSA-BPSO optimized NN model outperforms both NN and Kuhn-Munkres models, highlighting its potential for enhancing system throughput with fixed power settings. This research underscores the efficacy of the optimization technique in wireless communication scenarios.

339

Authors: Kossai Fakir, Jules Alexandre Haba, Chouaib Ennawaoui, Mahmoud El Mouden

To ensure proactive and intelligent control of the electrical power generated in industrial units self-producing energy, the construction of a data-driven model devoted to power prediction is an efficient solution that we have proven in this contribution. The remarkable added value of the paper was articulated around three main pillars: primarily, we have modelled a power station based on Rankine cycle that serves the energy needs of an industrial facility. Secondly, the connection of this plant to the public electric grid has been taken into consideration in modelling. Thirdly, we have used the model to avoid paying unwanted bills thanks to the forecasting of periods when there is an energy importation from grid. Before constructing the model, we have prepared a dataset composed of more than 25 million data points, reflecting the history of 3 years of archived data related to fifteen 15 features, in order to predict the target variable of this study. After finding that GRU and LSTM are among the most successful deep learning algorithms in energy prediction, we decided to use them in our particular application. Afterwards, a comparative study between them was first carried out on accuracy, where encouraging results were obtained on both sides, with respective scores of 98.73% and 99.36% during the training step, followed by 98.51% and 99.11% during the testing phase. Besides, the evaluation in terms of loss functions gave a convergence of the GRU model after 8 epochs, against 11 epochs for the LSTM algorithm. Therefore, we preferred to use LSTM in the definitive model because of its higher accuracy, providing that good computing power is used, so that the processing time could be acceptable by the end-users. Finally, we deployed the model to simulate a real case of forecasting the power drop one week in advance. In this case, we found that if the industrial unit had used the results of the model as a proactive decision support tool, it would have avoided importing 1456.4 MWh for 8 days from the national grid, which was equivalent to a loss of 119,094.8 USD.

366

Authors: Azzeddine Sardi, Abdelhadi Ennajih, Jamal Zbitou, Ahmed Errkik

In this work, a novel power divider/combiner based on microstrip technology is investigated and designed for ultra-wideband wireless applications. The design process adopted for this power divider consists of modifying the planar transformers of the conventional power divider using the open artificial transmission technique. The power divider was designed on the basis of the theoretical equations of the open stub technique. It is simulated and optimized using the ADS and CST Microwave Studio solvers. As the simulation results, over the operating band, a bandwidth of 3000 MHz between 1 GHz and 4 GHz was achieved, along the entire band. This gave a maximum return loss of -28 dB at the center frequency and an insertion loss of about 3.5 dB, while maximum isolation of -38 dB was obtained across the output terminals. On the other hand, a prototype of the proposed power divider, fabricated on an FR4 substrate using microstrip technology, gave results confirming those of the simulation. In addition, the final structure considerably reduced the overall dimensions to 75% less than the traditional power divider. Therefore, the proposed power divider could be a suitable solution for wireless applications including 5G, WiMAX, WIFI, and Bluetooth.

386

Authors: Anass Zaidouni, Mohammed Abdou Janati Idrissi, Adil Bellabdaoui

Information system (IS) and information technology (IT) Portfolio value management enables to measure the intended IS/IT project portfolio value, in pursuit of strategic objectives. Yet, the captured IS/IT project portfolio value should be articulated in the different IS/IT project portfolio segments in order to measure each category value contribution. Moreover, it should consider the strategic objectives variability throughout the portfolio lifespan. Therefore, this study proposes a novel methodology for the strategic portfolio value (SPV) calculation, using the rank order distribution (ROD) weights model for strategic objectives variability modelling, and the value for money (VfM) technique of the management of portfolios (MoPTM) guidance based on expert judgments. Besides, this study provides a new IS/IT project portfolio categorization. An IS/IT project management office (PMO) case study demonstrates the outcome of the new methodology, which is the calculated SPV of 0.68, and how it aids, decision-making and performance management, identify under uncertainty the most strategically valuable IS/IT Project Portfolio categories.

411

Authors: Swetha Pesaru, Naresh K Mallenahalli, B. Vishnu Vardhan

Diabetic retinopathy (DR) is a prevalent and potentially blinding eye disease that affects individuals with diabetes. On the other hand, internet of medical things (IoMT) incorporation into DR grading classification offers a promising future for the field of eye health. It optimizes the diagnostic process, increases accessibility to healthcare services, and ultimately improves patient care and outcomes. Therefore, accurate and timely grading of DR severity is crucial for effective disease management. In this article, DR-grading network (DRG-Net) is proposed, which is a comprehensive approach for DR labels classification using the indian diabetic retinopathy image dataset (IDRiD) dataset. To address the imbalanced nature of the dataset, synthetic minority over-sampling technique (SMOTE) is employed for data balancing, ensuring representative samples for each severity level. Then, transfer learning based residual network-50 (ResNet50) architecture is used to extract features from SMOTE outcome, which is a deep learning model renowned for its ability to learn complex image representations. Finally, graph-based K-nearest neighbours (GKNN) classification which utilizes the spatial relationships between samples to make informed decisions, considering the similarity of retinal images in a graph-based representation is introduced for enhanced grading classification of DR. The simulation results show that, the proposed DRG-Net resulted in improved performance as compared to state-of-the-art approaches such as MSA-ResNetGB, DLCNN-MGWO-VW, OHGCNet, and E-DenseNet BC-121 with an accuracy of 99.93%, and F1-score of 99.85%.

433

Authors: Hussein Mahdi, Nidhal El Abbadi

Glaucoma is a dangerous retina disease that can cause irreversible vision loss. It is called the "Silent Thief of Sight" because it has no symptoms in its early stages. A retinal fluid drainage imbalance causes elevated eye pressure. Over time, this pressure could damage the cells of the optic nerve that transmit visual data to the brain. Consequently, individuals with glaucoma can have progressive vision loss, eventually leading to total blindness if left untreated in the earliest stages. In recent years, there has been a rising interest in utilizing computer vision and machine learning techniques for developing an efficient automated diagnosis system for analyzing the retina fundus images to assist optometrists in diagnosing glaucoma at the early stages and reducing the time, cost, and human errors. All these issues motivate us to suggest an automatic glaucoma screening system as decision support for ophthalmologists at the early stages. The system based on combination decisions comes from three different approaches. Each approach uses different kinds of features for analyzing images. The first approach extracts medical features using the YOLOv8 algorithm to detect the optic disc and cup, then classifies features using the logistic regression model. The second approach extracts texture features using deep wavelet scattering and then employs the long short-term memory layer and sigmoid function to classify features. The third approach extracts deep features using convolutional neural network and then employs the attention layer and sigmoid function to classify features. The proposed system attained average results in testing images for the REFUGE, RIM-ONE-DL, DRISHTI-GS1, and ORIGA datasets, 96.21% accuracy, 98.65% specificity, 83.13% recall, 92% precision, 87.34% F1- score, 97.5% area under curve, and the average time is 0.4 seconds for classifying each image.

458

Authors: Spoorthi Basavarajappa, Shanthi Mahesh

Nodule segmentation in lung computed tomography (CT) images is a significant part of the detection and diagnosis of lung cancer. Automatic analysis of lung CT images is necessary to calculate lung nodule characteristics for recognizing malignancy. In recent years, deep learning and neural networks have been used in medical applications. Deep learning utilizes neural networks to train huge amounts of information which effectively learns the nodule features in lower to higher grades for segmenting and predicting the medical images. In this paper, the proposed cat swarm optimization (CSO) based recurrent neural network (RNN) is utilized for lung nodule segmentation. The proposed model is estimated on the freely accessible lung image database consortium and image database resource initiative (LIDC-IDRI) dataset. The proposed model is segmented by using the markov random field (MRF) based firefly algorithm (FA) and cuckoo search algorithm (CSA). The result shows that the proposed CSO based RNN model delivers performance metrics like dice coefficient (DC) loss and accuracy values of about 96.28% and 90.28% respectively, which ensures accurate nodule segmentation in lung CT images.

482

Authors: Optimal Integration of Photovoltaic Public Charging Stations in Multi-Consumer Electrical Distribution System Using Improved Honey Badger Algorithm

The penetration of electric vehicle (EV) loads in the electrical distribution system (EDS) is anticipated to increase dramatically over the next few years in response to rising global warming and fuel price scenarios. Planning for charging infrastructure and renewable energy sources (RES) is urgently required in this context. By carefully combining public charging stations (PCSs) backed by photovoltaic (PVs) systems and distribution static compensators (DSTATCOMs), this study proposes an optimisation strategy to reduce the adverse effects of EV load penetration in EDS. The proposed multi-objective function aims to reduce the voltage deviation index and distribution losses while considering various operational constraints. By combining the voltage stability index (VSI) and opposition-based learning (OBL) strategy with honey badger algorithm (HBA), the optimisation problem is resolved with a smaller search space for global minima. The effectiveness of the proposed technique on IEEE 33-bus EDS was evaluated in various situations. The real power losses are reduced to 242.35 kW, 75.32 kW and 41.76 kW from 350.25 kW by optimally integrating (i) PCSs alone, (ii) simultaneous PCSs and PVs and (iii) simultaneous PCSs, PVs and DSTATCOMs, respectively. Further, the VSI of the EDS is enhanced 0.7057, 0.8727, 0.9018 from 0.6104, respectively. The computational characteristics of HBA were also measured and compared with those of the COA and HBA. In terms of target and computational time, the outcomes produced by IHBA are very competitive with those of COA and superior to those of HBA. Additionally, even with a high EV load penetration, the suggested methodology produces reduced distribution losses and an appropriate voltage profile in EDS.

505

Authors: Raghavendra L R, Manjunatha R C

Achieving high spectral efficiency is a fundamental objective in cognitive radio (CR) networks, where fast and precise spectrum sensing plays a vital role. The cognitive radios rely on spectrum sensing to detect available frequencies, identify idle channels, and utilize them for transmitting data. A new approach to spectrum sensing (SS) is presented in this study, using a machine learning-based model. The model leverages different techniques for feature extraction and separates redundant and non-redundant features to enhance detection performance and reduce training overhead. A hybrid approach is introduced, combining energy, MME, and cyclostationary features extracted from sample data with NCA. The selected features are then trained with a Bayesian-optimized random forest classifier (RF) as a machine learning model. The proposed algorithms are evaluated in terms of average training time, classification speed, and accuracy. The experimental results demonstrate that the proposed bayesian-optimized random forest classifier (RF) achieved a probability of detection of 0.94.

531

Authors: K. Paul Joshua, D. Srinivasa Rao, D. Madhivadhani, Lily saron grace

Mobile ad hoc networks (MANET) are self-launching and self-building multichip wireless networks in which the system's construction deviates significantly owing to the mobility of the nodes. Network nodes serve as both hosts and routers, transferring data from other network nodes. MANETs necessitate an efficient routing protocol with a quality of service (QoS) component. They do not consider mobility as a potential restriction for routing concerns. It is challenging to maintain the necessary QoS in the network due to node mobility, which frequently causes connection failures and high error rates. The aim of the proposed study is to develop an effective MANET framework with dynamic routing and enhanced clustering. In this work, Fuzzy C means clustering is utilized to form the clusters identifying communities or groups of nodes with similar characteristics or behavior, whereas sea lion optimization (SLnO) is utilized to attain the best routing path for data transmission. The proposed model is empirically tested and compared with particle swarm optimization (PSO), ant colony optimization (ACO), grey wolf optimization (GWO), and genetic algorithm (GA) in terms of factors such as throughput, packet loss, energy consumed and end to end delay. The findings demonstrate that SLnO algorithm outperforms other protocols in terms of efficient routing generating a throughput of 680Mb/s and a packet delivery ratio (PDR) of 93.6%.

553

Authors: Asha Kethaganahalli Hanumanthaiah, Manju More Eshwar Rao, Soumyashree Muralidhar Panchal

Breast cancer is a deadly disease; an accurate and early diagnosis of breast cancer is the most efficient method to decrease the death rate. But, in the early detection and diagnosis of breast cancer, differentiating abnormal tissues is a challenging task. In this paper, a weight-based AdaBoost algorithm is proposed for an effective detection and classification of the breast cancer. An AdaBoost algorithm effectively classifies the breast cancer classes by adding the weights to the samples in the week classifier during the training phase. A weighted vote is performed on the results of each week classifier, and the strong classifier is integrated according to the weight of the week classifier. The breast cancer image datasets named CBIS-DDSM and MIAS are utilized for effective classification. Tumor-like regions (TLRs) are diagnosed by utilizing the optimum method of Otsu thresholding to enhance training abilities. The convolutional neural network (CNN) architectures of the AlexNet and ResNet50 are utilized for the feature extraction. A weight-based AdaBoost algorithm is proposed for the classification of breast cancer mammogram images into four classes benign calcification (BC), malignant calcification (MC), benign mass (BM) as well and malignant mass (MM). The results shows that the proposed weight based AdaBoost algorithm delivers the performance metrics such as accuracy, specificity, sensitivity, precision and F1-score values about 99.56%, 99.38%, 99.40%, 98.89% and 99.18% respectively, which ensures the accurate classification results compared with the existing methods such as IMPA-ResNet50, Gray difference weight and MSER detector, MLO and CC methods.

576

Authors: Amal R. Saleh, Motaz A. Elsaban, Mohamed M. Saleh, Hisham M. AbdelSalam

Real estate tax base assessment and evaluation systems are crucial for financing public services and digital economy transformation. To automate and unify complex tax procedures, an intelligent taxation model is needed. Deep learning neural network (DLNN) models have been limited for small-scale, high-dimensional data, but this paper creates an automated model using DLNN, offering superiority, interpretability, and dependability. The model offers lower error levels and high accuracy. The intelligent automated taxation model will improve real estate tax inspection offices efficiency by enabling precise tax base assessments and valuations. Deep learning techniques may enhance real estate price projections, resulting in more precise assessments of taxes. The findings reveal that, compared to other benchmark price predictors, the proposed model achieves greater accuracy (95%–99%) in different datasets and it has the potential to be generalized for real estate taxation authorities and tax inspection offices. The approach is helpful in automated real estate price prediction and taxation applications. This ground-breaking research study proposes a revolutionary strategy that employs deep learning neural network DLNN and challenges the conventional approaches to real estate tax base assessment.

600

Authors: Vijay Anand Krishnan, Abel Thangaraja Ganesan

Mobile adhoc network (MANET) is a type of wireless configuration that features self-organizing wireless mobile nodes and adaptive network connection. Security is a key concern for MANETs due to their dynamic nature and continually changing topology. To improve security, an adaptive trust threshold-aware secure energy-efficient protocol was designed, which adaptively predicts the threshold value using an artificial neural network (ANN) to evaluate the node’s trust for detecting and preventing the suspected nodes. In contrast, it fails to detect and mitigate conflicting behavior (CB) attacks, in which the suspected node may behave well towards a specific group of nodes and badly towards another group of nodes. Therefore, this article proposes a CB attack prediction using the shared learning-based ANN (CBAP-SLANN) algorithm to predict CB attacks in different nodes as well as different timeslots within the same node. The trust calculation should then take into account the consistency of behavior when employing different node-based observations along with the proposed different time-based observations. Initially, nodes are divided into overlapping clusters, and the trust and various network parameters of each node are observed for every group individually. Then, the ANN algorithm is trained in each group using the observed parameters and the trained model for each group is combined to get the global decision, which helps to predict the CB attack nodes in the network. At last, the simulation outcomes show that the CBAP-SLANN algorithm attains 93.1% accuracy when deploying 300 nodes compared to the trust-based routing algorithms.

620

Authors: Jincy C Mathew, V. Ilango, V Asha

Nowadays, retinal fundus images (RFI) play a crucial role in the early and exact prediction of glaucoma. Glaucoma is a vision-threatening eye illness that primarily impairs the eye nerves, and the serious condition of glaucoma leads to permanent vision loss. Several computer vision-based techniques have been explored in past studies for glaucoma detection, but those have certain misclassification results and are not much focused on early stage detection. This work presents an ensemble deep network (Ensemble DeepNet) model for the early detection of glaucoma via retinal fundus images. Initially, the kernel bilateral filter (kernel BF) is employed to improve the clarity of retinal images and reduces image noise. Then, the brightness of the input image is modified using linear histogram transformation (LHT). Further, the segmentation of the optic disc (OD) and optic cup (OC) is performed using two-stage thresholds U-network (Two-stage TUNet). Finally, a new ensemble DeepNet model is developed based on VGGNet, DenseNet, and CapsNet models using a weighted majority voting technique. The improved aquila optimization (IAO) algorithm is used to find the optimal weights for the ensemble DeepNet model. The proposed ensemble DeepNet model attains the highest of 99.45%, which shows that the proposed ensemble model is highly suitable for predicting glaucoma disease in an early stage.

650

Authors: Nirmala Velusamy, Jayanthi Boopathy

Nowadays, tree-structured deep learning classifier models have been widely used in different applications to ensure effective feature representation and learning. Amongst, dimensional sentiment analysis is the most interactive research field, which intends to identify continuous numerical values in the valence-arousal (VA) space. To achieve this, a tree-structured regional convolutional neural network with long short-term memory (T-CNN-LSTM) model was developed, which predicts the VA ratings of the texts for sentiment analysis. In contrast, the effect of a low prediction rate and difficulty of feature learning in a small number of class samples was not analyzed. Hence, this manuscript proposes an adversarial T-CNN-LSTM (A-T-CNN-LSTM) model for predicting the VA to achieve more fine-grained sentiment analysis. This model develops a semantic-enabled frequency-aware generative adversarial network (SFGAN) to produce more adversarial samples using the generator network and decrease the spectral data loss of the discriminator. It embeds the frequency-aware categorizer (FAC) into the discriminator to determine the input veracity in the spatial and spectral domains. Besides, semantic restricted sampling is employed in SFGAN for synthesizing the image subject to a semantic mask. Further, the created samples are classified by the T-CNN-LSTM for predicting the VA scores of sentences. Finally, the experimental results exhibit that the A-T-CNN-LSTM on stanford sentiment Treebank (SST) and CIFAR-10 databases achieves 90.12% and 91% accuracy than the other tree-structured CNNs.

673

Authors: Purba Daru Kusuma, Anggunmeka Luhur Prasasti

Migration algorithm (MA) and walrus optimization algorithm (WaOA) are two new swarm-based metaheuristics which are first introduced in 2023. As new metaheuristics, the modification of these two metaheuristics is still rare. Based on this circumstance, this work constructs a new metaheuristic called as migrating walrus algorithm (MWA) based on the hybridization of both MA and WaOA to create a better metaheuristic than them. MWA consists of five migrations: four directed migrations and one local migration. The references used in these directed migrations are the best walrus, a randomly picked better walrus, a randomly picked walrus, and two randomly picked walruses. In this work, two assessments are carried out: the comparative assessment and the individual migration assessment. The 23 functions are selected as theoretical use cases. In the comparative assessment, MWA is confronted with five new metaheuristics: MA, WaOA, attack leave optimization (ALO), coati optimization algorithm (COA), and osprey optimization algorithm (OOA). The result shows that MWA is better than ALO, COA, MA, OOA, and WaOA in 19, 19, 19, 17, and 17 functions. On the other hand, the individual migration assessment result indicates that the multiple migration approach is important to maintain the superiority of MWA with the directed migration toward the best walrus becoming the most important contributor. This result also strengthens the necessity of the multiple searches strategy rather than a single strategy.

695

Authors: Ricardus Anggi Pramunendar, Pulung Nurtantio Andono, Guruh Fajar Shidik, Rama Aria Megantara, Dewi Pergiwati, Dwi Puji Prabowo, Yuslena Sari, Lim Way Soong

This study aims to improve the classification accuracy of birdsongs by selecting the most pertinent features. This is important because birds are exceptional environmental regulators, but many species are endangered. The community can be assisted in distinguishing bird species and conserving the local environment if the classification is more precise. Nevertheless, because of disruptive noise and unfavorable qualities in the whispering of these bird species, feature selection focuses on enhancing performance accuracy. The use of the gray wolf optimizer (GWO) technique has been employed to identify the most optimum features from the data after outlier removal by the application of k-means clustering, reducing noise through YAMNet, and feature synthesis using gammatone cepstral coefficients (GFCC). This work utilizes the GWO algorithm to address the constraint management challenges associated with high-dimensional data in birdsong classification. The fitness functions used in this research are derived from the K-nearest neighbors (KNN) algorithm. The objective is to devise innovative ways for effectively managing constraints in the context of high-dimensional data. The number of features was reduced by more than 30.7% compared to the initial number of features and obtained an accuracy of 81.06%, as determined by the evaluation outcomes. This discovery improves precision by 4% and surpasses prior research. In summary, this work showcases the effectiveness of the optimization method, especially of GWO. It makes a valuable contribution to advancing a new workflow for analyzing high-dimensional data, specifically in enhancing the classification of birdsongs.

718

Authors: Anagha Deshpande, Krishna Warhade, Pratap Sanap

Human activity recognition (HAR) has become a highly researched area with numerous practical applications in public safety. Deep learning has revolutionized HAR by introducing novel approaches to tackle its challenges. Abnormal activity recognition enables prompt intervention and enhances public safety. Presently vision-based activity recognition techniques mainly use recurrent neural network (RNN) architectures like LSTM to handle sequential data dependency. However, this approach struggles to capture the spatial information between consecutive frames in video data, limiting their ability to learn spatiotemporal patterns. To address this issue, we introduce a layer called ResAttenConvLSTM2D, a variant of the ConvLSTM layer, and propose a novel architecture for solving the abnormal activity recognition problem. In the residual attention model, attention is applied to the residual connections, enabling the network to concentrate on portions of the input by calculating the attention score at each time iteration during model training. In addition, the proposed approach addresses the challenge of limited resources in handling video data by employing robust key frame extraction methods using an unsupervised K-Means algorithm. The proposed architecture is tested for benchmark datasets, i.e., AIRT Lab, hockey fight, and abnormal human activity with a classification accuracy of 90%, 96%, and 99% respectively, showing comparable accuracy or complexity compared to the state-of-the-art (SOTA) approaches.

740

Authors: Qassim Abd A. Hadi, Ali Saeed D. Alfoudi, Ahmed M. Mahdi

The internet of things (IoT) cybersecurity presents a crucial challenge in our daily lives. An intrusion detection system (IDS) is valuable for protecting IoT data against malicious attacks. Moreover, intrusion datasets are often imbalanced in the number of attacks, increasing the bias in machine learning towards classes with high frequency. Normally, this case affects a training model's performance and ability to make a correct prediction. This paper presents a hybrid model that merges dynamic evolving cauchy clustering (DECS) with ranking classification. The DECS model operates based on the self-similarity principle, strategically distributing data into clusters to counteract the impact of imbalanced data. Furthermore, the rank classification algorithm predicts classes for new attacks. The NF-ToN-IoT dataset was used to test the validity performance of the proposed model and compared with standard machine learning algorithms (K-nearest neighbours, random forest, and decision tree). The proposed model outperforms standard cauchy clustering regarding mean square error (MSE), exhibiting a noteworthy reduction to 0.0534. Furthermore, the silhouette score, which indicates clustering quality, notably improved, reaching 0.4707. Additionally, the proposed model attained an accuracy rate of 67.77% and an F1-score of 76.52%, while the standard Random Forest achieved better accuracy at 66.34% and an F1-score of 68.48%.

771

Authors: Vankayalapati Radhika, B. Sai Chandana

Skin cancer is a prevalent condition that may affect anybody, regardless of age. To lower the fatality rate by catching diseases in their earliest stages, automated skin cancer detection is required. To lower the fatality rate by catching diseases in their earliest stages, automated skin cancer detection is required. In recent years, skin cancer screening and detection have become more popular due to the imaging-based computer-aided diagnosis (CAD) approach. To identify and classify skin lesions, this research developed an automated integrative deep-learning network model. The hair is removed using a dull razor approach, and the noise is removed using an average median filter during image pre-processing. The afflicted lesion regions are found in the dermoscopic images using the Otsu thresholding approach and mathematical morphology. From the segmented lesions, the significant features are extracted using improved capsule network (Improved CapsNet). Finally, the enhanced optimized probabilistic neural network (EOPNN) is used for skin lesion classification. The improved artificial jelly optimization (IAJO) algorithm enhances the EOPNN classifier. A benchmark ISIC dataset is used to validate the proposed EOPNN-IAJO approach. The proposed structure has achieved encouraging results for several metrics with 99.23% specificity, 99.02% sensitivity, and 99.53% accuracy.

799

Authors: Azeddine Loulijat, Mouncef El Marghichi, Mohamed Makhad, Naoufl Ettalabi

The doubly fed induction generator (DFIG) is particularly sensitive to grid faults, representing a significant drawback of this generator type, because of the direct connection of its stator windings to the network. The electromagnetic coupling between the stator and rotor in the DFIG results in an undesirable consequence, where voltage dips lead to excessive stator current, causing high currents in sensitive inverters and overloading the DC-link condenser. This document outlines a comparative study that examines two protection scheme configurations with a proportional-integral (PI) controller for managing transient rotor current and overcharging of the DC-link capacitor in order to optimize the operation of the DFIG during network faults. One conventional design choice combines a crowbar circuit with a DC-chopper, in contrast the other design option proposes integrating a modified protection scheme (MPS) that includes an RpLp parallel circuit with the traditional crowbar. Both schemes are placed between the rotor windings and the rotor-side converter (RSC) to boost the low voltage ride-through (LVRT) capacity of the generator. The comparison of results obtained through MATLAB/SIMULINK simulations reveals the successful performance of these two schemes in reducing elevated intensity current of rotor and DC-link voltage levels in the DFIG machine. Furthermore, the traditional crowbar and MPS circuit design have effectively limited the current intensity of the RSC converter to levels lower than 0.22 kA and 2.95 kA, while absorbing up to 2.53 kA and 1.53 kA respectively. Thus, ensuring that the DC-link tension stays within the safe range when network failures occur. The notable distinction is that the MPS design prevents the decoupling of the rotor from the RSC during a symmetrical voltage dip, enabling effective control of all stator power through the RSC.

824

Authors: Mustafa Yahya Hassan, Ali Hamza Najim, Kareem Ali Malalah Al-sharhanee, Mohammed Ayad Alkhafaji, Ridha Mohammed Alfoudi, Worod Adris Shutnan

The firefly optimized Hybrid CNN-BILSTM architecture is a revolutionary AI-driven method proposed in this study for enhancing resource distribution and optimization, and it represents a significant advancement in the Internet of Things (IoT) network space. The importance of this research is in how it addresses the challenges of the utilization of resources in the rapidly growing IoT environment. Traditional distribution of resources methods usually struggle to capture complex chronological and spatial relationships in IoT networks. Convolutional neural networks (CNN) and bidirectional long short-term memory (BILSTM) systems are thus combined in a new Firefly Optimized Hybrid CNN-BILSTM technique to address these deficiencies. Furthermore, this hybrid structure enables excellent synchronous capture of the spatial patterns and temporal dynamics in the computer model by enabling extensive feature acquisition from IoT network information. The firefly optimisation technique is used to optimize the model's parameters, enhancing the model's effectiveness and resolution. Additionally, IoT networks may use an AI-driven strategy to allocate resources in a way that is intelligent and ecologically friendly, increasing resource usage, efficiency, and waste. By mimicking the seductive behaviour and recreating their illuminating patterns, the firefly optimization approach, which serves as the foundation for the algorithm's optimization process, makes it simpler to determine the most optimum resource allocation arrangement. The proposed AI-driven firefly optimised Hybrid CNN-BILSTM structure outperforms state-of-the-art approaches, as shown by the current study's deep evaluations with an accuracy of 99.88%. It also paves the way for more efficient and intelligent resource management in IoT networks and opens up new research directions for the field of AI-driven optimization for IoT applications.

850

Authors: Suhad Abbas Yassir, Haidar Raad Shakir

Information is an asset that profoundly affects every aspect of our lives and requires protection from malicious attacks. Although storing it in a repository can safeguard this information, techniques to maintain confidentiality during transmission over communication networks are essential. Due to the advent of digital technologies and easy communication channels, most of the information now exists in images or other multimedia formats. To safeguard such formats, it is crucial to encrypt information before transmitting it over a network. Encryption transforms the data such that only the intended recipients can reconstruct it. Image encryption is a widely researched topic and various methods have been proposed. As traditional methods often fail to provide adequate protection, advanced encryption standards are currently being investigated. Chaos-based techniques are strong contenders for cryptography because they ensure confidentiality, nonperiodicity, and randomness and are easy to implement. Therefore, they are frequently utilized and can be combined with other methods to bolster protection. In this study, we developed a hybrid image-encryption technique that leverages hyperchaotic maps and elliptic-curve cryptography to guard data against security vulnerabilities. The proposed method achieves an average entropy information value of 7.99%, indicating its resilience to entropy attacks. Furthermore, it showed a net pixel change rate of over 99% for all the test images acquired from the final diffusion stage. It also recorded peak security values of 0.9897, 0.9848, and 0.9676 in the horizontal, vertical, and diagonal directions, respectively. Overall, the scheme demonstrated a satisfactory performance across nearly all evaluation criteria.

874

Authors: Prema Muthusamy, Gomathi Pudupalayam Murugan

People who are deaf or hard of hearing typically communicate primarily through sign language (SL). A complication arises when a deaf\mute person tries to use SL with someone who aren't familiar with it. The use of modern technological advancements will be useful in this situation. In recent years, prediction of SLs has been greatly improved by deep learning (DL) techniques. Amongst, a spatial-temporal multi-cue (STMC) network combines a spatial multi-cue (SMC) module and temporal multi-cue (TMC) to learn spatial and temporal encoding of multi-cue features like full-frame, hands, face, and stance. While it outperforms single-cue algorithms on large SL datasets, it requires more time to pre-process the dataset. Also, annotating key points is required for the complete training of the STMC network. This article presents a spatio-temporal hybrid cue network (STHCN) using dynamic dense spatio-temporal graph convolutional neural network (DDSTGCNN) and VGG11+1D-CNN+BLSTM feature extractor network to address the fore-mentioned issues of ISL recognition and translation tasks. Initially, the skeleton and full frame data is extracted from the input video. The DDSTGCNN is a combination of dense-GCNN (DGCNN) and dynamic spatial-temporal convolution network module (DSTCNM) which learns spatial features temporal features of skeleton data. VGG11+1D-CNN+BLSTM is used to extract the features from full frame data. Then, the extracted features are fed into a bidirectional long-short term memory (BLSTM) encoder, connectionist temporal classification (CTC), and self-attention based LSTM (SA-LSTM) decoders for sequence learning and inference. Finally, The CTC and SA-LSTM predict ISL from input videos and sentences for ISL detection and translation. Finally, an experiment results reveal that the STHCN model achieves 93.65% accuracy in detecting the ISL.

896

Authors: Sneha G Venkateshalu, Santhosh Laxman Deshpande

The proposed spatial-spectrum sparse aware CNN (SSCNN) approach with a CNN architecture employing sparse aware fusion/compression technique to reconstruct high-quality HSI images, departing from prior approaches. The developed model comprises three phases: creating the dictionary, including sparse coding into the dictionary, and evaluating the quality of the parametric reconstruction for the fusion of spatial data. From fused HSI colors, multiscale features are extracted in the second phase. For classification, spatial, and spectral optimization used, a soft margin decision boundary method to reduce the misclassification error. The regularisation factor C was added to manage the commutation among margin and misclassification sensitivity. SSCNN network enables the training of high-level semantic features of fused bands with the best multi-categorization accuracy of 99.46% and kappa of 99.37% in addition to localization-preserved information within the 143.97 secs elapsed time. The findings of the proposed are helpful in spatial data analysis development in the study area.

925

Authors: V Sai Geetha Lakshmi, M Devika Rani, F Jeno Jasmine, Rathinam Muniraj, P Muthukumar

Power systems around the world are transforming into automated networks for more efficient, secure, reliable, and resilient operations, while satisfying technological, economic, and environmental goals. This study addresses the optimal network reconfiguration (ONR) problem by developing a multi-objective function that targets loss reduction, voltage profile improvement, and reliability enhancement. It recommends merging renewable energy sources (RES) with energy storage systems (ESS) to improve resilience. The complex nonlinear multi-objective, multi-variable, and multi-constraint problem is simplified by an efficient single candidate optimizer (SCO). Further, a novel severity index of interruption (SII) is introduced for reducing the search space and thus, improving the computational efficiency of SCO in the proposed hybridization algorithm. In terms of statistical metrics and computing time, simulations on IEEE test systems show that the SCO-SII approach outperforms the basic SCO, grasshopper optimization algorithm (GOA), cuckoo search algorithm (CSA) and Pathfinder algorithm (PFA) in reconfiguration, RES integration, and robust mode augmentation. The capacity of the methodology to handle uncertainty in current electrical networks emphasises its importance.

946

Authors: Sreenath Marimakalapalli Venkatarama Reddy, Annapurna Dammur, Anand Narasimhamurthy

The accurate and dependable prediction of traffic is essential for the stable and safe implementation system of intelligent transportation. It is difficult to examine both inter-series and intra-series temporal correlations at the same time. However, several recent research in the time domain have attempted to capture both correlations but numerous analyses only capture the correlations of temporal and rely on pre-determined priors as inter-series interactions. In this paper, an attention-based spectral temporal graph neural network (AStemGNN) is proposed for traffic flow prediction and identifying the critical node/link graph network. In the spectrum domain, both the correlations of inter-series and temporal dependency are captured by AStemGNN. It incorporates graph fourier transforms (GFT), which describe the correlations of inter-series, and discrete fourier transform (DFT) which represent temporal dependencies. Following the execution of GFT and DFT, the spectrum representations retain patterns and it was predicted efficiently by the modules of convolution and sequential learning. Furthermore, an AStemGNN automatically identifies the correlations of inter-series data without employing the help of pre-defined priors. The efficacy of the AStemGNN method is demonstrated by employing the PEMS04 and PEMS08 datasets. The existing methods such as attention-based spatial-temporal graph neural networks (ASTGNN), static and dynamic spatial correlation neural networks (SDSCNN), dynamic graph convolutional recurrent imputation networks (DGCRIN), and graph and attentive multi-path convolutional network (GAMCN) are used to justify the efficacy of the AStemGNN. When compared with the existing methods such as ASTGNN, SDSCNN, DGCRIN, and GAMCN, the AStemGNN achieves 0.65%, 1.12% mean absolute error (MAE), 0.78%, 1.05% root mean squared errors (RMSE), and 1.57%, 2.19% mean absolute percentage error (MAPE) in both PEMS04, PEMS08 datasets respectively.

965

Authors: Haidar Raad Shakir

Minimizing the computational volume during cryptography while preserving optimal security has long been a goal of computer scientists. Selective encryption is a scalable trend in multimedia content protection. This study builds on the findings of previous studies. It suggests employing the integer Haar wavelet transform and 3D-chaotic with the discrete fractional random transform to encrypt color images. First, each primary color was wavelet-processed into four sub-bands. The approximation sub-band of the wavelet transformation was encrypted using discrete fractional random. Subsequently, to construct encrypted images, an updated approximation and detailed sub-bands, followed by pixel permutation, were utilized. A secure hash algorithm for a plain image was used to produce encryption keys. Finally, a numerical analysis and robustness evaluations were performed using an encrypted test image. The results demonstrated the high efficiency of the proposed method for image encryption. Additionally, it is secure against a wide range of attacks such as entropy and asymmetric attacks. The proposed solution outperformed the encryption approaches proposed by other researchers in terms of security and performance evaluation when using images. Therefore, it fills the gaps in the literature on state-of-the-art methods.

992

Authors: Raad Farhood Chisab

There are many candidate waveforms for the next-era communication system. Filter bank multi-carrier (FBMC) This system depends on offset quadrature amplitude modulation (OQAM) is one of them. Compared to OFDM, it can offer faster data rates and improved spectrum efficiency. The OFDM has a few disadvantages which as a high PAPR resulting in the requirement for high-power amplifiers. So, to overcome this problem, the FBMC system with OQAM is used. In this manuscript, a proposed method was designed using the modified technique through the slantlet transform (ST) to raise the activity for the system. This scheme is suitable for providing lower losses, higher data rates, and for better spectral efficiency. a proposed method that uses the ST gives a modified performance than the fast fourier transform (FFT). Many parameters were applied to test the activity of the system such As Power spectral density, prototype filters, signal power, velocity, carrier frequency, modulation order, power delay profile, channel doppler model, overlap factor, and efficiency. In all these parameters it can be noticed that the proposed system based on ST has overcome the other systems and gives higher performance.

1021

Authors: Triyanna Widiyaningtyas, Didik Dwi Prasetya, Heru Wahyu Herwanto

Neighbor-based collaborative filtering is one of the prevalent recommendation approaches that apply similarity algorithms. Using a similarity algorithm to improve the accuracy of the recommendation system is one challenge in collaborative filtering. The computation of similarity now heavily relies on user rating and behavior scores. Users' preferences for each product kind (genre) demonstrate the user behavior’s value. The algorithm's weakness is that it only considers genre data when estimating user behavior, regardless of when the user rated the item. Therefore, this study aims to develop a new similarity algorithm by considering the genre and the time weight of the item rating, which is called time loss function-based similarity (TLFSim). Newly assessed items have a higher weight than those estimated for a long time. Our experiment tested the TLFSim’s performance compared to the user-score-probability-collaborative-filtering (UPCF) algorithm using the MovieLens 100k dataset. The experimental results demonstrate that the TLFSim algorithm surpasses the prior approach regarding recommendation accuracy, reducing mean absolute error (MAE) by 8.28% and root mean square error (RMSE) by 4.57%.

11

Authors: R. Ramesh, S. Sathiamoorthy

Diabetic retinopathy (DR) is a diabetic complexity that mainly affects the eye. Generally, an ophthalmologist defines the severity of the retinopathy by directly inspecting colour images and estimating them by visually examining the fundus. Due to the enormous amount of diabetic patients all over the world, it becomes an expensive process. The automated system was designed for accurate recognition of the disease using segmentation and fundus image. The blood vessel segmentation process is used to identify and separate blood vessels from the surrounding tissues in an image. This is a crucial stage in the detection of DR, as it enables physicians to measure and identify changes in blood vessels that are indicative of the disease. Numerous approaches are used for blood vessel segmentation and DR classification, along with deep learning (DL) and machine learning (ML) techniques. In this article, we introduce a novel dandelion optimization algorithm with deep learning based blood vessel segmentation and classification (DOADL-BVSC) model for DR grading. The presented DOADL-BVSC technique involves the concepts of blood vessel segmentation and DL-based classification for DR diagnosis on retinal fundus photographs. To accomplish this, the presented DOADL-BVSC technique uses the fuzzy set type-II approach for the image enhancement process. Next, U-Net with Bi-directional feature pyramid network (U-BFPN) model is exploited to segment the blood vessels in the retinal imaging effectively. Moreover, a squeeze and excitation (SE) network is applied for feature vector generation with DOA based hyperparameter optimizer. Finally, quantum autoencoder (QAE) approach is utilized for identifying and classifying DR into distinct stages. The experimental analysis of the DOADL-BVSC approach is investigated on benchmark DR datasets. The simulation results depicted the superior results of the DOADL-BVSC technique with increased accuracy of 99.93%.

31

Authors: Sripal Reddy Kethireddy, Phani Vidhyadhar Rallapalli, Lokanath Reddy Chilakala, Praveen Kumar Devulapalli

The world has taken a drastic look towards scalar mobile wireless multi media sensor networks (MWMSN) and almost all the nodes (IOT devices) are connected via the internet. The amount of image, video and data transmission has increased significantly. For battery-powered smart gadgets like smartphones and tablets, energy efficiency is a constant issue. To fully utilise the advantages of the multiple-input-multiple-output technology, MWMSN heavily relies on cooperative communication. Since each node in a cooperative mobile multimedia sensor network is mobile, energy consumption and routing present significant issues. Since each node in the network is mobile, energy consumption and routing pose significant problems for cooperative mobile multimedia sensor networks. To address these challenges optimal cooperative relay selection (OCRS) technique was proposed by utilizing velocity, distance, and connection quality factors. The suggested method bases its determination of the link quality for each pair of nodes on the signal strength and distance values. The highest quality of service (QoS) rating, which is determined to ensure route stability, reliability, and durability, is chosen as the relay nodes. Further, we have proposed an energy efficient cooperative multi media transmission (EECMT) to achieve a better compression rate with lower energy consumption. This approach utilizes the auto-correction code feature present in the QR code to further improve the performance of MWMSN. The simulation result shows that the proposed approach gives better performance and saves the energy up to 39% (approximately) compared to the efficient cooperative image transmission (ECIT), energy efficient relay node placement (EERP) and energy aware cooperative image transmission (EACIT) approaches.

68

Authors: Venkat Reddy Kumbam, A. Viji Amutha Mary

Epilepsy is a neurological disorder. It is a problem of abnormality in brain resulting in seizures, unusual behaviours, loss of awareness and sensations. Recently many deep learning-based methods to detect epilepsy from multi lead electroencephalogram (EEG) signals have been proposed. Most of these approaches fused the features extracted in one dimensional or two-dimensional mode from EEG through aggregation without any cross-reference learning. As the result, their performance gain in terms of accuracy is limited and also false positives are higher. Learning the cross-correlation information across multiple leads will solve this problem and there has been no works exploiting cross correlation learning. Existing approaches on usage of multiple modalities are mostly feature fusion or decision fusion approaches without cross correlation learning. This work proposes a cross model attention-based learning to learn enriched features from multiple modalities of EEG with aim to increase the accuracy and reduce the false positives. The performance of the proposed solution was tested against University of Bonn EEG dataset. Due to cross correlation learning from multiple modalities, the accuracy of epilepsy detection in proposed approach has increased to 98% which is atleast 2% higher compared to most recent approaches.

92

Authors: Sholiq Sholiq, Riyanarto Sarno, Endang Siti Astuti, Muhammad Ainul Yaqin

In COCOMO II, the primary input for estimating development effort in person month (PM), duration, and cost is the size of the software. Until now, there are two ways to get the size, namely (1) size is estimated using a line of code of software, and (2) size is estimated using unadjusted function points (UFP), which is one of the functional size measurements (FSM). In this study, we added a new way to obtain the size as the primary input in COCOMO II, namely with COSMIC function points (CFP). CFP has several advantages compared to other FSMs, including UFP. Therefore, like UFP, CFP is converted first to LOC, so the conversion equation must be obtained first. We applied four models to get the conversion functions: Ordinary least squares regression (OLSR), support vector regression (SVR) with linear, polynomial, and Gaussian kernel functions. The four models were applied using a dataset from small-scale business application software in Java. The results showed that PM estimation using the CFP model as the primary input produced better accuracy based on MMRE and Pred (0.25), namely 17%-19% and 67%-80%, than the UFP model on the COCOMO II of 135% and 10%.

113

Authors: B. Nisha, M. Victor Jose

Brain cancer is the 10th leading cause of death for men and women. Magnetic resonance imaging (MRI) based Brain cancer segmentation is challenged by less accuracy and high time consumption, hence, an efficient cancer segmentation method is needed for the medical world. In this research, a novel brain cancer segmentation method on MRI is proposed which is entitled 'Brain cancer object Segmentation using LPSIT method and back propagation network (CS_LPSITBP)'. The own contribution of this paper is the 'Unsupervised segmentation of cancer object using Lanczos-3 interpolation based pyramidal structured iterative thresholding (LPSIT)'. The LPSIT method effectively segments the brain cancer region and delivers the key guidelines to generate the training sample to assist the back propagation neural (BPN) network. Processing of multi size images based pyramidal structure is the novel concept of this paper. A five-stage pyramidal based work is influenced in this paper. True positive rate (TPR) analysis reveals that the proposed CS_LPSITBP method improves the TPR by 1.92% compared to the next best method. Proposed method reaches the time consumption of 16.89 sec, while that of the next best method is only 23.88 sec. Experimental results in terms of FScore, Segmentation accuracy and MCR prove the efficiency of the proposed method compared with existing methods; hence, it can be used as a tool for medical practitioners.

138

Authors: Sujono, Ardyono Priyadi, Margo Pujiantara, Mauridhi Hery Purnomo

This paper discusses optimizing DG location and size to reduce power loss and bus voltage deviation index in a 51-bus radial distribution network. Optimization of DG placement uses the firefly algorithm, and optimization of DG size uses the differential evolution (DE) algorithm. The results showed that the optimal locations were on buses 16, 45, and 15, with the highest sensitivity indices of 0.6667, 0.0612, and 0.0601. DG at a lagging power factor of 0.95 gives optimal results with sizes of 358.5157, 500.0000, and 499.9781 kW. Active and reactive power loss reduction is 44.6948% and 66.7038% of the system’s power loss without DG. In optimizing DG placement, AMFA converges faster than DE, GA, and ICA. In the case of DG size optimization, the DE algorithm converges quicker and gives the most optimal results with a fitness value of 0.12385 which is smaller than the FA, GA, and ICA algorithms.

162

Authors: Hashem B. Jehlol, Loay E. George

Managing big data backups is challenging due to high volumes of redundant data. Data deduplication is widely used but incurs significant computational and time costs. This paper proposes a hybrid deduplication system that combines file-level and block-level methods to enhance deduplication while reducing costs. File-level deduplication eliminates duplicate files, while block-level deduplication is applied to non-duplicated files using a dynamic list of divisors to enhance deduplication. A multi-hash function generates three hash values for each file or chunk to improve chunking speed and reduce hash collisions. The proposed hybrid system outperforms other state-of-the-art methods in terms of time, data deduplication ratio, and deduplication gain. Experimental results show reductions of 97.2%, 91.6%, and 82.1% in data size for Dataset 1, Dataset 2, and Dataset 3, respectively, and demonstrate that the proposed multi-hash function is faster and requires less storage than other hash functions.

185

Authors: Choupiri Shivakeshi, Sreepathi B

Currently, the high-load transmission system uses high energy resources, and due to the utilization of high energy, greenhouse gases are polluted. High load transmission caused more resource usage, causing traffic over the communication channel, and fewer packets were transmitted in more time. So to avoid the issue mentioned above, a novel buffalo-based gossip load balancing routing protocol (BGLBRP). Initially, this research created the desired number of nodes in the software defined network (SDN) environment. Avoid high-load data transmission; the system monitored the transmission with the help of the proposed model. For that, the system identifies the high-load data; it then shares the data into a free hub and performs the load-balancing operation. However, the proposed model needs a lower rate of energy resources for performing the packet transmission operation. The proposed model consumes lower energy about 0.051J, so it reduces greenhouse gas. Moreover, the performance rate of the proposed model was calculated, and a comparison was made through the existing model. In comparison, the proposed model attains a higher parameter rate than the existing models. Consequently, the parameters were measured in terms of node lifetime, communication delay, packet drop, packet delivery rate, and energy consumption. The presented model has recorded a high PDR of 97.5%. Compared to the past works, 10% of PDR has been improved. Subsequently, the communication latency was reduced by 25% than the compared models.

208

Authors: Arif Muntasa, Rima Tri Wahyuningrum, Durotun Nafisah

The purpose of the paper is to classify the Leukemia images. In this paper, we proposed a commutative model of a convolutional neural network for Leukemia image classification. We employ commutative hypercomplex modeling A[+1, -1] and A[-1, +1] to build the new model. We hire an augmentation model to enrich the image data sets for the training sets through rotation, zooming, and flipping. We evaluated our proposed method using acute lymphoblastic leukemia image database type 2 (ALL-IDB2). The results show that our proposed method has delivered the best average accuracy at 96.43% for A[+1,-1] and 97.05 for A[-1,+1]. We have measured and found maximum accuracy at 100% for A[+1,-1] and A[-1,+1]. Comparison results show that our proposed method outperformed K-nearest neighbor, support vector machine-radial basis function, support vector machine-linear, support vector machine-polynomial, naïve bayes gaussian, naïve bayes complement, decision tree, and colour hybrid modelling.

238

Authors: Karthick Natarajan, Kamatchi Annappan

Endometrial cancer (EC) rate is rising progressively worldwide so early diagnosis of EC using medical imaging is vital to increase a patient’s survival rate. To avoid the misdiagnosis of EC on magnetic resonance imaging (MRI) scans by clinicians, an automated staging model based on deep learning has broadly emerged in medical systems. Many deep learning models like convolutional neural networks (CNNs) with transfer learning schemes are developed to classify normal and cancer patients from a sequence of MRI scans. But such models have poor sensitivity to classify EC stages from other benign lesions, resulting inaccurate diagnoses of EC. Also, the classical transfer learning schemes reduce the accuracy of detecting medical images due to the discrepancies in data distribution between the source and target domains. Hence, this article proposes a novel deep EC prediction (DeepECP) model that involves image synthesis and classification processes. First, a feature entanglement generative adversarial network (FE-GAN) is proposed for MRI synthesis that creates a desired MRI sequence according to the complementary features of multiple MRI modalities. Then, a multi-modal CNN with long short-term memory (LSTM) network followed by the fully connected (FC) layer is developed to extract a sequence of cancer features from multi-modal MRI sequences. Moreover, an attention strategy is used to fuse those extracted features and get a final feature vector, which is given to the softmax function to classify EC stages. Finally, the extensive experiments show that the DeepECP model on the TCGA-UCEC and CPTAC-UCEC datasets reaches 93.2% and 93.3% accuracy in detecting EC stages, respectively compared to the support vector machine (SVM), VGGNet-16, InceptionResNet and CNN models.

263

Authors: Jutarut Chaoraingern, Vittaya Tipsuwanporn, Arjin Numsomran

To encourage the good health and well-being sustainable development goal, this article presents the design and implementation of real-time indoor air quality index (AQI) prediction using an artificial internet of things (AIoT) electronic nose integrated into a vacuum cleaner robot. The objective of the proposed method is to implement an effective embedded AIoT solution utilizing sensor fusion and the TinyML framework for the purpose of strengthening the environmental health system with suitable current technology. The high-accuracy sensor outputs of total volatile organic compounds (TVOC), humidity, equivalent carbon dioxide (eCO2), and PM2.5 gathered as the dataset are normalized in the data pre-processing state and utilized to create trained models using dense neural networks (DNN) deep learning algorithms. Tiny machine learning is responsible for neural network training, as it is capable of executing AI algorithms on embedded devices with extremely low power consumption and limited RAM and ROM resources. The testing results demonstrate that the predictive model performed well, with 99% accuracy for a maximum absolute regression error less than 15 and an 18.33 mean square error. The embedded device implementation uses Wio terminals with ARM Cortex-M4F microcontrollers for real-time indoor air quality index prediction and visualization. Experimental results demonstrating the average precision of the indoor AQI prediction were obtained at an average accuracy of over 98% with a computation time of 10 milliseconds and an acceptable usage of ROM and RAM resources of 8.5 KB and 1.1 KB, respectively, along with successive performances that satisfied web application data virtualization using the internet of things (IoT).

289

Authors: Arief Kurniawan, Winnona S. T. R. Gultom, Dion Hayu Fandiantoro, Eko Mulyanto Yuniarno, Eko Setijadi, Mochamad Yusuf, I Ketut Eddy Purnama

Cardiovascular disease (CVD) is dangerous and has a high mortality rate. One of the signs of CVD is arrhythmia. The method to find out the symptoms of arrhythmia is an electrocardiogram (ECG) long-term monitoring. The duration of ECG analysis on long-term monitoring is 6 hours, 12 hours, or 24 hours. The device used for long-term ECG is a Holter device. Arrhythmia analysis has followed: detect QRS complex and continue arrhythmia classification. Holter data is stored in local memory or on the server. Storage on local storage makes it difficult for cardiologists to perform analysis. However, using server storage causes access times to slow if patient data increases. Because of this, the cardiologist needs a powerful processing unit to analyze arrhythmia. This study proposes Arrhythmia analysis in the long-term ECG monitoring system. ECG acquisition is sent and stored in the processing unit. We use a single-board computer (SBC) Raspberry Pi as a processing unit. Besides storing data, SBC is also analyzing arrhythmias. The analysis steps are detecting R-peak using the Pan-Tompkins (PTK) algorithm, removing P and T waves using the Gaussian filter, and arrhythmia classification using the multi-layer perceptron (MLP). MLP is a low computational deep learning, which is suitable for SBC. The total storage delay consists of sending and storing data in the database. In the experiment, the propagation data to the broker is 0.023 s., and the storage time to the SQLite database is 15.16 s. The limited recording time for acquisition data is 21 hours and 36 minutes. The success rate of the device in detecting the QRS complex has a precision (+P) of 98.61% and a sensitivity (Se) of 99.8%. Our classification has good in Acc, Se, Spe, +P, and F1-scores are 99.77, 99.55, 99.55,99.85, and 99.55, respectively. The method is superior to several other arrhythmia classification studies.

313

Authors: Amer Tahseen Abu-Jassar, Hani Attar, Vyacheslav Lyashenko, Ayman Amer, Svitlana Sotnik, Ahmed Solyman

The paper presents a generalized system's structural scheme and components for monitoring and controlling access to robotic systems based on biometric data for decision-making. Mathematical decision-making models for choosing the best alternative based on fuzzy sets are proposed. The fuzzy analytical hierarchy process determined weights of 6 criteria (for k1 criterion weighting factor is equal to 2.2; for k2 criterion – 1.8; for k3 criterion – 0.6; for k4 criterion – 0.6; for k5 criterion – 0.6; for k6 criterion – 0.2, that is, most weighty would be k1 – recognition accuracy) against which the best option was evaluated. Thus, during evaluation of alternative on six criteria it was obtained that t alternative X1 (face recognition in full-face), will have the key value of membership function of resulting fuzzy set alternatives with ideal value of integral criterion, equal to 0.53, and as result, implemented rational choice, taking into account given criteria. The difference is that we have introduced such criteria as anti-spoofing. The access control system for robotic systems works in real-time. It is implemented based on an algorithmic decision-making complex, which includes two-factor authentication to increase security: registration involves entering employee data from the keyboard (password, login, surname, first name, position) and physiological authentication (identification by face). Face detection is proposed to be implemented on several images: face, profile, face, and profile in the mask. A distinctive feature of our development is that often, a main requirement for "access control systems with facial identification" is the frontal location of the face relative to the camera; in our case, the image of the face at an angle was added.

339

Authors: S. Subbalakshmi, K. Madhavi

It is becoming increasingly common to outsource data to the cloud. We recommend that the data owner encrypt his or her data ahead of outsourcing it to the cloud to ensure the confidentiality of the data. However, effective search for encrypted information is an essential and complex issue for cloud data sharing. In the past, searchable encryption (SE) has been proposed in many studies as the most promising solution for the efficient retrieval of encrypted data. However, these mechanisms are open to internal attacks that can be restored by assuming keywords from an open trapdoor (TPD) in an autonomous manner. This raises new security concerns, especially data confidentiality and security of cloud-based shared applications. In this paper, we present an efficient mechanism for the public key encryption method with authentication search (PKE-AS) which securely encrypts data using public-key cryptography and validates recipient authentication before retrieval with a shared secret key (SSK) exchange. The shared key is used by the sender to encrypt the keyword and by the receiver to generate a search TPD. The key idea of the proposal is to make sure that the system is safeguard the user data from intrusive keyword guessing attacks. This contribution provides a secure communication model for secure data sharing, searching and accessing by employing the key exchange protocol among the users. The comparison of PKE-AS with existing methods shows low executing time in analysis with different keyword search with encryption, TPD and Verification. It shows an average of 0.52ms less execution time in comparison results. The analysis of the experiment results demonstrates that the proposed method is suitable for practical use in cloud data sharing.

364

Authors: Veni Chinnasamy, Sridevi Rajasekaran

Cyber security is an essential area of study because of the positive effects that networks have on modern society. As it becomes simpler for cybercriminals to launch novel assaults, the scale and complexity of today's networks continue to grow. Therefore, it is crucial to create an efficient instruction detection system (IDS) capable of constantly monitoring network traffic for suspicious activity and issuing alerts as necessary. Several researchers have employed machine learning (ML) and deep learning (DL) techniques to create an effective IDS. Amongst, an effective IDS detection model convolutional neural network (CNN)- long short term memory (LSTM extracts temporal and geographical aspects of network traffic to lower the false alarm rate (FAR) and raise the detection rate (DR). However, the epistemic uncertainty in intrusion data affect the efficiency of CNN-LSTM model. This paper handle uncertainty information by proposing a multivalued neutrosophic convolutional LSTM (MVN-ConvLSTM) which extracts different deep features. MVN-ConvLSTM uses a neutrosophic set (NS) which considers truth (T), indeterminacy (I), and falsity (F) memberships of every features. First, the intrusion features are mapped to NS domain as six sets, positive degree of truth-membership (T_A), positive degree of falsity membership (F_A), Positive degree of indeterminate towards falsity and indeterminate towards truth membership (I_A), negative degree of truth-membership (T_B), negative degree of falsity membership (I_B), and negative degree of indeterminate towards falsity and indeterminate towards truth membership (I_B). Next, an MVN-ConvLSTM is generated with four parallel paths, two of which take input from T and two from I, and a suitable combination of these paths is used to produce the output. The weights of the neural network are updated in four different directions at once using a back propagation approach. The efficiency of MVN-ConvLSTM to handle uncertainty in intrusion dataset is proved by comparing conventional CNN models which provides lower FAR and high DR. Finally, the test results show that the MVN-ConvLSTM achieves accuracy value of 94.63%, 92.84% and 91.84% on three different datasets CIC-IDS2018, WSN-DS and UNSW-NB15.

386

Authors: Anjaneya P, Rajini G. K

Light field imaging, which can gather global visual data, has garnered attention from the computer world in recent years. Inter and intra frame correlation, big data storage, and poor reconstructed depth quality still plague light field area. In this work, greedy discrete wavelet and poincare recurrence network (GDW-PRN) is proposed for robust lossless image compression of hyper spectral images. First, spatial preprocessing is carried out using discrete reduction wavelet transform to remove unwanted artifacts. Next, dimensionality reduced visual data representation is applied by means of greedy band to the pre-processed images, therefore addressing the issues related to large data storage. Finally, lossless image compression is performed by utilizing poincare recurrence network. The entire proposed work was carried out on light field hyperspectral imagery data using MATLAB simulation tool. The proposed work has been compared to the existing methods such as compression based on differential pulse code modulation (C-DPCM), and 3D wavelet transform and spectrum learning with regression vector (3DWT-SRV). On comparison the acquired findings demonstrate that the suggested GDW-PRN process has high compression proportion light field image storage performance as well as leading to high peak signal to noise ratio (PSNR).

406

Authors: Amel Austine, Suji Pramila Richard Nelson

The proposed system focuses more on maintaining D2D links as long as possible which requires an optimum resource allocation initially. Considering this as the optimization issue, this work finds a solution to solve this by proposing a method based on a hybrid optimization concept with game theory. The hybrid concept includes the hybridization of ant colony optimization and Beluga whale optimization (BWO) with a game theory known as ant Beluga with game theory based optimization (AB-GTO) for resource exchange. This enables frequent updates of interference level indicators of each link and maintains consistent links between devices. The proposed solution will be under the consideration of minimization of interference and maximum sum rate as well. The adopted AB-GTO-based REFD2DC in the LTE-A system employing AB-GTO has a high sum rate value over ACO-Game, T-REX RAN, T-REX DRAN, and T-REX CYC.

428

Authors: Abdalla El-Dhshan, Hegazy Zaher, Naglaa Ragaa

This paper proposes a novel metaheuristic technique called two-stage crow search algorithm (TS-CSA). The TS-CSA extends the original crow search algorithm (CSA), that mimics the intelligent foraging behavior of crows. TS-CSA integrates a two-stage search process to improve the CSA in three aspects. First, the two search stages are designed to balance the algorithm's global search and local search capabilities. Second, TS-CSA incorporates a "leaders group" to enable high-quality individuals to guide other individuals and perform global searches by expanding the search space using the best individuals. Third, TS-CSA proposes a new local search strategy for fine-tuning the individuals of the first stage. The proposed TS-CSA and the original CSA are evaluated alongside other metaheuristic algorithms: Grey wolf optimizer (GWO), marine predators algorithm (MPA), and pelican optimization algorithm (POA) using a well-known benchmark of thirteen optimization functions. These functions are commonly employed to assess optimization algorithms due to their complex landscapes and multiple local optima. The results show that the TS-CSA outperforms all competing algorithms, including the original CSA, in terms of convergence and solution quality across all two categories of optimization functions. Furthermore, the proposed algorithm shows potential for solving complex optimization problems across various domains, making it a valuable tool for practitioners and researchers in related optimization fields.

449

Authors: Zuhair A. Ahmed, Safanah M. Raafat

This paper presents an innovative approach to global path planning (GPP) that uniquely integrates an enhanced Bidirectional A* search algorithm, gradient descent smoothing, and orientation filter interpolation. These methods are applied within a quadratic approximation-based potential field and global cost map. The key idea is successfully integrating these powerful techniques, ensuring minimal impact on computational time while enhancing path size and smoothness. Rigorous testing in environments like the Amazon web services (AWS) RoboMaker Hospital Gazebo environment and the sections at the control and systems engineering building at Iraq’s University of Technology (CSE-UOT) in Iraq shows significant improvement over existing planners like wavefront GPP and robot operating system (ROS) default GPP. The proposed algorithm reduced pathfinding time by approximately 60.3% and path size by 9.0%. These findings highlight the efficiency of the proposed global planner, suggesting it as a robust solution for autonomous robotic navigation in complex architectural scenarios.

473

Authors: Muhanad Abdul Elah Alkhalisy, Saad Hameed Abid

The efficiency of distance learning can be assessed by examining specific student behaviors during online courses and assessments. Detecting abnormal student behavior can guide evaluating the effectiveness of learning. Detection speed, accuracy, and model efficiency are essential considerations in distance learning environments. This study proposes a model for detecting abnormal student behavior based on a modified YOLOv5 object detector. Firstly, to achieve model efficiency, the YOLOv5 feature extraction network is pruned by removing the portion responsible for extracting high-level feature maps of small objects, which consumes significant computing resources and parameters. Also, the model's feature fusion part and prediction layers are adjusted accordingly. A convolutional block attention module (CBAM) is added between the model's neck and prediction head to boost the model's focus on students' areas and detection accuracy. Five behaviors were tested on a recently created dataset. The results show that the suggested SPL-YOLOv5 detection method outperforms the SSD-MobileNet, Faster R-CNN, the original YOLOv5 algorithms and state-of-the-art methods regarding performance. The suggested approach increased mAP@0.5 and mAP@0.5:0.95 by 0.14% and 0.13%, respectively, compared to the original YOLOv5. In addition, the model's inference time decreased by 2.7%, computational resources (FLOPs) by 40%, and parameters by 47%.

501

Authors: Syed.Karimunnisa, Yellamma Pachipala

Cloud computing benchmarks the dream of rendering computing as a utility, providing high agility and reachability from an existing set of technologies. It facilitates a wider dimension to architect and manage remote resources. Cloud technology with exponential growth is drilling towards issues that tend to lower its explored possibilities. As cloud systems by virtue deal with various virtualized resources, scheduling is opted as an important metric for measuring and leveraging performance. But scheduling efficiency is deteriorated by various parameters that pave scope for our research and projects immense need for improvising the overall makespan of the system. The proposed work aims at projecting a greater drift in the first phase by witnessing a sequence of phases like pre-processing the user tasks for improved accuracy, classifying the tasks with respect to resource demand and execution time using the improved density based clustering method (IDCM). The second phase deals with enhanced coot optimization algorithm for task scheduling (ECOA-TS) that proceeds and proves its novelty by adopting Cauchy mutation overcoming the convergence backdrop for generating an optimal mapping between clustered user tasks and VMs. The overall performance of the proposed work overrides by reduced makespan against existing state-of-the-art optimization algorithms like particle swam optimization (PSO), grey wolf optimization (GWO) and whale optimization algorithm (WOA) by 27.41%, 19.8%, and 15.33% respectively.

523

Authors: Arif Nugroho, Eko Mulyanto Yuniarno, Mauridhi Hery Purnomo

Inverse kinematics plays a significant role in the robotic motion control. The flexible way to formulate the inverse kinematics can be achieved by using a neural network. The drawback of the neural network-based inverse kinematics is that it has no feedback mechanism to compensate for the remaining error. To improve its performance, further development should be conducted. In the design of the robotic motion control for the NAO robot arms, this paper proposed a new approach that combines the neural network-based inverse kinematics with the Jacobian. This combination yields a closed-loop control system. This control system utilizes the neural network as the feedforward controller and the Jacobian as the feedback controller. In particular, the neural network-based inverse kinematics has a function to estimate a set of required joint angles for the joint actuators, and the Jacobian function is to compensate for the remaining error of the neural network-based inverse kinematics in the estimation of joint angles. By using this proposed approach, we obtained more accurate joint angles for controlling the joint actuators. The comparison result showed that the averaged MSE for the particle swarm optimization (PSO) was 3.47 x 10-3 rad, 1.19 x 10-3 rad for the neural network, and 3.72 x 10-5 rad for the proposed approach. The performance comparison result indicated that our proposed approach has a lower averaged MSE than the other ones. Accordingly, the result of this research confirmed that our proposed approach can provide more accurate joint angles for controlling the joint actuators such that the robot’s end-effector can be driven along the desired path in the cartesian space.

552

Authors: Adil L. Albukhnefis, Amar A. Sakran, AtaAllah Saleh Mahe, Maryam Imran Mousa, Ahmed Mohsin Mahdi, Aqeel hamza al-fatlawi

Intrusion detection systems are critical in identifying and mitigating cyber threats. However, the intrusion data often contains insufficient features, which can adversely affect the classification accuracy of machine learning algorithms. To effectively select optimal features from intrusion attacks, a highly efficient model is required to extract highly correlated features. Nevertheless, traditional detection systems may experience low accuracy and high false-positive rates. This paper proposes a hybrid model for improving intrusion detection systems using mean-variance mapping optimization (MVMO) and random search (MVMOR). The strategy of the proposed model is MVMO algorithm is used to search for the optimal feature subset. while, the random search is employed to optimize the hyperparameters of the machine learning algorithm (classifier). The proposed hybrid model seeks to optimize the feature selection and parameters of classifiers at some time. The performance of the hybrid model is evaluated on the NSL-KDD as a benchmark dataset. The proposed MVMOR achieves an accuracy of 88%, while the conventional MVMO achieved only an 80% accuracy rate. The empirical results show the proposed model has the potential to offer better protection against various cyber threats, thus making a valuable contribution to the field of Cybersecurity.

576

Authors: Narjes N. Jasem, Sadiq A. Mehdi

This research paper introduces a novel encryption algorithm that utilizes a hyper six-dimensional chaotic system to enhance image security. The algorithm incorporates randomization, switching, diffusion, and XOR operations in a series of stages to ensure robust encryption. Two sets of keys are generated using the chaotic system, adding random and unpredictable elements to enhance the algorithm's security. During the encryption process, the image's color values are transformed into red, green, and blue vectors, enabling precise manipulation at the pixel level. Pseudo-random shuffling, guided by chaotic keys, introduces an additional layer of randomness, making the encrypted image resistant to decryption and tampering attempts. XOR operations further modify the pixel values, while interconnecting the color vectors enhances the encryption's complexity and security. Experimental evaluation of the proposed algorithm demonstrates its efficacy. A high PSNR value of 7.9997 indicates preserved image resolution after encryption. The NPCR value of 99.6002 indicates robust resistance to pixel modifications and the UACI value of 35.3623 showcases uniform pixel distribution. The algorithm also offers a substantial key space of 2947, further strengthening its overall security. Notably, its sensitivity of 1015 highlights its resilience against potential attacks.

598

Authors: Purba Daru Kusuma, Ashri Dinimaharawati

This paper promotes a new swarm-based metaheuristic called four directed search algorithm or FDSA. FDSA is designed as a directed search-based metaheuristic without deploying any neighbourhood search. It contains four directed searches that are carried out sequentially. Each search has its reference. These four references are the best member; the resultant of three shuffled members within the swarm; a shuffled member within the swarm; and the resultant of the best member, a shuffled member within the swarm, and the corresponding member. FDSA implements a strict acceptance procedure so that new solution is accepted only if it provides improvement. The investigation is carried out to evaluate the performance of FDSA in solving the 23 functions. FDSA is also confronted with five new metaheuristics: northern goshawk optimization (NGO), average subtraction-based optimization (ASBO), coati optimization algorithm (COA), mixed leader-based optimization (MLBO), and attack leave optimization (ALO). This work also investigates the contribution or dominance of each search in the context of finding the optimal solution. The result shows that FDSA is superior to all these confronters by consecutively outperforming NGO, ASBO, COA, MLBO, and ALO in the 17, 13, 11, 18, and 11 functions. Its superiority is mainly in the high-dimension functions. Through investigation, there is no dominant search among the four directed searches in FDSA. Meanwhile, multiple search strategy is proven to improve performance significantly. On the other hand, the contribution of neighbourhood search is not significant.

627

Authors: Ahmed Abdulmunem Hussein, Esam Taha Yassen, Ahmed N. Rashid

This research proposes an efficient algorithm for solving the green vehicle routing problem (GVRP), capable of generating high-quality solutions while considering environmental impact and computational efficiency. We employ an adapted grey wolf optimizer (GWO) algorithm and Q-learning (QL) for parameter optimization, introducing a discrete grey wolf optimizer (DGWO), a discrete variant of the GWO. The DGWO leverages the 2-opt technique and the Hamming distance concept, making it suitable for addressing discrete problems like GVRP. The key novelty of our approach is the use of QL to refine the parameters of the DGWO, specifically the number of iterations and number of wolves. This application of QL significantly enhances the efficiency and effectiveness of the algorithm compared to DGWO with manual parameter tuning, highlighting the significance of QL in parameter optimization. The proposed discrete grey wolf optimizer-Q-learning (DGWO-QL) algorithm is extensively validated on benchmark instances of GVRP, demonstrating promising results. For smaller benchmark instances comprising of 20 customers and 3 stations, our approach outperforms in 12 out of 16 instances. When tested on larger benchmark instances, within a range of 111 to 500 customers and 21 stations, it achieves success in 10 out of 12 instances. Compared to existing methods, our approach demonstrates improved performance in terms of solution quality and computational efficiency. The results show the robust performance of the DGWO-QL, particularly under stochastic route scenarios, which underscores the advantage of the proposed technique. This study represents a significant contribution to the current body of literature by underscoring the potential of the DGWO-QL algorithm in generating high-quality solutions for GVRP.

649

Authors: Raghul S, Jeyakumar G

Evolutionary algorithms (EAs) in the repository of the evolutionary computing (EC) paradigm are simplistic and stochastic in nature. The desirable characteristic of adaptive simplicity has sustained research community’s rampant curiosity in EAs. Distributed evolutionary algorithmic (DEA) framework is an instinctive extension of EAs. This paper summarizes the design and implementation of a multi-threaded DEA (MTDEA) framework. Empirical analysis between a traditional DEA framework and a simulated MTDEA is presented. The MTDEA framework was outfitted with three plug-in algorithms to handle commonly occurring faults in distributed environments. The propriety of the MTDEA framework was validated in two customary applications - Image thresholding and Image reproduction. The thresholding and reproduction problems were addressed by incorporation of the differential evolution (DE) and genetic algorithm (GA) methods respectively, into the MTDEA framework. The performances of DE and GA routines were compared with their sequential counterpart frameworks. The MTDEA framework was evaluated for reliability and scalability on a high-performance computing (HPC) system.

677

Authors: Namdeo Baban Badhe, Vinayak Ashok Bharadi, Nupur Giri, Sujata Alegavi

Remote sensing image captioning is the challenging task due to low global information, single feature extraction and lack of detailed image captions. To address these issues, this research proposed a deep attention based DenseNet with visual switch added bidirectional long short-term memory (DADN-BiLSTM) for captioning. In this research, initially the images and captions are collected from captioning dataset to smooth away small structures. After that, a double attention mechanism is applied to DenseNet for capturing weak features and to improve the problem corresponds between image feature and captioning information. At the same time, a clustering-based segmentation is more useful and easier to segment the image as smaller parts to make the access easily. Moreover, a decoder is used to improve the use of captioning context information. Then the proposed system is implemented in PYTHON and the performance is evaluated against existing methods in terms of some relevant evaluation metrics such as, recall-oriented understudy for gisting evaluation, accuracy and bilingual evaluation understudy. Finally, the experimental results achieve higher scores in all evaluation indicators such as 0.8925 BLEU1, 0.8514 BLEU2, 0.8252 BLEU3, 0.8312 BLEU4 and 0.8611 ROUGE score on UCM captions, 0.8532 BLEU1, 0.7912 BLEU2, 0.8351 BLEU3, 0.7215 BLEU4 and 0.8139 ROUGE score on Sydney captions and 0.8125 BLEU1, 0.7501 BLEU2, 0.6812 BLEU3, 0.7254 BLEU4 and 0.8245 ROUGE score on RSICD captions.

702

Authors: Santhosh Kumar S, B.K Raghavendra

Coffee is used by about two-thirds of the population in their daily life in one way or the other. However, their significance is greatly affected by the diseases that occur in the coffee leaves. This work proposes a colour-based filtering technique to determine the diseases that arise in the coffee leaves. Initially, the image obtained from the dataset is pre-processed, and contrast enhancement takes place using top–hat transform-contrast limited adaptive histogram equalization (THT-CLAHE) to enhance the visual characteristics. Next, the background present in an image is removed and the leaf is filtered depending on its colour into healthy and diseased leaves via supremum distance-fuzzy C means (SD-FCM) machine learning technique. Then, to obtain minute details, patches are extracted from the diseased leaf. Afterward, by employing double adaptive weight strategy-mexican axolotl optimization (DAWS-MAO), feature extraction is carried out followed by the selection of crucial features. For the classification of leaf diseases, the selected features are further fed into the tuned hard sigmoidal-multi-layer perceptron neural network (Tuned HS-MLPNN). In the end, based on the area of disease prediction, the severity of the disease thus classified is predicted using machine learning. The proposed Tuned HS-MLPNN method achieves the classification accuracy of 98.65%. Finally, the superiority of the proposed model is analysed with the Support Vector Machine (SVM), Naïve Bayes (NB), and artificial neural network (ANN).

728

Authors: Riyadh Rahef Nuiaa, Saif Ali Abd Alradha Alsaidi, Bahaa Kareem Mohammed, Ali Hakem Alsaeedi, Zaid Abdi Alkareem Alyasseri, Selvakumar Manickam, Mohammed Abdulridha Hussain

In recent years, there has been an increase in distributed reflective denial of service (DRDoS) attacks, particularly those that target open lightweight directory access protocol (LDAP) servers. These attacks involve transmitting a small request to a large number of available LDAP servers, seeking information from all users. Consequently, the servers respond with significantly more data than the original request, amplifying the traffic and overwhelming the target with massive amounts of data. Therefore, this paper proposes a novel model for detecting LDAP-based DRDoS attacks by utilizing an enhanced particle swarm optimization (PSO) algorithm based on an adaptive weighted threshold (AWTPSO) model. The proposed AWTPSO model incorporates network traffic features and LDAP protocol characteristics to identify attack patterns. It further employs an adaptive weighted threshold model to dynamically adjust the threshold value for each feature. The enhanced PSO algorithm optimizes the threshold values, thereby improving the detection accuracy of the proposed model. The proposed AWTPSO detection model has been validated using the recent CICDDoS2019 dataset (LDAP sub-dataset). The experimental results demonstrate that the AWTPSO model effectively detects LDAP-based DRDoS attacks with exceptional accuracy of 99.99% and minimal false positives of 0.01%, surpassing other state-of-the-art techniques. Consequently, the proposed model presents a highly promising and robust solution for detecting the threat of LDAP-based DRDoS attacks on enterprise networks.

748

Authors: Agus Wahyu Widodo, Samingun Handoyo, Irfany Rupiwardani, Yusi Tyroni Mursityo, Imam Nurhadi Purwanto, Heni Kusdarwati

The implementation of a CNN1D model is a big challenge because it involves many hyperparameters which have to be tuned well. The research has goals to compare the classification performance of the C4.5 tree and CNN1D models applied in the imbalanced medical dataset in six performance metrics. The oversampling method is applied to the training dataset to form the oversampling training data which is divided into 5 folds of cross-validation data for tuning hyperparameters of both models. By using the grid search method, there are obtained the optimal CNN1D hyperparameters are [256, 4] for the filter and kernel sizes, and the optimal C4.5 hyperparameters are [25,7] for the minimum number of instances in the splitting node and the depth of C4.5 tree. The models with optimal hyperparameters are trained using the oversampling training data and evaluated their performance in both of original training and testing datasets. In the training dataset, the CNN1D outperforms the C4.5 tree in all six metrics with a value of 99% except in the matthews correlation coefficient (MCC) metric with 73% besides the performance gaps of both models being very large. In the testing dataset, the CNN1D also outperforms the C4.5 tree in four metrics with a value of around 99%, but in the MCC and the area under curve (AUC) metrics, the C4.5 tree outperforms the CNN1D although their performance gaps are narrow enough. An interesting future work is to use the MCC or AUC criteria in the tuning hyperparameters of models for the classification of imbalanced class datasets.

12

Authors: Shreenath Kannughatta Narasimhamurthy, Vasantha Kumara Mahadevachar, Ram Kumar Thayur Narasimhamurthy

Copy-move forgery detection (CMFD) is a well-known digital image forgery technique that is used in detecting forgery regions of images. The copy-move forgery in images occurs when a specific section of an image is attached to a new component of the same image to replicate the forged image parts as an original. The forgery appears to be realistic because even after being forged in the objective region, the image acquires all the basic qualities of the original image. Some post-processing functions, such as scaling or rotation invariant, noise handling, hybrid image manipulation, etc., limit the capability of CMFD models. To overcome these limitations, the features are extracted from the Hybrid feature extraction using modified speeded up robust feature (M-SURF) with accelerated KAZE (A-KAZE) feature description and detection algorithm. The main advantages of the SURF descriptor are: the SURF features are resistant to several post-processing attacks and use Laplacian of gaussian to differentiate background and foreground features clearly. The A-KAZE descriptor is robust in detecting scale-invariant of forged regions and poorly localized key points of objects. A-KAZE constructs the invariant scaling levels using nonlinear diffusion filtering. It smooths the image while preserving the edges and decreasing speckle noise. As a result, it captures the precise location of features during the feature extraction process. Following the feature extraction, the matched features are compared, and removed false matches by applying random sample consensus (RANSAC) technique. The performance evaluation of the proposed Hybrid MSURF with A-KAZE CMFD method is validated on MICC-F220, MICC-F2000, CoMoFoD datasets and surpasses the existing CMFD methods. The proposed CMFD achieved better results compared to existing methods such as when CMFD based on DHE-SURF features along with mDBSCAN clustering and CMFD based on SURF, BRISK features along with DBSCAN clustering measured in terms of Precision of 99.93%, Accuracy of 99.46%, F1-score of 98.21%, true positive rate (TPR) of 99.93%, and false positive rate (FPR) of 4.67%.

39

Authors: Amir Yasseen Mahdi, Siti Sophiayati Yuhaniz

This paper proposes an improved binary sparrow search algorithm (IBSSA) as a search strategy within the feature selection (FS) methods. Its main objective is to use clinical texts to improve COVID-19 patient categorization. The constant need for an efficient FS system and the favorable outcomes of swarming behavior in numerous optimization situations drove our efforts to develop a novel FS strategy. Additionally, clinical text data are frequently highly dimensional and contain uninformative features, which have a major impact on the classifier's accuracy, making FS a key machine-learning step in data pre-processing to reduce data dimensionality. The bi-stage FS approach is used in this work to elect the features. At the initial stage, we employed a term weighting scheme (TWS) that assigned a weighted score to each feature by measuring the significance of the features obtained from the pre-processing model using a new weight calculation method called root term frequency-core-inverse exponential frequency (RTF-C-IEF). Next, finding the most relevant and almost optimal feature subset for COVID-19 illness diagnosis is done in the second stage using a freshly developed methodology that was inspired by the way sparrow’s behavior. The suggested modification method for the sparrow’s algorithm is composed of several stages of advancement. The main objectives are to promote the exploration of the search space and increase the algorithm's variability. In order to evaluate the proposed model, various classifiers were employed on two datasets, each of which had 1446 and 3053 cases, respectively. The experimental and statistical results demonstrate that the proposed IBSSA is significantly superior compared to other comparative optimization algorithms, and it successfully upgrades the shortcomings of the original SSA. Moreover, the IBSSA has the highest accurate performance when compared to other rivals by the SVM classifier, Where, average removed features are 77.99% and 83.5%, with improvement percentages by F1-scores: 84.95% and 95.94 % for both datasets, respectively.

69

Authors: F. Al-dolaimy, Rabei Raad Ali, Noor Nabeel, Wisam Subhi Al-Dayyeni, Fatima Hashim Abbas, Hussein Muhi Hariz, Salama A Mostafa, Mohammed Ahmed Jubair

Vehicular ad hoc networks (VANETs) recently covered a wide range of intelligent transportation systems (ITSs) and applications. VANETs consist of convinced unique characteristics such as dynamic movement and high speed. Due to these characteristics, link failures occur, and delay and routing overhead is greatly increased, directly affecting the effectiveness, Quality of Service (QoS), and stability of VANETs. To achieve an efficient and reliable network performance, this paper proposes QoS Aware Hybrid Optimization for Improving Path Selection (HOIPS-VANETs) in VANETs. This hybrid optimization combines the Improved ant colony optimization (ACO) and Effective Whale Optimization Algorithm (EWOA). The EWOA algorithm is used for initial optimal path selection, and ACO is used to find the best optimal solution to achieve effective communication in VANETs. This optimization technique is also applied to low-density, medium-density, and high-density scenarios, as it is compared with the earlier methods. During the experimentation process, our findings reveal no noticeable change in the performance of the earlier methods when applied to the low-density and medium-density scenarios. Still, the performance is gradually reduced when applied to the high-density scenario. On the other hand, the performance of the proposed approach is better for all three tested scenarios and demonstrates effective communication for the VANETs.

94

Authors: Mohammed Kdair Abd, Luay G. Ibrahim, Firas M. F. Flaih

Incorporating renewable energy storage systems in power grids has presented significant challenges in maintaining a stable power generation structure and load frequency within interconnected grids. A promising solution to support the reliable and safe operation of the power system is the use of pumped storage units because of their excellent adjustment characteristics, including fast power response and convenient start and stop features. To optimize the performance of the pumped storage system, this article proposes the implementation of a linear-adaptive disturbance rejection controller (LADRC) in conjunction with the segmentation particle swarm optimization (Se-PSO) algorithm for parameter tuning. This article establishes a precise hybrid load frequency control (LFC) model for a two-area interconnected power grid incorporating pumped storage power plants (PSPP). It determines the optimal LADRC parameters for varying load disturbances in different areas of the power grid. The proposed control method systematically simulated pumping and power generation conditions in a PSPP. Simulation results indicate that the proposed method enhances dynamic stability by approximately 85%, offering greater anti-disturbance ability and adaptability than the traditional proportional-integral (PI) controller.

115

Authors: Arundhati Nelli, Rashmi Jogdand

Maximizing resource utilization by minimizing service level agreement (SLA) violation is considered to be extremely difficult process for scheduling of realtime workload on multi-cloud platform. In particular the proposed work considers both energy and performance as SLA minimization constraint for scheduling of workload in multi-cloud platform. The scheduling optimization of SLA minimization is done as single objective function using dragonfly soft computing approach. Further, the scheduling optimization of min-max is done as multi-objective for maximizing resource utilization with minimal SLA violation using dragonfly soft computing approach. The results show that the min-max workload scheduling (MMWS) has improved the resource utilization by 16.02%, 7.92%, and 2.52% respectively for montage workload and 9.92%, 4.07%, and 1.11% for SIPHT workload in comparison to the existing workload-scheduling adaptive-dragonfly algorithm (WS-ADA), reliability multi cloud-scheduler (REL-MC) and service-level agreement-based workload-scheduling (SLA-WS) method respectively. The results show that the MMWS has reduced the SLA violation rate by 81.92%, 69.23%, and 30.11% respectively for montage workload and 84.57%, 61.32%, and 12.62% for SIPHT workload in comparison to the existing WS-ADA, REL-MC and SLA-WS methods respectively. Hence, from the experiment outcomes it is shown that the proposed MMWS technique reduces SLA violation with improved resource efficiency in comparison with WS-ADA, REL-MC and SLA-WS.

137

Authors: Lakhvinder Singh, Dalip

An intelligent information technology-based system offering practical health advice can improve the superiority of lifespan for individuals with chronic illnesses. This study develops a BFCNN (Bees featured CNN) model for the classification or prediction of heart, liver, and kidney diseases. The main objective of the proposed classification model is to accurately diagnose a patient's disease. The research model aims to construct a highly efficient deep-learning system for patients with heart, liver, and kidney diseases. A novel approach for detecting multiple diseases in medical datasets using a BEE-featured CNN model. The BEE-featured CNN model is a modified version of the traditional CNN model that incorporates the behavior of bee swarms to enhance the accuracy of the model. The bee-inspired algorithm used in the model involves the use of multiple artificial bees that simulate the behavior of real bees to search for optimal solutions. The proposed model was trained and tested using a dataset consisting of medical datasets of patients with different diseases. The images were collected from various sources and labeled with the corresponding disease type. The dataset was split into training and validation sets, with 80% of the datasets used for training and 20% for validation. The results of the experiments showed that the proposed BEE-featured CNN model outperformed the traditional CNN model in detecting multiple diseases in medical datasets. The results of the prediction model demonstrate outstanding performance, with an accuracy of 96.7%, specificity of 99%, and Sensitivity of 97%. The research system demonstrates promising results in classification accuracy and holds potential for usage in medical and e-health applications.

164

Authors: Robert Marco, Sharifah Sakinah Syed Ahmad, Sabrina Ahmad

One of the main reasons that hinders making software effort estimation remains a most of the unresolved problem due to the heterogeneous nature of software data with complex structures. In processing nonlinear data, the long short-term memory (LSTM) model is often used for the purpose of solving discriminative and generative problems. Taking into account the fact that the LSTM network have low computational efficiency, due to the need to set a large number of hyperparameters. However, training deep learning models requires expensive work in terms of specifying hyperparameter configurations in the model. The grid search (GS) optimization method is used to find the best hyperparameter values for deep learning networks, which have many different hyperparameters that affect how well the network architecture works. In this paper, we proposed the grid search method as a quick step toward optimizing the parameters of the LSTM model. An empirical study was conducted using five datasets. From our results, we have seen that the LSTM-grid search model consistently performs better across datasets in mean absolute error (MAE) and root mean squared error (RMSE) compare to existing work on using machine learning approach for software effort estimation. The main advantage of training the LSTM show that the grid search finds hyperparameters which results in faster convergent, the possibility of generalization, and better coefficient of determination in estimating software effort.

191

Authors: Asmaa Ftaimi, Tomader Mazri

Recently, much effort and interest have been devoted to developing accurate and reliable blockage prediction systems. The latest research tends to apply artificial intelligence to enhance blockage prediction systems' accuracy, especially when dealing with non-line-of-sight radio signal propagation. However, AI models can carry inherent vulnerabilities that might damage the relevant system. Therefore, this paper aims to examine the effect of adversarial examples on AI-based blockage prediction systems. By performing adversarial example attacks on the AI-based blockage prediction model, we have shown the drastic impact on root mean square error that has arisen from 0.26 to 0.82 after introducing adversarial examples with 0.08 magnitude. Additionally, we have proposed a defensive approach based on encryption techniques to proactively prevent further compromise of the targeted system. The proposed method not only enhances data privacy but also help to prevent adversarial attacks that exploit transmitted data.

215

Authors: Shruti. R. Danve, Manoj S. Nagmode, Shankar B. Deosarkar

The extreme growth in smart mobile users and the demand for higher data rates have paved the way for developing 5G networks. Additionally, increased wireless data traffic implies more power consumption in network equipment. Mobile network service providers are concerned about the energy efficiency (EE) of cellular networks. In this era of advanced wireless communication, Massive multiple input multiple output (MMIMO) technology satisfies the demand for a hyper-connected society. To deal with the problem of increased hardware power consumption and cochannel interference in MMIMO, a quad methodology is proposed to evaluate the performance of a cellular network. This quad package comprises linear precoding (LP), power consumption reduction (PCR), User selection (US), and antenna selection (AS). The proposed quad methodology is used for enhancing the energy efficiency and capacity of a cellular network. Two LP techniques, zero forcing (ZF) and minimum mean squared error (MMSE) are compared to evaluate the EE of MMIMO with antenna selection. It is observed that EE for MMIMO with AS using ZF is three times greater than MMSE. MMIMO system with the suggested quad methodology provides 45% and 20% improvement in capacity and energy efficiency respectively over only the norm US.

239

Authors: Saif Mohammed Al-azzawi, Mohanad A. Deif, Hani Attar, Ayman Amer, Ahmed A. A. Solyman

Due to global lockdown policies implemented against COVID-19, there has been an impact on electricity consumption. Several countries have emphasized the significance of ensuring electricity supply security during the pandemic to maintain the livelihood of people. Accurate forecasting of electricity demand plays a crucial role in ensuring energy security across all nations; accordingly to achieve this objective, this study employs metaheuristics optimization algorithms to enhance the prediction model's operation, such as Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Random Forest (RF), at an optimized level to minimize errors. Two metaheuristics optimization methods, Particle Swarm Optimization (PSO) and Genetic Algorithms (GA), are utilized. The suggested prediction models are trained using daily power usage data from three US urban regions. In terms of prediction accuracy, the findings show that KNN with PSO surpasses the other models. The COVID-19 pandemic reduced power usage by 20% relative to pre-pandemic levels.

264

Authors: Purba Daru Kusuma, Faisal Candrasyah Hasibuan

This paper introduces a novel swarm-based metaheuristic called swarm magnetic optimizer (SMO). SMO imitates the behaviour of two magnets close to each other: pushing toward or pulling away from each other. This push-pull mechanism is then adopted to become a novel search in SMO. SMO is set up as a swarm of magnets that move autonomously. In the early iteration, the pull strategy is dominant. Meanwhile, it declines during the iteration and is replaced by a push strategy. The determination between these two strategies is calculated stochastically. SMO consists of three sequential phases in every iteration where the corresponding magnet and its reference run a push or pull search in each phase. The reference used in each phase is the global best magnet, a randomly selected magnet, and a randomly generated magnet. This paper evaluates SMO through simulation to find the optimal solution for 23 functions. The performance of SMO is compared with five latest metaheuristics: mixed leader-based optimizer (MLBO), guided pelican algorithm (GPA), zebra optimization algorithm (ZOA), coati optimization algorithm (COA), and clouded leopard optimizer (CLO). The result shows that SMO is better than MLBO, GPA, ZOA, COA, and CLO in consecutively 22, 15, 11, 13, and 14 functions. The superiority of SMO, especially, is in solving high dimension functions. Meanwhile, in the fixed-dimension multimodal functions, SMO is still superior to MLBO but less superior to GPA, ZOA, COA, and CLO.

289

Authors: Kunjan D. Shinde, Vijaya C

The advancement in very large scale integration (VLSI) technology and field programmable gate array’s (FPGA) parallel constructive nature for digital circuits has made the implementation of finite impulse response (FIR) filters increasingly relevant in real-time. In FIR filters the computational complexity increases with the length of the filter; numerous techniques have been developed to design viable architectures for realizing FIR filters. The multiple input multiple output (MIMO) based parallel FIR filter architecture often requires a large area for realization as the level of parallelism and filter order increases; this study presents an architecturally enhanced, novel parallel architecture for FIR filter design which eliminates the resource dependencies on the level of parallelism. The proposed architecture addresses the issue by limiting the number of multipliers required irrespective of the change in the level of parallelism and further modifies the data flow to improve the iteration period in the proposed design. The level of parallelism (L) is fixed to 8 in the presented study as the value is neither too low nor high; it is important to note that the increase in the level of parallelism will increase the number of samples generated at a given time. The presented work is demonstrated using tap 16 FIR filter implemented on FPGA VIRTEX 4 xc4vsx35-10ff668 and VIRTEX 5 XC5VSX95T-1FF1136 platforms, and filter-order 16 FIR filter is implemented on ARTIX 7 xc7a200t-2fbg676. The functionality is verified using Xilinx ISE 14.7 and Matlab 2018 environments. The post-synthesis results and comparative study show the design validation; the proposed architecture outperforms the benchmark and conventional MIMO-based parallel FIR filter architectures with an improvement in total delay by 70% w.r.t. [2] and 93% w.r.t. [3] and 2% w.r.t. [4] as compared in case studies 1 – 3. The reduction in area metric is observed as dipping in the slice requirement by 31%, 47%, and 110% and dipping in the LUT requirement by 58%, 66%, and 65%, further, the delay metric is improved by 49%, 80%, and 67% when compared with MIMO-based parallel architecture of [1] on the same platforms suggested in [2-4] respectively.

318

Authors: Rafael M. J. O. Silva, Attilio Sbrana, Paulo A. L. de Castro, Nei Y. Soma

Deep learning models, despite their potential, often function as “black boxes”, posing significant challenges to interpretability, particularly in sensitive fields such as healthcare and finance. Addressing this issue, we introduce a novel, human-understandable metric aimed at enhancing the interpretability of local interpretable model-agnostic explanations (LIME). Distinct from previous methodologies, this metric is designed to assess the shift in classification probability upon the removal of features (words), thereby providing a unique insight into interpretability. We deploy a convolutional neural network (CNN) for sentiment analysis, interpret predictions utilizing LIME, and evaluate these explanations using three distinct metrics: our proposed metric, a conventional model-based metric, and human evaluations. Through rigorous validation, our metric demonstrated high recall performance, a key indicator of relevant instance retrieval. Results showed worst-case and best-case recalls of 80.29% and 98.19% respectively, against a logistic regression metric for “good” and “excellent” classifications. Comparisons with human evaluations using single-word explanations revealed worst-case and best-case recalls of 82.03% and 94.37%, respectively. These high recall values highlight our metric's effectiveness in aligning with both human judgments and model-based metrics, emphasizing its capacity to capture essential explainability aspects. Furthermore, our study also outlines certain LIME limitations, setting the stage for future interpretability-focused AI research.

348

Authors: Abhishek Singhal, Devendra Kumar Sharma

Voice signals are the most convenient and essential method to establish the communication between human beings. The linguistic characteristics of the human being can be recognized with the help of voice signal analysis such as the ascent of language, health status, emotions, age, gender, etc. Gender is an unprecedented peculiarity of the speakers. This peculiarity can be recognized through the analysis of the voice signals. This paper presents the precision value of gender, i.e., transgender, female and male. The live voice samples for females and males are recorded as well as the live voice samples for the transgender in India are recorded first time because the voice samples of the transgenders are not available at the authenticated sources. The error factor and accuracy is also calculated for the proposed model, which is utilized for the gender identification of the speakers by analyzing the voice signals. In the proposed work, bidirectional long short-term memory (BiLSTM) architecture with recurrent neural network (RNN) model is used as a classification algorithm with mel frequency cepstral coefficients (MFCCs). The MFCC is utilized as a trait of the signals used to identify the gender of the human beings. The outcomes of the proposed model are not dependent on the content of the articulation and the dialect of the speakers. The precision values for female, male and transgender, the accuracy and error rate of the proposed model, depend on applied quantities of voice signals in testing and training datasets. The average precision value is 93.91%, 94.70%, 97.43% and 95.35%for the male, transgender, female and proposed model, respectively. It is found that the female precision value is the highest, and the male has the lowest. Further, the average error rate of the proposed model is 0.045. The gender identification accuracy is 95.52%.

372

Authors: Robert Marco, Sharifah Sakinah Syed Ahmad, Sabrina Ahmad

The uncertainty inherent in the predictive environment of software effort estimation, is due to the limited availability of data information and the expensive costs associated with data collection. Consequently, there is difficulty in making accurate predictions with insufficient software effort estimation data, due to the limited information in the data. The probabilistic deep generative model conditional variational autoencoder (CVAE) is capable of producing synthetic data and modeling complex data distribution characteristics that is similarity to the real Unfortunately, because the method was previously developed for the classification task, the application of the technique to the regression task has received little attention. This study aims to construct a CVAE model combined with the relevance function contained in the re-sampling method. Relevance function method aims to create a label on the target. Statistical tests, such as the Levene test, t-test, and Kolmogrove Smirnov test, are utilized to compare the results to the real data. This study considers seven popular algorithms for comparison, such as synthetic minority oversampling technique regression (SMOTER), generative adversarial networks (GAN), conditional tabular generative adversarial networks (CTGAN), gaussian-copula (GC), copula GAN (CGAN), and variational autoencoder (VAE). CVAE approach has the best augmentation performance quality with accuracy values (MAE; RMSE; RAE; R2) compared to existing models in each china dataset (923.330; 1470.536; 0.436; 0.848) and desharnais dataset (9.540; 11.694; 0.041; 0.978), respectively. The proposed model generates synthetic data which is similarity to real data in the software effort estimation context. In addition, synthetic data improves the quality of performance on baseline machine learning in software effort estimation contexts rather than using real data.

398

Authors: Seema Sabharwal, Priti Singla

Sign language translation through deep learning is a popular topic among researchers nowadays. It opens the doors of communication for deaf and mute people by translating sign language gestures. The translation of input sign language gestures into text is called the sign language translation system (SLTS). In this paper, optimised machine learning-based SLTS for Indian sign language (ISL) has been proposed to facilitate deaf-mute persons. Further, this paper presents a simulation analysis of the impact of the number of convolution layers, size of stride function, epochs, and activation function on the accuracy of translation of ISL gestures. An optimised ISL translation system (ISLTS) for fingerspelled alphanumeric data of 36 classes using a convolution neural network (CNN) with a novel RADAM_NORM optimiser has been proposed. The proposed system has been implemented using two datasets- the first customised ISL alphanumeric dataset has been taken from Kaggle and the second dataset has been prepared by the author consisting of 36 classes and nearly 50K images. The accuracy of the proposed ISLTS on the first dataset is 99.446 % and on the second dataset is 97.889%.

422

Authors: Ibrahim A. Zedan, Mona M. Soliman, Khaled M. Elsayed, Hoda M. Onsi

SIFT-based techniques have achieved satisfying performance in detecting copy-move forgery (CMF). Typically, these techniques find the matched regions of an image and re-examine them using a variety of methods to determine whether CMF has occurred or not. However, these techniques have some shortcomings related to how they handle false matches, which usually occur due to image continuity or self-similarity. First, a spatial distance threshold or segmentation-based methods are commonly utilized to handle image continuity. Second, several external methods along with manually created thresholds are utilized to handle image self-similarity. In this paper, we propose a new matching strategy that is resistant to false matches while reducing reliance on external methods and thus avoiding several thresholds. We model the keypoint as a whole region rather than a single point and employ the intersection over union measure to deal with image continuity. To reduce false matches caused by image self-similarity, we combine the cross-matching test with a modified distance ratio test. This combination takes into account the ability to detect multiple cloning. Moreover, we utilize a support vector machine to learn the threshold(s) needed to decide if CMF has occurred or not. The proposed methodology is evaluated over three challenging datasets: MICC-F600, Coverage, and MICC-F220. On MICC-F600 dataset, our proposed methodology outperforms other state-of-art techniques and achieves high precision of 99.38%, recall of 97.5%, and 98.42% of F1 score. Additionally, the comparative evaluation using Coverage, and MICC-F220 datasets proved the effectiveness of the proposed methodology to handle a variety of attacks.

450

Authors: Suadad Zaidan Khalaf, Mohamed Ibrahim Shujaa, Ahmed Bahaaulddin A. Alwahhab

Law enforcement and civilian protection are increasingly dependent on surveillance technologies. To prevent social, economic, and environmental harm, video surveillance situations like those in train stations, schools, and hospitals must automatically detect aggressive and suspicious behavior. Intelligent video surveillance systems now enable human interaction monitoring. Despite its security benefits, crowds and the camera's vision make it difficult to distinguish regular from abnormal activity. Therefore, there is a great deal of investigation into the many methods of identifying violent behavior. The detection approach presented in this research can be broken down into three distinct subfields. The classification methods employed led to the formation of these classes. Classifications include Gradient Descent (SGD), Adaptive boosting (ADA), Naive Base (NB), and Logistic Regression (LR) for detecting violent acts with machine learning. Methods for feature extraction and violence detection are derived using Principal Component Analysis (PCA). Dataset learning- based models for violence detection can also be evaluated using the AIRTLab dataset, a model developed to test the robustness of algorithms against false positives. In order to determine if the proposed model's resistance to false positives, accuracy (ACC) metrics were computed on the proposed model and used to set a benchmark for the AIRTLab dataset. According to the research literature, the proposed models are accurate of 98.31% with the logistic regression classifier having superior generalization skills.

472

Authors: Lusia Rakhmawati, Hapsari Peni Agustin Tjahyaningtijas, Wiyli Yustanti, Wiryanto Wiryanto

Due to extensive Internet usage, digital media can be quickly altered, copied, and distributed via information networks. As a result, digital information's illicit reproduction became a serious issue. As a means of copyright protection for digital media, digital watermarking has been demonstrated to be effective. Based on a discrete cosine transform (DCT), an improved robust watermarking technique considering image characteristics, a 1-D linear transformation, and an optimal embeddable AC coefficient is proposed in this paper. The host image is first divided into 8 x 8 non-overlapping block and block selection for DCT transformation is performed. The variance in each block is used to identify the embedding watermark location. A linear map is used to scramble the watermark image, which is embedded into the AC coefficients which has a maximum difference between embeddable and non-embeddable non-zero AC coefficients. Experiment results show that the suggested approach can withstand a range of a non-malicious attack with very satisfactory results for BER close to 0, and NC near to 1.

497

Authors: Noora Nazar, Qusay Al-Doori, Omar Alani

Low density parity check (LDPC) codes are highly regarded for their exceptional error correction capabilities, making them a preferred choice for error correction coding (ECC) in modern communication standards. These codes are implemented in specialized integrated circuits (ICs) that facilitate data transmission. However, the power requirements of complementary metal-oxide-semiconductor (CMOS) circuits, which operate across a wide frequency range, present a significant challenge for future communication systems. To address this challenge, this paper proposes a novel multi-frequency power reduction strategy for LDPC encoders. The strategy utilizes dynamic voltage and frequency scaling (DVFS), a well-established power reduction method in CMOS circuits. By combining DVFS with fuzzy logic control, the system optimizes the voltage supplied to the LDPC encoder, achieving substantial power reduction while maintaining coding efficiency, flexibility, and performance. The proposed approach dynamically adjusts the encoder's voltage levels based on real-time conditions, ensuring efficient power consumption across different frequencies. This innovative strategy aims to overcome the power challenges faced by future communication generations, which have been largely overlooked in previous research efforts. This paper aims to assess the effectiveness of a multi-frequency power reduction strategy for LDPC encoders. Through extensive analysis and evaluations, it demonstrates significant power reduction while maintaining LDPC coding efficiency. These findings contribute to the development of power-efficient LDPC encoder designs, meeting the evolving needs of communication systems. Results reveal the system's performance across different frequency ranges. In the low-frequency range (1 MHz to 100 MHz), a remarkable 90% power reduction is achieved. In the medium-frequency range (100 MHz to 1 GHz), the reduction is 50%, while in the high-frequency range (1 GHz to 5 GHz), it reaches 20% to 14.5%. Notably, the system operates most effectively in the low and medium frequency range, providing substantial power savings. These results highlight the potential of the multi-frequency power reduction strategy for LDPC encoders in addressing power challenges across different frequencies. The significant power reduction achieved demonstrates its effectiveness and adaptability to varying frequency demands. This contributes to the development of power-efficient LDPC encoder designs, optimizing performance and energy consumption in various communication systems.

523

Authors: Dhiza Wahyu Firmansyah, Riyanarto Sarno

Breath acetone concentrations were found to be correlated with blood ketone levels. Based on this evidence, predicting blood ketone levels using breath analysis and machine learning (ML) becomes possible. Nevertheless, a good ML model requires a large amount of training data. Under certain conditions, it is difficult to collect large amounts of data such as during the Covid-19 pandemic. To overcome this problem, we propose an augmentation technique to extend the number of training datasets using two step synthetic minority oversampling (SMOTE). The first step was to increase the amount of training data by combining it with synthetic data, while the second step was to balance the data at each ketone level. The strategy for using SMOTE with regression was further explained since this study aims to predict ketone levels with numerical output values and SMOTE is typically used in classification cases. The proposed method was evaluated by entering the data into several ML methods such as deep neural network regression (DNN-R), linear regression (ML-R), ransac regression (RC-R), K-nearest neighbour regression (KNN-R), decision tree regression (DT-R), random forest regression (RF-R), Ada boost regression (AD-R), Gradient boost regression (GB-R) and XG-boost regression (XGB-R). Based on the test results, when compared without the proposed method, an increase in accuracy was obtained on DNN-R, ML-R, RC-R, KNN-R, DT-R, RF-R, AB-R, GB-R, and XGB-R by 0.958%, 9.51%, 35.74%, 18.133%, 8.236%, 11.348, 9.47%, 5.093%, and 11.264% respectively.

548

Authors: Wissal Farsal, Mohammed Ramdani, Samir Anter

The graded multi-label classification (GMLC) is an extension of multi-label classification. Whilst a multi-label classifier is limited to predicting the set of relevant labels, a graded multi-label classifier predicts the degree of relevance of a set of given labels. A key challenge of this learning problem consists of modelling the dependencies among the labels to improve the predictive accuracy. The algorithm adaptation-based solutions, which modify the algorithms directly to handle GMLC were proven effective in modeling these dependencies in comparison to the transformation-based models which reduce the graded multi-label datasets into a set of multi-class or binary datasets. In this paper, we propose an adaptation of random forest algorithm (GML_DT) with an adapted CART (classification and regression trees). The adapted algorithm is based on a modified formula for the Gini Index which fully models the label dependencies. The performance of the new model was tested on a 101 benchmark datasets and compared against the most influential methods for GMLC. The evaluation metrics considered in the experimental study are proper to the graded multi-label setting, i.e., hamming loss and vertical 0-1 loss. The experimental results show that the proposed model outperforms the considered models for the graded multi-label performance metrics. The increase in performance and overall accuracy is quantified by a decrease in the hamming loss of more than 13% and a decrease in vertical 0-1 loss that exceeds 10% when compared to the state-of-the-art models.

568

Authors: Christy Atika Sari, Muhammad Hafizh Dzaki, Eko Hari Rachmawanto, Rabea Raad Ali, Mohamed Doheir

The exchange of confidential information should be done in a secure environment. Therefore, security is needed if the exchange of information is carried out using Internet media. The way to secure the information can be done using cryptography and steganography techniques. and vigenere cipher had been applied as cryptography techniques and the LSB method as steganography techniques. This research has also compared the effect of the Fibonacci sequence in the encryption process. In this study, six images were used, each with a different extension, size, and type of images. All these images will be inserted with the secret message resulting from the encryption that applies the Fibonacci sequence or without applying the Fibonacci sequence. Fibonacci series is used to generate a random key, which is used for encrypting the message in playfair encryption algorithm. The level of effectiveness of the Fibonacci sequence has been determined and measured using MSE, PSNR, Entropy, UACI, and NPCR. The highest PSNR was in a 512x512 pixel grayscale image with a result of 7.9632, the best PSNR obtained was more than 85 dB, while the best UACI obtained was more than 49% using Fibonacci and 34% without implementing Fibonacci. The NPCR obtained is both above 99%, it is just that the image that has been implemented by Fibonacci produces a slightly higher UACI.

591

Authors: Balaji Sreenivasulu, Anjaneyulu Pasala, Gaikwad Vasanth

Early discovery and precise classification of plant-lead diseases are used to handle their spread and enhance the overall yield and product quality. The deep learning approach attains significant achievements in image classification and recognition. Since the classification using deep learning mainly depend on large-scale dataset for preventing the overfitting issue. Image augmentation is required to be developed to eliminate the risk of overfitting during the classification. In this research, the deep learning model based augmentation namely uncertainty based progressive conditional generative adversarial network (UPC-GAN) is developed for improving the plant leaf disease classification. The UPCGAN is used to map the images from one domain to another domain in a paired manner and estimate the uncertainty with the created images. Moreover, UPCGAN performs pixel wise residual distribution using the independent distributed zero mean generalized Gaussian distribution (GGD). The progressive learning of UPC-GAN increases the differences in the augmented synthetic images for improving the classification using DenseNet121. The dataset used to evaluate the proposed UPCGAN-DenseNet121 method is PlantVillage dataset. The performance of UPCGAN-DenseNet121 is analysed using accuracy, precision, recall and F1-score. Existing research such as deep convolutional GAN (DCGAN)-GoogleNet, conditional GAN (CGAN)-DenseNet121 and Fast wide and deep feature extraction block (WDBlock) based GAN namely FWDGAN are used to evaluate the UPCGAN-DenseNet121. The accuracy of UPCGAN-DenseNet121 for 10 classes is 98.2%, which is high when compared to the DCGAN-GoogleNet, CGAN-DenseNet121 and FWDGAN.

611

Authors: Fatima Abdul Sattar AL-Taie, Ahmed Sabah Al-Araji

In this research, a predictive voltage neural controller and remote monitoring for the nonlinear proton exchange membrane fuel cell (PEMFC) system are implemented in real-time. This paper's major purpose is to precisely and rapidly determine the appropriate hydrogen partial pressure (PH2) control action to improve the fuel cell's nonlinear performance under varying load currents and to enable remote monitoring of the nonlinear PEMFC system based on the internet of things (IoT). The laboratory, virtual instrument engineering workbench (LabVIEW) package is used to demonstrate the real-time performance of the proposed predictive voltage controller applied to the 150-watt PROTIUM PEMFC, which will be used to generate the appropriate amount of hydrogen partial pressure control action that will enter the fuel cell for stabilizing the desired output voltage. The message queuing telemetry transport (MQTT) protocol and a Raspberry Pi 4 acting as a local server are the building blocks upon which the monitoring component of the proposed system is implemented in order to monitor the desired output voltage, the fuel cell output voltage, and PH2. The Raspberry Pi collects the necessary fuel cell data and sends it to the node-red dashboard for monitoring. According to the simulation and the experimental results obtained using the proposed predictive controller on PROTIUM PEMFC, the proposed controller can generate an accurate, prompt, and timely reaction to the hydrogen partial pressure control action to reduce the tracking voltage error and to get rid of the fuel cell output voltage oscillation. The proposed experimental work was compared to the simulation findings to confirm its effectiveness in terms of effectively tracking the desired output voltage, providing a fast response, and achieving the optimal partial pressure of hydrogen. However, in the simulation findings, a voltage error of 0.01 volts was observed without any oscillation. On the other hand, the experimental results indicate a slightly higher voltage error of approximately 0.1 volts, accompanied by oscillations of around ± 0.1 volts.

642

Authors: Yamamh Alaa Alamari, Ahmed Fanfakh, Esraa Hadi

Currently, there are around 4.95 billion people who use the internet, which creates a large audience with an increasing demand for activities that can be done online. Some examples of these activities include social networking, information sharing, and online shopping. Therefore, there is an urgent need for improved levels of secrecy and privacy. It has recently come to light that one of the most serious challenges to the mainstream adoption of business apps is that of online fraud. As a direct consequence of this, issues pertaining to authentic cation, authorization, and identification have emerged as critical considerations in today's open and accessible society. The act of recognizing an entity, whether it be a human, a computer, or a software program, is referred to as the identification process. Authentication and authorization are complementary processes that are used in security systems to decide which persons are permitted to access information resources over a network. Authentication is performed by the user's device, while authorization is performed by the security system. However, a variety of potential solutions have been suggested, one of which is the use of parallelism in order to boost the efficiency of authentication methods. This work proposes a new message authentication algorithm that is applied in parallel over a multicore processor. The proposed message authentication algorithm uses two PNGRs and two substitution boxes to encrypt and authenticate the plain message. The encryption process has a one-round operation, making it a fast technique to encrypt and decrypt blocks of messages. In comparison to the existing method, the proposed authentication method outperforms the parallel speck-based authentication method by an average of 3.27 times faster when executed over a multicore CPU. The average speedup compared to the sequential version of the proposed algorithm and its parallel implementation is 2.99. The proposed method passes the most difficult randomness test, and the obtained MAC values are tested further to meet other security measurements.

669

Authors: Al_hussein M. Alturfi, Sonia Goyal, Amrit Kaur

Artificial bee colony (ABC) is a very popular algorithm and has been widely used in the literature. The algorithm has some inherent drawbacks, including slow convergence, and poor exploration versus exploitation, among others. In order to deal with these problems, we are hybridizing the ABC algorithm with the JAYA algorithm. The proposed algorithm has the added properties of both ABC and JAYA and has been named as JABC algorithm. The key idea is to add prospective equations of JAYA into ABC for better exploitation, and adding new mutation operators for enhanced exploration and better convergence. The proposed algorithm is tested on CEC 2005 benchmark problems and real world synthesis of linear antenna array (LAA). SLL reduction in five different LAA’s is done, for position, amplitude, and phase optimization. We use 10-element, 24-element, 28 element and 40-element LLA for optimization. For performance evaluation, the algorithm is tested with respect to basic ABC, JAYA, spider monkey optimization (SMO), Moth flame optimization (MFO), Chameleon Swarm Algorithm (CSA) and other algorithms. Experimental results on both benchmarks and LAA problems, show that JABC is significantly better as compared to others. Also, statistical analysis using Wilcoxon’s test and Friedman tests shows the superior performance of JABC algorithm with respect to other algorithms.

19

Authors: Ashraf Ali SK, Prasad M.V.D, Hima Bindu G, P. Praveen Kumar P, Anil Kumar D, Kishore P.V.V

Sign language recognition in real time has been leveraged by the continuously varying hand movements in both shape and orientation across Spatio-Temporal dimensions. This is accomplished by either independent view or shared view feature learning. However, information movement between views is neither total nor restricted to achieve view insensitivity during testing where all views are needed. The objective is to perform pose based multi view sign language recognition by applying triplet loss on a pair of specific and shared view features. Shared view features are obtained using view compatibility matrix which maps within class between view features and between class within view features. This mapping helps in increasing information flow between views from the same class and restricting it between classes thereby making highly discriminative feature representation for all views. Subsequently, metric learning enables to build a view invariant feature embedding by stacking view specific and shared features from different layers for training deep models. In the end, a blended view feature representation is obtained per class. Experiments were designed on our multi view skeletal sign language video dataset and three benchmark action datasets. The results of the experimentation have shown that the performance of the classifier has improved by 8% over the linear view combiners such as Laplacian eigenmaps. Further, the proposed model is useful in constructing a view invariant feature for recognition of multi view sign language.

48

Authors: Md. Irshad Hussain B., Mohamed Rafi

Now-a-days biometrics is used to recognize an individual, and is thought of a more secure way to authorize an individual. Face is the most important factor to recognize an individual. In order to enforce security features, many verification systems use face recognition to identify a person, such as - automobile security, security of home, smartphones owner recognition, attendance system, identification of criminals, etc. Face biometric works in two phases –face detection and face recognition. In this paper we are proposing a hybrid model that works in two phases, face detection is the first phase which includes two stages, in first stage it detects the face from the acquired image and then in the second stage acquired image is subjected to intensity equalization. The second phase is of Face recognition, where finally the individual is recognized as authenticated user or not. We are using Viola-Jones algorithm to detect the face, histogram stretching for intensity equalization and principal component analysis (PCA) algorithm for image recognition. The effectiveness of the suggested facial recognition system is shown through experimental findings using the face dataset constituting of 100 subjects using VTU-BEC-DB multimodal biometrics database with constraints of pose and illumination in the face images, without the intermediate step of intensity equalisation, we had a recognition rate of 93.60%. However, when the intermediate step of intensity equalisation was included, a novelty was added to the hybrid algorithm, and we achieved a recognition rate of 98.20%, which is higher recognition rate than the state-of-the-art methods.

75

Authors: D. Shankar, G. Victo Sudha George, N kanya

Security is considered one of the major challenges in the network due to the increase in network services. Many systems linked to the network play a major role in business and other applications that provide network services. Therefore, it is necessary to identify effective ways to secure the system. One of the significant information security technologies is the intrusion detection system (IDS) that utilizes various deep learning (DL) and machine learning (ML) algorithms to identify network issues. As a result, a novel DL technique is used to classify and detect attacks in the network. To perform this approach, pre-processing is initially done for the collected data using Z-score normalization (ZsN). Then, feature selection is performed using the hybrid pattern search whale optimization algorithm (HPS-WOA). Followed by, network attacks are detected and classified using the optimum bi-directional long short-term memory with the gated recurrent unit (OptiBiNet_GRU). Finally, the hyper parameters such as loss and weight of the proposed DL model are optimized using the pelican optimization algorithm (POA). The effectiveness of the proposed method is validated using two datasets on various performance metrics with some existing baselines. The results show that the proposed method outperforms other methods in terms of accuracy of 99%, precision of 95%, f1-score of 97%, specificity of 99%, and recall of 98% for UNSW-NB15 dataset, whereas accuracy of 99%, precision of 95%, f1-score by 99%, specificity of 99%, and recall of 99% for CICIDS 2017 dataset, respectively.

105

Authors: Gopalsamy Vanitha, Palaniswamy Amudha, Subramaniam Pillai Sivakumari

WSN has been widely used in many sensitive applications and it also has novel possibilities for laying the groundwork for using ubiquitous and pervasive computing, but it has also presented a number of issues and challenges, such as a dynamic network topology and a congestion problem that hinders not only network bandwidth utilisation but also performance. Proficient rate control and fair bandwidth allocation (PRC-FBA) was one of the schemes in the literature to solve issues of WSN by combining the ideas of traffic class priority and bandwidth fairness. However, because of the nature of WSN, the energy of nodes near the sink node is diminished when packets move from lowly congested nodes to highly congested nodes. This paper proposes a proficient rate control with data aggregation and fair bandwidth allocation (PRCDA-FBA) to address this problem by using an effective data aggregation approach for reducing the number of transmissions. In the proposed method, fair bandwidth allocation is simplified by an artificial intelligence-based bandwidth prediction method. Thus, PRCDA-FBA increases the network's durability. Despite having lower bandwidth utilizations, energy-critical sensor nodes require careful power management to avoid being eavesdropped upon. Along with data aggregation and fair bandwidth allocation, the effects of overhearing packets by energy-critical nodes are mitigated through network-wide route adjustments based on the energy level of nodes. Thus, in the proposed method, data aggregation is scheduled based on the availability of bandwidth, energy, queue size and packet priority. The proposed method is named as energy-aware proficient rate control with data aggregation and fair bandwidth allocation (EPRCDA-FBA). The proposed algorithms have been deployed on the Network Simulator 2.35 platform, and a comparative analysis has been performed using several metrics, including throughput, packet loss, End-to-End (E2E) delay and energy utilization. The EPRCDA-FBA method archives highest throughput which is 9.17%, 5.48%, 4.68% and 2.45% higher than congestion control strategies like discrete-time sliding mode congestion controller (DSMC), weighted priority based fair queue gradient rate control (WPFQGRC), PRC-FBA and rate adjustment-based congestion control (RACC).

126

Authors: Zuhair A. Ahmed, Safanah M. Raafat

Accurate localization and mapping are essential for autonomous navigation systems. The simultaneous localization and mapping (SLAM) algorithm, gmapping, is widely used for creating two-dimensional occupancy grid maps (2D-OGMs) due to its low cost and effectiveness in indoor environments. According to the SLAM interaction between localization and mapping terms, the evaluation of map accuracy implicitly means the evaluation of localization. This study focuses on improving the accuracy of the estimated 2D-OGMs by fine-tuning the 32 initialization parameters of gmapping using a turtlebot3-burger robot. An improved experimental procedure was developed, incorporating image registration and similarity measurement to evaluate the map accuracy. The results show a substantial improvement in map accuracy, from 78.84% to 94.18%. The study highlights the importance of fine-tuning the SLAM algorithm for improved map accuracy and provides valuable insights for developing autonomous navigation systems. The key contribution of this paper lies in the systematic classification, fine-tuning, and evaluation of the Gmapping initialization parameters.

151

Authors: Ujjwala S Rawandale, Sanjay R. Ganorkar, Mahesh T. Kolte

In recent days, one of the most serious health problems in humans is hearing loss. For helping the hearing impairment, the Hearing Aid (HA) system is used, but it may lack in various aspects such as delay, matching error, power consumption, low complexity and unwanted noise. Many techniques were introduced to overcome these issues, but they still have problems. Therefore, this research uses a novel Aquila-based Adaptive Filter (AbAF) for improving the HA system with the optimized parameters of filter. Here, the incorporation of the Aquila optimizer along with the adaptive filter is considered as the key novelty of this present work. The main motive of this proposed model is to filter the noisy features more possible range for improve the HA performance. Hence, the Aquila agent removes the unwanted noise by updating the adaptive processor's filter coefficient and optimizing the filter parameters such as matching error, delay, complexity, and bandwidth and power consumption. Moreover, the suggested model is designed and executed in the MATLAB platform. Finally, the performance parameters were estimated and compared with the past studies to validate the performance improvement. The presented model scored the reduced Matching error as 0.3% and reduced power consumption as 10%, which quite lower than the compared models.

175

Authors: Ahmed R. Kadhim, Furkan Rabee

Cloud computing has become an essential technology in many industries due to its scalability and cost-effectiveness. The dynamic nature of cloud computing, including elasticity, on-demand provisioning, diverse resource types, and varied pricing model, presents a significant challenge in scheduling tasks for cloud-based systems, especially when considering user quality of service (QoS) constraints such as deadlines and budgets. Therefore, in order to optimize the performance of the cloud systems and end-user satisfaction, an efficient budget and deadline-aware scheduling model is necessary. Game theory provides a framework for modeling and analyzing the strategic interactions between self-interested entities, which makes it an ideal tool for task scheduling in cloud computing. Additionally, the versatility of game models enables the analysis of various cloud computing architectures. This paper proposes a dynamic Stackelberg (leader-follower) game model for modeling the interactions between tasks, scheduler, and cloud resources to find an equilibrium for the game under both budget and deadline constraints. The proposed dynamic task scheduling based on Stackelberg game (DTSSG) model is assisted by the pricing model and satisfaction factors to select the optimal virtual machine for processing the user task. To achieve high average resource utilization, the utilization factor of the cloud resources is considered in the proposed work. Experimental results show that the Stackelberg model equilibrium has been very effective in scheduling the user tasks across the data center resources by selecting the optimal virtual machines. The results demonstrate improved execution efficiency in terms of decreased makespan by 30%, reduced number of deadline violations by 52%, decreased total gain cost by 27.13% and increased provider profit by 19.15 % on average as compared to existing deadline budget scheduling (DBS), genetic algorithm, and MAX–MIN methods. Also, the results show the effectiveness of the proposed work in terms of increased throughput by 59.4 % and decreased makespan by 27.95% using Google cloud jobs dataset (GoCJ) as compared to existing gradient-based optimization (GBO), multi-verse optimizer (MVO), enhanced multi-verse optimizer (EMVO).

201

Authors: Abbas M. Ali Al-muqarm, Naseer Ali Hussien

The unpredictable and huge data generation nowadays by smart devices from IoT and mobile crowd sensing (MCS) applications like sensors, and smartphones requires processing power and rapid responses in a specific time frame (deadline), and user cost, i.e., budget. Cloud provides these capabilities to serve organizations and customers, but when using cloud appear some limitations, the most important is task scheduling. Nevertheless, the most of earlier scheduling algorithms have focused on only one evaluation parameter such as makespan or cost and so on, as the scheduling process goal. Optimizing a single metric may not ensure an improvement in the cloud performance in general, so the need may arise for algorithms that concentrate more broadly on enhancing more than one parameter. Therefore, this paper presents a deadline-budget multi-objective dynamic scheduling scheme (DB-MODS) to execute user tasks on VMs within QoS limits in order to finish the task within the deadline and budget if possible. DB-MODS using K-Means based on task length and deadline for clustering incoming tasks into groups and categories VMs based on capacity thresholds. In addition, the task belonging to every cluster is assigned to a suitable VM in VMs groups. based on objective functions, which can be one for user request depending on (deadline and budget) and the second is for system depending on (execution time and cost). This paper used CloudSim plus to simulate and evaluate our approach. The DB-MODS approach performance was compared to scheduling algorithms from the previous literature. Both random and Google cloud jobs (GOCJ) workloads are used to evaluate the efficiency of DB-MODS. The results show that DB-MODS outperforms the other algorithms by minimizing makespan by 41%, energy by 46%, and increasing success ratio by 35%, in comparison to existing load balancing and scheduling methods: EEVS, LAS, Greedy-R, DTS, Random, and Greedy-P in the first scenario. Minimizing makespan by 46%, cost by 44%, and maximize throughput by 80% when compared with MOCS, Max-Min, HABC-LJF, FCFS, MOPSO, Q-learning, MOABCQ-FCFS, and MOABCQ-LJF in a second scenario, and minimizing makespan by 46%, and increase throughput by 39% when compared with HESGA, GA, and ACO in a third scenario in average. In addition to the possibility of applying and employing DB-MODS for scheduling deadline-critical tasks in a heterogeneous cloud system.

231

Authors: Mohamed Cherradi, Anass El Haddadi

The evolution of the huge amount of heterogeneous data sources introduces the emergence of several fresh new concepts. Among one of the most well-known concepts emerging as a recent and trending topic in big data is the data lake. Which represents a central repository that stores heterogeneous data sources in its native format without any predefined schema. In the absence of any enforced schema, effective metadata management based on metadata models remains an active research topic to address the data lake issues knowing by a data swamp. However, the examination of existing metadata models shows that none of them proposes a complete model. In this article, we propose a generic and extensible metadata model that supports high flexibility and better scalability in the integration of metadata. With these capabilities, EMEMODL enables comprehensive metadata management for data lakes. We show EMEMODL’s feasibility through a prototypical implementation based on TPC-H datasets of different sizes to prove the scaling feature, using MySQL and Neo4J. The findings of these experiments revealed encouraging results for big data queries that give us a graph database, which shows an efficient ability to manage and process large amounts of data compared to a relational database in terms of retrieval time queries and resource consumption, including cpu and memory usage.

258

Authors: Feby Artwodini Muqtadiroh, Tsuyoshi Usagawa, Riris Diana Rachmayanti, Supeno Mardi Susiki Nugroho, Eko Mulyanto Yuniarno, Mauridhi Hery Purnomo

Classification of uncertain conditions requires computational modeling to obtain exact non-vague results for making the right decision, such as opening and closing school cases during a pandemic. We cannot rely solely on normative and textual government regulations because of numerous constraints and uncertainty in implementation. Unsupervised classification techniques can deal with such issues without needing prior references that contain definitive hesitancy. This motivates us to use a fuzzy system based on knowledge-based composition rules for complex problems such as the dynamics of COVID-19 because of its ability to adapt to changes and uncertainties. Therefore, we construct rules based on knowledge about COVID-19 to the issue of opening/closing schools using three fuzzy approaches: conventional fuzzy, intuitionistic fuzzy system (IFS), and fuzzy c-means (FCM). We can demonstrate a correlation between the number of school openings and the COVID-19 dynamics by utilizing the fuzzy approach to reduce the degree of hesitance. Experiments on available public time-series datasets demonstrate that the IFS is more efficient in forming rigidly distinct two classes. The results indicate that the accuracy of IFS is 99.47%, FCM is 91.28, and conventional FS is 84.33%, including the IFS silhouette score, which is higher than the others, at 0.91 or closer to 1, indicating excellent classification results. IFS is less superior in running time, while FCM is the fastest. This is because there are multiple stages in the IFS by considering non-membership functions.

280

Authors: Mohammad Osman, Kaleem Fatima, P. Naveen Kumar

Security and privacy are the major concerns in the internet of things (IoT) which are uncertain and unpredictable. Trust aware routing is one of the recent and effective strategies which ensure better resilience for IoT nodes from different security threats. Towards such concern, this paper proposes a new strategy called independent onlooker withstanding trust aware routing (IOWTAR) for IoT. IOWTAR introduced a new compound trust metric by combining three individual metrics namely independent trust, onlooker trust, and withstanding trust (a combination of resilient trust and immovability trust). Independent trust and onlooker trust are assessed based on direct and indirect experiences of nodes about their neighbor nodes. Withstanding trust is assessed based on the stability and resilience of nodes towards dynamic topological changes and network failures respectively. Further, this work adapted the Firefly algorithm (FFA) to optimize the weights of individual trusts and establishes a secure path. Simulation experiments carried out over the proposed method had shown its superiority in terms of packet delivery ratio, delay, and throughput. The proposed method has gained an average improvement in the throughput is of 23.71%, 20.18%, 17.27%, and 2.88% from PSO, GSA, WOA, and CBBMOR-TSM-IOT methods respectively.

302

Authors: Nadia A. Khalaf, Hazim G. Daway, Baida M Ahmed

Improvements in aerial images have an important role, given the increase in dust storms due to global warming and climatic changes. Apart from improving aerial images, image enhancement is important in many applications, such as tracking and monitoring studies during wars and for the environment. However, aerial images often suffer from a lack of contrast due to dust and pollutants. In this study, we propose a perceptual dark channel prior (PDCP). In this method, the light value (Y) is improved by using stretch and perceptual color space, and then the chromatic compounds (CbCr) are improved by dark channel prior (DCP). The quality measures lightness order error (LOE), entropy, and color-fullness metrics (CM) and then compares these with the improved methods (DCPMV, YCBCR, MLA, SEA, DCP, and IEIF) to determine the method efficiency. Results show that the proposed PDCP effectively enhances the aerial images better than other methods, because owning it the average are LOE (194.096), EN (7.361), and CM (39.403).

322

Authors: Ernando Rizki Dalimunthe, Erwin Susanto, Kharisma Bani Adam

The most frequently used maximum power point tracking (MPPT) technique is the perturb & observe (P&O) algorithm as a power tracking tool in PV system. The P&O algorithm is easy to compute and implement, but the algorithm is prone to oscillations at the maximum power point. Hence, the power becomes inaccurate due to a lot of power loss. This study utilizes the deep q- network (DQN) algorithm to improve P&O performance by correcting the output value algorithm using various step sizes by DQN. The proposed method increased the tracking speed when receiving the same value at different times by 33.3%–50%, and the oscillation rate was successfully reduced by 73.99%–83.5%. The advantages of increasing tracking speed and decreasing oscillation rate are accompanied by tracked power with averages of 95%, which is better than the P&O and DQN algorithms. It shows that the proposed method can work optimally regarding efficiency and oscillation rate and be the fastest in tracking maximum power from previous related works.

345

Authors: Usman Ependi, Adian Fatchur Rochim, Adi Wibowo

This research presents a novel hybrid sampling technique, implemented at the data level, to effectively address imbalanced and noisy data in classification processes. The proposed technique expertly combines two established methods, namely, the random over sampling (ROS) and neighbourhood cleaning rule (NCL) approaches, to tackle imbalance and noise issues, respectively. The study carried out an empirical evaluation of the proposed approach using crowdsourced text data that primarily emphasized the triple bottom line (TBL) dimension of a smart social, economic, and environmental city. The study used the long short-term memory (LSTM), convolutional neural networks (CNN), and CNN-LSTM classification models to validate the efficacy of the proposed hybrid sampling technique and compare its performance with other existing approaches, including ROS oversampling, NCL undersampling, synthetic minority over sampling & tomek links (SMOTE-Tomek), and synthetic minority over-sampling and edited nearest neighbours (SMOTE-ENN) hybrid sampling. The results are impressive, with the ROS-NCL hybrid sampling technique achieving high accuracy rates across all three classification models, at 97.71%, 98.01%, and 98.11%, respectively. This approach provides a robust and effective solution for handling impure data and holds great promise in identifying complex data patterns in real-world classification problems.

378

Authors: P. Muthukumar, M. V. Ramesh, Ponnam Venkata Kishore Babu, P. Rohinikumar, S. V. Satyanarayana

Most electrical distribution systems (EDS) are radially structured so that primary and secondary protection control devices can work together well. Because they have a high R/X ratio in their design, they also have a bad voltage profile, high distribution losses, and less stability margins. As a result, integrating reactive power compensation devices like the distribution-static VAr compensator (D-SVC) can solve these issues. Yet, the situation can get worse if D-SVCs are placed and rated incorrectly in radial distribution networks (RDNs). Determining the optimum locations and their ratings simultaneously requires an updated version of the Firefly algorithm (FA) with adaptive parameters, which is introduced in this paper as the adaptive firefly algorithm (AFA). The multi-objective function that has been presented relates to improving loadability, voltage stability, and reducing active power loss. On the IEEE 69-bus, simulations are run for three different VAr compensation levels. In comparison to the base case, the losses are reduced by 34.04% and 33.14% with 50% and 75% VAr compensation, respectively. But for the optimal VAr compensation of 73.14 percent by AFA, the losses are reduced by 35.29 percent, which is higher than both under and over compensation cases. Similarly, the loadability margin is increased to 3.099 p.u. with optimal VAr compensation, but it is observed as only 2.833 p.u. and 2.939 p.u. with 50% and 75% VAr compensation, respectively. On the other hand, the findings produced with APF demonstrate its efficiency for resolving complex optimization issues and outperform those obtained with previous research. Also, the proposed D-SVCs allocation has improved RDN's overall performance, demonstrating how well it adapts to real-time applications.

405

Authors: Vivek Kadam, Surendra Bhosale

Inflated communication traffic and expanding radar applications result in frequency congestion and require extra spectrum resources. Dual-functioning multi input multi output radar benefits both sensing and communication operations via real-time cooperation. This decongests the radio frequency environment and allows a single hardware platform for both functionalities. Analog beamforming fails to provide multiplexing gain and digital beamforming is not preferred practically because of heavy power consumption. A reliable hybrid beamforming design is an inherent requirement due to the concerns related to limited RF chains, interference and the cost of fully-digital beamforming. In this paper, Enhanced-Social Ski Driver’s algorithm is presented to investigate the performance of hybrid beamforming in dual-functioning MIMO radar. This study investigates location-based direction sensing for Rayleigh and Rician fading channels. This amendment provides improved performance with a minimal bit error rate, maximum gain, and significant normalized array power distribution. The simulation results show 24% reduction in bit error rate, 15% improvement in gain and persistent array power than existing social ski driver’s algorithm.

428

Authors: Asif Abdullah, Omkar S Powar, Krishnan Chemmangat

Surface electromyography (sEMG) based silent speech interface (SSI) is an actively investigated topic among the broad area of human computer interaction studies which is currently dominated by acoustic sound based speech recognition research. This research is an attempt to help people who have an impaired vocal system if they are having no issues with their facial muscle functions. The basic idea is to reduce the total number of sEMG electrodes that has to be affixed on the face thereby reducing the invasiveness of the silent speech recognition module. This is achieved by incorporating a new detrended fluctuation analysis (DFA) based feature along with the already existing features associated with electromyographic signals. DFA is used for the first time in literature in the area of surface electromyography based silent speech recognition. The main idea is to incorporate the DFA feature along with the state-of-the-art features to improve the performance of a sEMG based SSI model so that an efficient channel reduced model can be realised. Different channel combinations were tried to analyse the impact of each channel in word recognition accuracy and the optimal channel combination was identified. As a result of this research work, a reduced channel setup with 5 electrodes was proposed in place of the conventional 7 channel data acquisition setup. This was achieved while maintaining an accuracy of 83.88 % and 92.92 % using the decision tree (DT) model and K-nearest neighbours (KNN) model respectively.

455

Authors: Md. Irshad Hussain B., Mohamed Rafi

In India, online digital transaction has become prominent means of financial transaction in everyone’s day-to-day life as per reserve bank of India's (RBI) reports. Presently, Indian banks protect users' online transactions with only username and password, CAPTCHA, and dynamic 6 or 8-digit one time passwords (OTPs), which are not very secure. According to literature, this straightforward method of authentication is easily vulnerable to numerous threats. The time offered to enter the OTP with numerical pattern is more than 100 seconds, which is in defiance of RBI guidelines and can be used by fraudsters to commit fraud. In this paper we are suggesting a novel 3-Level authentication which is in-line with RBI authentication guidelines along with the authenticator assurance standards of National Institute of Standards and Technology of the United States and the European regulation for electronic payment services, for securing the users online transactions aided by username and password, OTPs and biometrics. At Level-3 user is verified with biometrics or by OTP. For biometric authentication, we have chosen facial trait, a hybrid algorithm for face detection and recognition is designed, implemented and tested on a standard dataset of VTU-BEC-DB. Suitable OTP and time constraints were identified with the implementation of proposed algorithm being tested by conducting series of trials involving 200 participants with age group of 22 to 55 years. Based on online-survey, we are suggesting for the use of fall-back authentication technique powered by security question, in-case the authentication is only on the basis of OTP. We have achieved 98.20% recognition success rate for facial recognition. The significant time to enter dynamic 6 digit OTP and dynamic 8 character alpha-numeric OTP with time limits of 45 and 60 at Level 2 and 3 is 33.821.10 and 45.310.92, respectively.

482

Authors: Purba Daru Kusuma, Ashri Dinimaharawati

A new metaheuristic can be developed by constructing from scratch, modifying the existing metaheuristics, or hybridizing some metaheuristics. This work presents a new metaheuristic: extended stochastic coati optimizer (ESCO). ESCO is developed by expanding the shortcoming coati optimization algorithm (COA). ESCO expands the number of searches and references used in COA. ESCO also implements a stochastic process for each unit to choose the searches that will perform. It differs from COA, which splits the population into two fixed groups, each performing its strategy. ESCO implements three sequential phases in every iteration. Two options can be chosen in every phase. ESCO has three references in its guided search: the global best unit, a randomly selected unit, and a randomized unit within the search space. In this work, ESCO is challenged to solve 23 classic functions and benchmarked with five shortcoming metaheuristics: guided pelican algorithm (GPA), puzzle optimization algorithm (POA), average subtraction-based optimizer (ASBO), and coati optimization algorithm (COA). The result presents the superiority of ESCO among five shortcoming metaheuristics by outperforming the GPA, POA, GSO, ASBO, and COA in solving 13, 21, 23, 16, and 13 functions, respectively. Through investigation, the multiple search approach is more effective than the single search approach.

509

Authors: Seema Swamy Gowda, Ambika Ramalingappa

Underwater wireless sensor networks (UWSNs) have a huge amount of sensors located underwater to collect data from the underwater scenario. UWSN is considered a promising method for monitoring and exploring an underwater scenario. Energy-efficient and reliable data broadcasting are considered challenging tasks, because of the limited energy source of sensors. To address this issue, an energy-efficient cluster head (CH) selection and multi-hop routing are developed in UWSN. The multi objective energy based improved jellyfish swarm optimization (MOEIJSO) is proposed to select optimal CHs from normal sensors. The input parameters of MOEIJSO are residual energy, neighbor node distance, sink distance, and CH balancing factor. Next, multi-hop routing is developed by using ant colony optimization (ACO) for delivering the data packets. Therefore, the proposed MOEIJSO-ACO method is used to improve energy efficiency while increasing data delivery. The proposed MOEIJSO-ACO method is evaluated by using the alive and dead nodes, total energy consumption, data received in base station (BS), and life expectancy. The existing researches such as metaheuristics-based clustering with a routing (MCR) for UWSN, modified low energy adaptive clustering hierarchy (MLEACH) and cooperative energy-efficient routing (CEER) approach are used to compare the MOEIJSO-ACO method. The alive nodes of the MOEIJSO-ACO are 399 for 800 rounds, which is high when compared to the MCR-UWSN.

532

Authors: Shalini Sharma, Syed Zeeshan Hussain

A mobile Ad Hoc network (MANET) is a dynamic wireless network developed using wireless nodes without using any infrastructures. The wireless nature of mobile networks causes several challenges in secured communication which makes it susceptible to malicious attacks. Therefore, security is a very essential factor in facilitating a secured message transmission among mobile nodes in the wireless medium. The energy and trust aware - multi objective african vultures optimization (ETA-MAVO) method is used in this paper to provide high security for the data packets, thereby preventing malicious attacks. Using the ETA-MAVO algorithm, the network nodes' energy efficiency and trust value are calculated. For choosing the best cluster heads (CHs), ETA-MAVO considers fitness functions, namely: trust value, energy ratio, communication cost, the total number of gateway hops, and network load. To discover malicious nodes in the network and minimize packet loss, the trust value of a node is calculated from its neighbours. Because of trust value, updating routing information is simpler and increases network throughput. The results obtained from the simulation show that the proposed ETA-MAVO achieved better performance with respect to parameters such as delay, energy consumption, throughput, and detection rate when compared with the existing cat slap single-play algorithm (C-SSA) and data-driven zone based routing protocol (DD-ZRP), angular based energy proficient trusted routing (ACEPTR) protocol and aquila optimizer (AO) and puzzle optimization algorithm (POA). The proposed method achieved a detection rate of 93% while the existing C-SSA, DD-ZRP, ACEPTR, AO and POA achieved a detection rate of 90%, 68%, 72%, 91% and 90% respectively.

552

Authors: R. Sridevi, V. Sinthu Janita Prakash

Clustering is one of the important functionalities of networking, which has a dominant impact on the performance in terms of Throughput, packet delivery ratio and communication delays. The challenge in performing the clustering process is more challenging for heterogeneous networks. The divergence between the member nodes is highly variable for a network that contains IoT nodes. As the result of vast availability of different IoT hardware, organizing them under one roof is a critical task which is very vital too. This work is intended to develop a new hierarchical based clustering to improve the performance and the lifetime of the IoT / Wireless sensor network nodes. Three legacy functional blocks such as idiosyncratic fuzzy energy estimator, cumulative fuzzy energy hierarchy builder and energy based cluster head exchanger are integrated in this work and submitted here as “Idiosyncratic Fuzzy Low-Energy Adaptive Clustering Hierarchy (IFLEACH)”. An industrial standard network simulator is used to evaluate the performance of IFLEACH in terms of throughput, packet delivery rate, latency, end-to-end delay and energy consumption by simulating the real-worlds IoT network environment replica. An achievement around 11 kbps throughput and 4 % security level are obtained by the proposed method in high density network.

579

Authors: Meeradevi, Monica R. Mundada

Agriculture is been majorly contributing to the country’s GDP. So, it is foremost important to improve the production quality and quantity to improve the agricultural economy. Using modern technologies like IoT, big data, blockchain, robotics, 5G technologies, etc., farming can be improved by replacing humans in agricultural operations. The proposed work focuses on forecasting of yield and prices of agro-products which will be useful for farmers to increase their productivity and which in-turn increase their economy. Data science techniques can be used to perform this by predicting which crop can be grown for given environmental features and what is the outcome before harvesting period. The proposed model uses computational data-driven approach for crop yield prediction and price forecasting of agro-products. The study uses hybrid approach for crop yield prediction which integrates LSTM and genetic algorithm based evolutionary algorithm known as enhanced long short-term memory (ELSTM). The weight assignment plays a major role in learning the behaviour of the network. The gradient value with respect to weight is calculated for each epoch, and weight is updated to compute the new weight of the network to minimize error. The model accurately predicts the crop yield for the dataset which comprises of features like soil data, rainfall, history of production, fertilizers etc., the accuracy of the model is measured using metric root mean square error (RMSE) and mean absolute error (MAE). In the proposed study, the computation model is used to enhance the knowledge about agricultural yield before the crop sowing period. The proposed model will help farmers and government agencies to improve production. The proposed ELSTM model is compared with other machine learning models such as naïve bayes, decision tree, which shows accuracy between 75% to 80% and the experimental result show the proposed ELSTM model is performing better with 85% accuracy. The price forecasting model is necessary to identify the commodity prices well in advance. The agro-product price forecasting is made using auto-regressive integrated moving average (ARIMA) which uses dataset collected from various agricultural produce market committee (APMC). Auto correlation function (ACF) and partial auto correlation function (PACF) time series plots have been used to assess the proposed model's performance in predicting prices of crops.

604

Authors: Nelli Sreevidya, Penamakuru Siva Jyothi, Gaddam Sumalatha, Shanthi Pannala

Breast cancer is considered one of the main reasons for the mortality of females around the world, therefore the recognition and classification of the initial stage of breast cancer are essential to help the patients to perform the required action. However, the precise classification of breast cancer is a difficult task in medical imaging because of the complexity of breast tissues. In this paper, Otsu's thresholding based segmentation (OTS) is used for segmenting the cancer regions from the images of mammographic image analysis society (MIAS) dataset. Subsequently, feature extraction using Alexnet is done for extracting the features. The feature selection using multi-objective improved ant colony optimization (MIACO) is proposed in this paper to select the optimal features from the overall features acquired using Alexnet. The MIACO is mainly selects the features based on correlation coefficient to choose the features with higher correlation. Further, the stacked autoencoder (SAE) is used to perform an effective breast cancer classification using the features selected by MIACO. The performance of MIACO-SAE method is analyzed by means of accuracy, precision, recall, characteristic stability index (CSI) and F-measure. The existing research namely threshold-based and trainable segmentation (TTS) with machine learning (ML), infinite feature selection with genetic algorithm (IFS-GA) and deep convolutional neural networks (DCNN) are used to evaluate the MIACO-SAE method. The classification accuracy of MIACO-SAE is 99.36%, which is high when compared to the TTS-ML, IFS-GA and DCNN.

625

Authors: Mahmoud Zaki Iskandarani

The objective is to investigate effect of slot probability and its effectiveness in vehicular communication. The work carries out MATLAB simulation covering three different levels of slot probability; low, medium, and high. The goal behind such simulation is to uncover the process that leads to high reduction in throughput. The normal explanation for the reduction is high levels of collisions and re-transmission of data through new attempts. However, this work shows that such process can be characterized and controlled as the work shows through sinusoidal regression, the oscillatory behavior caused by collisions, attempts, retransmission, as a function of slot probability. The simulation results, which covers 20 vehicles at various slot probability levels, shows that there is a change in the data traffic-throughput shape function. This is due to the effect of slot probability and reflects on the level of obtained oscillations. The work also discovered an oscillation pattern that infiltrate and affect communication channels at different levels as a function of collisions and data delivery attempts, which is related to slot probability and data traffic. Such oscillatory pattern should be investigated further and the conditions for such pattern should be eliminated, in order to achieve better utilization of communication channel and higher throughput, with lower attempts and less collisions. This is also related to both slot probability and number of available slots. The obtained results in this work showed that at the upper part of the mid slot probability range and all the way to through the high slot probability part, a noticeable oscillatory behaviour is observed, with almost constant values of average data traffic and throughput values. These effects are accompanied by large acceleration and accumulation of collisions. Thus such new approach, and in conjunction with NP-CSMA, can be used at both the design stage of communication channels and vehicular networks, and during operations. This will enable adaptive slot allocation and dynamic transmission of messages, which will result in collisions reduction and better channel utilization.

654

Authors: Ali H. Mhmood, Hazem I. Ali, Amer B. Rakan, Mohammed R. Subhi, Yaseen Kh. Yaseen, Hameed A. Mohammed

This study proposes a new approach for controlling COVID-19 through vaccination, where an adequately descriptive mathematical model is created for COVID-19 using the susceptible exposed infectious recovered (SEIR) model of epidemic diseases. The presented control approach is synthesized using a combination of feedback linearization and H_∞ control, and incorporates model reference control to achieve optimal time responses with the aid of the black hole optimization (BHO) algorithm. The effectiveness of the designed control law is evaluated using data from the lombardy region of Italy. The results of the simulation show that the proposed control approach is able to effectively control the COVID-19 outbreak by accurately implementing the desirable vaccination, while effectively addressing nonlinearity and uncertainty in the COVID-19 system with a desirable control action. The control method has achieved the required immunity of 6.6 million individuals after approximately 25 days with a transmission rate reduced to zero in a short time, and a vaccination rate of 170 thousand people per day.

677

Authors: Karnam Sreenu, Sreelatha Malempati

From the past few decades, the cloud computing has been arisen as an extensively used platform to provide storage, compute and analytics services to organizations and end users on the basis of pay-as-you-use. This enabled the organizations as well as individuals to access the larger set of resources without establishing a costly and high performance computing platform. However, the major issue in cloud computing is the task scheduling which is declared as NP-hard problem and most of the researchers applied meta-heuristic algorithms to solve it. However, they experienced a slower convergence speed which has a direct impact on the efficiency of cloud computing environment. To achieve a faster convergence along with efficient quality of Service, this paper proposes a simple and effective task scheduling mechanism based on multiple resource attributes and conditional logic. This method considers totally four resource attributes such as Resource reaction time resource location, resource availability and resource reliability rate. Based on these four attributes, the proposed method constructs an index called as task scheduling index (TSI) and assigns tasks for resources based on their TSI value. The TSI is constructed through the proposed conditional logic and high priority is given for resources those have higher TSI. For experimental validation, we used two benchmark datasets such as GOCJ and synthetic dataset. Three performance metrics namely Makespan, Throughput and convergence speed are measured and compared with state-of-the-art methods like PSO, GA and GWO. On an average, the reduced Makespan of proposed method is 48.22% and 46.87% for GOCJ and Synthetic datasets respectively.

709

Authors: Muntasser A. Wahsh, Zainab M. Hussain

This paper presents a novel method for optimizing rectangular boundaries in stitched images using a combination of deep learning and genetic algorithms. The proposed method utilizes an enhanced structure of convolutional neural network (CNN) in order to extract features for residual regression and a genetic algorithm to tune CNN parameters. The performance of the proposed method was evaluated using various metrics, including mean squared error, peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and Fréchet inception distance (FID). The outcomes from the conducted experiment demonstrated that the suggested method achieved high accuracy in rectangular boundary optimization, even for images with complex shapes and orientations. The method was also tested with different parameters, and results indicated that increasing the number of generations and population size led to improved performance. Also, the combination of deep learning algorithm specifically (CNN) and optimization algorithm specifically (genetic algorithm (GA)) led to an increase in the accuracy of the rectified images where the average PSNR reached 23.98, SSIM is 0.8066, and FID is 18.72. The proposed method has potential applications in various areas of study, such as image cropping, object detection, and image segmentation.

14

Authors: Mantena Krishna Satya Varma, Raja Kulasekaran, Nynalasetti Konadala Kameswara Rao

Hyperspectral image (HSI) segmentation and classification is trending research in military and civil applications area. However, HSI classification is facing various challenges in analyzing spectral and spatial regions. In order to improve the performance of HSI classification models, segmentation is essential step. Therefore, this article is focused on implementation of unified HSI segmentation network (HSIS-Net) using active learning. Initially, HSI preprocessing operation is performed to normalize the spectral-spatial regions. Then, joint spatial-spectral boundary extraction operation is performed using spatial information divergence (SID) and spectral correlation mapper (SCM). Finally, segmentation of boundary estimated HSI bands is performed using multi-view active learning network based fully convolutional segmentation network (MAL-FCSN). The simulations revealed that the proposed HSIS-Net resulted in superior segmentation performance with segmentation accuracy (SA) of 0.999, and segmentation F1-score of 0.999 as compared to the existing HSI classification approaches for four publicly available HSI datasets.

35

Authors: Duaa Al-Safaar, Wathiq Laftah Al-Yaseen

Network violations are currently society's major challenge. For networks to be protected against hostile threats, an intrusion detection system (IDS) is important. To create effective IDS, deep learning (DL) is used in various fields, including information security. In this paper, a hybrid deep learning approach is proposed to effectively identify network intrusions using Autoencoder (AE) and Multi-layer perceptron (MLP). We use Autoencoder which can reduce the number of the original attributes based on the number of attributes, we first enter the original data on the Autoencoder and produce new compressed data, then enter it on the MLP classifier. The NSL-KDD dataset is thoroughly evaluated to determine the efficacy of the hybrid AE-MLP model the best outcomes are reached, with an accuracy rate of 87.6% and 81.06% (binary classification and multi-classification). In addition, the proposed hybrid method was compared with various recently proposed DL-based attack detection mechanisms. In terms of performance on the available dataset, it is observed that the proposed model outperformed.

64

Authors: Gouri Bompem, Nagaraju Chiluka, Dhanalakshmi Pandluri

In this research article, an effective model is implemented for predicting the outcome of the Indian general election 2019 and farmer’s protest by utilizing the sentiment analysis of Twitter data. In the initial segment, the raw Twitter data are acquired from the Indian Political tweets 2019 and farmers protest tweets databases. Further, the denoising operations such as removal of unnecessary space, tabs, new-line, hashtag symbols, non-English characters, punctuations, numbers and special characters are used to enhance the quality of the acquired data. Then, the keyword trend analysis and topic modeling utilizing latent dirichlet allocations (LDAs) are performed for better data representation. Next, the extraction of the feature is carried out utilizing skip-gram and term frequency-document level frequency (TF-IDF) techniques, and further, the feature optimization is accomplished using enhanced dragonfly optimization algorithm (EDOA) for selecting the optimal feature vectors. In the EDOA, a Brownian motion is added for improving the probabilistic behaviors. Lastly, the selected features are given to the deep belief network (DBN) model to classify the people’s sentiments into negative, neutral, and positive classes. The experimental evaluations demonstrated that the EDOA-DBN model has obtained 99.22% and 98.83% of accuracy on both Indian political tweets 2019 and farmers protest tweets databases, which are maximum related to other existing models.

92

Authors: Nadia A. Khalaf, Hazim G. Daway, Baida M Ahmed

The use of hazy images is amongst the fastest ways to identify natural resources. Aerial photography has overcome the obstacles faced in land areas, such as rugged areas, swamps and sand dunes. It has important applications in tracking and monitoring studies during wars and peace. However, aerial images often suffer from the lack of contrast due to dust and pollutants. Therefore, this study attempts to improve aerial images by modified dark channel prior (DCP) and adaptive histogram equalisation (AHE) algorithms with YIQ. The images are first improved through DCP and then converted into YIQ colour space that saves the chromatic compounds (IQ). The lighting component comes out after improving the original aerial image via the stretch technique and AHE. Afterwards, all the compounds are merged, and reverse transformation is performed. The proposed method for improving aerial photographs was compared with several other methods. By analyzing the results, the proposed method obtained the best measures of quality, including an entropy value of (7.771), an average gradient value of (14.207) and a naturalness image quality evaluator value of (3.700).

115

Authors: Waleed Kareem. Ahmed, Rana Saad Mohammed

The Internet of Things (IoT) was a new concept that connected various physical things to the internet. The IoT is fast expanding and will soon significantly impact everyday life. Although the increasing number of connected IoT devices is simplifying life, they risk our data. Radiofrequency identification (RFID) assists in the automated identification of connected IoT devices. Furthermore, the primary challenges for RFID tag-connected technology are privacy and security. With the improving safety of solutions RFID for many apps, RFID necessitates a centralized database widening. Blockchain techniques are speedily establishing themselves together as a new distributed and decentralized replacement. And it provides enhanced transparency, data security, immutability, dependability, and lower maintenance costs. Because of its inherent benefits, RFID is projected to play a significant part in enabling identifying technology on the IoT. However, due to its association with sensor technology, it has the potential to be applied in a wide range of industries. Security, however, is one of the more complex components of creating an RFID system. RFID Security focuses around authentication and privacy concerns. From the previous works, we found that the computing cost and uptime are higher. To solve these problems, we offer improving lightweight authentication using Arnold Chaotic map and Markov chain for IoT applications. The proposed method uses random algorithms (Markova chain, Arnold map chaotic, and ECC), which add high security. We also used blockchain technology to transfer keys through a non-security channel. Based on parameters such as (communication cost, throughput rate, storage requirements, transmission delay, and computational cost) the results of the proposed RFID protocol were compared with several RFID-based security solutions. As a result, the proposed lightweight scheme is more efficient and secure than existing RFID systems in terms of performance, according to simulation results, and is fully compatible with actual applications.

142

Authors: A Sequence-to-Sequence Text Summarization Using Long Short-Term Memory Based Neural Approach

The massive development in the different fields of engineering and science brought a significant change in the modern years. In the field of natural language processing, automatic text summarization is known as one of the important research directions. The text's primary concepts and flow should be considered to provide a solid summary that limits the repetition in the text as a summary. A sentence is referred to as a basic language unit and there are many semantic links present among the prison terms like example-of, cause-effect, sequential, etc. within a meaningful text. In this research manuscript, an automated text summarization model is developed and its performance is validated on the two challenging datasets such as daily-mail and Gigaword. In this manuscript, a transformer: long short term memory (LSTM) neural network is used along with the Huber loss function and Adam optimizer. The transformer used in this research (LSTM with Huber loss function and Adam optimizer) includes the advantages like requiring limited memory, computationally effective, and easy implementation. The proposed neural network mainly aims in exhibiting the summaries that are composed by the groups or paragraphs that includes more keywords or phrases than summaries composed by sentences. The non-differential evaluation metrics are utilized in the proposed sequence-to-sequence model to provide semantic information of input text and it stores important features for effective text summarization. The size of the parameters is specified by the maximum number of sentences in the same group. The proposed LSTM with Huber loss function and Adam optimizer has 44.51 ROUGE-1, 20.43 ROUGE-2, and 40.08 ROUGE-L on dailymail dataset. Experimental results of the trial demonstrated that the proposed model outperformed the existing models like dynamic residual network, convolutional neural network with LSTM, and knowledge powered topic level attention model in terms of the rouge parameter.

166

Authors: Kelly Rossa Sungkono, Riyanarto Sarno, Maisie Chiara Salsabila, Chintya Prema Dewi

A process model describes business process flow as the activities that employees must carry out. Nowadays, many companies have similar business processes, so they do not establish their process model from scratch but build the model based on an existing process model or a combination of some process models. Several process mining methods approaches matching rules to define similarities of a model, and others consider the semantic side; however, none use the similarity to merge some business process models. This paper proposed graph-based semantic similarity, a method that merges two process models considering the semantic similarity between those activities. The utilized semantic similarity methods are SBERT and TF-IDF. The evaluations compare SBERT and TF-IDF with other methods and use a similarity method with the highest score in graph-based semantic similarity. Based on the semantic similarity score, graph-based semantic similarity with SBERT has higher similarity scores than existing graph-based semantic similarity, i.e., node similarity and Jaro-Winkler distance. With the highest similarity scores among existing methods, the evaluations also prove that graph-based semantic similarity with SBERT correctly combines business process models based on semantic similarity.

192

Authors: Zahraa Ch. Oleiwi, Ebtesam N. AlShemmary, Salam Al-augby

Arrhythmia is one of the cardiovascular disease types that affect humans and often leads to death. generally, ECG signals uses to diagnose the patient’s heart state where the ECG illustrates the electrical activities and physiological state of the heart. This paper proposes ECG classification model to classify four types of heartbeats for the early detection of Arrhythmia. Detail wavelet coefficients of ECG were extracted using discrete wavelet transform (DWT) to produce new datasets of ECG with for the dimensions and processed information to ensure the efficiency of proposed classification techniques. In addition, power spectral density (PSD) has been calculated for approximate wavelet coefficients of ECG to extract more relevant features that improve the performance of the classifier. The two classifier models use, the convolution neural network (CNN) utilizes for deep learning networks with artificial neural network (ANN) and the Random forest ensemble method. The experiments and results show that, the proposed model with RF archives 98.5% classification accuracy considering all decomposition levels of DWT. Additionally, the solution with CNN-ANN achieves 96% classification accuracy at the third decomposition level. Therefore, the results show the impact of the proposed solution with high efficiency in terms of fewer dimensions and high accuracy.

215

Authors: Jaafar Ahmed Abdulsaheb, Dheyaa Jasim Kadhim1

This work considers the best path planning algorithm for a mobile robot that travels independently in an unknowable environment. To get around the limitations of unstable searches in the conventional Hunter-Prey Optimization Algorithm (HPO), the improved HPO optimization algorithm is used and introduces a new control parameter referred to as randomization adjustment in order to avoid stagnation and early convergence. The absence of the transfer parameter from exploration and exploitation is a significant flaw in the HPO algorithm, which results in an unstable search and additional time waste. Another new parameter called the changing parameter (CP) is used to address this flaw. It is used in an environment with erratic static and dynamic obstacles and a static and dynamic target. Finding a collision-free path that is also the objectively shortest path and the smoothest path can solve the path-planning problem. The proposed algorithm attempts to mimic the real world by taking into account the actual size of the mobile robot, a kinematic model, and the robot's specifications. The proposed algorithm is evaluated by comparing it on 30 dimensions using 13 benchmark test functions. The performance of the proposed algorithm is evaluated against the results of five swarm optimization algorithms. According to the results of the standard deviation tests, the proposed algorithm performs the best in 92% of the 13 test functions. Furthermore, the average outcomes for three complex maps (10×10) m in size demonstrate the potency of this approach for robot paths from the starting point to the target. The average distance over ten runs for maps 1, 2, and 3 is 12.6436 meters, 12.4961 meters, and 17.6547 meters, respectively. It demonstrated how quickly and easily it could avoid both stationary and moving obstacles.

240

Authors: Mazin Ismael Al-Saedi

In this paper, a modified block backstepping controller was formulated for double lane changing control of a two-degree-of-freedom (2DOF) autonomous vehicle. For a systematic procedure, a 5th-degree polynomial double lane change trajectory was created. Next, the state model dynamics of this underactuated 2DOF autonomous vehicle is presented, which was transformed into the block-strict feedback form. Thereafter, the block backstepping technique was implemented to achieve the control input (steering angle). For enhancing the steady state performance during the trajectory tracking, an integral action was included in the proposed controller. The stability of this controller was established through the Lyapunov theory. To investigate the effectiveness of this proposed controller, we compared it with a human driver's transfer function controller and LQR controller, in addition to the sliding mode controller. Simulation results were achieved using CarSim-Simulink. Later, this controller was implemented to track the same lane change path with a different road coefficient of friction. The supposed controller characteristic was further investigated by inspecting the motion of the vehicle when exposed to an idealized lateral step force disturbance. The simulation results indicated the power of modified block backstepping control and it has higher following accuracy, better yaw rate tracking, and stable front steering angle, at high speed and at high and low friction roads.

268

Authors: Purba Daru Kusuma, Meta Kallista

This paper introduced a novel metaheuristic that is developed based on the tournament mechanism, namely quad tournament optimizer (QTO). As its name suggests, QTO proposes a new approach of metaheuristic in which there are four searches conducted by each agent in every iteration. These searches are: (1) searching toward the global best solution, (2) searching toward the middle between the global best solution and a randomly selected solution, (3) searching relative to a randomly selected solution, and (4) neighbourhood search around the corresponding solution and the global best solution. A solution candidate is generated by each search. Then, a tournament is carried out to find the best candidate. This strategy is novel because most of metaheuristic deploys only single search or multiple searches where each search is conducted sequentially. QTO is challenged to find the optimal solution of 23 classic functions. In this challenge, QTO is benchmarked against five shortcoming metaheuristics: marine predator algorithm (MPA), slime mould algorithm (SMA), golden search optimizer (GSO), hybrid pelican Komodo algorithm (HPKA), and guided pelican algorithm (GPA). The result indicates that QTO outperforms all these benchmark metaheuristics. QTO is better than MPA, SMA, GSO, HPKA, and GPA in solving 22, 19, 21, 20, and 13 functions consecutively. The result also indicates that QTO has successfully found the global optimal solution for nine functions: Sphere, Schwefel 2.22, Schwefel 1.2, Schwefel 2.21, Rastrigin, Griewank, Six Hump Camel, Branin, and Goldstein-Price.

291

Authors: Azza. R. Abd El-maksoud, A. H. Ahmed, S. Hasan

In this paper, for four different types of distributed generation (DG) units, a graph theory-based modified particle swarm optimization (MPSO) algorithm is proposed for the simultaneous optimal distribution network reconfiguration (DNR) and allocation (placement and sizing) of multiple DG units to minimize the total real power loss of the radial distribution network (RDN) while fulfilling all system operational constraints. Different cases are carried out for DNR and DG integration to evaluate the effectiveness of the proposed algorithm. To evaluate the objective function, an intelligent graph theory-based backward/forward (BW/FW) sweep load flow technique that can manage any topological alteration owing to DNR and DG integration is introduced. The proposed algorithm is assessed using IEEE 33-bus and IEEE 69-bus radial distribution systems and results are obtained using MATLAB software. The obtained simulation results show that the proposed algorithm can provide a wonderful solution in terms of real power loss minimization and voltage profile enhancement (compared with the base case, loss reduction of 74.29%, 92.15%, 54.3%, and 45.87% for DG type1, DG type2, DG type3 and DG type4, respectively for IEEE 33-bus, and loss reduction of 84.16%, 97.79%, 70.62%, and 63.01% for DG type1, DG type2, DG type3, and DG type4 respectively for IEEE 69-bus).

319

Authors: Nada Fitrieyatul Hikmah, Rachmad Setiawan, Mohammad Daffa Gunawan

Poor sleep quality is a common sign of a variety of sleep and health problems. Thus, sleep quality assessment is necessary as it can be a first-step predictor of physical and mental health. Several studies were completed for this objective. However, no prior study in sleep quality assessment has explored a comprehensive heart rate variability (HRV) analysis by including feature extraction in the time and frequency domain, and non-linear analysis. This study proposed a full evaluation of sleep quality, by incorporating multiple physiological signs in subjects to detect exhaustion after a period of sleep. The primary contribution was the development of an algorithm to estimate sleep quality based on the combination of electrocardiography (ECG) and electromyography (EMG) signals by using machine learning. HRV analysis of ECG signal included time domain, frequency domain, and non-linear analysis. Mean power frequency (MPF) was extracted from the EMG signal by using spectral analysis. In addition, determination of fatigue level as an indicator of the subject's sleep quality was evaluated with fatigue severity scale (FSS) questionnaire. Based on results, the accuracy values of logistic regression, random forest, k-nearest neighbor (k-NN), support vector machine (SVM), and SVM with grid-search were 93%, 93%, 93%, 40%, and 100%, respectively. The proposed method was applicable for investigating sleep quality.

343

Authors: Fatima Abdul Sattar AL-Taie, Ahmed Sabah Al-Araji

This paper presents a new development of a predictive voltage neural controller to control the stack terminal output voltage of a nonlinear proton exchange membrane fuel cell (PEMFC) system based on a neural network technique and a back-propagation learning algorithm. The main objective of this paper is to precisely and quickly identify the best control action of the hydrogen partial pressure to enhance the nonlinear performance of the fuel cell output voltage under a variable load current. This optimal control action prevents damage to the fuel cell membrane, thereby prolonging the fuel cell’s lifetime. The proposed predictive voltage controller consists of three sub-controllers. The first one is the numerical feed-forward controller (NFFC), which is used to decide the steady-state hydrogen partial pressure (PH2) control action depending on the desired voltage. The second sub-controller is a feedback neural controller that uses a multi-layer perceptron (MLP) and a back-propagation learning algorithm to generate the hydrogen partial pressure feedback control action to track the desired output voltage of the fuel cell during transient conditions. The third sub-controller is the predictive control law equation, which is based on the modified Elman recurrent neural network (MERNN) as an identifier for the PEMFC model and the multi-objective performance index. From the simulation results, the proposed controller, which is composed of the three sub-controllers, has the capability to generate a precisely and quickly timed response to the hydrogen partial pressure control action in order to minimize the tracking voltage error and eliminate oscillation in the output voltage of the fuel cell. Finally, the suggested predictive voltage control strategy's numerical simulation results are then verified by comparison with those of other types of controllers in terms of the minimum number of steps ahead prediction (reducing from 10 to 1 step ahead prediction) and enhancement of the tracking voltage error by 81.8% when comparing with a predictive neural controller and improvement of the tracking voltage error by 87.5% when comparing with an inverse neural controller. Moreover, the oscillation effect in the output voltage is completely eliminated, resulting in a response without any overshoot.

375

Authors: Suneetha Mopidevi, Prasad M.V.D, Venkata Vijay Kishore Polurie, Anil Kumar Dande

Multiview video processing for recognition is a hard problem if the subject is in continuous motion. Especially the problem becomes even tougher when the subject in question is a human being and the actions to be recognized from the video data are sign language. Although many deep learning models have been successfully applied for sign language recognition (SLR), very few have considered multiple views in their training set. In this work, we propose to apply meta metric learning for video-based sign language recognition. Contrasting to traditional metric learning where the triplet loss is constructed on the sample-based distances, the meta metric learns on the set-based distances. Consequently, we construct meta cells on the entire multiview dataset and perform a task-based learning approach with respect to support cells and query sets. Additionally, we propose a maximum view pooled distance on sub-tasks for binding intraclass views. The results of experiments conducted on the multiview sign language dataset and four action datasets show that the proposed multiview meta metric learning model (MVDMML) achieves 11% higher performance than the baselines.

401

Authors: Dian Candra Rini Novitasari, Fatmawati, Rimuljo Hendradi, Yuniar Farida, Ricky Eka Putra, Rinda Nariswari, Rizal Amegia Saputra, Rr Diah Nugraheni Setyowati

Diabetic retinopathy (DR) initially derives from damage to the eye blood vessels which leads to bleeding up-to permanent blindness. The severity of DR is not easily known. Therefore. it is necessary to create a system that is able to identify the severity level of DR. In this study, the identification of DR was conducted using hybrid CNN and ELM method. Hybrid CNN-ELM is useful for obtaining the most effective model in the classification system and computational time. CNN architecture is useful for extracting fundus data in image feature recognition. Several modified ELM methods (KELM, MLELM, DELM) were used to classify the severity of DR based on the results of CNN feature extraction. The classification system was tested with two datasets, namely DRIVE and Messidor. Based on the average value, the best architecture evaluation in extracting fundus data was DenseNet compared to GoogleNet, ResNet18, ResNet50, and ResNet101. Based on the computation time, the KELM method was faster than the MLELM and DELM methods, with an average time of 43 seconds. The results on the DRIVE dataset produced good evaluation values, while Messidor obtained good results on the MLELM method. It showed that the Messidor data was able to be separated well linearly. The modified CNN-MLELM method produced more stable values in both datasets with an average accuracy of 99.21%, a sensitivity of 99.29%, and a specificity of 99.21%.

427

Authors: Abbas A. Kareem, Bashra K. Oleiwi, Mohamed J. Mohamed

Path planning is one of the most crucial aspects of implementing unmanned aerial vehicle (UAV) missions. Therefore, it is essential to figure out the optimal path from the starting point to the target point in different scenarios. In this paper, we propose employing a hybrid algorithm, named FPA-GA, generated by combining the flower pollination algorithm (FPA) and genetic algorithm (GA), to find an optimum path in a real modern building’s environment. In addition, a cubic polynomial algorithm via two points was proposed to make the route adequate and smooth. Because the GA has a good capability for exploring the search space, it is employed for exploration while the FPA and GA are employed to increase the exploitation capability in the proposed algorithm. Five different scenarios are utilized to evaluate FPA-GA's ability to find the optimal path in a variety of situations, and then the proposed algorithm's performance is compared with that of seven other algorithms: GA, FPA, bat algorithm (BA), particle swarm optimization (PSO), whale optimization algorithm (WOA), improved whale optimization algorithm (IWOA), and IWOA-PSO. The best path, the mean path length, the standard derivation, and the worst path length are the four parameters used to compress data. In all scenarios, the results demonstrate that FPA-GA is capable of locating the shortest and most collision-free paths, and the hybrid algorithm FPA-GA is always superior to other algorithms. The proposed algorithm provided the mean path length enhancement in all scenarios, where the maximum enhancement equals (33.6%), (23.1%), (55.5%), (29.8%), (50.5%), (53.9%), and (26.4%) compared with GA, FPA, BA, PSO, WOA, IWOA, and IWOA-PSO, respectively. On the other hand, the proposed algorithm is guaranteed to find the best path in all scenarios, where the standard deviation of FPA-GA is always less than that of other algorithms.

454

Authors: Keerthi Prasad Ganeshaiah, Vinay Hegde

Handwritten character recognition (HCR) is a growing field in the applications of pattern recognition, image processing, communication technologies, and so on. But, the identification of handwritten characters affected because of different styles of writers, or even one writer’s style differed according to the conditions. Moreover, the huge amount of features from the handwritten characters also affect the classification. To address the aforementioned issues, the hybrid feature extraction (HFE) with morlet stacked sparse auto-encoder (MSSAE) based feature dimensionality reduction is proposed for improving the classification of HCR. Here, the HFE is the combination of different textural and shape features such as histogram of oriented gradients (HOG), gray-level co-occurrence matrix (GLCM), discrete wavelet transform (DWT), and skeleton features. The morlet wavelet activation function is used in the MSSAE to enhance the dimensionality reduction ability that used for an effective reduction of feature dimensions. The reduction of feature dimension using MSSAE is used to improve the classification using multi-class support vector machine (MSVM). In this research, the real-time indigenous languages i.e., English and Kannada handwritten characters from the Chars74K dataset are used for the analysis i.e., offline recognition. On the other hand, the online recognition of Kannada characters is also done for analyzing the HFE-MSSAE method. The HFE-MSSAE method is analyzed in terms of accuracy, precision, recall, CSI and F-measure. The existing researches namely convolutional neural network (CNN), hybrid feature based long short term memory (HF-LSTM) and elephant herding optimization - long short term memory (EHO-LSTM) are used to evaluate the HFE-MSSAE method. The accuracy of the HFE-MSSAE for Kannada is 96.73% which is higher than the HF-LSTM and EHO-LSTM.

477

Authors: Ammar Abdulsalam Al-Asadi, Mahdi Nsaif Jasim

Many deep learning-based recommender systems have been proposed recently. Where they involve all the users in datasets to build the latent representation of input data to be used later for predicting the missing rates. Despite the fact that, users have different interests, and these differences reduce the model prediction accuracy. This paper proposed a novel cluster-based denoising autoencoder model (cluster-based DAE) for rate prediction recommender systems. Instead of building a single model, it builds K models by using k-means algorithm to divide the users into groups based on their preferences. Each group trains a DAE model to generate recommendations for its members. The proposed method was trained and tested with MovieLens (100K, 1M, and 10M) datasets where 80% of the data are used for training and 20% for testing. The performance of the proposed method compared against other state-of-the-art methods that use deep learning to build rate prediction models. It outperformed the other compared methods in term of mean absolute error (with 12.9%, 14.7%, and 22.3%) and root mean squared error (with 24.2%, 18%, and 21.1%) using MovieLens 100K, 1M, and 10M datasets respectively.

497

Authors: Aruna Kumar Bananaik Thulajanaik, Naganaik Manjanaik

The mmwave (millimeter wave) with massive MIMO (Multiple-input and multiple-output) leverages larger antennas with spatial diversity and multiplexing gain brings about positive vibe for future generation wireless networks for provisioning bandwidth hungry multimedia applications However, the fronthaul link that links a remote radio head (RRH) and carries video frames as bitstreams to the baseband unit (BBU) can fluctuate the overall speed and capacity of massive MIMO if suitable data compression (encoding-decoding) techniques are not functional. This paper presents an error detection aware compression (EDAC) scheme which first performs encoding using low-rank approximation considering both spatial and time domain to reduce video frame size; later, the encoded video frames are compressed using Huffman codewords. Experiment outcome shows the proposed video compression model achieves much better compression efficiency with improved bit error rate (BER), symbol error rate (SER), error vector magnitude (EVM), and compression ratio when compared with the fixed code dictionary huffman video coding (FCDHVC), exiting video compression (EVC) technique and huffman video coding (HVC). The proposed EDAC model achieved compression efficiency of 30.52 whereas the FCDHVC, EVC and HVC model achieved compression efficiency of 26.50, 21.75 and 22.35 respectively.

517

Authors: Kavitha Chandrashekar, Anitha Tuluvanooru Narayanreddy

The use of machine learning (ML) within medical field is on the rise, notably as a means to enhance both the speed and precision of diagnosis. Through evaluating large volumes of patient information, machine learning is able to provide disease prediction, giving both patients and doctors more control over their health. Predicting and preventing heart disease has become a major area of study in medical data processing as a result of the increased expense of therapy. Since there are so many factors that come into play, estimating one's heart disease risk manually is a challenging task. Moreover, there are very few methods which provide better accuracy for the prediction of the heart disease. Hence, by using openly accessible cleveland heart disease dataset, this research aims to design and evaluate several advanced technologies constructed employing machine leaning algorithms for diagnosing if an individual is going to get heart disease or not. In this paper, we propose an ensemble feature optimized (EFO) learning method which uses an enhanced extreme gradient boosting tree and feature level cross validation scheme for effective heart disease (EHD) prediction. The presented EFO prediction algorithm and other existing machine learning algorithms have been used for the prediction of the heart diseases. The performance of the existing algorithms (XGB-based, ensemble tree hyper optimization (ETHO), and MLP-PSO) and proposed EFO algorithm has been evaluated using the classification metrics. When compared with the XGB-based, ensemble tree hyper optimization (ETHO), and MLP-PSO algorithm, the EFO algorithm has attained an accuracy of 98.61%. The EFO algorithm provides the doctors to able to predict the heart disease more efficiently and effectively.

536

Authors: Varsha Pravin Hole, Nikahat Mulla, Pravin Basavraj Hole

Cloud storage has gained extensive attention due to its low cost, global scalability, and improved agility. Data privacy is an important issue in cloud storage because breaches of personal information or business secrets are occurred by malicious hackers or unauthorized cloud operators. To address this issue, a Modified Key Sifting Scheme (MKSS) is proposed for developing an effective Quantum Key Distribution (QKD) with Two-Way Identity Authentication (TWIA) to provide security in the cloud storage. The Tree Parity Machine (TPM) is used for proposing a MKSS which used to minimize the key distribution time based on the synchronization. This MKSS-QKDTWIA considers four different objects such as data user, data owner, server and data storage in a communication scenario. The performance of the MKSS-QKDTWIA is analyzed by means of key computation time, processing time, encryption & decryption time, and memory utilization. Existing research such as Modified Random Fibonacci Cryptographic (MRFC), Software Stack-QKD (SS-QKD) and Enhanced Cloud Security Model based on QKD Protocol (ECSM-QKDP) are used to evaluate the performance of MKSS-QKDTWIA. The key computation time of MKSS-QKDTWIA for 50 attribute sets in each key is 16.7s which is less when compared to the MRFC, SS-QKD and ECSM-QKDP.

561

Authors: Narumon Wannoi, Chaisit Wannoi, Chai Chompooin-inwai, Nirudh Jirasuwankul, Churirat Boonkhun

This article presents the impact study of electrical vehicle charging stations (EVCS) loading to the Thailand’s North eastern power system. Main purposes of this paper are to find the maximum number of EVCS that can be loaded to power system and to rank the critical areas under EVCS load changing by using the repeated power flow calculation technique. Results from the calculation are for the integration planning of distributed renewable generators (DRG) to reduce system congestion. Typically, the critical areas are buses or devices that violate of system control limits. The system control limits are consisting voltage limit and load percentage limit of power transfer devices. All critical areas are needed to improve system stability and reduce system congestion for support the EVCS growing. In this study, The EVCS have been installed on main roads in Thailand’s North-eastern area from Bangkok to Nong-Khai province with a length of 624 km. The EVCS were installed every 50 km, totally 11 stations, and connected to 115 kV substations in the system. The impact study results found that the system experienced with congestion problem on following elements: a) 11 critical buses at 115kV network, b) one critical bus at 230kV network and c) one transmission line is overload to 100 percent at 115kV network when each of EVCS station load increase to 30 MW. In addition, study results reveal that when installed DRG at the right and proper locations, these can reduce congestions situation, reduce system losses and enhance system stability.

580

Authors: Rana H. Saloom, Hussein K. Khafaji

Molecular biology and bioinformatics are fields that alter knowledge and abilities for gathering, managing, storing, analyzing, interpreting, and disseminating biological data. In order to get insight into the design and testing of life sciences, it is necessary to make use of high-performance computers, cutting-edge software tools, and unique algorithmic approaches for data analysis, interpretation, and prognostication. The computing challenges of processing biological sequences are used in this research to describe a significant technique for producing DNA sequence alignments. One of these challenges is to put forward computational models that aid in speeding up the computation of the substitution matrix of the needleman-wunsch algorithm. Another difficulty is the implementation of a parallel program for two threads to find the values of the matrix concurrently in order to achieve high efficiency and find the best sequence alignment in less time. This paper introduces a novel approach to constructing the substitution matrix of the Needleman-Winch algorithm. This approach enhances the overall performance of the algorithm by adjusting the values of the penalties imposed for the gap, mismatch, and match in addition to computing the matrix using two parallel threads depending on the progress made in the devices, especially with regard to the capabilities and performance of the GPU. Numerous tests have been run using different data sets and different lengths of sequence. A comparison was made between the amount of time needed to implement the Needleman method and the amount of time needed to implement the suggested model using the same data in order to evaluate the performance of the proposed model. Processing time and speedup for parallel performance are experimentally calculated. The recommended model has high scalability in terms of workload and machine capacity, according to the performance evaluation and scalability assessments. The proposed approach reduced the execution time ratio by 47% to 90%, and this improvement rate rises as the length of the reference and query sequences does.

607

Authors: Ajay Mamindla, Ramadevi Y

One of the most prominent indicators for the detection of breast cancer is a breast mass. In this regard, effective mass segmentation for any type of mammography is crucial for improving breast cancer detection accuracy and lowering mortality. In order to pace up the process of mammogram segmentation for breast mass, an ABC3D (artificial bee colony based 3 dimensional) Otsu method is proposed in this paper. Firstly, convergence speed of bees in basic artificial bee colony (ABC) is improved by adopting the epsilon greedy method for scout bees. Secondly, proposed improved ABC method is paired with optimal 3D Otsu multilevel thresholding technique to get the better thresholding set for medical mammogram images. Epsilon greed based scout bee technique streamline the exploration-exploitation problem of standard ABC while searching for best threshold set in 3D space. The proposed ABC3D is tested on eight mammogram images collected from the authoritative and publicly available database mini MIAS (mammographic image analysis society). PSNR (peek signal to noise ration), SSIM (structural similarity index) and time cost are measured to record the effectiveness of ABC3D method. The results of experimentations indicate that the proposed ABC3D achieve superior segmentation results than the teaching learning ABC (TLABC), ABCDS (directed scout), gbest guided ABC (GABC), improved particle swarm optimization (IPSO) with 3D Otsu as objective functions.