International Journal of Intelligent Engineering and Systems

11

Authors: Manaswini Pradhan, Alauddin Bhuiyan, Subhankar Mishra, Thanh Thieu, Ioana L. Coman

In the recent decades, the lung cancer is the most dangerous disease that causes 1.6 million deaths per year. Therefore, the early recognition of lung cancer is an effective way to reduce the mortality rate. Compared to other imaging techniques, the histopathology images are effective in identifying the location and level of cancer. In this manuscript, a novel model is implemented for an automatic classification of histopathological images related to lung tissues. Initially, the color normalization technique is applied for improving the contrast of the histopathological images, which are acquired from the LC25000 lung histopathological image dataset. Additionally, the cancer segmentation is accomplished utilizing saliency driven region edge based top down level set (SDREL). Further, the feature descriptors: Alexnet and gray level co-occurrence matrix (GLCM) features were used for extracting the feature vectors from the segmented histopathology images. Lastly, the enhanced grasshopper optimization algorithm (EGOA) and random forest classifier were used for optimal feature selection and lung tissue classification. The simulation result shows that the EGOA-random forest model obtained 98.50 % of accuracy on the LC25000 lung histopathological image dataset.

35

Authors: Valliammal Narayan, Sudhamathy Ganapathisamy

Machine learning methods are widely applied in credit card fraud detection to improve its efficiency and automate the process. The existing methods in credit card fraud detection have limitations of imbalance data problem. In this research, Hybrid Sampling (HS) - Similarity Attention Layer (SAL) – Bidirectional Long Short Term Memory (BiLSTM) is proposed to improve the classification performance of credit card fraud. Two datasets such as European data and Revolutionary analytics are applied to evaluate the SAL-BiLSTM model. Hybrid sampling of SMOTE-ENN model over-sample the minority class and under-sample majority class that helps to reduce difference between original data and generated data. The SAL is introduced to measure similarity of sequence of data to provide weight to unique features and reduces overfitting problem in classification. The proposed method has the advantage of removing the overlapped samples in the majority class and increasing the instances in the minority class. The SAL method in the proposed method helps to focus on unique features in the datasets to improve the classification performance. The BiLSTM model performs forward and backward analysis to find the relevant features for classification. The proposed HS-SAL-BiLSTM has a 99.2 % recall value and the existing RF-SMOTE-Support Vector Machine (SVM) has a 97.7 % recall value.

54

Authors: Dedy Kurnia Setiawan, Widya Cahyadi, Bambang Sri Kaloko

Uninterruptible power supply (UPS) is a form of electrical supply that is frequently used to regulate electrical current and perform as a backup power source when the primary power source fails. Apart from providing backup power, the inverter system proposed in this study can reduce harmonic effects and increase power factor. The control system is based on a synchronized reference frame (SRF) and uses online combined error adaptive fuzzy (OCEAF). The load voltage and current are decomposed into their symmetrical components: positive, negative, and zero. These components are then transformed to a synchronous reference frame (dq), which OCEAF controls. Controlling the inverter is accomplished by converting the controller output to abc coordinates. OCEAF combines the delta error adaptive fuzzy (DEAF) and the absolute error adaptive fuzzy (AEAF) techniques with the NN adaptor. This adaptive fuzzy-PI automatically modifies the Kp and Ki values when the system changes, significantly when the source value alters or is faulty. According to power factor measurements, OCEAF performance is superior, more consistent, and closely unity power factor. As for evaluating network harmonics, systems with OCEAF controllers may reduce network harmonics from 6.24 % to between 1 and 1.5 %. The DEAF and CEAF techniques reduce harmonics by 2-2.5 and 1.5-2 %, respectively.

74

Authors: Yenni Astuti, Risanuri Hidayat, Agus Bejo

Speaker recognition system is intended to recognize a person’s identity. This task can be done by knowing the feature of the contained voice signals. The feature can be extracted using a feature extraction technique. One of the most popular feature extraction techniques is Mel-Frequency Cepstral Coefficient (MFCC). Until this time, the MFCC is still a challenging technique to be developed. In this work, a proposed technique is developed based on the MFCC. The original MFCC is modified in the process of filter bank and by adding spectrogram. The target of this work is to obtain a better recognition accuracy than the original MFCC. This target can be done by applying a technique called Mel-weighted spectrogram. The output of the proposed technique are spectrogram images which contain the feature of the voices. The spectrogram result is then classified using dissimilarity space based on Euclidean distance to identify the person’s identity. For the dataset, this work uses 315 recorded voice signals, consisting of 3 speakers, each pronouncing five words repeatedly 21 times on seven different days. The performance of this system is evaluated by comparing the percentage of accuracy among the proposed technique, the original MFCC, and two other MFCC-based techniques. In this work, the proposed technique is better than the three other techniques with an accuracy up to 88.57%. From these results, the Mel-weighted spectrogram can be considered as a recommendation for obtaining a higher recognition rate in speaker recognition system.

91

Authors: Preethi Srinivasalu, Aishwarya Palaniappan

In recent times, brain tumor detection become an important task in medical image processing applications. The early detection of brain tumor improves the treatment process and increases the survival rate of the patients. However, the manual segmentation and classification of brain tumor is a complex and time consuming process. Therefore, a new automatic brain tumor detection model is implemented in this manuscript for effective brain tumor detection. After collecting the scans from the cloud, the normalization and adaptive histogram equalization techniques are used to enhance the acquired brain scan quality. Further, the tumor regions are segmented by integrating the Adaptive Kernel Fuzzy C Means (AKFCM) clustering algorithm with the Otsu thresholding technique. Next, the deep and textual feature values are extracted from the segmented regions using the Gray Level Co-occurrence Matrix (GLCM), Local Ternary Pattern (LTP), and LeNet-5. The dimension of the extracted feature vectors is optimized by using the Modified Particle Swarm Optimization (MPSO) algorithm, which are given as the input to the Multi-Support Vector Machine (MSVM) for tumor classification. The experimental outcomes confirmed that the MPSO-MSVM model obtained high accuracy of 98.89%, which is superior related to the existing machine learning techniques.

111

Authors: Thuan Thanh Nguyen, Huu Truong Trinh, Hoai Phong Nguyen, Tan Loc Vo, Thanh Long Duong

This paper demonstrates an artificial ecosystem optimization (AEO) for the multi-goal electric network reconfiguration (ENR) problem (ENRP). The membership goal functions of the ENRP comprise power loss reduction, voltage deviation reduction, reduction of load unbalance index among branches and reduction of number of switch operations. To show the advantages of AEO for the multi-goal ENRP, the multi-goal ENR approaches relied on particle swarm optimization (PSO), genetic algorithm (GA) and cuckoo search algorithm (CSA) are applied to contrast with the AEO method. The result comparisons among methods on the 33-node distribution network show that the AEO method can archive the optimal solution with the higher success rate and the lower average value and standard deviation of the fitness values than GA and PSO. In addition, the result comparisons with other previous approaches also show the reliability of the proposed AEO method for the single and multi-goal function ENRP. Therefore, the AEO can be an effective and useful approach for the ENRP to optimize the single and the multi-goal functions.

132

Authors: Anjani Parvathi K, Kotaiah N.C, Radha Rani K

One of the challenging tasks in an active distribution network (AND) embedded with intermittent renewable energy sources (RES) under islanding conditions is the maintenance of frequency and voltage profiles within tolerable limits. Failing to maintain these operational requirements may lead to voltage collapse or complete blackout of the network. In order to avoid this scenario, ADNs may function with contemporary load shedding schemes but these schemes may result in an inadvertent and excessive amount of load shedding, thereby consequently causing unsatisfactory and low reliability at the consumer level. Thus, this paper used Pelican optimization algorithm (POA) approach for minimising the amount of load to be shed by determining the optimal amount of load to be shed within the consumers’ specified limits. Simulations are performed on IEEE 33-bus by assuming different kinds of renewable and distribution generation (DG) units. The computational efficiency of POA is compared with literature works and its performance is also characterised based on 25-indipendent runs of each case. The results obtained by POA are observed to be superior in terms of global optima and less run time. Also, the ADN is observed as satisfactory operation with fewer distribution losses, an improved voltage profile, and enhanced stability margins by imposing optimal load controls to the consumer.

152

Authors: Sumaya A. Ibraheem, Omar Y. Shaaban

Nowadays, latency factor in network virtualization takes a serious attention in 5G networks upgrading. Flex-grid/elastic optical network (EON) virtualization recently become known as the fifth-generation (5G) enabling technology for the optical network slicing efficiency. As a consequence, the solution of the virtual optical network embedding (VONE) problem over EON must be solved with the latency targets guarantee. In this work, a new loading factor was suggested after adding the latency effect to the existing EON constraints by identifying its contributing factors in the network. Then, a virtual network mapping algorithm was presented to solve the VONE problem including latency constraint. Network topologies (NSFNET, USNET) were used under the dynamic scenario and four optimization methods which are; alignment and consecutiveness-aware virtual network embedding (ACT-VNE), the local resource capacity and the shortest path first fit (LRC-SP-FF), greedy algorithm and the shortest path first fit (Greedy-SP-FF), load and resource-aware based on ant colony optimization (LRA-ACO) were used and results have been compared to get the optimal solution. The blocking probability on average was increased by approximately 50 % in both topologies affected by adding latency (between 20-50 ms) as compared to the non-latency cases of previous works. Also, during all simulations, the LRA-ACO gave the best embedding performance.

179

Authors: Purba Daru Kusuma, Anggunmeka Luhur Prasasti

This paper presented a new metaheuristic technique, namely the guided pelican algorithm (GPA). GPA has the improvements for a shortcoming algorithm, namely the pelican optimization algorithm (POA), that mimics the behaviour of pelican birds during hunting prey. It improves the original POA in three ways. First, GPA replaces the randomized target with the global best solution as a deterministic target in phase one. Second, GPA replaces the pelican's current location with the search space size in determining the local search space size in phase two. Third, GPA implements multiple candidates in both phases rather than a single candidate as it is used in the original POA. Simulation is implemented to observe GPA’s performance in optimizing both theoretical and real-world problems. GPA is compared with four algorithms: marine predator algorithm (MPA), particle swarm optimization (PSO), komodo mlipir algorithm (KMA), and POA. The result shows that GPA outperforms all sparing algorithms in optimizing most benchmark functions. GPA is also implemented to optimize the portfolio problem. The result shows that GPA outperforms all sparing algorithms in optimizing the portfolio problem. It outperforms three sparing algorithms in optimizing the portfolio problem. Its performance is 9%, 11%, and 13%, better than PSO, MPA, and KMA consecutively. Meanwhile, its less than 1% worse than POA.

201

Authors: Periya Nayaki Anbarasu, Thanabal Muniyandi Suruli

Nowadays, clinical decision support system (CDSS) is emerged broadly due to the growth of smart applications. Yet, labeling a massive amount of clinical data is expensive and needs domain expertise. Also, how to design a robust CDSS model in a healthcare platform with a minimal number of annotated clinical data was an essential problem. To tackle this issue, a generative adversarial network (GAN)-based semi-supervised training model was developed, which increases the quantity of annotated data and solves the unbalanced annotated labels. As well, support vector machine (SVM) and K-nearest neighbor (KNN) classifiers were applied as the base learners to predict the label for unannotated data. But, the result from both base learners was not often similar, which impacts the prediction efficiency. So, this article introduces a deep ensemble learner model (DELM) for predicting labels to the unannotated data by stacking multiple machine learning classifiers as base learners. In this model, two levels of prediction: the initial level and the second level. During the initial level, multiple base learners including SVM, KNN, naive bayes (NB) and random forest (RF) learn the dataset with both the annotated and unannotated samples independently to predict the class of the unannotated instances. Then, those prediction outcomes are merged during the second level, which applies the deep neural network (DNN) as an ensemble classifier to get the final predicted class of the unannotated instance. Finally, the experimental results exhibit that the DELM with GAN achieves 86.54 %, 84.83 % and 86.72 % accuracies on SPECT, WDBC and Hallmarks databases, correspondingly, compared to the Fuzzy-AHP+ANN, HTM+LSTM, DBSCAN+SMOTE-ENN+XGBoost and GAN-based semi-supervised models.

224

Authors: K. Soumya, Vijay Kumar Garg

Detecting suicide ideation from social media texts is challenging as it necessitates analyzing the content and context of the text utterances. Modeling the temporal trajectory of suicide through various stages like stress, depression, thought and strengthening of the thought becomes difficult with content features alone and this needs context cues too. This work proposes a hybrid feature representation incorporating both content and context features. The richness of features is enhanced using adversarial learning. Neural machine translation is done on hybrid feature representation to provide named entities with emotional intensity tagging. The sequence of emotional intensity tagging is mapped by Bi-directional Long short term memory (LSTM) to suicidal ideation label. The proposed integration of emotion tagging to SI detection with Bi-directional LSTM provided an SI detection accuracy of 95.54% which is atleast 3% higher compared to existing works.

247

Authors: Saif K. Jarallah, Sawsen Abdulhadi Mahmood

Video summarization technologies strive to provide a succinct and thorough description by selecting the most informative frames from the original video. The essential concept of query-based video summarization methods is represented by constructing a video summary related to user query in term of user interest. This paper aims to address the problem of query-based video summarization construction task by adopting a lightweight deep learning model for object detection task in order to evident how concerning a frame or shot is to a given query. Both YOLOv3 and Tiny YOLOv3 deep learning models are used separately in the proposed system to train the networks using images and videos dataset including diverse types of objects such as; (motorcycles, bicycles, cars, buses, and trucks). Subsequence, the most relative frames to a given query are selected and assembled as keyframes using a modified K-mean clustering scheme to provide different interesting summaries from the original video. Based on the experimental results of object detection phase, we have obtained an average object detection accuracy around 93%, 83% based on YOLOv3 and Tiny YOLOv3 deep learning models respectively. Comprehensive experiments were performed to evaluate the proposed video summarization system, which exhibited an efficient summarization rate close to 33% of the original video. Further experiments were conducted using standard UTE dataset to exhibit the competitive performance of the proposed method compared to state of art query-based video summarization methods.

273

Authors: Neny Sulistianingsih, Edi Winarko, Anny Kartika Sari

The Girvan-Newman (GN) method is one of the most popular methods for detecting communities. However, the method is applied to graphs with only positive weights, while graphs with positive and negative weights are in the real world. This paper proposes improving the GN method to work on graphs with positive and negative weights. The method is called Girvan-Newman Parallel Positive Negative (GN-PPN). Other than the benefit related to the use of negative weights, GN-PPN has a parallelization process that accelerates processing time. This method also uses edge betweenness and pair dependencies as calculations. The GN-PPN method is evaluated using modularity score, Normalized Mutual Information (NMI), and processing time. Our experiments show that the modularity score of the GN-PPN is similar to GN, modified GN (mGN), and Parallel GN (PGN). The accuracy of the GN-PPN method in detecting communities evaluated by NMI shows that the GN-PPN is better than similar methods such as Infomap, which ranges from 0.651 to 0.937. The processing time of the GN-PPN method shows a significant acceleration, which is a decrease of around 45% to 95% compared to other methods such as GN, mGN, and PGN.

293

Authors: De Rosal Ignatius Moses Setiadi, Rahmawati Zulfiningrum, Eko Hari Rachmawanto, Pulung Nurtantio Andono

Telemedicine is a communication in the medical field that requires privacy and confidentiality, so encryption is needed as a security method. The development of encryption methods must accompany technological developments. This research proposes a cryptography method developed and tested on medical images. This method consists of bit plane slicing, system chaos, and hash function. Bit plane slicing to break up the image bits so that they can be scrambled independently and dynamically with the chaos method. The hash function encrypts key 1, which is user input. The chaos method is run with dynamic parameters generated with a hash function to perform the confusion process on each bit plane. Then bit-plane join is performed, followed by the diffusion process using a logistic map with the second key for the parameter. The method is sensitive to slightly modified plain images or keys. Several measurement tools use to prove the method's performance. The result is a mean value are 7.999 for entropy information, 99.6027 for the number of pixels change rate (NPCR), 33.0925 for unified average changing intensity (UACI), 0.0021 for correlation coefficient, 231.8679 for chi-square and histogram, 50.0550 for avalanche effect, and perceptual analysis have proven the proposed method has satisfactory performance. The decryption is also done perfectly, as verified by the structural similarity index (SSIM)=1, peak signal-to-noise ratio (PSNR) is ∞, and bit error ratio (BER) is 0. Finally, this method can also work very fast in less than one second, so it can be implemented in real-time encryption.

315

Authors: Nova Rijati, De Rosal Ignatius Moses Setiadi, Pulung Nurtantio Andono

Tamper detection and localization are essential things in fragile image watermarking to carry out the authentication process. The tampering method continues to evolve from general and complex tampering. This study proposes a tamper detection and localization technique that can withstand a variety of standard and complex attacks with a combination of bidiagonal singular value decomposition, blockwise and group block for authentication. The least significant bit method is used in the embedding process to increase imperceptibility. By combining these methods, fragile image watermarking is produced that is robust against various common tampers such as type I and type II copy-paste, rotate, text addition, and noise addition, as well as various complex attacks such as vector quantization, collage, content only and constant feature. The tamper detection rate is more than 0.99, and the average PSNR value is 51.85 dB. This shows that the proposed method is robust against various tampers and has excellent imperceptibility.

337

Authors: Abousserhane Zakaria, Abbou Ahmed, Bouzakri Hicham

The aim of this paper is developing and validating the sliding mode control application (SMC) for hybrid renewable energy system (HRES) featuring stand-alone and on-grid operations modes. The proposed system composed by a photovoltaic (PV) and wind turbine (WT) as principal sources. The PV is connected to the overall system via boost converter which controlled using MPPT algorithm depending to Adaptive Perturb and Observe (APO) method managed by SMC to extract maximum of power of PV modules. The WT depends to a Permanent Magnet Synchronous Generator (PMSG) and a controlled rectifier to maximize the operating point of the WT. Therefore, the battery and supercapacitors are connected to the system via a bidirectional converter to ensure the charging modes operations. The voltage source converter (VSC) is controlled by SMC had an aim to control the frequency and amplitude of the system outputs injected into the grid and load utility. To expose the performance of the proposed sliding mode control approach, we propose a different comparatives tests include a conventional PI control and hysteresis current control under two operating grid modes depending to different climate scenarios. The developed control strategy for HRES have shown great results such as the control performance, effectiveness and delivers low harmonic distortion values (Max of THD are 3,43% and 4,88%) compared with hysteresis current controller (Max of THD are 4,63% and 10,98%) and PI control (Max of THD are 4,92% and 12,86%) featuring OFF and ON grid, respectively. Finally, the proposed control enhances the HRES quality and stability.

364

Authors: Eman K. Ibraheem, Ekhlas K. Hamza

The increasing number of mobile devices challenges the current radio frequency (RF) networks. A hybrid RF/VLC network (HLRN) is proposed to mitigate the spatial fluctuation of data rate, offering a system throughput greater than that of standalone visible light communications (VLC) or radio frequency (RF) networks. In hybrid networks, the main problem is load balancing, which reduces network performance. Therefore, to solve this problem, the load balancing (LB) schemes in HLRNs are studied and focus on the AP assignment for the users. In that, efficient spectrum sensing is found essential to prevent the wrong holes detection in the band using five technologies divided into three stages, namely: Received signal strength (RSS), particle swarm optimization (PSO) and deep learning methods: Feed forward neural network (FFNN), convolutional neural network (CNN) and cascade back propagation neural network (CBPNN). An event such as high energy noise availability or primary user fluctuation (mobility) impact on the throughput is studied, which was carried out in three stages depending on MATLAB. In the first stage, the received signal strength (RSS) algorithm is proposed, which is used the SNR to calculate throughput for fixed and mobility user and user satisfaction. In stage two, the particle swarm optimization (PSO) algorithm is proposed to achieve a better performance than the RSS algorithm. At last, in stage three, the DL algorithms (FFNN, CNN, CBPNN) are used to enhance the performance's accuracy and compare them. The results in stage one show that throughput using RSS technique is decreasing when the number of users increases by achieving 390 Mbps approx. is detected with single user existence while 180 Mbps approx. is detected with 10 users' existence. And it can be noted that most of the users are connected to wireless fidelity (Wi-Fi) AP. Hence the Wi-Fi is overloaded. Further, only 34 % of the users in RSS based scheme will achieve the desired performance if a user satisfaction threshold of 0.5 is considered for the system. The results in stage two shown that throughput using the PSO technique is outperformed over the others by achieving high average throughput of 960 Mbps approx. Also, it has offered acceptable load balancing for the access points with good user satisfaction reach to 80 % of the users will achieve the desired performance when the user satisfaction threshold is considered to be 0.9 for the system. In stage three, deep learning methods for system performance enhancement are used (as compared with the standard RSS method). throughput is improved, and this improvement is lesser than that in the case of the PSO algorithm. 800 Mbps approx. is achieved using the CNN method, and it is realized that 70 % of the users will achieve full user satisfaction when the user satisfaction threshold is considered 1.2 for the system.

392

Authors: Mina Ibrahim, Muhammad Mahmoud, Reda M. Albadawy, Hatem Abdulkader

Liver cancer is a common type of cancer that causes death, because there are no noticeable symptoms at an early stage, as this disease is not detected in most patients until cancer has reached the advanced stage only. Researchers are developing algorithms that doctors can use to detect liver tumours early by examining images of tissue from a biopsy or an abdominal medical image. The tissue expert must put in the time and effort required at this stage to determine whether or not this tumour is cancerous and in need of treatment. This model can then be used by a histology expert to make an initial diagnosis. Convolutional neural networks (CNNs) are employed in this paper to propose a novel combination of deep learning models that can transfer information from previously trained global models. After that, this information was decanted into a solo model to improve our model approaches to increase the performance in time and accuracy with tuning to an encoder, decoder, shortcuts, and skip connections to custom convolution layers for three classes such as background, origin, and Tumour shape. Regarding semantic segmentation, our model has proved to be a highly effective way to make results more accurate and valuable to assist in the diagnosis of the liver Tumour using CT scans. As a result, we were able to develop a hybrid model that is capable of recognizing CT images of a liver tumour. Our research yielded the greatest possible results, which we got, reaching 99.50% accuracy, a 86.40% precision and a recall of 97.90%. This accuracy with multi-class is higher than that obtained using other previous models that obtained the best accuracy of 0.991 during the annual periodic examination campaigns for liver cancer detection. Using this model, experts in this field can save time and effort while becoming more informed choices. It also keeps the time and effort that would otherwise be required to administer this treatment, especially during the annual examination campaigns.

418

Authors: B. Uma Rani, A. R. Naseer

Underwater Acoustic Wireless Sensor Networks (UAWSN) is a collection of sensor nodes that are strategically placed at different depths under water to monitor the activities of interest. In contrast to Terrestrial Wireless Sensor Networks (TWSN), a UAWSN is constrained by several challenges like limited bandwidth, multipath propagation, path-loss, noise, Doppler spread, etc. Also, other parameters like salinity, depth and pressure influence the propagation speed and in turn the achieved throughput. Most of the existing works in this direction focus on designing new medium access control protocols to minimize collisions and maximize bandwidth utilization. Other existing routing and data forwarding protocols use different metrics to maximize network objectives like network lifetime, increase network throughput, minimize energy drain, etc. In this paper, we propose a routing and data forwarding mechanism using link bandwidth by considering path-loss and multipath propagation effects. These cross-layer metrics allow us to select high-capacity links increasing overall network throughput. In addition, we propose different optimizations to the data forwarding and routing mechanisms to save energy and increase lifetime of the network. The simulation results show that the proposed mechanism improves 6% packet delivery ratio, 10% throughput, and minimizes energy consumption in comparison to state-of- the-art protocol CLOR.

443

Authors: Mohammed Kh. Al-Nussairi, Sadeq D. Al-Majidi, Ali Jber Mshkil, Adel Manaa Dakhil, Maysam F. Abbodm, Hamed S. Al-Raweshidy

An Automatic Generation Control (AGC) is considered a substantial stage in power systems to ensure that the area-frequency response and the tie-line power of an interchanging steady state errors are acceptable values especially at the transient state. Whiles, several techniques have been designed for the AGC, Fuzzy logic control is commonly used. However, it is faced a longer processing time issues to adjust the accurate single in the transient state. In this paper, a Single Input Fuzzy Logic Gain Scheduling PID Controller (SIFL-PID) is designed as a supplementary loop for AGC of two area interconnected power system to reduce the processing time. The lower number of rules and the simple parameters of the SIFL-PID will contribute to minimise the design time required for the Fuzzy Logic Gain Scheduling FL-PID. The SIFL-PID also reduced the signed distance technique when it is generated one input variable simple mathematical model of the system. In comparison with the FL-PID, the number of rules controlling system can be significantly reduced. The main outcome of this work is that it is assessed the effectiveness of the SIFL-PID for two power systems that are interconnected when it is compared with the FL-PID using the MATLAB/SIMULINK environment. The results prove that, the simple design of the SIFL-PID control is validated under various condition tests. Performance analysis standards are also performed. The results of all simulations demonstrate that the suggested controller provides better performances such as like as minimum Performance analysis standards, frequency deviation power deviations and low overshoot and undershoot.

465

Authors: Nguyen Dinh Hung, Le Cao Quyen, Le Van Dai

The primary goal of this paper is to develop and apply an adaptive fuzzy (AF) based on the proportional integral derivative (PID) controller for the automatic voltage regular (AVR) system in the synchronous generator (SG) to improve output voltage stability. This proposed AF-PID is applied to ameliorate the shortcomings of the traditional PID controller by determining the optimal coefficients for the controller. Firstly, the initial coefficients of the traditional PID controller are calculated by using the Ziegler-Nichols method. After that, it develops the AF algorithm based on the fuzzification, rules of the fuzzy controller, and defuzzification of the voltage signals for self-tuning the coefficients to add the traditional PID controller. The transient response of the system is simulated and analyzed by considering the different step changes in reference voltage. The efficacy of the proposed AF-PID controller for the AVR system in the SG is illustrated by comparing it with traditional PID and without the controller based on the obtained results from the case studies. It can be concluded that when applying the proposed method, the transient response characteristics of the system from the perspective of simulating via the time domain under any sudden load changes in system operation, such as the rise time, steady-state error, max peak overshoot, and settling time are greatly improved. The simulation has been performed using the MATLAB/Simulink program.

492

Authors: Dwi Sunaryono, Riyanarto Sarno, Joko Siswantoro, Diana Purwitasari, Shoffi Izza Sabilla, Rahadian Indarto Susilo, Naufal Rafi Akbar

Epilepsy is a common chronic brain disease caused by abnormal neuronal activity and the occurrence of sudden or transient seizures. Electroencephalogram (EEG) is a non-invasive technique commonly used to identify epileptic brain activity. However, visual detection of the EEG is subjective, time consuming, and labour intensive for the neurologist. Therefore, we propose an automatic seizure detection using a combination of one-dimension convolution neural network (1D-CNN) with majority voting and deep neural network (DNN). EEG signals features are extracted using discrete Fourier transform (DFT) and discrete wavelet transform (DWT) which then these features will be selected with XGBoost to minimize features classified with CNN. The proposed method experimental results show that it can detect epilepsy from EEG signals perfectly with an accuracy of 100%. However, the proposed method only yielded classified EEG signals from the University of Bonn Dataset as its results. The performance of the suggested approach might not be similar to other EEG datasets.

511

Authors: G. Krishna Raju, P. Padmanabham, A. Govardhan

Introduction of the World Wide Web (WWW) and progressions in the field of multimedia and computer technology have enlarged the amount of image collections and databases including art galleries, digital libraries, and medical imageries that extend to millions of images. From these large-scale datasets, the retrieval procedure of images is carried out by classical approaches like chi square distance, colour histogram and text-based image retrieval, which however requires more time for getting the desired images. Thus, there is a need of proposing an effective retrieval system for images by handling these large numbers of images. For this reason, a new Content-Based Image Retrieval (CBIR) system has been implemented in this paper for retrieving the desired images of users from the collected images. Initially, different kinds of images like medical images, texture images, and environmental images etc., are collected from the standard image database. The deep feature extraction using Visual Geometry Group network (VGG-16), Inception v3, and Xception are used to get the features of all the images in the database. The parameters in these three deep learning techniques are tuned with an enhanced optimization algorithm of Modified Bypass-Rider Optimization Algorithm (MB-ROA). The features from VGG16, Inception and Xception are used for optimal weighted fused feature selection using enhanced ROA under training phase. During testing with query images, it undergoes deep feature extraction with the same set of techniques and, then fed into the optimal weighted fused feature selection to get the features of query image. Then, the multi-similarity function considering the cosine, Euclidean and Jaccard similarity are checked between the optimal features of database images and query image. The database images with the minimum similarity with the query image are retrieved from the database. The overall precision and recall to retrieve the top ten images related to the provided query image from the Corel dataset are 77.25% and 76.89%, respectively, while 70.15% and 70.26%, respectively from the VisTex dataset. The experimental findings show that the recommended model is effective at retrieving images from the database.

541

Authors: Yunifa Miftachul Arif, Hani Nurhayati

One of the challenges of learning mathematics is making students like the subject to understand the material being taught more efficiently. Therefore, several studies propose the use of gamification elements in the mathematics learning process. Another important consideration in learning mathematics is the accuracy of the subject matter given to students. This study proposes a metaverse-based mathematics pedagogy media (MMPM) equipped with learning material selection (LMS) to adaptively select subject matter according to the level of student knowledge. We use the multi-criteria recommender system (MCRS) to support LMS in providing recommendations for determining the choice of subject matter. The method used to calculate similarity is cosine-based, while the ranking is based on average, worst-case, and aggregate. We built the MMPM system using the unity game engine in the experimental stage. At the same time, the subject matter that becomes the focus of scenario visualization is about beam, cube, prism, and pyramid. The test results show that the LMS system works well in carrying out its duties by adaptively choosing the appropriate subject matter scenarios for students based on their pre-test results. In this study, MCRS-based LMS produces the highest accuracy of 92% for two to three input items and the lowest 90% for four input items.

563

Authors: Hussein A. Hussein Al-Delfi, Fadhil Sahib Hasan

This paper suggests the transmission of secure speech signals through the MIMO-OFDM system over a doubly selective fading channel. The two-dimensional chaotic map like logistic (TDLM), standard (TDSM), and triangle function combination discrete chaotic map (TD-TFCDM) are used to generate pseudo-random bit generator (PRBG) and index permutation (IP) to encrypt the information based on ciphering and scrambling techniques. Different speech quality and residual intelligibility measures including log-likelihood ratio (LLR), frequency-weighted segmental signal-to-noise ratio (fwSNRseg), signal-to-noise ratio loss (SNRLOSS), correlation coefficient (CC) and spectrogram measures as well as Bit Error Rate (BER) performance are used to test the performance of the proposed security system. The simulation results show that the speech signal over the MIMO-OFDM system behaves well with high security. Also, the speech encryption system using TDSM and TD-TFCDM outperforms TDLM in most residual intelligibility measures.

589

Authors: Mie Mie Oo, Nu War

Acoustic scene classification is a difficult issue among artificial intelligence, signal processing, and machine learning. Scene recognition performance has a robust relation with feature learning using deep convolutional networks. In the following research, end-to-end deep residual network embedded channel attention is explored to learn the discriminative features from the audio scene. Log-Mel spectrogram is obtained from input raw audios. It is forwarded to proposed attention network. An extracted feature layer is concatenated with the SoftMax classifier in the proposed attention network. The experimentation is carried out on Detection and Classification of Acoustic Scenes and Events (DCASE) 2016 and 2017 datasets. The proposed channel-attention-based residual network achieves classification results with an average accuracy of 80.27% and 80.82%, respectively.

609

Authors: Saritha Ibakkanavar Guddappa, Rajeshwari Mahabhaleshwar Hegde

Cyber physical system (CPS) is an engineered system that integrates the physical world with the cyber world through enhanced computation, communication and control (3C) abilities. But, the CPS’s sensors are susceptible to malicious threats due to its dynamic topology and open medium of the network. Therefore, an effective secure adaptive cluster-based routing is required to be developed for improving the security of CPS. In this paper, the multi objective-trust based hybrid optimization algorithm (MO-THOA) is proposed to improve CPS security. The MO-THOA is the combination of trust based flower pollination algorithm (TFPA) and trust based ant colony optimization (TACO). In that, the TFPA is used to select a secure adaptive cluster head (ACH) and TACO is used to identify the secure routing path via ACHs. Therefore, the proposed MO-THOA improved the security against malicious attacks while broadcasting the data. The performance of the MO-THOA is evaluated using packet delivery ratio (PDR), average end to end delay (AEED) and normalized routing overhead (NRO). The existing methods namely grey-wolf updated whale optimization algorithm (GU-WOA), moving centroid based routing protocol (MCRP) and multi-objective ant colony optimization (MOACO) are used to evaluate the MO-THOA method. The PDR of the MO-THOA for 20 nodes is 0.981, it is high when compared to the GU-WOA, MOACO and MCRP.

627

Authors: Sara Ahmed, Fatma A. Omara

Cloud computing has gained many attentions worldwide. Workflow systems become a significant method for develop scientific applications. Therefore, workflow scheduling is considered one of the most important issues in cloud computing. It concerns about mapping tasks on cloud resources (i.e., Virtual machines (VMs)), to improve scheduling performance. Because the existing heterogeneous earliest finish time (HEFT) algorithm is considered one of the best and simplest algorithms, many algorithms have been proposed to improve the performance of the HEFT algorithm. According to our previous work, a modification has been done to HEFT algorithm to enhance the performance, called modified heterogeneous earliest finish time (M-HEFT). The goal of M-HEFT algorithm is to reduce make span, maximize resource utilization and increase load balance. According to the work in this paper, an enhancement has been added to our previous M-EFT algorithm to reduce the tradeoff among make span, resource utilization, and load balance, called enhanced modified heterogeneous earliest finish time (EM-HEFT). The enhanced EM-HEFT algorithm consists of two phases; task prioritization and task-VM mapping. In task prioritization phase, a priority will be provided to each task in directed acyclic graph (DAG) by introducing new factors in priority value to be more aware about task requirements. According to task-VM phase, tasks are allocated to resources as in our previous M-HEFT algorithm. To evaluate the performance of the proposed EM-HEFT algorithm, a comparative study has been done among the proposed algorithm and four existed algorithms (HEFT, Efficient scheduling algorithm use critical path and static level attribute, Optimized Min-Min (OMin-Min) and our previous M-HEFT). The experimental results show that the proposed algorithm outperforms other algorithms by minimizing make span by 25%, improving resource utilization by 43% and load balance by 14% in average.

657

Authors: Bakr S. Shihab, Hadeel N. Abdullah, Layth A. Hassnawi

Recently, path planning algorithms have been one of the primary and important functions of unmanned aerial vehicles (UAVs). Path planning algorithms in UAVs focused on path length, average path length, computation time, and standard deviation from the mean path length. In spite of this, it faced many difficulties and problems, such as many obstacles, path segmentation, and the increasing number of obstacles and paths in urban environments. This work proposes polynomial functions for path planning and obstacle avoidance. Since it enables us to plan the path in static internal environments, it enables us to plan the path quickly and with less computing time because it does not require high memory and does not require pre-compute of the path. Instead, the route is plotted in real time, Where the appropriate equation is entered into the program, so that the vehicle follows the curve of the entered equation. An accurate data set and metrics were used to measure the efficiency of the proposed method. The experimental results showed a clear improvement in the work of the polynomial function on A*, PSO and genetic algorithms, as this improvement appears very clearly when compared to the computing time, which was reduced by 15% in the method of polynomial functions where the path calculation took only parts of The second, as well as the path length was halved in the polynomial method as the results showed, which reduces the time of battery and memory consumption, the cost of calculating the path and the time to reach the goal.

682

Authors: Ahmed Wasif Reza, Khairum Islam, Sidratul Muntaha, Omair Bin Abdur Rahman, Rabeya Islam, Mohammad Shamsul Arefin

The emergence of new and improved technological advances created severe problems in the security state of the educational certification system. Throughout this paper, a proposal has been made to improve security. Here, Blockchain technology has been introduced as reliable secure storage for the educational certification system, providing an additional facility to the users. That is the validation and authentication of the student’s academic records. Moreover, for security purposes, Blockchain technology can replace the traditional academic certification system and contribute to a new model for sharing student information. After completion of data inclusion and hashing, the blocks will be inserted into the Blockchain network. This proposed model enhances document security and fraud reduction and additionally reduces a significant amount of authentication time almost up to double the current speed. With this system, we will get a certification process in which all data will be digitalized and secured in an unbreakable database with proper authentication and with a noticeable amount of time efficiency.

12

Authors: Arnaud Nanfak, Charles Hubert Kom, Samuel Eke

As a major piece of equipment in the electrical power transmission and distribution, the rapid and accurate assessment of emerging or existing internal faults in power transformers is a key factor in the safe and stable operation of the power grid. This paper proposes a hybrid approach to fault diagnosis for these transformers. This approach is based on ensemble bagged tree classification and training subsets obtain by a conventional pre-processing method. Two pre-processing approaches are performed, the first based on the maximum concentrations of the dissolved gas samples and the second based on the minimum concentrations of the dissolved gas samples. For each training subset, an ensemble classifier is constructed with as inputs the Rogers ratios, Gouda ratios, dissolved gas concentrations and their associations. The proposed hybrid methods are established with 475 samples of training dataset, tested on 117 samples dissolved gas analysis (DGA) data and validated on International Electrotechnical Commission (IEC) TC10 database. The performances of the proposed diagnostic models are evaluated and a comparison is done compared with the following diagnostic methods: IEC ratios method (IRM), Rogers ratios method (RRM), three ratios technique (TRT), Gouda’s triangle (GT), and self-organizing map (SOM) clusters. The results found by computer simulations carried out by the matrice laboratory (MATLAB) software show that, of the two pre-processing approaches used, the one based on the minimum sample concentration gives better results than the one based on the maximum concentration. In terms of fault type, the best diagnostic model using the minimum concentration-based pre-processing approach has a diagnostic accuracy of 94.02%, compared to 92.31% for the best diagnostic model using the maximum concentration-based pre-processing approach. This is lower than the 97.25% for SOM clusters and 96.58% for GT but higher than the 59.83% for RRM, 81.19% for IRM and 93.16% for TRT. In terms of fault severity, the best diagnostic model using the minimum concentration-based pre-processing approach has a diagnostic accuracy of 81.20%, compared to 74.36% for the best diagnostic model using the minimum concentration-based pre-processing approach. This result is lower than the 83.76% for TRT and 89.74% for GT but higher than the 49.57% for RRM, 66.67% for IRM and 78.90% for SOM clusters.

35

Authors: Billel Arbaoui, Yusraw O-manee, Patimoh Yuso, Yuttachai Hareebin

Self-awareness emotional intelligence symptoms will be unavoidable in the job. These symptoms are generally regarded as acceptable by certain standards, as they are an important part of keeping people informed (or commonly known as situational awareness). However, the most recent research focuses on external components of situational awareness aspects and lacks adaptable methods to deal with the individual's situational awareness dynamics. This study investigated the use of an agent-based modeling strategy for situational awareness in the workplace. It focuses on environmental and personal factors that influence the level of situational awareness at work in a dynamic way. The outcomes included a range of scenarios that corresponded to various personality traits and environmental conditions. Finally, equilibria analysis and automated logical verification were employed to evaluate this computational model in order to check whether there were any conceptual defects, as indicated in the literature. The suggested computational agent-based model has shown rational behavior patterns that are consistent with existing psychology literature on situational awareness in the workplace.

57

Authors: Manar Bashar Mortatha, Samer Salah Thabit, Hawraa R. Abdul Ameer, Riyadh Rahef Nuiaa

During the image enhancement process, hidden information, poor capturing device quality lead to poor image contrast, insufficient user experience, and an inappropriate data collecting environment setting have all been noted as serious concerns. Histogram equalization techniques have been used to solve the challenges described above. Nonetheless, the images obtained using these methods are frequently impacted by unwanted artifacts, and unnatural appearances effects. Due to that, this research presented a novel strategy for enhancing contrast called Dynamic Clip Limit Window Size Histogram Equalization. The proposed technique uses a new fitness function of combining the discrete entropy and root mean square error parameters. The proposed strategy's qualitative and quantitative result is validated and assessed against 5 state-of-the-art strategies (HSQHE, ACLTSHE, CLAHE, AEIHE, and IAECHE). The proposed technique proved its high capability to produce the best average DE, CII, and SSI values for the Faces-199 dataset (7.869, 1.000, and 0.999, respectively).

81

Authors: Mohammed Shakir Oudah, Abeer Tariq Maolood

With the rapid use of the Internet, and the increase in the numbers of smart and mobile devices, the internet of things (IoT) fields had become more crucial fields in the interest of developers. Many aspects related to authentication and privacy communication during an interaction between remote users and IoT devices should be enabled and achieved in a lightweight and secure manner. Due to the elliptic curve digital signature (ECDSA)’s features that are secure and lightweight compared with other public key algorithms; this paper presents a new IoT authentication model that incorporates a modified ECDSA. But unfortunately, the familiar use of ECDSA in Blockchain showed some problems related to the capability of revealing the random private integer, which leads to private key disclosure, and hence funds theft, this paper highlights this problem and proposes modifications to the ECDSA to make it more reliable. In addition, the proposed model combines a modified ECDSA and Shamir’s secret sharing (SSS). The combination can give better results in establishing a more securely authentication agreement and robustness to resolve the standard algorithm attenuation, this modification involved a variant of ECDSA’s calculations in signature processing, and employing Shamir’s Secret Sharing to further protect the random private integer. Also, the proposed model achieves lower overhead communications by splitting and redistributing authentication calculations roles between the system’s entities. After security analysis, the proposed algorithm exhibits ability to resist potential threats, also, a comparison with other related works demonstrates that the proposed algorithm performs fewer arithmetic operations, relatively, by decreasing the number of modular inverse operations, so this feature lowers resources-efforts and high performance, moreover, the theoretical analysis indicates that the proposed algorithm efficiently manages the scalability of the system.

103

Authors: Abbas Salman Hameed, Dheyaa T. Al-Zuhairi

This paper proposes a peak to average power ratio (PAPR) reduction companding scheme for single carrier frequency division multiple access. The proposed technique has multi-level compression (MLC) stages where a signal is adjusted according to its instant value with respect to these levels. To validate MLC function, PAPR, bit error rate (BER), average power (P_av), and power spectral density (PSD) are evaluated throw simulation. MLC improved PAPR significantly with low BER degradation. Using 16-QAM, MLC enhances PAPR by 4.38 dB while only 1.6 dB additional SNR is required to maintain 10^(-3) BER. MLC performance is compared with the recent companding techniques; Mu-law, Exponential, Trapezoidal, and tangent rooting. The BER simulation results show the superiority of MLC over these techniques. For PAPR reduction, different from other techniques, both Mu-law and MLC reduce PAPR significantly with close values. Finally, the Exponential and MLC preserve P_av with less side lobes in PSD.

128

Authors: Ganapathy Kavitha, Kandasamy Vanitha

Psoriasis diagnosis from gene expression data requires efficient machine learning classifiers with the finest pre-processing methods to overcome the outlier and missing value problems. This paper developed relative entropy fuzzy score (REFS) normalization and genetic weighted k-nearest neighbors (GWKNN) imputation for enhancing pre-processing and hybrid multi-kernel universum support vector machines (MKUSVM) - radial basis function extreme learning machine (RBFELM) classifier for accurate psoriasis recognition. REFS normalization utilizes improved fuzzification and relative entropy in the gene fuzzy score estimation to reduce the skewness and outlier gene data. GWKNN imputation performs symbolic regression of weighted k-nearest neighbors (KNN) with genetic programming (GP) for missing value estimation. Finally, the hybrid MKUSVM-RBFELM classifier classifies the psoriasis gene data with features selected using mutual-information parameter. Evaluated over public dataset GSE55201, the proposed model achieved 91.67 % accuracy, 92.58 % precision, 90.94 % recall, 91.75 % F-measure, and 0.8452 MCC values with a reduced processing time of 0.373 seconds.

151

Authors: Deepak Jayaprakash Doggalli, Sunil Kumar B S

One of the primary causes of cancer-related mortality is liver cancer. Computed tomography (CT) is a commonly utilised imaging technique for the assessment and staging of hepatic tumors. Manually scanning volumetric CT scans is time intensive and ambiguous. Despite the fact that numerous deep learning models for semantic segmentation have been developed, the U-Net model remains extremely successful. In this work, we propose a method for segmenting liver tumors from abdominal CT images that is entirely based on the U-Net model and demonstrate the model's simplicity and efficacy. The U-Net architecture is employed at two levels. The first level of segmentation is used to segment the liver from CT slices, while the second level is utilised to segment tumours from masked CT images. Using CT scans from the LiTS dataset, the proposed technique achieved a dice global score of 94 percent for liver segmentation and 73 percent for tumour segmentation.

172

Authors: S. Aarthi, M. Shanmugasundari

This research provides a two-class single transmit queuing model. We also calculate the model’s execution proportions under a vague environment. The main purpose of this inquiry is to compare the results of a single transmit queuing model based on fuzzy queuing theory and intuitionistic fuzzy queuing theory. Triangular fuzzy numbers and triangular intuitionistic fuzzy numbers are used to describe the entry (arrival) and administration (service) rates. The fuzzy queuing theory model’s evaluation metrics are supplied as a range of values, but the intuitionistic fuzzy queuing theory model offers a wide range of values. An analysis is offered to discover quality measures utilizing a proposed methodology in which the fuzzy values are retained as is without being altered into crisp values, hence we can use the proposed method to draw scientific conclusions in an uncertain environment. Two numerical problems are solved to demonstrate the sustainability of the suggested method. Subsequently, prototype components were exposed to sensitivity analyses. Sensitivity testing is used to find discrepancies between the two groups when calculating their execution proportions. We employed the triangular fuzzy number in an intuitionistic fuzzy environment in this study, accounting for the degree of comfort and refusal so that the sum of both values is always less than 1. For this type of fuzzy integer, we gave various non-normal arithmetic procedures. The proposed formulations are simple and direct, having been devised using classical algebraic mathematics. This strategy is simple and straightforward to use in actual situations. The nearest interval number is then used to round a TIFN. The key benefit of this approach is that using a multi-section algorithm, we can quickly solve a bound unbridled optimization problem with coefficients as TIFN. The present methodologies and strategies are intended to be applicable to a variety of contemporary decision-making challenges in areas of economic share, finance, administration, and ecology, which are the focus of our future study.

194

Authors: Pallavi Hallappanavar Basavaraja, Shanmugarathinam Ganesarathinam

Osteoporosis is a condition wherein bone tissue deteriorates and bone strength deteriorates. Over time, the disease that can lead bone to become more permeable and fragile makes it more prone to breaking. Early recognition of osteoporosis via an X-ray approach will enhance diagnosis and treatment practices, but it will also assist in preventing national economic loss via mass screening and awareness. A novel early osteoporosis diagnosis model is developed for X-ray images in this research work. The following four primary steps are used to construct a unique Osteoporosis detection model in this study: “(a) pre-processing, (b) feature extraction, (c) optimal feature selection, and (d) Osteoporosis detection”. Gabor filtering (noise reduction) and histogram equalization are used to pre-process the obtained raw data (X-ray) (quality enhancement). Features such as “Active shape model (ASM), active appearance model (AAM), gray level co-occurrence matrix (GLCM), mean local gradient pattern (M-LGP), and improved median ternary pattern (I-MTP)” are recovered from the pre-processed data. Following that, a new hybrid optimization model chooses the best features from the retrieved features. The cat guided hummingbird foraging algorithm (CGHFA) is a conceptual combination of the basic artificial hummingbird algorithm (AHM) and the cat hunting optimization algorithm (CHOA). The deep learning classifiers in the Osteoporosis detection phase are trained using these ideally selected characteristics. The newly created ensemble-of-classifiers model is used to represent the osteoporosis diagnostic phase. “Quantum deep neural network (QDNN), improved deep convolution neural network (I-DCNN) and recurrent neural network (RNN)” are some of the deep learning classifiers that is employed here. All of these classifiers are trained using the optimal features available. The loss function of DCNN is improved via harmonic mean based cross-entropy function. The final detection performance will be calculated by combining the results obtained from all of these characteristics (by taking the mean). Finally, the effectiveness of the anticipated model is validated by a comparative examination. Accordingly, the detection accuracy attained by the proposed deep ensemble model +CGHFA at Learn_rate=60 is 90.7%, at Learn_rate=70 is 92.14%, at Learn_rate=80 is 93.482% and at Learn_rate=90 is 94.8%, which is higher than the existing models.

219

Authors: Loai Alamro, Yuhanis Yusof, Nooraini Yusoff

Human head attribute classification (HHAC) is a fundamental and substantial research domain in pattern recognition and computer vision. However, recent HHAC networks do not consider the correlation of common characteristics among the attributes over different regions of the human head. To address the above problem, this study proposes a multi-output convolutional neural network to jointly learn the features of human head attributes with common characteristics. The proposed network contains two convolutional blocks and five output layers, where each output layer learns to predict a specific group of human head attributes. In order to properly learn the correlation among the human head attributes, this study divides these attributes into five groups: hair, face, style, accessories, and appearance. Extensive experiments showed that the proposed network obtained an average classification accuracy of 95.29% and 97.93% on the challenging CelebA and LFWA datasets, respectively. Thus, the proposed network is approximately 2% and 10% superior to the closest competitor (i.e., PS-MCNN) on both datasets. In addition, the proposed network achieved higher classification accuracy compared to the existing networks almost in all human head attributes. That findings demonstrate the effectiveness of the proposed network and the attributes grouping method in learning the correlations among human head attributes correctly.

244

Authors: Noor Jabbar Abraham, Hazim G. Daway, Rafid Abbas Ali

Enhancement of images with low light has become an important role in the field of digital image processing, especially when you take the image at low or irregular lighting levels. In this study, a new algorithm was proposed to enhance images with low light based on the development of the Dark Chanel Prior (DCP), Where the image was first improved using this DCP then the lightness component in (Lab) space was improved using the sigmoid mapping. The proposed method was compared with several algorithms by using non-reference quality measures as naturalness image quality evaluator and image quality evaluator using LIME data. By analyzing the results we note the success of the proposed method in improving images with low light, with obtaining the best contrast and lightness compared to the rest of the methods, where the proposed method obtained the best quality values of natural image quality evaluator (3.462) and perception image quality evaluator (35.714).

261

Authors: Abdullah Fawzi Shafeeq, Inaam Ibrahim Ali

This study proposed a simple method for determining the best position and size of distributed generation (DG) and static VAR compensator (SVC) in the radial distribution system (RDS), as well as the impact of employing two different types of DG on total losses. The target functions are designed to reduce the system’s overall power losses, improve the voltage stability margin (VSM), and reduce drop voltages. This method relies on the deployment of many sizes of DGs or SVCs in each bus and comparing their impacts of them on the system’s overall losses and taking optimal sizing and placement of them if that given minimum losses. Moreover, the proposed strategy does not require extra work to set the parameter settings and weighting factors to obtain the objective functions. To assess the performance of the suggested approach, five instances were evaluated during DG and SVC installation in this study. The suggested approach is tested on the RDS’s IEEE 33_bus and 69_bus to ensure its practicality. This method decreased losses by 92.72 % for IEEE 33_bus and 96.91 % for IEEE 69_bus. In the 33_bus, the minimum VSM is improved from 0.6674 to 0.9697 p.u, and the minimum bus voltage is improved from 0.9038 to 0.994 p.u. While in the 69_bus, the minimum VSM is enhanced from 0.6835 to 0.9761 p.u and the minimum bus voltage is enhanced from 0.9093 to 0.9958 p.u, with the results compared to other existing methodologies.

284

Authors: Anindita Septiarini, Rizqi Saputra, Andi Tedjawati1 Masna Wati, Hamdani Hamdani

Sarong is a traditional cloth typically worn during formal or religious events which conventionally woven using the traditional loom. Samarinda is one of Indonesia’s regions with a sarong with a distinctive pattern. Nevertheless, the majority of indigenous people cannot distinguish the various motifs of Samarinda sarongs from those of other regions in Indonesia (non-Samarinda). Therefore, it is necessary to classify the motif of sarongs. This work proposed a pattern recognition method based on computer vision for sarongs motif classification. This method required adequate features to achieve the optimal results. Accordingly, the appropriate color and texture features were investigated to obtain the most discriminative ones. This work generated features using color moments, Gray Level Co-occurrence Matrix (GLCM), and Local Binary Pattern (LBP). The most discriminatory features were selected using the Correlation Based Feature Selection (CFS) and then fed into Artificial Neural Network (ANN). A total of 1000 images were used to evaluate the method and achieved the highest performance with an accuracy value of 100%.

309

Authors: Noor Abdul Khaleq Zghair, Ahmed Sabah Al-Araji

The primary goal of a mobile robot is to reach the desired goal by traversing an optimized path defined according to some criteria such as time, distance, and safety of the robot from any obstacles that may be in its way. Therefore, the backbone of autonomous mobile robots (AMRs) is path planning and avoiding obstacles. Many algorithms for path planning and obstacle avoidance have been presented by many researchers and each of these algorithms has several benefits and drawbacks. This paper focuses on comparing the performance of several metaheuristic algorithms that result in a more efficient, smoother, and shorter path for the mobile robot to reach the target in a complex environment. These algorithms include particle swarm optimization (PSO), chaotic particle swarm optimization (CPSO), modified chaotic particle swarm optimization (MCPSO), and firefly algorithm (FA). On the other hand, the paper proposes a hybrid algorithm by combining the FA and the MCPSO, namely the (HFAMCPSO). To demonstrate the effectiveness of the proposed algorithm in terms of the optimum cost function and obtaining the shortest path length, the optimal solution is compared to those of other path planning algorithms. Moreover, inverse dynamic and kinematic modeling are utilized to obtain the best torque and the best velocity actions for the wheels of the autonomous mobile robot. The proposed hybrid (FAMCPSO) algorithm provides enhancement on the path length equals (43.3%) and (25.5%) compared to the radial cell decomposition (RCD) and the A* algorithm, respectively. Moreover, the enhancement on the path length equals (2.3%) and (22.7%) compared to the firefly algorithm (FA) and the genetic algorithm (GA), respectively. All methods are simulated in an environment with static obstacles using the 2018b MATLAB package.

336

Authors: Sara Ahmed, Fatma A. Omara

Cloud computing has gained many attentions. Workflow scheduling one of the most important issues in cloud computing. It involves mapping tasks onto cloud resources – Virtual machines (VMs), to improve scheduling performance. Because the existing heterogeneous earliest finish time (HEFT) algorithm is considered one of the best algorithm, so the work in this paper propose a new algorithm based on HEFT algorithm; called modified heterogeneous earliest finish time (M-HEFT); to reduce the tradeoff among make span, resource utilization, and load balance. The proposed M-HEFT consists of two phases; task prioritization and task-VM mapping. In Task prioritization phase, a priority will be provided to each task in directed acyclic graph (DAG) as in the original HEFT algorithm. According to task-VM phase, tasks allocate to resources according to length of tasks and the load of available VMs with considering load balance. To evaluate the performance of the proposed algorithm, a comparative study has been done among the proposed algorithm and three existed algorithms. The experimental results show that the proposed algorithm outperforms the other algorithms by minimizing make span by 29%, improve resource utilization by 53% and load balance by 18% in average.

365

Authors: Prakit Intachai, Peerapol Yuvapoositanon

In this paper, the performance and robustness in remaining useful life (RUL) estimation of turbofan engines of the singular spectrum analysis and long short term memory (SSA-LSTM) algorithm with the window length optimization are investigated. Specifically, we are interested in how the optimized SSA-LSTM algorithm, i.e., the SSA-LSTM algorithm with its window length being optimized, responds to the variation in the number of cells and in the number of hidden layers of the LSTM structure. The performance and robustness of an SSA-LSTM algorithm are evaluated by two metrics, namely the root mean square (RMSE) ratio and the sensitivity ratio. Four optimized SSA-LSTM algorithms and two non-optimized SSA-LSTM algorithms were tested with the prototype dataset from the turbofan engines datasets. The simulation results showed that all the optimized SSA-LSTM algorithms performed much better in terms of both the RMSE ratios and the sensitivity ratios than the non-optimized SSA-LSTM algorithms. The RMSE ratios of the optimized SSA-LSTM algorithms with the window length of L=T/2,L=T/4,〖L=log⁡(T)〗^2.3 and L=24 were 38.737%, 33.967%, 22.896% and 11.780% respectively. In the same order, the sensitivity ratios of all the optimized algorithms were 2.067%, 1.495%, 1.555% and 1.444% respectively. Among all the algorithms being evaluated, the L=24 optimized SSA-LSTM algorithm provided the best performance in terms of both the RMSE ratio and the sensitivity ratio and hence confirming its best performance and robustness for RUL estimation.

390

Authors: Kittapon Chatwongtong, Tosaphol Ratniyomchai

In general, the train has consumed energy from the traction substation via the conductor during acceleration or motoring mode. This mode consumes most of the energy. However, during deceleration or braking mode, energy can be returned to the power terminal by regenerative braking management. This paper presents the energy loss reduction in the conductor transmission system and energy distribution mitigation from the traction substations by using the on-board supercapacitor, which is a storage device for storing regenerative braking energy. The results include the first case without a supercapacitor and the second case with supercapacitor operated on piece-wise linear state of charge (SOC) control. They are obtained by the single-train simulation on MATLAB and the power flow calculation by current injection method (CIM) on the peak time of the DC Mass Rapid Transit (MRT) Purple Line in Bangkok, Thailand. This research can effectively save energy distribution, energy loss and energy waste by approximately 3.5 %, 10 % and 20 % respectively.

416

Authors: Hind Zeyada, Sayed Abdo Mohamed, Gamal Tharwat, Hany Harb, Ayman Nasr Hamed

The interference of reflected waves from multiple elementary scatterers produces speckle, which appears as a granular noise in synthetic aperture radar (SAR) images. These speckles in SAR images cause difficulty in image interpretation, which reduces the effectiveness of image segmentation and classification. In this paper, we propose an effective solution using generative adversarial networks (GAN) to decrease speckle noise while preserving texture features.The convolutional block attention module (CBAM) boosts the essential features in upsampled data and supports the creation of a final denoised real InSAR data.The proposed deep-learning method proved that CBAM enhanced the peak signal-to-noise ratio using the GAN. Furthermore, CBAM improved structural similarity (SSIM) to 99 and achieved the minimum mean squared error. The despeckle performance was enhanced using GAN-ResUNet in which the SSIM equals 0.999. The denoising performance proved that the use of GAN with CBAM as the generator inspired by ResUNeT achieved the best performance compared to other experiments results.

439

Authors: Abdul Azeez, Suraiya Tarannum

To meet the increasing demand for wireless applications requires efficient performance in orthogonal frequency division multiplexing (OFDM) and multi-input multi-output (MIMO). The existing methods involve applying various optimization techniques to increase the performance of the partial transmit sequence (PTS) and OFDM system to reduce the peak average to power ratio (PAPR). This research proposes prime learning ant lion optimization (PL-ALO) method to reduce the PAPR and bit error rate (BER) of the system. The tournament selection technique is applied to increase the exploitation related to the best fitness agent that improves the PL-ALO model performance. The tournament selection technique randomly performs tournaments among the best fitness search agent in the system. The square-root raised cosine (SRC) precoding and Mu law companding techniques were applied to improve the efficiency of the system. The comparison between discrete cosine transform (DCT) and fast fourier transform (FFT) techniques was carried out in the performance analysis. The DCT-based method has higher efficiency than FFT-based techniques in the system. The PL-ALO method has higher efficiency than existing techniques of particle swarm optimization (PSO)- grey wolf optimization (GWO) and multi-objective mayfly algorithm (MOMA). The average PAPR of selective mapping (SLM) is 8.243, PTS-ant lion optimization (ALO) is 3.962, MAMO is 5.279, PSO-GWO is 5.854, and PL-ALO is 3.099.

461

Authors: Poornima Krishnamoorthy Balasamy, Kangaiammal Athiyappagounder

An effective E-Learning system must identify learning content appropriate for the needs of the specific learner from among the many sources of learning content available. The learning content can consist of different types of media content namely Text, Video and Image and to determine a rating. Recommendation systems become increasingly used in information systems and especially in e-learning platform. These systems are used to propose and recommend content of these platforms to users according to needs of the later in order to allow them to have the maximum information for learning. In this paper, a recommendation system based on data mining and deep learning has been proposed to help the learner by recommending the appropriate learning content. To improve the recommendations a content matrix is created and based on this, the logistic regression and deep learning methods could classify recommendations. Experimental results show that the precision, recall and f measure for Top 5 & 10 Recommendations using Deep Neural Network performsby 1.439% better than Top 5 & 10 Recommendations using Logistic Regression among Chi Squared, SA, PSO and ICA methods.

483

Authors: Amandeep Kumar, Balwinder Singh Dhaliwal, Damanpreet Singh

In recent years, researchers have focused their efforts on improving agricultural production by automating farming applications using wireless sensor networks (WSNs). The agricultural information must be accurate and updated in order to take the necessary actions. Sensor nodes (SNs) deployed in agricultural fields are power-constrained and require lot of energy to transmit data on regular basis. As a result, robust approaches are required to manage energy-related concerns for long-distance communication in large farms. To address long-distance agricultural needs, a cross-layer-based energy-efficient wireless sensor network model called CL-EEWSN that employs the wild horse optimizer (WHO) has been proposed. The primary goal of this research is to select the optimal cross-layer based cluster heads (CH) in terms of energy efficiency, communication delay, and latency. The physical layer, medium access control layer and network layer properties of each SN are used to evaluate and select the best CH. Further, a deep learning-based approach is used to determine the best data transmission path. The CL-EEWSN reduces energy consumption, end-to-end delay, and improves packet delivery ratio when compared to existing state-of-the-art protocols. The average energy consumption of CL-EEWSN for 100 nodes and 400 nodes is 0.16 nJ and 0.31 nJ respectively and is lowest in comparison to existing techniques.

505

Authors: Pradeep Krishnadoss, Chirag Chandrashekar, Vijayakumar Kedalu Poornachary

Cloud computing, a term which has become very popular on the internet platform which has evolved over time and gained more attention due to various features that are being introduced like multiplex numerous users on the identical physical structure, computing of resource on request and many more. Cloud computing is considered to be an enormous pool having essential resources which the users can make use of to complete their task through the internet. Therefore, in order to complete the tasks requested by the users, a scheduling algorithm is required making scheduling a vital part of cloud computing. Though numerous solutions with respect to scheduling techniques have been proposed in in recent literature, researcher’s continuously carryout performance upgradation by considering a mixture of QoS parameters thereby enhancing the cloud performance. In this regard, we had introduced an optimal and ideal task scheduling algorithm and challenge the other prevailing algorithms in terms of QoS parameters like makespan and energy. The proposed algorithm increases the performance of the scheduling in cloud by making use of an enhanced meta-heuristic hybrid algorithm called Rider Cuckoo Optimization Algorithm (RCOA), which is an advanced version of the already existing Rider Optimization Algorithm. The proposed RCOA technique had produced an improvement of 5.6%, 4.36% and 2.27% for makespan and had reduced the energy to the tune of 16.8%, 19.18%, and 16.15% when compared with Particle Swarm Optimization (PSO), Cuckoo Search (CS), Rider Optimization Algorithm (ROA) algorithms respectively when used with 25 VMs. Also the proposed approach attains the objective of optimal scheduling swiftly and achieves convergence in a small duration of time.

527

Authors: Emtinan Mohammed Jawad, Haidar J. Mohamad, Hazim G. Daway

Colour retinal image enhancement plays an important role in eye diseases and classification techniques. Optical coherence tomography devices are used to take images, which often suffer from a lack of lighting and contrast. In this study, a new algorithm for improving colour retinal images was suggested. The suggested algorithm was developed on the basis of improving the lighting (L) based on (Lab) colour space while preserving the colour information (a and b). The lighting enhancement included the wavelet fusion between the original lighting component and the improved component twice using contrast-limited adaptive histogram equalisation (CLAHE). Data containing 40 image forms (driver rental images dataset) were used, and the proposed method of improvement was compared with several other methods, such as principal component analysis using reflection model (PCAURM), luminosity and contrast adjustment (LCA), fuzzy logic by stretch membership function (FLSMF), CLAHE, contrast enhancement approach (CEA) and modified color histogram equalization (MCHE). The collected results showed that the proposed method obtained excellent quality rates in terms of natural image quality evaluator (4.680), entropy (6.985), contrast enhancement measurement (0.569) and structural similarity index (0.963).

548

Authors: Jaafar Ahmed Abdulsaheb, Dheyaa Jasim Kadhim

This paper discusses the best path planning algorithm for an autonomous mobile robot in an unknown environment with irregular static and dynamic obstacles and a static and dynamic target based on the improved COOT (ICOOT) optimization algorithm. The ICOOT overcomes the drawbacks of unstable searches in the conventional COOT. The path planning problem is solved by finding the collision-free path between multi-objective shortest path and smoothness. The ICOOT tries to imitate the real world by adding the mobile robot's actual size and the kinematic model with specifications for mobile robots. In order to evaluate the proposed algorithm, thirteen benchmark test functions are used to make a comparison with 30, 100, and 500 dimensions. To test the efficiency of the proposed technique, results are compared with five swarm optimization algorithms. The standard deviation results show that the proposed algorithm gets the best results in 84% of the thirteen test functions for 30 dimensions and 92% for 100 and 500 dimensions. Also, in four complex maps (10×10) m, the mean results show that this method is very useful for robot paths from the start to the target, and the mean distance for ten runs is 13.3797 m for map 1, 13.5164 m for map 2, and 11.9312 m for map 3, and 16.3937 m for map 4. It showed how well it could move quickly and easily around both fixed and moving obstacles.

579

Authors: Mohammed Jabbar Mohammed Ameen, Saad S. Hreshee

This paper introduces a novel voice encryption wireless system, which is based on characteristics of massive Multiple Input Multiple Output (MIMO) wireless channel. By exploiting the Minimum Mean Square Error (MMSE) precoding technique, channel fading values are permutated and substituted using various chaotic generators. The voice samples are mixed with channel values and a chaotic sequence before being transmitted. The proposed model was applied on a 5G network that included Generalized Frequency Division Multiplexing (GFDM), Parallel Spatial Modulation (PSM), and massive MIMO. Two chaotic sequences are generated, the first one used for the substitution stage by QR decomposition for the Henon map and then combining the outcome with Bernoulli and logistic maps to Q and R matrices, respectively. New chaotic signals are added to the real and imaginary parts of the MMSE precoding matrix. The second key is generated by the Tent map to perform the permutation stage for the overall precoding matrix. Several security metrics tests were used to verify effectiveness the of the proposed method resistance against attacks .The obtained tests results are: Signal to Noise Ratio (SNR=-23.1223), Root Mean Square (RMS=0.88565), Crest Factor (CF=10.6717), correlation coefficient (r_xy=0.000471), Linear Predictive Code Distance (dLPC=1.2758), Spectral Segment SNR (SSSNR=-27.6149), Cepstral Distance (dCD=9.4159), number of samples change rate (NSCR=33.334%), unified average changing intensity (UACI=99.9998%), keyspace=2500, key sensitivity, and speed=0.0329.The simulation results show that the proposed scheme has an excellent level of security and resistance to various forms of attacks, which outperforms many recent similar voice encryption methods.

601

Authors: M. Syauqi Hanif Ardani, Riyanarto Sarno, Mikhael Ming Khosasih, Malikhah Malikhah, Doni Putra Purbawa, Chastine Fatichah, Dwi Sunaryono, Rahadian Indarto Susilo, Shoffi Izza Sabilla, Kelly Rossa Sungkono

Human axillary odour is a unique discussion because it stores various useful information, including health, food quality analysis, and the similarity to the smell of the food eaten which can be done using an electronic nose (e-nose). However, rarely for research use an e-nose to determine the similarity of human axillary odour to food material odours. This research aimed to determine the similarity between male and female odours based on food material odours, such as garlic, shallot, and red chilli using an e-nose. We propose a method using an electronic nose with 8 Taguchi gas sensor (TGS) and 1 digital & humidity (DHT) sensor, then processing the resulting data using the fast fourier transform (FFT) smoothing method. Sensor data that have anomalies are removed by the quantile method. The standardization process is carried out to the signal data and feature extraction is processed with mean, standard deviation, and minimum value. This study found that the proposed method can produce a fairly good cluster of aroma food materials, faithfully getting the highest accuracy of 96.67 % using support vector classifier (SVC) and k-nearest neighbour (KNN) with each best parameter. We also found that the smell of human armpit sweat generally has a lot of similarity with food materials, specifically that male has the highest similarity to the shallot, while female has the highest level of similarity to garlic. In contrast, red chilli has the lowest level of similarity with human armpit odour.

625

Authors: Aldinata Rizky Revanda, Chastine Fatichah, Nanik Suciati

Manual classification of acute lymphoblastic leukemia carried out by doctors will certainly take a lot of time and effort. The challenges in automated computer-based systems for classification of acute lymphoblastic leukemia are when providing proper lightning in stained white blood cell microscopy images and when segmenting the touching or overlapping cells in the image. Previous studies related to the classification of acute lymphoblastic leukemia still require many steps when using conventional methods, whereas when using deep learning methods are still limited to classification without providing analysis for the instance segmentation of lymphoblast in the image. Therefore, we propose instance segmentation using Mask R-CNN on white blood cell microscopy images to classify acute lymphoblastic leukemia that can support the diagnosis process efficiently and effectively. In this study, we implemented Mask R-CNN by transfer learning method to fit the instance segmentation task on white blood cell microscopy images. We added a contrast enhancement process to the image dataset to overcome the bad lightning problem in stained white blood cell microscopy images. We used the real dataset obtained from hospital to evaluate our method. The method we used was able to get 83.72 % accuracy, 85.17 % precision, and 81.61 % sensitivity.

652

Authors: Weerapong Chanbua, Unnat Pinsopon

Steady state and dynamic LuGre friction models of an electro-hydraulic system were constructed. Indirect electro-hydraulic cylinder force control under friction compensated PID and fuzzy controllers were tested and compared. Feedback force signal was indirectly measured and calculated from pressures to emulate the applications that direct force measurement is impossible. Estimated friction was compensated to desired force command of the PID controller. The proposed fuzzy controller contained two modules. The first, as a friction compensator, calculated modified error according to estimated friction and feedback force error. The second module calculated control action based on PD control scheme. Friction compensated PID and fuzzy controllers could reduce tracking errors at the maximum force commands by 80% and 90%, respectively, compared to the PID controller without compensation. The steady state friction compensation yielded better tracking performances compared to the dynamic friction. Tracking performance of the fuzzy controller was always better than the PID controller.

676

Authors: Anik Vega Vitianingsih, Zahriah Othman, Safiza Suhana Kamal Baharin, Aji Suraji, Anastasia Lidya Maukar

The difficulty of acquiring data from numerous intergovernmental agencies/institutions for prone road traffic accidents (PRTA) spatial datasets produces a small-scale dataset that causes dataset imbalance. Class imbalance in small-scale datasets causes uncertainty in the results of the modeling PRTA classification. The proposed research is a scenario-based case representation model on the pre-processing data stage to increase the sensitivity of algorithm classification in a small-scale dataset that causes dataset imbalance using machine learning (ML), the synthetic over-sampling method. The retrieval of attributes from the spatial dataset is transformed into the raw dataset, the normalized dataset, the synthetic minority over-sampling technique (SMOTE) raw dataset, and SMOTE normalized dataset scenarios. Balancing datasets using four variants of SMOTE, namely ADASYN, Borderline-SMOTE, K-Means SMOTE, and SVM-SMOTE resampled. To evaluate how well the PRTA classification model performed, we utilized the hyper-parameters optimization technique and the genetic algorithm (GA) search cross-validation. The experiment was run with the ML classifier method, including the k-nearest neighbor (KNN), support vector machines (SVM), multilayer perceptron (MLP), naive bayes (NB), logistic regression (LR), and random forest (RF). The Area Under Curve (AUC) was used to evaluate the results of the experiments. The results of the dataset test in a predetermined scenario conclude that a single algorithm that is computationally light to produce an optimal classifier tends to use a raw dataset that is balanced using SMOTE. The KNN method as a single algorithm for classification based on the distance between samples is superior, with an AUC value of 0.89, which is included in the good classification category of all ML classifiers proposed to handle small data sets imbalanced classes using SMOTE raw datasets for K-Means variants SMOTE.

11

Authors: Hetal Vipul Gandhi, Vahida Zakirhusen Attar

Aspect Based Sentiment Analysis (ABSA) aims to perceive the opinion of the majority of reviewers for each feature (aspect category) of the product. We target both Aspect Term Polarity (ATP) and Aspect Category Polarity (ACP) subtasks of ABSA. We retrieve different word vector-based representations of sentence and augment them with n-gram and sentiment features using novel algorithm. We employ supervised machine learning approaches and propose the ensemble of Logistic Regression, k-Nearest Neighbor, and Support Vector Machine models for these subtasks. We experiment with four major domains from Hindi ABSA Dataset and report increase in performance accuracy by 16% for ATP subtask and 2% to 13% across domains for ACP subtask over results from researchers who released this dataset. Secondly, we also propose a novel WFBOS method to compute the Summary Score [range: 100 to 100] of each category from a particular domain. We make use of frequency of positive, neutral, negative, and conflict sentiment, of each aspect category from among the reviews of a particular domain in Hindi ABSA Dataset to do this computation.

31

Authors: Barlian Henryranu Prasetio, Edita Rosana Widasari, Fitra Abdurrachman Bachtiar

The nonverbal communication processes a critical parcel. In some cases verbal communication is incapable since the speaker does not utilize non-verbal communication well at the same time. Non-verbal communication which falls in unconscious emotion is important in determining in function of cognition, language comprehension, and decision making. However, a little research studied in this area. Many years, researchers are amazed by the reliability of Mel-Frequency Cepstral Coefficients (MFCC) feature extraction technique in recognizing stressed speech. In this paper, we propose a simple feature extraction technique that effective but strong enough to recognize stressed speech. There are the speech energy and frequency. We attempted a basic approach to classify unbiased or stretch on female and male discourse. The highlight extraction is based on control and recurrence. This investigate utilized 10 female and 10 male discourse datasets. There are 5 classification strategies utilized. The classification models are Neural Arrange, k-Nearest Neighbour, Bolster Vector Machine, combination of NN-k-NN and combination of NN-SVM. Test comes about approved utilizing k-fold cross-validation strategy. The tests are assessed utilizing R-index to distinguish whether the highlights contributing to the push discourse acknowledgment. Based on exploratory comes about, the number of inputs impacts the esteem of R-index. In general, combining the Neural Organize and Back Vector Machine is the most excellent classification strategy by appearing stretch acknowledgment rate of 85% precision.

53

Authors: Nischaykumar Hegde, Linganagouda Kulkarni

Mobile sink-based sensor networks deployed in unattended environment are characterized by infrequent sink visits. In these applications, ensuring the survivability and confidentiality of data is important till it is collected by the sink. The data must also be prevented from collection by agents other than sink and leakage by neighbours. Data collection in unattended environment must also be energy efficient to prolong the life time of the network. This work proposes a novel Energy Aware Adaptive Compressive Sensing (EA-ACS) assisted secure data survivability scheme which is able to optimize energy consumption at cost of data accuracy level desired by applications. The solution introduces three novelties: adaptive compressive sensing for reducing the foot print of data and energy consumption during transmission, a novel bi party authentication scheme for secure data collection by sink and low energy overhead piggy bagging for data replication. Through performance in NS2, the proposed solution is found to provide at least 6% higher data survivability and 20% lower energy consumption compared to existing works.

76

Authors: Puppala Ramya, Tumati Jashwanth, Dupaguntla Venkata Sathvik

Technological advancements in block chain (BC)-based frameworks have empowered scientists to create innovative inventions such as e-casting ballots. The traditional agreement models utilized proof-of-work (PoW) in the Bitcoin that affected energy utilization and bargained the adaptability for the ballot framework. The existing works evaluates the trust basically only on the centralized party as it was not feasible because of the dynamic changes in the pervasive social networking (PSN) topology and their characteristics. The present research work proposes a block chain based trust evaluation model for the PSN based BC. The proposed hybrid proof of stake-trust (PST) BC is based on the proof-of trust (PoT) and also the proof of stake (PoS) overcomes the issues that are occurring in the e-vote casting. The trust evaluation is performed for public verification and becomes transparent for each node of PSN. The advantage of the proposed method is that a new block will be designed for trust evaluation during the block generation. The process of sharding erases the workload when the network works fast for the individual nodes provides the sum of their alternative parts. Therefore, the model utilizes the agreements for generating the safe process that guarantee the precision to vote it from the time of the election results. The present research work utilizes the proof of stake-trust based BC resulted in security improvement. The model improves the adaptability and execution of the BC based on the ballot framework provided a secured voting system for the government. The proposed PST-BC model showed better results in terms of latency as 15/s when compared with the existing models merkle hash tree -bloom filter that obtained 107.3/s and performance constraints based electron of 18/s.

97

Authors: Krishna Reddy Gajulapalli, Merlin Sheeba Gnanadhas

The hybrid innovation of Orthogonal Frequency Division Multiplexing (OFDM) through Multiple-Input Multiple-Output (MIMO) delivers a feasible substitute to increase the Quality of Service (QoS) to accomplish better data rate and spectral efficiency for the transmission network. An extraordinary Peak-to-Average Power Ratio (PAPR) and Bit Error Rate (BER) are the important parameters that can be considered in the analysis of the MIMO-OFDM network. Partition Transmit Sequences (PTSs) is one of the capable procedures and direct methods to attain a reasonable PAPR performance. However, it needs a serious restoration process to discover the vital features that produce a computational multi-layered design with sub-blocks. Here, a reduced computational complexity PTS scheme is proposed which is completely depends on the hybrid procedure named as Moth Flame Optimization with Improved Firefly Algorithm (MFO-IFFA). The simulation outcomes demonstrated the capability of the proposed MFO-IFFA approach decreases the PAPR reduction up to 3.6 dB. Similarly, the proposed MFO-IFFA overcomes the significant evolutionary procedures mentioned in the existing works such as Additive Signal Mixing (ASM), Adaptive Simplified Optimized Iterative Clipping and Filtering (ASOICF) and Hybrid Independent Component Analysis (HICA).

116

Authors: Bharathi Ramachandra, T. P. Surekha

Wireless Sensor Network (WSN) is developed as a promising technology for a wide range of applications due to its low installation cost. However, the nodes in the WSN are susceptible to different security threats, because these nodes are located in hostile or harsh environments. The important goal of this paper is to obtain secure data transmission while minimizing energy consumption. In this paper, an Improved Moth Flame Optimization (IMFO) is used for selecting the optimal Cluster Head (CH) from the network. Moreover, the secure routing path is generated using the IMFO where the fitness function considers residual energy, distance, trust and node degree. Therefore, the Secure Clustering and Routing using IMFO (SCRIMFO) provides the resistance against the Distributed Denial-of-Service (DDoS) attack whereas the energy consumption is minimized by identifying the shortest path. The proposed SCRIMFO method is analysed in terms of Average Energy Consumption (AEC), Packet Loss Ratio (PLR), Packet Delivery Ratio (PDR) and routing load. Here, the SCRIMFO method is compared with the Secure Routing Protocol based on Multi-objective Ant-colony-optimization (SRPMA), Ant Colony Conveyance Algorithm (ACCA) and Energy-aware Trust and Opportunity-based Routing (ETOR) approaches. The PDR of the SCRIMFO is 94.64 % for 2 DDoS attacks that is high when compared to the SRPMA, ACCA and ETOR.

142

Authors: Shaymah Akram Yasear, Hayder M. A. Ghanimi

This paper proposes a modified honey badger algorithm (MHBA) for solving the optimal power flow (OPF) problem. This problem is a highly non-linear, non-convex and complex optimization problem with several decision variables and constraints. The original honey badger algorithm (HBA) has the problem of trapping in local optima due to the loss of population diversity, especially in solving complex optimization problems. Therefore, the MHBA aims at sufficient improvement in finding the optimal solution and feasibility. Opposition-based learning strategy (OBL) is integrated with the MHBA to preserve the diversity of the population and enhance the convergence toward the optimal solution. The effectiveness of the MHBA algorithm is evaluated on five objective functions of the OPF problem namely, total generation fuel cost minimization, active power and reactive power transmission losses minimization, voltage deviation and voltage stability enhancement. The performance of the proposed algorithm is tested and validated on the IEEE 30-bus test system. The proposed MHBA is compared with the HBA and other nature-inspired optimization algorithms reported in the literature. The results indicate that the proposed MHBA algorithm has the superiority to jump out of the local optimal and better convergence in solving the OPF problem. This is due to the strategy used in the algorithm which helps in maintaining the population diversity and provides a proper balance between exploration and exploitation.

167

Authors: Ammar Abdul Ameer Rasheed, Ahmed Sabah Al-Araji, Mohammed Najm Abdullah

During the previous years, the number of researchers who discussed the field of path planning algorithms has increased. In this paper, a proposed hybrid technique for path planning based on the benefits of sampling-based algorithms and heuristic path planning algorithms was enhanced. Rapidly-exploring Random Tree star (RRT*) was used to generate the candidate nodes that represent a collision-free path. These candidate nodes were applied to the Particle Swarm Optimization (PSO) algorithm to generate the shortest and the smoothest path planning (the optimal path planning) for the mobile robot. The RRT*PSO algorithm was applied in two scenarios (static and dynamic environments) with a workspace of [500×500] cm using MATLAB 2021a. In the static environment, a workspace full of obstacles was chosen to find the shortest collision-free path. In addition, a reference path equation was found to calculate the reference linear and angular velocities of the mobile robot as well as the linear and angular velocities of the right and left wheels. In this work, the suggested hybrid RRT*PSO algorithm improves path length 34.27% compared to the A* algorithm and the fuzzy analytic hierarchy process (A*–FAHP hybrid) algorithm and 0.35% compared to the Self-adaptive evolutionary game-based particle swarm optimization (SAEGBPSO) algorithm. In the dynamic environment, an algorithm named (contour path down and contour path up) was suggested to avoid collisions with dynamic obstacles. By using this suggested algorithm, the reference linear and angular velocities of the mobile robot as well as the linear and angular velocities of the right and left wheels were calculated to obtain a smooth path followed and free-navigation with dynamic obstacles in an environment.

198

Authors: Najla Akram AL-Saati

In this work, the wild horse optimization (WHO) algorithm, known for its ease of use, efficiency, and fast convergence, is explored in solving the reliability redundancy allocation problem (RRAP) for series-parallel systems. This problem has as of late caught the attention of researchers in this area, especially in today’s rapidly growing field of artificial intelligence. The NP-hard RRAP problem deals with maximizing of reliability under certain constraints. This work uses WHO algorithm to maximize the overall system reliability by determining how many redundant components are to be used along with their reliabilities in each subsystem, such reliability is constrained by cost, volume, and weight. Testing is carried out to show the effectiveness of this algorithm using four known numerical examples, results are to be compared with simplified swarm algorithm (SSO), attraction-repulsion imperialist competitive algorithm (AR-ICA), hybrid salp swarm algorithm and teaching-learning based optimization (HSS-TLBO), particle swarm optimization (PSO), and gradient based optimization (GBO). Computational results show that WHO was able to find better feasible near-optimal solutions effectively and efficiently in terms of population size and number of iterations.

216

Authors: Sritha Sreedharan, Ranjith Nadarajan

Dimensionality reduction is the most prominent process in artificial intelligence and data science because of using a massive amount of high-dimensional information. In recent days, many dimensionality reduction algorithms focused on the harmonic mean-based linear discriminant analysis (HLDA) which was the enhanced version of classical LDA. In particular, two different variants such as HLDA and HLDA pairwise (HLDAp) have been applied to reduce the high-dimensional data by using the harmonic mean between-class distance. However, its computation time complexity was high during the initialization phase since it comprises the matrix Eigen decomposition/inverse. Hence this article proposes the Fast HLDA (FHLDA) and FHLDA pairwise (FHLDAp) algorithms for reducing the high-dimensional data during classification. In this algorithm, a joint diagonalization scheme is introduced instead of Eigen decomposition depending on Taylor expansion for lessening the number of iterations in the initialization step to produce the discriminant. It does not a choice to a sweeping task so that every element of the Eigenvector matrix at every iteration is calculated to minimize the computation time burden. As well, a first-order approximation of the inverse Eigenvector matrix and the complete matrix of Eigenvectors are updated at every iteration. On the contrary, the overlap among the samples of dissimilar classes tends to miscategorization. So, the optimal discriminant vector is discovered to solve this issue by extending the between-class scatter matrices. Finally, the experimental outcomes show that the FHLDA and FHLDAp algorithms achieve 10 % higher average accuracy than LDA, ALDA, WLDA, G2DLDA, HLDA, and HLDAp algorithms.

237

Authors: Abdul Azeez, Suraiya Tarannum

In wireless communication systems, Multiple-Input Multiple-Out (MIMO) and Orthogonal Frequency Division Multiplexing (OFDM) were widely used technology. Spectrum utilization efficiency is needed to improve in OFDM technology for effective performance. A Nested Particle Swarm Optimization and Grey Wolf Optimization (HPSO-GWO) is proposed with the Low-Density Parity-Check (LDPC) coding scheme for minimizing the errors that occurred while transmitting the data bits over the MIMO-OFDM. The Nested PSO-GWO process involves in update the velocity of PSO using GWO method that helps to increases the convergence rate and balance the trade-off between exploration-exploitation process. The optimal velocity update in the PSO helps to escape from local optima trap in the search process and improve the efficiency of the model. Generally, the linear error-correcting codes are applied in Low-Density Parity-Check (LDPC) that is used to transmit the message in a noisy channel. Fast Fourier Transform (FFT), CP removal, and the Serial to Parallel (S/P) are accomplished as well as the received data bits are demodulated to obtain the output bits on the receiver side. The proposed method has 10-2 CCDF in 5.6 PAPR and the existing method requires more than 6 PAPR to achieve 10-2 CCDF.

257

Authors: Ahmed H. Aliwy, Ayad R. Abbas, Mustafa J. Hadi

Sentiment analysis (SA) is one of the most important tasks in the natural language processing (NLP) field. Many researchers have been trying to build an efficient SA system for many applications such as detection of terrorist activities, customer support management, analyzing customer feedback, market research, competitive research, and many others. Almost all these researches deal with a classification task, and they tried to solve one or two of the challenges that face this task. In this research, a complete SA system was constructed for processing five main challenges in SA and studying the effect of each one on the system. These challenges are the processing of negation, multi-polarity of words, text multi-polarity, semantically ambiguous words (exact meaning), and sarcasm. Some solutions were introduced for these challenges for getting high accuracy. It is the first work for studying the effect of these five challenges collectively with novel solutions to some of them. For aspect-based SA, three different classifiers were used; support vector machine (SVM), maximum entropy (MaxEnt), and long short-term memory (LSTM). The best results for f-measure were 0.833, 0.852, and 0.875 using the LSTM method on Foursquare ABSA, SemEval2014 (laptop and restaurant) datasets, respectively. We induced the most effective negation processing on the SA system from many test scenarios, followed by multi-polarity of words, semantically ambiguous words, text multi-polarity, and sarcasm, respectively. Also, the proposed sarcasm processing technique was evaluated on two annotated corpora with sarcasm, iSarcasm, and Rillof datasets, before applying it on SA.

279

Authors: Chada Sampath Reddy, G. Narsimha

Wireless sensor networking (WSN) is an important subject among the researchers in recent years and it is recognized as a familiar approach for upcoming applications like traffic monitoring in real time, battlefield, and eco-systems for surveillance. The network has receptive data and it is essential that the networks secure the several types of attacks like eavesdropping, capturing the node, rejection of service. Various sensor networks routing approach has been proposed to secure the networks. This work offers a secure routing scheme in WSN with deep learning approaches. Initially, trust verification is performed to select the non-malicious node, which is carried out through long short-term memory (LSTM), where the malicious nodes are removed. Secondly, data transmission is done through encrypting the data using elliptic curve cryptography (ECC) algorithm. This encrypted data is forwarded to the secured shortest paths, in which the secure shortest path is selected by dragonfly algorithm (DA) through solving the multi-objective function by incorporating the measures like distance, energy, delay, throughput, and trust. Evaluation of cost function of the proposed DA-LSTM model is correspondingly secured 22 %, 27 %, 31 %, and 18 % enriched performance than the PSO-LSTM, GWO-LSTM, WOA-LSTM, and CSA-LSTM at iteration 10th. Finally, the numerical simulations show that the suggested secure routing and data transmission in WSN is validated with other optimization algorithms and shows an advanced performance based on analysis with various performance measures by concerning with the number of sensor nodes and the maximum number of iterations.

303

Authors: Jatinder Pal Singh, Anuj Kumar Gupta

Wireless sensor networks (WSNs) have become a dominant technology in recent years due to their vast range of applications. However, the creation of energy-efficient WSN remains elusive. Clustering methods have long been employed in WSNs to reduce energy consumption and extend network lifetime, but efficient cluster head (CH) selection and competent routing always pose a major obstacle. This paper, proposes a new routing technique in which CH selection is performed using whale based tunicate swarm algorithm (WTSA) and routing between CHs is implemented using aquila optimizer (AO). In the proposed technique WTSA is used to identify efficient CHs from the WSN network, and remaining nodes join these CHs by assessing the cost function value. CHs residual energy, node degree, distance from base station (BS) and intra-cluster distance are being used to derive this cost function. AO is used for inter-clustering routing between CHs that are far from the BS. With the help of a fitness function, AO is used to cultivate optimal routes between CHs and to weed out ineffective routes. The performance of the proposed technique WTSA-AO is compared with state-of-the-art techniques BOA, PSO-GSA, BERA, WTSA, and LEACH in terms of delay, number of alive nodes, throughput and average energy consumption. The results show that the proposed technique improves the performance of wireless sensor networks by reducing average energy consumption by 8.3 % compared to WTSA and striving to achieve a delay of 0.121 seconds for 200 nodes.

329

Authors: Srinivasarao Thumati, S Vadivel, M Venu Gopala Rao

Several studies in the literature have focused the use of optimum network reconfiguration (ONR) and/or optimal allocation of renewable energy (RE) based distributed generation (DG) methodologies to improve performance in electrical distribution networks (EDNs). However, a few studies have only focused at simultaneous ONR and DG allocation in the context of demand increase, network loading unpredictability, or RE power. Furthermore, there is no much focus on the influence of emerging electric vehicle (EV) load penetration on EDN performance. This study proposes a novel meta-heuristic strategy for solving the simultaneous optimal ONR and RE-based DGs allocation problem for minimising distribution losses, improving voltage profile, and lowering greenhouse gas (GHG) emissions while considering the load penetration of electric vehicles (EVs). For solving the complexity inherent in the multi-objective optimization problem with numerous constraints and multi-type variables, a new and efficient honey badger algorithm (HBA) is proposed. On the IEEE 33-bus EDN, simulations are run with various EV load penetration levels. In terms of fitness function, HBA is observed to be superior to COA, TLBO, CSA, and PSO. As observed in the results, for 50% EV load penetration, the losses, AVD, and GHG emissions are increased to 1571.676 kW, 0.02658 p.u., and 25780.05 103 (lb/h), respectively. However, by having DGs optimally and with ONR, the losses, AVD, and GHG emissions are decreased to 77.08 kW, 0.00038 p.u., and 2175.01103 (lb/h), respectively. This indicates that the performance of EDN is improved significantly by having simultaneous DGs allocation and ONR solution irrespective of EV load penetration level. This also indicates the suitability of the proposed methodology for practical applications.

352

Authors: Harmandeep Singh, Vipul Sharma, Damanpreet Singh

Recently, breast cancer has achieved top position in terms of cause of mortality among women of all age groups by surpassing the lung cancer. To improve the survival rate, timely assessments are essential. Mammography is the best modality among the most widely used timely detection modalities. The radiologists’ manual reading may have an impact on the accuracy of the diagnosis. As a result, the computer-aided diagnosis (CAD) systems are being developed as tools to reduce the false alarms and to increase the diagnosis accuracy. In this study, an attempt has been made to improve the diagnosis performance of the CAD systems by incorporating recently developed marine predators algorithm (MPA) in conjunction with three different neural network classifiers including feedforward neural network (FFNN), cascade forward neural network (CFNN), and recurrent neural network (RNN). Unlike other existing studies, fully digital mammogram images from INbreast dataset have been employed for testing of the system proposed in this study. Experimental results reveal that the best classification performance [Accuracy 97.34%, Sensitivity: 98.40%, Specificity: 100.00%] is obtained when MPA is used in conjunction with RNN classifier. To demonstrate the usefulness of the proposed system, the obtained results are compared with the results obtained using already invented CAD systems in previously published studies using the same dataset. The findings suggest that the proposed system is acceptable for real-time clinical applications.

382

Authors: Suwito Suwito, Mochamad Ashari, Muhammad Rivai, M. Anis Mustaghfirin

This paper presents a multisector drip irrigation system (DIS) powered by solar photovoltaic (PV). A binary particle swarm optimization (BPSO) method possibly determines irrigation in multisector, depending on the power availability of the solar PV. The power from solar PV in this study was optimized through maximum power point tracking (MPPT) using an incremental conductance (INC) method. A prototype was built using a PV emulator in 10 controlled irrigation sectors. Three operating modes applied include control of all sectors with BPSO, partly sectors with a combination of linear programming, and all sectors with bypassed without BPSO. Simulations and tests showed very similar results, indicating that the BPSO method provides an optimal and accurate irrigation system with 0.7 psi of an average irrigation operating pressure (IOP) error, 4.01 % of water volume error, and 96.55 % of the uniformity coefficient of water volume.

406

Authors: Sudha Rani Chikkalwar, Yugandhar Garapati

An intrusion detection system monitors the networks and identifies the malware or suspicious activity in the network. Machine learning techniques were applied in the Intrusion detection system to improve its efficiency in the identifications. Imbalance data problem in intrusion detection affects the performance of identification and deep learning methods have overfitting problems. The autoencoder – support vector machine – grasshopper optimization (AE-SVM-GO) model is proposed to overcome the limitation of the overfitting problem in intrusion detection. The hybrid technique of AE-SVM-GO is applied to solve imbalance data problem and overfitting problem in intrusion detection. The autoencoder model is applied to generate the instances of minority classes to balance the dataset. The Grasshopper optimization performance hyper-parameter optimization in the SVM model to learn the features to adaptively set the parameter in classification. Four datasets such as UNSW-NB15, CICIDS2017, NSL-KDD, and Kyoto 2006+ dataset were used to test the proposed AE-SVM-GO model. The proposed AE-SVM-GO model has 95.3 % accuracy, whereas the existing convolutional recurrent neural network (CRNN) and SVM-naïve bayes model has 76.82 %, and 93.75 % accuracy respectively.

425

Authors: Ravi Mohan Sharma, Chaitanya P Agrawal

With the fast advancement of smartphone technology, the smartphone has emerged as the most prevailing instrument for accessing the Internet and obtaining a wide range of services with a click. Increased use of smartphones for online payments has attracted fraudsters, adding to an increase in malware outbreaks. Mobile application vulnerabilities and malware are the origins of various types of fraud and numerous cyber-attacks. Large datasets are frequently used for malware analysis; however, large datasets may contain many redundant, inappropriate, and noisy features, resulting in misclassification and low detection rates. This paper presents a hybrid approach to Android malware detection that reduces the dimensionality of datasets to reduce resource-intensive computation while preserving critical information. We present a novel hybrid approach for detecting Android malware based on a meta-heuristic (modified Intelligent Water Drop Algorithm (IWD)) and Deep Learning (DL) techniques. The studies show that the proposed approach efficiently removes irrelevant attributes and attains significant detection performance with an F1-Score of 93.7%, a precision of 95.35, an accuracy of 99.12%, and a recall rate of 96.68%.

447

Authors: Kavitha Muruganantham, Subbaiah Shanmugasundaram

Online digital marketing achieves their revenue according to their advertisements or sales assignment when companies have the profitable attention for recommending their products to customers via ranking them. Online customers are not able to guarantee that the items delivered through the recommendation by big data are either comprehensive or applicable to their essentials. In the past few years, recommendation frameworks were broadly applied to analyze the massive amount of data. Among those, a Distributed Predictive model with Matrix factorization and random Forest (DPMF) has achieved high efficiency to predict the item ratings. However, it accounts only for user preferences and opinions whereas the other contextual data are necessary for enhancing the efficiency of rating prediction. In this article, a Distributed Improved Predictive model with a Matrix factorization and random Forest (DIPMF) framework is proposed that considers the elements of social context and the dynamic characteristic of every user for every item to enhance the quality of prediction. The primary aim is to combine the information from the preferences, opinions and social context of each user. The social context of users is multiple features of the context such as differences in the current opinion with earlier opinion, behavior, relationship and interaction. Since each user is connected through relations and interactions. At first, the training dataset is split into an optimal amount of splits for accelerating the parallel and distributed training process. Then, the training process is carried out by the DPMF with the Distributed Improved Predictive model with Matrix factorization-Improved variant (DIPMI) to create the representation of every user's preferences, opinions and social contexts in the training set. Further, the prediction of rating is formulated as a regression challenge and solved via the Random Forest (RF) algorithm that predicts the customer’s rating behavior with their opinions and social context for every product. Finally, the experiments are conducted on trip advisor and Amazon datasets to evaluate the efficiency of DIPMI and DIPMF compared to the state-of-the-art recommendation frameworks. The findings exhibit that the DIPMF on the trip advisor dataset achieves an average of 0.6826 Root Mean Square Error (RMSE), 0.5925 Mean Absolute Error (MAE), 0.8369 recommendation quality and 0.0023 Confidence Range (CR) 95% compared to the other frameworks. Similarly, the DIPMF on Amazon dataset achieves an average of 0.7591 RMSE, 0.5704 MAE, 0.8298 recommendation quality and 0.0032 CR 95% compared to the other frameworks.

468

Authors: Binuja Philomina Marydasan, Ranjith Nadarajan

In mobile adhoc networks (MANETs), the most essential problem is to ensure the quality-of-service (QoS) during data transmission via multiple paths from a source to the destination node. To tackle this problem, a topological change adaptive adhoc on-demand multipath distance vector (TA-AOMDV) routing protocol was developed, which ensures QoS according to the different parameters for high-speed node mobility. But, it did not consider the path stability related to the node density, which also affects the routing efficiency. Hence, this paper develops a reliable and stable TA-AOMDV (RSTA-AOMDV) routing protocol that applies a new forwarding strategy to enhance the path reliability and stability for data transmission. In this protocol, every origin node relays data packets to the target in a hop-by-hop manner depending on local information gathered from its one-hop adjacent during the forwarding phase. Also, two different metrics are considered: (i) destination region selection (DRS) and (ii) weighted closeness and connectivity (WCC) to find the stable path from the source to the destination node. During the adjacent finding phase, a forwarding node self-selection mechanism is introduced to reduce the communication overhead due to the high node density and severe congestion. Finally, this protocol is simulated for 2 different cases: (1) a varying node mobility and (2) varying number of nodes. In the case of high-speed node mobility, i.e. 50m/s, the RSTA-AOMDV protocol achieves a 50.7 % packet delivery ratio (PDR), 109.4ms end-to-end delay (E2E-D), 158.51Kbps throughput, 73.4 % normalized routing overhead (NRO) and 11J mean energy consumption (MEC) compared to other existing protocols. Similarly, in the case of high node densities, i.e. when the number of nodes is 100, the RSTA-AOMDV protocol achieves a 66.5 % PDR, 1485.74ms mean E2E-D, 639.7Kbps throughput, 15 % NRO and 22.7J MEC compared to the existing protocols.

489

Authors: Valarmathi Srinivasan, Vijayabhanu Rajagopal

Diabetic Retinopathy (DR) is a common complication of Diabetes Mellitus (DM) that produces retinal abnormalities and can lead to blindness if not diagnosed and treated on time. To address this concern, an adaptive Convolutional Neural Network (CNN) model with Gradient Boosting (GB) called ResNetGB has been used from the literature where a Principal Component Analysis (PCA) based Fully Connected (FC) layer is used to capture the discriminative characteristics from the Retinal Fundus (RF) samples. It is essential to extract more effective features to categorize the DR grades. Hence, in this article, the Multi-Scale Attention (MSA) strategy is incorporated into the ResNetGB model for effective DR grade classification. First, the encoder network is used to embed the RF image in a high-level interpretational space in which the mixture of mid and high-level characteristics is considered to enhance the representation. Then, a Multi-Scale Feature Pyramid (MSFP) is added to define the retinal pattern in various localities and the MSA strategy is applied to the high-level interpretation. Moreover, the entire MSA-ResNetGB framework is trained by the cross-entropy loss to categorize the patients with respective DR grades. Finally, the experimental analysis exhibit that the MSA-ResNetGB model achieves the 94.40% and 94.17% accuracy on two benchmark datasets: Kaggle-APTOS and IDRiD, respectively compared to the cutting-edge models.

509

Authors: Muhammad Kaleem, Ismail Fauzi Bin Isnin

Due to technology scaling, network-on-chip (NoC) become the viable solution for on-chip many-core systems. The most critical concern of NoC is congestion management caused due to heavy communication traffic between nodes. Without an appropriate congestion resolution strategy for reducing heavy in-network traffic, the efficiency of the entire network is damaged severely. In this paper, an interval based record-keeping mechanism is presented to reduce network traffic and congestion by maintaining a history table and previous packet transaction records at each node. Proposed method performs certain validity checks before allowing using previous transaction record from history table. The performance of the technique is investigated in terms of average delay and compared to the state-of-the-art routing algorithms using the Access Noxim simulator. The simulation results demonstrate that the proposed method has outperformed in terms of global average delay, with 8-12% improvement, the average number of hits is 26-61% greater than misses under different synthetic traffic. The proposed algorithm has been tested under various topological configurations for efficiency evaluations.

533

Authors: Muhammad Aidiel Rachman Putra, Tohari Ahmad, Dandy Pramana Hostiadi

Botnets are a severe threat to a computer network, affecting various aspects of security systems, including spreading malicious programs, phishing, sending spam messages, and click fraud. Because of their negative consequences, botnets must be identified early. Nevertheless, their different characteristics have made them challenging to detect. This research proposes a bot patterns communication detection from traffic flows analysis consisting of three main activities: bot detection, extraction, and communication behavior analysis phases. This proposed model aims to obtain a specific behavior of bot attacks, which can be used as an early warning bots attack system. The process of bot patterns communication detection depends on the accuracy of bot detection, so the model improves the pre-processing phase and uses multi-model classification. Improvement in the pre-processing phase is carried out in the feature engineering section using the concept of one-hot encoding. Several machine learning classification models are used to obtain the best detection accuracy: Decision tree, Random forest, Logistic regression, k-NN, and Naïve Bayes. Furthermore, the model has been tested on two different datasets, namely the NCC and CTU-13. The experimental results show that the proposed model is optimal and recognizes bot activities well. The accuracy detection is obtained at 99.99%. Besides, the model can also identify the bot’s attack activity scenario and communication behavior in three types: centralized, distributed, and spread.

555

Authors: Salma El bakkal1* Abdellah Lakhouili1 El Hassan Essoufi

Intelligent traffic controller leads to manage traffic at intersection in order to minimize traffic congestion and has been intensively researched for a several decades. Multi-intersection cooperative traffic signal control (CTSC) is an efficient system that has received a great deal of attention and development in recent years. One problem with multi-intersection CTSC is that controller’s actions are based only on the traffic state or on the decisions taken at previous time step (t-1) at adjacent intersection. To address this problem, in this work a Double Deep Q Network Cooperative Traffic Signal Controller (CTSC-DDQN) is proposed. The CTSC-DDQN algorithm is a reinforcement learning agent that, depending on current traffic conditions, changes the traffic phase distribution order for n-intersection simultaneously. Experimental results under real scenarios show that the proposed approach outperforms other static approach which fixe the time length and the phase order despite the traffic situation and actuated controllers which change the traffic light properties based on the queue length in term of average Q-length and average waiting which eventually leads to mitigate traffic congestion. The results show that our could minimize the average waiting time by up to 79% and the average queue length by up to 80%.

575

Authors: Sai Ram Inkollu, G.V. Prasanna Anjaneyulu, Kotaiah NC, Nagaraja Kumari.CH

Currently global warming is increasing significantly around the world and become one of the typical issues for sustainability. In this connection, many sectors are transforming towards sustainable solutions. Integration of renewable energy (RE) is one such adaption in electrical power systems, by which the burden on conventional plants as well as their greenhouse gas (GHG) emission can be reduced significantly. However, due to radial structure, electrical distribution networks (EDNs) may not support for RE integration inappropriately. There are numerous methods have been introduced on optimal allocation of RE sources in radial distribution networks (RDNs), but not highly focused on maximizing their RE hosting capacity (HC). In this paper, a recent and efficient meta-heuristic algorithm, hunter-prey optimization (HPO) is introduced for finding the optimal locations and sizes of photovoltaic (PV) systems in RDNs. In addition to the loss minimization and voltage profile improvement, maximization of PV hosting capacity (HC) is focused as a major objective. Simulation results are done on IEEE 33-bus RDNs for different scenarios. The computational efficiency of proposed HPO is compared with other recent algorithms and it is observed that the results of HPO are better than other compared methods in terms of global optima. In addition, the enhanced HC of PV systems in RDNs is ensured improved performance in terms of reduced grid-dependency, GHG emission and distribution losses along with improved voltage profile.

599

Authors: Shatha Abdulhadi Muthana, Ku Ruhana Ku-Mahamud

This study presents a comparison between multi-objective optimization methods used to obtain solution for generator maintenance scheduling (GMS) problem. The GMS problem with three objectives which include the total operation cost minimization, system’s reliability (gross reserve) maximization and convenience is considered in this study. Convenience objective is represented by the minimization of the violation in the number of units’ maintenance outage constraint. To solve the problem of GMS, there is a need for GMS model to represent the requirements of electrical power system and optimization method to implement the model and obtain solution for the GMS. The proposed Pareto ant colony system (PACS) algorithm is based on ant colony system algorithm and Pareto approach. Pareto approach is used to make trade-off between the obtained solutions based on the three objectives. In this study comparison is made based on the results of experiments using the IEEE RTS 32- and 36-unit systems while the demand for the 32- and 36-unit systems is based on IEEE RTS demand systems. In addition, four common multi-objective algorithms based on Pareto approach i.e., the Non-dominated Sorting Genetic Algorithm II, Strength Pareto Evolutionary Algorithm 2, Multi-objective Simulated Annealing, and Multi-objective Particle Swarm Optimization are used in the evaluation of the proposed PACS algorithm. The multi-objective GMS model is implemented by all the algorithms and five performance metrics i.e., the grey relational grade (GRG), coverage, distance to Pareto front, overall Pareto spread and the number of non-dominated solutions are used in the evaluation. The Friedman test is also used to evaluate the algorithms’ performance statically, which is made based on GRG metric. The experimental results showed that the proposed PACS algorithm was able to obtain a robust solution by considering different initial operational hours of the units. In term of GRG metric, the PACS algorithm was able to obtain the best results for the 32-unit systems in all the maintenance windows. However, for the 36-unit system, the PACS algorithm secured the second-best results at the early stages of the operation time but outperformed other algorithms during other operation times. For other metrics, overall the PACS algorithm has the best performance in terms of coverage, distance to Pareto front and overall Pareto spread metrics while the NSGAII has the best result in terms of the number of obtained non-dominated solutions. Friedman test implies that the PACS algorithm is significantly better than the other comparative algorithms.

627

Authors: Saka Uma Maheswara Rao, K Venkata Rao, Prasad Reddy PVGD

The medical and health service has become advanced and the smart health care platform has made the diagnosis more robust for the treatment. The accurate analysis of medical data is dependent on early disease detection and the value of accuracy is reduced when the medical data quality is poor. However, the existing approaches failed to deploy the learning model to handle the heterogeneous medical data. The present research work has used the machine learning algorithm effectively for the chronic disease prediction such as heart disease, cancer, diabetes, stroke, and arthritis for the frequent communities and the medical data are available from the disparate sources including a wide variety of information has made difficult to analyse and retrieve. The detailed information about the attributes is required to be known as it is significant in analyzing the medical data. The process of selecting the attributes plays an important role in decision-making for medical disease analysis. The proposed model was tested for its effectiveness and was validated with various benchmark data which was collected from distinct domains. The present research work utilizes Spark Streaming layers for data streaming to diagnose further based on Long Short Term Memory (LSTM) Co-learning with whale optimization approach is from the heterogeneous medical data. The results obtained by the proposed method analysed the disease abnormality better. The existing model obtained SVM-RBF of 81.30 %, k-fold cross-validation and hyperparameters tuning with Random Forest of 94.9%, CNN of 90%, and the proposed LSTM based Co-learning model with Whale optimization approach obtained 98.6 % which is better compared to the existing models.

12

Authors: Ravikumar Mugadhanam, Eranna Uppara

The development of high-performance Radio Frequency (RF) receivers requires an innovative RF front-end design to ensure the best performance out of good technology. Recently, in the industry of wireless technology, the production of receiver designs with better efficiency and performance is increased. A Low Noise Amplifier (LNA) plays a vital role in the performance of an RF receiver. LNA is most commonly designed with the increased number of stages which renders high gain by conquering the noise in the subsequent stages. The previous LNA design topologies (single-stage and two-stage) offer minimum Noise Figure (NF) and high voltage gain with increased power consumption and dissipation. The standalone parameter ‘linearity’ is indirectly dependent on power and it is clear that to achieve high linearity more quantity of power is required. In this research work, an improved three-Stage LNA with Cascode Topology (3S-LNA-CT) is designed with efficiency by satisfying the requirements like minimum NF, improved linearity, low power and high voltage gain. The proposed LNA architecture is realized using three stages in cascade connection because, in the receiver design, low noise and improved performance are the prior design constraints. Cadence Virtuoso software is used to implement the proposed three stage LNA design in 45nm Complimentary Metal Oxide Semiconductor (CMOS) technology. At end of the simulation, the 3S-LNA-CT design achieved 0.33 dB of NF, 7.92µW of power consumption, S11 and S22 values are evaluated -11.36dB and -17.54 dB respectively. The simulation performances of the 3S-LNA-CT is improved compared to the conventional LNA design such as Noise Cancelation Technique (NCT) LNA, Ultra-Wide Band (UWB) LNA, and Particle Swam Optimization (PSO) LNA.

36

Authors: Umasankar Loganathan, Charles Rajeswaran, Harindrakumar Jayaraj, Manoj Magesh

In the present scenario, the DC-DC converter becomes the most significant device and it is used in various applications such as Electric vehicles, fuel cells, sustainable systems, etc. A bidirectional converter used in the above-mentioned applications should be lesser in magnitude and lighter in weight. In case, if the magnitude of the traditional bidirectional DC-DC converter is more, the substituting consistency has been amplified that results in more switching losses. Because of this switching loss, the magnitude of the structure is minimized. To overcome the losses during the process, a novel topology of dc-dc converters is designed with Falcon Optimization Algorithm (FOA) to increase the efficiency of converters. This research proposes the design of Bidirectional DC-DC converter (BDDCDC) for C and π Filter through Fractional Order Proportional Integral Derivative (FOPID) which is tuned by FOA. This proposed controller synthesis considers both robustness and closed-loop performance to ensure a fast and stable regulation characteristic. A simple tuning method provides the appropriate gains to meet design requirements. The outcomes of proposed FOA-FOPID is simulated in MATLAB Simulink which represent the transient performances such as steady state error (1.1 V), rise time (0.21 sec), settling time (0.33 sec) and peak time (0.24 sec) is much better than existing PID and Adaptive Neuro Fuzzy Inference System (ANFIS) control.

59

Authors: Abdelhakim Idir, Khatir Khettab, Yassine Bensafia

The goal of this research is to develop a high-performance fractionalized proportional–integral–derivative (FPID) controller based on Henry Gas Solubility Optimization (HGSO) for controlling the speed of a direct current (DC) motor. The suggested HGSOA-based Fractionalized PID technique with Matsuda approximation method was used to obtain the optimal FPID controller by minimising the integral of time multiplied absolute error (ITAE) as the objective function. Index of performance and disturbance rejection analyses, as well as transient and frequency responses, were all employed to validate the suggested approach's effectiveness. The proposed HGSO-FPID controller with Matsuda approximation was then compared not only to the original HGSO algorithm-tuned PID controller, but also to other controllers tuned by cutting-edge meta-heuristic algorithms such as Atom Search Optimization algorithm (ASO), Grey Wolf Optimization algorithm (GWO), Particle Swarm Optimisation (PSO), Invasive Weed Optimisation (IWO), and stochastic fractal search (SFS). The results showed that the proposed HGSOA-FPID controller has better performance with lower settling time, Ts which 0.1003 s, with lower rise time, Tr which is 0.0579 s, negligible overshoot, D which is 0.0052% and strong output disturbance rejection when compared to the performance of the other controllers.

80

Authors: Velusamy Murugesan, Ponnunaicker Balamurugan

Breast cancer classification through Gene expression analysis is very challenging due to the large variations and missing values problem. However, the traditional normalization and missing value imputation (MVI) methods perform poorly in the occurrence of significant batch effects, heterogeneity, artefacts, and low resolution. Therefore, an efficient pre-processing technique is presented in this paper using Weighted Fuzzy Score (WFS) and Bayesian Independent Principal Component Analysis (BIPCA) for upholding the quality of the expression analysis. In this proposed WFS-BIPCA technique, the large variations are reduced through sustainable transformations by WFS. BIPCA impute replaces the missing expression values without degrading the quality, consistency, and coherence of the output results without repeating all microarray experiments. The proposed WFS-BIPCA technique is evaluated with different classifiers over breast cancer gene expression datasets from Mendeley Data. Results showed that WFS-BIPCA with Support Vector Machine (SVM) classifier achieved high accuracy of 92.36%, 91.86%, 90.02% and 93.89% for BC-TCGA, GSE2034, GSE25066 and Simulation Datasets, respectively. Similarly, it achieved 91.66%, 96.15%, 89.32%, and 95.36% precision, 93.94%, 92.22%, 86.25%, and 97.48% recall, 92.79%, 94.14%, 87.76%, and 96.41% f-measure values and low processing time of 1.98, 0.96, 3.56 and 0.55 seconds for BC-TCGA, GSE2034, GSE25066 and Simulation Datasets, respectively.

103

Authors: Hadeel I. Abdulameer, Mohamed J. Mohamed

In this paper, we present four control structures for the Fractional/Integer Order Fuzzy Proportional Integral Derivative controllers that are used for a 2-link rigid robot manipulator. The manipulator is dealing with the trajectory tracking problem. A metaheuristic optimization technique, namely the most valuable player algorithm, is presented to optimize the controller's parameters while minimizing the integral of time square error. Furthermore, the proposed controllers’ robustness is examined for changing the initial condition, exterior disturbances, and parameter variations. MATLAB code outcomes show that the Fractional Order Fuzzy Proportional Integral Derivative controllers ensure the best trajectory tracking and also improve the system's robustness to change the initial condition, external disturbances, and parameter variations. The best structure is the Fractional Order Fuzzy Proportional Derivative -Fractional Order Proportional Integral Derivative controller among all structures with the minimum integral of time square error that is equal to 7.7481×10-5 for trajectory tracking, 2.4334×10-5 for changing of the initial position, 2.1893×10-4 for disturbances rejection and 2.4990×10-6 for parameter variation. The result showed also that the response of the trajectory tracking for theta1 and theta2 without overshoot and it has minimum settling time in the case of Fractional Order Fuzzy Proportional Derivative -Fractional Order Proportional Integral Derivative controller.

127

Authors: Wiwik Anggraeni, Eko Mulyanto Yuniarno, Reza Fuad Rachmadi, Pujiadi, Mauridhi Hery Purnomo

Climate and geography factors significantly influence the spread of dengue fever. It's critical to figure out the specifics of climatic and geographic conditions and the relationships between current patients. For further preventive measures, it is also necessary to identify the transmission source patients. This study aims to improve the understanding of dengue fever patients spreading under climate and geographic locations. Patients are clustered based on climatic and geographical variables, and influential patients are found in the established network using Fuzzy C-Means and Social Network Analysis. The scenario of cluster numbers' alteration and degree of fuzziness with Fuzzy C-Means made the three groups of patient clusters. A total of 52% of the patients are included in the lowland cluster, based on climate conditions and altitude. The patients grouped better with this approach than with the other compared methods. The Calinski Harabasz score has an average difference of 1644.105. The following relationship in the network is constructed once the cluster has been formed. It is given a fuzzy rule representing the distance of residence and the period of illness between patients. According to the Social Network Analysis approach applied to the region and month scenario, the three areas sensitive to the spreading center are Dau, Kepanjen, and Karangploso. The most significant patient data distribution occurred during the rainy season, peaking in January-February. The centrality algorithm shows that patients with male characteristics and the age range of children and adults could be the source of the disease's spread every month. Kepanjen area is the site of residence with the most significant impact with a proportion of 62.5 % in a year, and the initial illness is the fever related to dengue. Analysis results of this study can use by the Public Health Office to plan and manage resources to prevent extensive spread.

161

Authors: Zainab E. Kanoon, Ahmed Sabah Al-Araji, Mohammed Najm Abdullah

Due to the widespread use of autonomous mobile robots, the path planning of mobile robots take great importance in the research field. This paper aims at developing a path planning method that provides the shortest path with collision avoidance between the starting and the target points in a static robot environment. This paper proposes an intelligent hybrid optimization method that combines two algorithms; the first algorithm is called the Quarter Orbits (QO) algorithm, which tries to enhance the behavior of the cell decomposition path planning algorithm in terms of reducing the path length required to reach the target by steering the mobile robot toward the target using the quarter orbits cells instead of the grid cells and the vertical lines' cells. The second algorithm uses the Particle Swarm Optimization (PSO) algorithm to get a more efficient, smoother, and shorter path for the mobile robot to reach the target in a complex environment. The generated hybrid method is called the Quarter Orbits Particle Swarm Optimization (QOPSO). All simulation results clearly show that the proposed hybrid algorithm provided the shortest path with collision avoidance compared to the results of the Vertical Cell Decomposition (VCD) algorithm and the Radial Cell Decomposition (RCD) algorithm. The proposed hybrid QOPSO algorithm achieves enhancement on the path length equals 29.42% compared to the VCD algorithm and 24.25% compared to the RCD algorithm for different maps using MATLAB 2021a.

193

Authors: Marwa Jaleel Mohsin, Ibrahim A. Murdas

To overcome the RF spectrum crunch in the wireless communication networks and perform high-speed transmission with increased channel capacity, Light fidelity (Li-Fi) was presented by Harald Haas as a promising technology for the fifth generation and beyond. A high-speed Li-Fi system based on all-optical orthogonal frequency division multiplexing (AO-OFDM) was proposed and simulated in this paper. The optical signal was transmitted through a Free-space optical channel link making the proposed system suitable for outdoor application. All-optical OFDM architecture was improved the overall system performance. The 8 and 16 subcarriers were multiplexed demultiplexed through optical inverse fast Fourier transform/fast Fourier transform (OIFFT/OFFT) for the purpose of orthogonality. Optical IFFT was implemented at the transmitter side based on an optical frequency comb generator (OFCG) and dense wavelength division multiplexing (DWDM), while at the receiver side, Optical FFT was implemented based on multi-Mode interferometers (MMI) and time delays. The system was simulated by the VPI photonic design suite and analysed in terms of BER, Constellation diagram, and received power with different FSO path links from 1 to 100Km. Our system achieved a well-received power with high data rate reaches 1.2Tbps and low BER about 1×10-12.

215

Authors: Hatma Suryotrisongko, Yasuo Musashi

We explored the potentials of the quantum machine learning (ML) approach for cybersecurity domain generation algorithm (DGA) detection applications employing two quantum ML approaches: hybrid quantum-classical deep learning (DL) and variational quantum classifier (VQC) models. We implemented quantum noise models and benchmarked the hybrid quantum-classical DL with the VQC-based one. Our datasets include statistical analysis (entropy, relative entropy, information radius, and reputation score) of 1,000,000 Alexa and 803,333 DGA domain names. Qiskit and Pennylane were utilized for the quantum simulations in our experiments. We found that our proposed hybrid quantum DL outperforms the VQC-based model (94.9% maximum accuracy). Investigating the combinations of 12 types of optimizer algorithms, four feature maps, and four variational form circuits reveal that feature maps and variational forms are critical for the VQC algorithm. We show the usefulness of a quantum circuit architecture consisting of Angle Embedding and Random Layers for a hybrid quantum DL model.

233

Authors: Agus Tri Haryono, Riyanarto Sarno, Rachmad Abdullah

Financial headlines and microblogs usually have sentiment of finance and research further about aspect of sentiment analysis still needed. From dataset FiQA 2018 challenges 4 aspect represent about financial, which is corporate, economy, stock and market. This research proposes method to determine financial headlines and microblogs to financial aspect. A sentence form dataset pre-processed into keyword. Aspect categorization using semantic similarity calculate similarity measurement on word vector of sentence with term list of each aspect, the highest similarity value determines the aspect. Neural network is used for sentiment classification of headlines and microblogs, the method used word embedding and bidirectional long short-term memory (BLSTM). The sentiment classification results are combined with the aspect categorization results to determine which aspects have the highest positive sentiment. The highest aspect categorization performance is obtained combined semantic similarity and bidirectional long short-term memory which reach 88.1% and semantic classification accuracy reach 77.0%. The stock aspect has received more positive sentiment compared to the sentiment of other aspect, this can be used for started investment in stock.

253

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

Missing data in software attributes as a common occurrence that can lead to incorrect estimates and poor predictability. For this reason, the imputation approach was used in our study. We model incomplete data using an Autoencoder (AE) approach for missing value imputation, which substantially minimizes the complexity of data modeling when dealing with small data in regression situations. In this study, we introduce the imputation approach through Stacked Denoising Autoencoder (SDAE) with dropout regularization and perform hyperparameter tuning. The scenario presented in this research is built up by eliminating data intervals from the whole dataset at various missing rates (10 percent - 80 percent missing rate). Experimental findings from 11 datasets support the efficacy of the suggested technique (on Promise and ISBSG10). The proposed method's performance is compared to that of other techniques (such as: Generative Adversarial Imputation Networks (GAIN), k-Nearest Neighbor imputation (kNNI), Multiple Imputation by Chained Equations (MICE), and Random Forest Imputation (MissForest)), demonstrating that our method outperforms others even with an 80% data missing rate, with producing the best value (low) on mean absolute error (MAE: 0.0839) and root mean square error (RMSE: 0.1061).

281

Authors: Asmaa A.Ghany, Ahmed A. Zaki Diab, Abo‐Hashima M. Elsayed, Yahia S. Mohamed, E. G. Shehata

Nowadays, great attention has been paid to wind energy conversion systems (WECS), particularly high-power stations equipped with high-power electronic converters. Direct-driven permanent magnet synchronous generator (PMSG) with three-level converters is the preferred type for such wind energy systems. To regulate the operation of the generation system, a finite control set model predictive control (FCS-MPC) has a superior performance for power converters control techniques due to tight control dynamics, simple design, multi-variables control, no modulation control stage, and only a discrete-time model is needed. But on the contrary, variable switching frequency output waveforms is the main drawback. The main objective of the paper is to design a controller of a three-level neutral point clamped (3L-NPC) converter with a novel fixed switching frequency for regulating the stator current of PMSG and ensure the balance of the DC-link capacitors voltages. In addition, a low voltage ride-through (LVRT) control technique is provided for the grid side converter of the wind power system to improve the wind turbine system performance throughout the grid fault. The futured system has been demonstrated via simulation work under steady states, transient conditions, and different types of grid voltage faults. The comparative results between MMPC and classical FCS-MPC show the effectiveness of the proposed MMPC in enhancing the results and reducing the simulation execution time and the computational effort associated with the calculation of the duty cycle of the converters also the total harmonic distortion (THD) has reduced by 31.34% lower than classical FCS-MPC and obtaining a constant switching frequency with a reduction in execution time by 51.17%. In addition, the comparative results of WECS during symmetrical and asymmetrical grid faults with and without LVRT controller show the effectiveness of LVRT controller in supporting the grid during faults and preventing the dc capacitor overvoltage.

304

Authors: Andi Kurniawan Nugroho, Dinar Mutiara Kusumo Nugraheni, Terawan Agus Putranto, I Ketut Eddy Purnama, Mauridhi Hery Purnomo

Stroke is a condition in which the blood supply to the brain is cut off. This occurs due to the rupture of a blood vessel. In addition, this can lead to fatal conditions for the patient where causes damages in several areas of the patients’ brain. The examination by health workers is generally carried out to receive an image of the part of the patient’s brain that has a stroke disorder. Commonly, stroke detection is done using magnetic resonance imaging (MRI). The purpose of this study is to use convolutional neural network (CNN) as a method for classifying MRI stroke images. The data used in this study is the DWI stroke MRI image dataset 5,226 images. The data consisted with 1,742 normal images, 1,742 intra cerebral hemorrhage (ICH) images, and 1,742 acute ischemic images, which are grouped into 3 parts, training, validation, and testing. The images produced by the MRI are labeled manually by the doctor to obtain certain types of strokes. This study employed the multi optimizer which is Root Mean Squared Propagation (RMSprop), adaptive moment estimation (Adam), and stochastic gradient descent (SGD). This study compared between the scenario original CNN model and the scenario hepta convolution layer neural network (HCL-NN) which proposed by this study. Both of the scenario of CNN tested using magnetic resonance –diffusion weighted (MR-DWI) images. Transfer learning in the CNN model (inside the CNN library) was tested with the author's data for performance testing. Then, the results of the performance trials that have been carried out earlier are compared with the results of the original CNN and HCL-NN. The experiment shows by using scenario HCL-NN display data training accuracy value 99.23 percent and data testing accuracy 97.40 percent. The both values are achieved with using RMSprop optimizer. Furthermore, this study also tested the values of precision, Recall and F1-scores. The scores for testing the HCL-NN scenario 97.43, 97.40 and 97.40 are higher compare to the other scenarios (original CNN) and other transfer learning (RESNET, VGG19 and mobilenet V2). As a summary of this study, the proposed HCL-NN performed better than other CNN scenarios for classifying strokes for MR-DWI images.

343

Authors: Ledya Novamizanti, Andriyan Bayu Suksmono, Donny Danudirdjo, Gelar Budiman

In medical applications, the slightest distortion in the patient's medical image cannot be tolerated, as such distortion can lead to misdiagnoses and misleading conclusions. The robust reversible watermarking technique has received considerable attention, as it allows the complete extraction of the embedded watermark signal and the comprehensive reconstruction of the masking signal. This paper proposes a robust reversible watermarking (RRW) scheme based on Stationary Wavelet Transform (SWT) and Multibit Spread Spectrum (MSS). The host image is broken down into several subbands using SWT. Then, the watermark is embedded into the subband diagonal detail coefficient of the host image using MSS. Finally, the watermarked subband is combined with other subbands using inverse SWT (ISWT) into a watermarked image. The embedded watermark is extracted on the receiving side, and the original medical image is precisely reconstructed without involving auxilary information. By the experimental results, the proposed robust reversible watermarking technique can restore the watermark and rebuild the host medical image perfectly without any attack. The watermarked images produced by the proposed reversible data hiding technique have better visual quality with a peak signal-to-noise ratio above 41 dB. In addition, the proposed RRW scheme is robust to various attacks such as compression, noise addition, sharpening, and image enhancement.

366

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

The fundamental challenge in developing a prediction model for software effort estimation (SEE) is related to the availability of inadequate public data or the small amount of information contained in the data. As a result, because the information contained in the data is small, it can be challenging to make accurate estimates with insufficient SEE data. Data augmentation techniques, and synthetic data have been used successfully to overcome problems with limited data sets. In this study, conditional variational autoencoder (CVAE) and an inverse normalization transformation (INT) were investigated as methods for data augmentation and synthetic data generation. Eleven data sets in the field of software engineering (such as the PROMISE repository and ISBSG) that will be used as case studies. Overall, the CVAE-INT model was used for the augmented data set (synthetic data). Based on the Mann-Whitney test, it shows that our method provides a significant level with a mean p-value greater than 0.90 in the data set. Our model yields lower mean absolute error (MAE) and root mean squared error (RMSE) values. Increased mean error for the datasets Albercht (0.0269; 0.0842), China (0.0444; 0.1079), Cocomo81 (0.0544; 0.1155), Desharnais (0.0702; 0.1258), IFPUG (0.0225; 0.0448), ISBSG10 (0.0996; 0.1836), Kemerer (0.0776; 0.1457), Kitchenham (0.0001; 0.0010), Maxwell (0.0746; 0.1230), Nasa93 (0.0980; 0.1488), and UCP (0.0427; 0.0899). Furthermore, we assessed the influence of synthetic data using five machine learning prediction approaches (such as CART, k-nearest neighbors (kNN), Support Vector Regression (SVR), MultiLayer Perceptron (MLP), and Random Forest (RF)). The results show that our method can provide data augmentation techniques for the transformation of variance data that can produce synthetic data that has the same distribution as real data. In addition, this study shows that the synthetic data produced has a significant effect on increasing the accuracy of the five predictor methods in the machine method.

397

Authors: Anggun Fitrian Isnawati, Mas Aly Afandi

The development of wireless technology, especially small cell communication systems, is now very flexible to be developed. As a form of network densification to improve the performance of the macrocell network, the combination with smallcell, in this case the femtocell network, forms a heterogeneous networks (Hetnets). However, with many networks running concurrently, a combination network between macrocell and smallcell networks, interference is unavoidable. To overcome this problem, it can be solved by applying the power control technique independently by the user. One of the independent power control methods is using a game theory approach. The use of game theory in power control or Game-Theoretical Power Control is often also referred to as Power Control Game (PCG). By determining the appropriate utility function, optimal power is obtained each time using the power update iteration process. The study addresses the issue of achieving the target SINR for both femto and macro users on heterogeneous networks by proposing a PCG that is capable of exceeding the target SINR upon convergence, implying improved communication quality. Based on the results of the feasibility and convergence tests, the proposed system has been proven to meet the requirements for existence and uniqueness, which are the requirements for meeting the feasibility and convergence of the system. The results of the performance comparison also show that when reaching convergence, both femtocell and macrocell users using the Proposed PCG method are able to exceed the specified target of signal to interference and noise ratio (SINR) in each network type. Meanwhile, using the Koskie Gajic (KG) and Al Gumaei (AG) methods, both femtocell and macrocell users could not reach the target SINR. So it can be concluded that in terms of achieving the target SINR, the Proposed PCG method is better than the other two methods.

416

Authors: Putra Yudha Pranata, Wahyono

Illegal parking is a violation that often occurs in urban areas. Supervision is conducted in places where parking is prohibited to overcome this problem. Manual monitoring requires high staff performance and costs. Therefore, a video-based illegal parking detection is proposed. Previous studies have several weaknesses in video-based illegal parking detection, especially those that use background modeling techniques. Examples of drawbacks include changes in illumination and imperfect segmentation caused by noise, leading to false-positive errors. This study aims to overcome the problem of frequently occurring false-positive errors by using the improved visual background extractor (I-ViBe). This method develops the ViBe approach to detect illegal parking by modifying the background model update mechanism with the static region extraction and vehicle verification processes and integrating tracking processes. Experimental results show that the values of precision, recall, and f-measure are 100%, 60%, and 75% respectively, which means that the proposed system has overcome all false-positive problems.

441

Authors: Najib Ababssi, El alami Semma, Azeddine Loulijat

Voltage instability and voltage collapse are the serious problems that can occur due to reactive power deficits caused by increased load or contingencies. Detecting potential voltage collapse in power systems is essential to maintain voltage stability during high demand. The stability of power systems is an important issue in the planning and operation of these systems. This paper determined the optimal location and size of a FACT STATCOM device for improving the static voltage stability margin of a transmission system. The study network is the IEE New-England transmission network. The problem has been formulated as a multi-criteria optimisation with the objectives of maximising load margin, minimising power losses and minimising voltage deviation. The objective function of the problem is adjusted using the analysis and optimisation method (CPF). The appropriate values and placement for STATCOMs are found using the CPF method based on the objectives below. The proposed method is verified using a simulation test on the IEEE-100 kV network which is part of the IEEE- 39 bus New England power system. The simulation results showed the efficiency of the CPF for the nominal values and the optimal location of the STATCOM. The results showed that the improvement of the voltage stability margin, the voltage profile of IEEE-100 kV is increased and the power losses are reduced. Finally, STATCOM ensures the overall stability of the transport network.

467

Authors: Shaik Imam Saheb, Khaleel Ur Rahman Khan, C. Shoba Bindu

In the dense sensing environment, deployment of multiple sensor nodes is included in the WSNs. Based on the sensor deployment scheme, the WSNs’ effectiveness has been provided up to the extent that relied on the network coverage. Some of the major objectives relevant to the sensor nodes' deployment that are required to be satisfied are the network connectivity, number of deployed sensors, lifetime, overall consumption of energy, and area coverage. The deployment of sensor nodes has been carried out based on a constrained problem of multi-objective (MO) that includes the objective of determining the sensor node arrangement for improving the coverage area, decreasing the energy consumption, improving the network lifetime, and network connectivity between SINK and node for accurate data transmission. Based on Bat Algorithm (BA), the node deployment with the proposed technique is discussed for improving the sensor nodes’ coverage rate. However, each bat is determined the solution based on the sensor nodes’ deployment. Further, the bat algorithm (BA) has been improved to enhance the sensor nodes' coverage rate. According to the value of fitness function, the bat movement is chosen in the enhanced bat algorithm. The node deployment using the improved bat algorithm have been compared with RSSI based node deployment method, called as hybrid DP-HOP and particle swarm optimization approach CPSO. Compared with Hybrid DP-Hop & CPSO techniques, the proposed node deployment technique achieved 60% throughput gain, improves PDR by 30% and reduce the end to end delay by 40% and optimized energy consumption.

488

Authors: Firas Muneam Bachay, Mohammed Hasan Abdulameer

Palmprint authentication has received a lot of attention as one of the most prevalent biometric methods. A palmprint is a portion of the palm’s surface that has special characteristics that could be used for authentication. Getting the most valuable features out of a palmprint, on the other hand, is a major challenge. Another challenge is coming up with an efficient strategy to authentication that uses fewer images, especially with approaches that require a high number of images in the training phase, which is a major issue. The majority of recently developed approaches rely on primary lines, wrinkles, and creases, which in some situations are insufficient to separate two people due to resemblance. Deep learning methods have recently been viewed as a critical component for extracting deep features such as texture features in these types of tasks. We concentrated on the palmprint authentication challenge in this work by creating a hybrid model called AE+CNN, which is based on the autoencoder (AE) model and a convolutional neural network (CNN) model. The proposed model comprises three phases: pre-processing, region of interest extraction (ROI), and feature extraction and matching using hybrid AE+CNN. The experiments used the COEP palmprint database, which has a limited number of palmprint images, posing a significant challenge for deep learning models that require a large number of images for training. The F1-score and the accuracy metric were both employed in the evaluation. with a score of 97.85 % for accuracy and 96.81 % for F1-score. Gradient-weighted class activation mapping (Grad-CAM) was also applied to figure out which parts of the palmprint are the most discriminative for class classification.

514

Authors: Moch Deny Pratama, Riyanarto Sarno, Rachmad Abdullah

The Hotel website always provide sentiment reviews of the service, then users can easily choose the desired facility according to the best advice results from the previous user. In this study, the sentiment of the reviews are determined based on five hotel aspects, those are: food, service, location, comfort and cleanliness. Every data hotel reviews is pre-processed to produce a term list. Extract related hidden topics using Latent Dirichlet Allocation (LDA). The extracted documents are categorized by matching for similarity to determine the term documents into five hotel aspects. Each term document is expanded using synonyms to increase the similarity value to LDA with 100% expanded document using Cosine Similarity produces the highest performance value of 0.856. Labeling the sentiment of each review based on the aspect and sentiment classification using the Support Vector Machine method gets an average value of 0.940 for all fifth aspects. The ranking of the most important aspects shows that users talk a lot about the cleanliness aspect, having the highest positive sentiment value of 39.450 and having a negative sentiment of 3.861, indicating that each review sentiment is influenced by certain aspects.

536

Authors: Rajeswarappa Govardanagiri, Vasundra Sanjeevulu

In wireless senor networks (WSNs), the process of handling the sensor nodes’ restricted energy resources from the dimensions of data routing, collection and aggregation is highly challenging. These potential challenges of WSNs demand efficient energy stabilization in the network for prolonged lifetime that attributes towards improved rate of reliable data dissemination. The maximization of network lifetime is essential for attaining success in data delivery, energy conservation and scalability. Clustering schemes provide low overhead and efficiently allocates the resources for ultimate improvement in energy consumptions and minimize interfaces among the sensor nodes. Swarm intelligent meta-heuristic clustering schemes provide optimal solution for this non-deterministic polynomial (NP)-complete problem of clustering. In this paper, Hybrid grasshopper and improved bat optimization algorithm (HGIBOA)-based cluster head selection scheme is proposed for enhancing lifetime expectancy by sustaining energy stability in WSNs. This HGIBOA utilized variable coefficient-based Levy flight for enhancing the exploration potentialities of GOA. It inherited the local searching operation of bat algorithm for establishing the balance between exploitation and exploration. It further included a random strategy for employing it over high quality population with the objective of improving the capability of exploitation. It also included the fitness function evaluation based on residual energy, distance between cluster head and base station, and distance between cluster head and cluster member nodes. The simulation results confirmed that this proposed HGIBOA with different number of sensor nodes sustained residual energy by 18.21%, improved throughput by 21.84%, prolonged network lifetime by 17.92% and maintained stability of 16.28%, better than the benchmarked Cuckoo search and PSO-based QoS-aware multipath routing protocol (CSPSO), clustering scheme using firefly and modified ABC (FMABCCS) and integrated GWO and WOA-based clustering strategy (GWWOA) approaches used for investigation.

554

Authors: Bethelli Pavani, Lavadya Nirmala Devi

Sensors in an internet of things (IoT) network are energy-consuming devices and they operate on batteries that have limited energy capacity. This issue impacts the network lifetime. One of the promising solutions for this problem is simultaneous wireless information and power transfer (SWIPT). In SWIPT, a receiver node harvest the energy by itself based on the quantity of RF signal. When the transmitted RF signal becomes weak, the receiver node not able to forward the information to the next node due to not sufficient harvested energy. It can reduce network performance. Here, we propose a new SWIPT mechanism called as hybrid SWIPT (H-SWIPT) to enhance the harvesting energy at which the receiver node not only depends upon the previous node's RF signal but also depends on the neighbor node's RF signal, sink broadcast energy, and co-channel interference. Furthermore, to minimize the total energy consumption of the path a new routing metric is introduced. Based on this, it will select the minimized energy cost path from source to destination. The network performance is evaluated in terms of energy cost and aggregative energy cost and compared with existing schemes: information transmission (IT) & power splitting protocol based SWIPT (SWIPT-PS). From the results, for varying node count, the average energy cost is observed as 5.9mW, 5.433mW, and 4.7mW for IT, SWIPT-PS &H-SWIPT respectively.

576

Authors: Thaair Ameen, Ali Adil Ali

To provide more accurate, diverse, and interpretable recommendations, one must go beyond modeling user-movie interactions and consider user social correlation graphs and movie-movie correlation graphs. However, most previous research has only considered correlations between user preferences and social correlations, while ignoring the correlation between movies. In this paper, we describe a model for movie recommendations that include a representation of the user's preference in movies based on graph attention networks and multi correlations as in the following graphs. 1) user-movie graphs, which include user-movie interactions, as well as users’ opinions on movies, 2) user social correlation graphs, which contain social correlations among users, and 3) movie-movie correlated graphs, which contain the correlation among the movies themselves. Extensive experiments are conducted on two real-world (Netflix and Movie Lens) datasets to verify the efficiency and stability of the proposed model.

601

Authors: Aicha El filali, Aissam Jadli, El Habib Ben Lahmer, Sanaa El filali

Demand forecasting is an indispensable key to planning and achieving objectives, as it feeds all the processes in the company’s supply chain. Forecasting is a difficult task that requires a lot of analysis using powerful complex mathematical equations to build a model capable of predicting customer behavior according to the market variability involved. Therefore, the objective of this paper is to build a new hybrid deep learning method named (LSTM-GRU) based on sequential learning using long short term memory (LSTM) and gated recurrent units (GRU). The proposed method builds automatically the best prediction model by considering various combinations of LSTM and GRU hyperparameters using the gridsearch technique. It can predict highly fluctuating demand data while taking into consideration the non-linear characteristics of time series. The new method’s performance was compared to several deep learning models, namely single layer LSTM, single layer GRU, stacked LSTM, and stacked GRU, using real electrical product data from an industrial Moroccan company. The evaluation of the experimental results with mean absolute error (MAE) and root mean squared error (RMSE) methods reveal that the predictions produced with the new model are the most accurate with the lowest error rates MAE= 237682.56 and RMSE= 366633.28 respectively.

12

Authors: Mahesh Boddu, Soumitra Kumar Mandal

This article presents an efficient hardware architecture of lossless image compression mechanism using colour filter array (CFA) for wireless camera networks. Recently, Quantum-dot cellular automata (QCA) technology has been widely using in the field of VLSI, thus the proposed compressor is developed using QCA technology to mitigate the area, and power as compared to standard CMOS technology. In addition, an improved and extended Golomb-Rice entropy coder (IEGREC) is proposed to reduce the memory requirement, and computational complexity. The IEGREC approach eliminates the requirement of context module, which is a major component in lossless image compressor architectures and its memory to lessen the hardware resource utilization. Further, to maintain the pixel connectivity and higher compression ratio (CR), the proposed architecture utilizes the adaptive Golomb-Rice (GR) parameter prediction and control module. Finally, packer module is used to maintain the flow of output compressed bit stream. The extensive simulation results show that proposed QCA-IEGREC method performs superior as compared to state-of-art approaches in terms of lesser area as 3700 gate count, and reduced power of 1.02 mW, and even image quality statistics such as 18.78dB of peak signal-to-noise ratio (PSNR), 152.3 of figure-of-merit (FOM), and 29.3 of CR.

36

Authors: Omar Abdullah Murshed Farhan Alnaggar, Basavaraj Ningappa Jagadale, Swaroopa Hebbar Narayan

Early detection of the brain tumors can help in providing sufficient treatment to completely cure the disease. Machine Learning (ML) and Deep Learning (DL) algorithms are utilized predominantly in recent years for the tumor detection process from the brain Magnetic Resonance Imaging (MRI). In this paper, an effective tumor detection framework is proposed by utilizing a hybrid segmentation algorithm and a new hyper-parameter optimized Convolutional Neural Networks (CNN) based classifier using Chimp Optimization Algorithm (COA). The framework includes pre-processing, segmentation, feature extraction and classification stages. The pre-processing stage performs the denoising using wavelet filter and image enhancement using adaptive histogram equalization. Then the Boosted Crossbred Random Forests (BCRF) is used to segment the regions of interest to identify skull, tumor and lesions. The features are extracted using the Grey-Level Co-occurrence Matrix (GLCM) and Gabor filter. Finally, the tumor classification is achieved using the COA-CNN. The COA-CNN is developed by utilizing the COA to tune the hyper-parameters of CNN to obtain optimal CNN architecture. Experimental evaluation was performed on a dataset of 3264 MRI brain images containing normal and tumor images. No tumor, Glioma, Meningioma, and Pituitary tumor are classes of the MRI images which are correctly classified by the proposed BCRF and COA-CNN based detection model with a testing accuracy of 95.18% and reduced processing time. The results indicate the efficacy of the proposed method in classifying brain tumors for assisting physicians in early tumor diagnosis and treatments.

57

Authors: Fikri Budiman, Pulung Nurtantio Andono, De Rosal Ignatius Moses Setiadi

This research proposes an encryption method for images based on dual chaos systems and dual hash functions. SHA-256 and SHA-512 are hash functions chosen to process plain images and private keys. The purpose of using hash functions on plain and key images is to increase encryption resistance against differential attacks and perform local encryption in each image zone. The proposed method consists of two stages of encryption, the first involves dual hash functions and confusion with a chaotic system by performing encryption based on zoning and rotation of the image. The second stage is a diffuse process using a logistic map. The proposed method is proven to be strong against various attacks that have been tested with various measuring tools such as entropy, histogram analysis, chi-square, number of changing pixel rate (NPCR), unified averaged changed intensity (UACI), and the avalanche effect (AE). This method also proved to work better than the state-of-the-art method in this research, significantly based on entropy, UACI, and NPCR.

79

Authors: Nataraj Kalaivani, Raman Beena

Heterogeneous Cross-Project Defect Prediction (HCPDP) predicts the defects by incorporating the project datasets with different distribution and metrics. However, the class imbalance problem and shallow feature learning of classifiers reduce the overall performance. This paper presents an HCPDP using Improved Synthetic Minority Oversampling Technique (ISMOTE) and hybrid deep learning classifier of Golden Eagle Optimized Siamese Neural Networks (GEO-SNN). Initially, Peters filter removes irrelevant instances in source-target dataset based on heterogeneous metrics. ISMOTE, enhanced through fuzzy mutual information based KNN attribute weights assignment, is applied solve the class imbalance problem. Features are scaled using Z-Score standardization and metric matching by Spearman's Rank Correlation (SRC). Finally, GEO-SNN, developed by SNN parameter optimization using GEO, performs deep semantic feature learning for defect prediction. Experiments using benchmark datasets showed that the proposed ISMOTE and GEO-SNN based HCPDP technique has provided 99% defect prediction accuracy and reduced processing time by 20%than the state-of-the-art methods.

99

Authors: Sudhakar Karna Narayana, Naveen Thimmahanumaiah Hosur

Wireless Mesh Networks (WMNs) are considered as an important structure for the next generation wireless networks that have numerous nodes organized in the mesh topology. In WMN, the data packets are forwarded through the multi-hop model. Nowadays, the combination of mesh clients with the Internet of Things (IoT) is gaining substantial importance for connecting numerous machines and it is used to acquire faster coverage. Moreover, the nodes in the WMN are susceptible to the malicious nodes which transmit the compromised and manipulated data to the destination. In this research, the Priority based Trust Efficient Routing using Ant Colony Optimization (PTER-ACO) is proposed to achieve data privacy and reliable routing over the WMN. Subsequently, Advanced Encryption Standard (AES) and Rivest Cipher 6 (RC6) are used to provide added security to the data. The PTER-ACO is analyzed by means of energy efficiency, network throughput, Packet Loss Rate (PLR), computational overhead, and latency. The existing methods such as Improved version of the Dijkstra Algorithm (IDA), Round Trip Time (RTT) based detection and Robust and Trusted Scheme (RTS) are used to evaluate the PTER-ACO, against these methods. The PLR of the PTER-ACO method is 21 % for 10 malicious nodes, which is less when compared to the IDA, RTT and RTS.

116

Authors: Madha Bramarambika

The MRI is assisted by doctors to evaluate the tumors that help them for planning the required treatment. The physicians are dependent on the medical MRI diagnosis but that required lots of effort in finding out the tumor regions. The existing models speed up the scanning process using the MRI technique that reduced the burdens from radiologists but showed abnormality due to the presence of outliers when extracting hyperparameters. The proposed Hybrid GLCM-LDTP-Le-Net Feature extraction technique is developed by combining the Grey level covariance Matrix (GLCM), Local Direction Ternary Pattern (LDTP) and Le-Net features. The feature extraction technique results with hybrid parameters, which are applied to the Bidirectional Long Short Term Memory (Bi-LSTM) model to classify the brain MRI as a Malignant or Benign. The proposed hybrid feature extraction increases the accuracy of learned models by extracting the combined features from the input data. The proposed hybrid feature extraction obtained an accuracy of 97.88 %, better when compared with the existing CNN-based models' methods that obtained accuracy of 91%.

135

Authors: Agus Subhan Akbar, Chastine Fatichah, Nanik Suciati

Many studies have been carried out to segmentation brain tumors on 3D Magnetic Resonance Imaging (MRI) images with 3D or 2D approaches. The 3D approach pays attention to the interrelationships between slices in a 3D image. However, this requires high resources, while the 2D approach requires lower resources but ignores the voxel relationship in 3D space. The 2.5D approach seeks to combine the lightness of the 2D approach and the voxel interconnection of the 3D approach. This article proposes SDA-UNet2.5D, a shallow UNet 2.5D architecture that pays attention to the interconnectedness of 3D images by involving five slices to get one slice of segmentation prediction results. The architecture is trained using the Brain Tumor Segmentation (BraTS) 2018, 2019, and 2020 datasets. Compared to other architectures, this proposed architecture has a high segmentation speed with 4.05-4.24 seconds to segment one patient data. Online validation resulted in superior average dice performance of 75.70, 88.82, 77.33 for the BraTS 2018, 71.29, 88.00, 76.55 for the BraTS 2019, and 70.80, 87.95, 75.89 for the BraTS 2020 validation datasets in the areas of Enhanced Tumor (ET), Whole Tumor (WT), and Tumor Core (TC).

163

Authors: Arun Prasath Govindan, Annapurani Kumarappan

Hand gestures and voice inputs have been measured as the vital communication component for the past few decades. Here, deep learning-based Reversible Convolutional Neural Network (Rev-CNN) is explicitly modelled to predict gesture-based sign language. Similarly, this work concentrates on modelling a reversible model to identify the voice gesture into sign language. It shows reversible representation by attaining superior accuracy with a lesser amount of model parameters and various CNN architecture. Here, the efficiency of the reversible model is evaluated with the prevailing G-CNN, VGG-11/16 model over the testing and training environment. Here, two diverse datasets like ROBITA Indian Sign Language Gesture Database and the standard voice-input dataset, is considered for evaluation purpose. The highest prediction accuracy of 94.38 % and 97.89 % is attained using the proposed reversible CNN model over the other approaches like GCNN, VGG-11 and VGG-16 model. The experimental outcomes and metrics like loss function, error rate and execution time are measured and compared with different methods like GCNN, VGG-11/16. Additionally, other efficiency metrics are utilized to determine the efficiency of the anticipated model. The model outperforms the existing approaches by categorizing the gestures with reduced error rate. The prediction accuracy of the reversible CNN (dataset 1) is 95.38 % and for dataset 2 is 96.69%. Similarly, the execution time is 5.5 minutes.

190

Authors: Santhosh Kumar Gorva, Latha Channagiri Anandachar

In recent decades, the rapid growth of cloud computing has led to increasing concerns about the security and energy requirement of cloud data centers. To overcome the concerns of load balancing and data security, a new hybrid model is proposed in this article. Firstly, a Modified Particle Swarm Optimization (MPSO) technique is proposed for balancing the tasks between heavy-loaded Virtual Machine (VMs) and light-loaded VMs. An effective load balancing improves the performance and availability of website, database, application and other computing resources. In the MPSO, a linear decreasing inertia weight is included to achieve optimal solutions in both VM migrations and load balancing. Secondly, an Enhanced Elliptic Curve Cryptography (EECC) algorithm is proposed for effective data security. In the proposed EECC algorithm, a new pseudo-random key is combined with a public key to improve the security of cloud data centers. In this work, the effectiveness of the proposed MPSO-EECC model is validated in terms of Service Level Agreement (SLA), execution time, energy consumption, energy SLA violation, encryption, and decryption comparison time. As represented in the experimental section, the proposed MPSO technique almost reduced 30%-70% of energy consumption compared to the existing optimization techniques. Similarly, the proposed EECC algorithm reduced 10ms to 30ms of decryption comparison time related to the comparative cryptography algorithms.

213

Authors: Kawther Dawood Salman, Ekhlas Kadhum Hamza

Due to the rapid growth in mobile data traffic, academia and industry are considering Li-Fi (Light Fidelity) as an alternative to Wi-Fi (Wireless Fidelity). Li-Fi is a high-speed, two-way wireless network system that has several advantages over traditional radio frequency systems. To fully exploit the potential of Li-Fi systems, users must be provided with a seamless connection, which must be achieved by carefully studying user mobility. Nowadays, most wireless Li-Fi network applications focus their design on solving some unspecific user-location problems that may fully achieve some preferred results in the domain they serve. For example, locating an unknown user in an indoor environment is considered a kind of solution for maintaining the QoS (Quality of Service) and delivering the signal to the user without distraction. Where the main research problem lies in the signal interference, loss, and scattering in the Li-Fi network, it is a big problem because it reduces the network performance and coverage. Therefore, to solve this problem, this research investigates the locating of the mobile user within the Li-Fi equipped room to obtain the best possible coverage of the user's service. The aim of the research was divided into two stages. In the first stage, the method used in our work was presented by showing the triangulation method in simulating user location data (sensor simulation), the first stage was not affected by noise, as it serves as proof of the correctness of our work and depends in the second stage when it is under the influence the noise. In the second stage, the method of RSS - triangulation (Receiving Signal Strength -Triangulation) was adopted to simulate the user’s location data (a simulation of the sensor) and under the influence of noise and implemented on a deep learning algorithm to determine the accuracy of the user’s location and how to deal with noise. In the simulation results for the data in the first stage, our work was validated using the triangulation method in data simulation. The average error for the X-Axis was 2.17344 〖× 10〗^(-14) cm; Y-Axis was 6.44762 〖× 10〗^(-14) cm and Z-Axis 4.65690 〖× 10〗^(-11) cm the results obtained from the triangulation simulation were close to the real results. In the second stage, when adopting the RSS-Triangulation method under effect the noise, the average error was achieved on the X-axis 2.05612 〖×10〗^(-3 )cm, Y-Axis was 4.56249〖 ×10〗^(-3 )cm, and Z-Axis 5.10474 〖×10〗^(-3 )cm where The DNN (Deep Neural Network) showed how to deal with noise, the highest error was obtained on the X-axis 2.51535 cm, the Y-axis 2.25903 cm, and the Z-axis 4.22898 cm for an indoor environment of 5m x 5m x 3m.

243

Authors: Nirmala John, Varaprasad Janamala, Joseph Rodrigues

Due to numerous operational restrictions and economic purposes, optimal load management for energy balance in the smart grid (SG) is one of the compensating responsibilities. This research provides a novel multi-objective optimization technique for attaining energy balance in SG, with the goal of avoiding fines due to excessive upstream network power extraction beyond contractual demand. Due to a lack of capacity to create the whole optimization towards the global optimum after each run, optimal load control (OLC) is a prevalent challenge. Adaptive-TLBO, the most recent variation of Teaching Learning Based Optimization (TLBO), comprises both alterations during the exploitation and exploration phases (ATLBO). Because the ATLBO is used on a modified IEEE 33-bus system, the results obtained in this mode are extraordinary. The energy balance has improved in addition to the enhancement of the voltage profile and the reduction of distribution losses. As evidenced by comparisons with PSO, basic TLBO, backtracking search algorithm (BSA), and cuckoo search algorithms, the suggested ATLBO algorithm has precedence over any other proposed algorithm (CSA).

260

Authors: Anthony Anggrawan, Mayadi, Christofer Satria, Lalu Ganda Rady Putra

Providing scholarships to students is an important action in helping students succeed in their studies. The scholarship recipients are students who meet the specified criteria. However, it is not easy to decide which scholarship recipients are genuinely eligible to receive scholarships, especially if the selection process for prospective scholarship recipients is done manually. Many prospective scholarship recipients must be selected, and more than one criterion is required. That is why it is not surprising that awarding scholarships to selected candidates takes a long time and the accuracy of the results is dubious. It means there needs to be a system that can assist in choosing prospective students who meet the criteria for the best requirements as scholarship recipients. Therefore, the purpose of this research is to build an application system that can provide recommendations for eligible candidates for scholarship recipients based on consideration of the criteria possessed by prospective scholarship recipients. This research method is an experimental study using a combination of the Analytical Hierarchy Process (AHP) and the Multi-Objective Optimization Method by Ratio Analysis (Moora) method to rank scholarship recipients. The study results indicate that using an application system integrated into the database in recommending prospective scholarship recipients according to the desired conditions is more accurate using a combination of AHP and Moora methods (up to 94.07%) than using only one Moora method (which is only 89.62%). This means that the selection of scholarship recipients according to the desired qualifications using the AHP method for weighting and the Moora method for ranking is a combination of methods that have relatively more reliable accuracy.

287

Authors: Salwa Khalid Abdulateef

The image vectorization based on geometrical contour representation is an active research topic. It involves approximating the contour of the objects inside the image using geometrical definition or estimation. Typical approach of estimating the parameters of the geometrical model is fitting-based estimation. However, the noise that exists in the image causes degradation in the performance. In order to overcome this degradation, we propose integrating evolutionary based optimization. In this article, the design of the operators of the genetic optimization for improving the contour detection results of segments-based estimation of the contour is proposed. The operator includes both crossover and mutation. The evaluation shows that Structural Similarity Index Metrics (SSIM) measure has increased after applying it from 0.64 to 0.77 on UEC Food 100 Dataset and from 0.54 to 0.66 after applying it to UEC Food 256 Dataset.

307

Authors: Mays D. AL-Bayati, Duraid Y. Mohammed, Mohammad Sarfraz

Arrhythmia identification using electrocardiograms (ECGs) is a critical component of biomedical signal processing and pattern recognition. The classification of ECG arrhythmias is presented in this article using features extracted from the Extreme Value distributor of the probability density function (PDF), also training model built using the Alexnet architecture of a conventional neural network (CNN). A continuous wavelet transform (CWT) was used to convert the features space to a two- dimensional image. Additionally, a comparison of the two approaches was conducted. In the first case, we used CWT to convert ECG data to two-dimensional images. In the second case, features were extracted from PDF files and converted to the image domain using a CWT. With a learning rate of 0.001 and batch size of 250, the accuracy of tests was 98.67 %. The results indicated that the proposed method is more accurate than the conventional method when it comes to feature extraction.

329

Authors: Korawitch Kaiyawong, Keerati Chayakulkheeree

This paper proposes a mixed-integer particle swarm optimization (MIPSO) for coordinated optimal placement of energy storage system (ESS) and capacitor bank (CB). In the propose method, optimal ESS scheduling (OESSS) is solved by particle swarm optimization (PSO), as a subproblem, the optimal coordinated placement (COP) for ESS and CB, simultaneously. The distribution system annual loss minimization (DSALM) is used as the objectives of COP problem. The proposed method was tested with the IEEE 33-bus radial distribution test system. The results demonstrated that the proposed method is successful and robust in minimizing system losses, which loss saving of 35.12% when compare to the based case, which is the best solution among other existing methods.

348

Authors: Hayder M. A. Ghanimi, Shaymah Akram Yasear

The global pandemic (Covid-2019) has severely affected all aspects of our life and even changed the way we live and work. As the pandemic outspread, healthcare professionals need urgent interventions to control the harmful consequences. Encountering such a crisis makes them more prone to negative psychological ramifications in their work environment, making them unable to provide the proper support. Burnout is the most negative feeling increased among healthcare professionals while compacting the virus. One essential move to remedy the impacts of burnout is understanding its determinants and their causal relationships. This paper addresses the design of a computational (cognitive) agent model of burnout for healthcare professionals using a temporal-causal network model. Several determinants of burnout with their causal relationships were identified from the literature and formalised to construct the proposed cognitive agent model. In addition, different simulation experiments were implemented to obtain a clear insight into the causal relationships among burnout determinants and those experiments are exhibited similar behaviours to exiting literature. Furthermore, the developed model was evaluated using two different methods: mathematical analysis to prove its implementation was achieved right and automated logical verification to check several properties as shown in the literature to confirm that the suitable model was built. The obtained cognitive agent model could be helpful to develop a covid-19-aware analytics software agent that can monitor healthcare professionals' mental health.

371

Authors: Tossapol Kiatcharoenpol, Sakon Klongboonjit

A reliable and sensitive technique for predicting quality of a plastic work-piece produced in injection molding process is essential help for practicing engineers. A system based on the process parameters that can estimate both two prime characteristics, %volume shrinkage and warpage of work-piece before it produced is significantly beneficial. In this paper, a fast feed forward network, Hybrid Neural Network (HNN), is proposed to construct the predictive model for those two quality characteristics. The unique algorithm of HNN based on the optimization of the weights of each layer is changed to a linear problem by linearization of the sigmoid functions. As iteration procedure used in Backpropagation algorithm is eliminated, the network training time is significant reduced. With this fast convergence of using HNN, the intelligent predictive model for injection molding process that can learn online is possible for further study. To entitle the network to cater for various process parameter conditions, a knowledge base as training and testing data have to be generated on the experimental data in a comprehensive working range of a plastic injection molding process. Consequently, the experiments were performed in 256 conditions based on the combination of nine basic process parameters. The neural networks were trained and the architecture of networks was appropriately selected by benchmarking the Root Mean Square error (RMS). The results of the novel network, HNN, have shown the ability to accurately predict the percentage of volume shrinkage with the 1.02% and 4.87% error at training and testing stages, respectively and for warpage with the 3.76% and 2.47% error at training and testing stages, respectively. These accuracy results are similar to those of backpropagation neural network (BPNN), but HNN has shown the superior fast converging about 38.5% and 66.7% over than those of BPNN.

394

Authors: Malikhah Malikhah, Riyanarto Sarno, Sozo Inoue, M. Syauqi Hanif Ardani, Doni Putra Purbawa, Shoffi Izza Sabilla, Kelly Rossa Sungkono, Chastine Fatichah, Dwi Sunaryono, Arief Bakhtiar, Libriansyah, Cita RS. Prakoeswa, Damayanti Tinduh, Yetti Hernaningsih

Viral respiratory infections are the most common diseases suffered by all age groups worldwide. The gold standard for diagnosing viral respiratory infection is through the molecular method, but this diagnosis is expensive, requires sophisticated equipment, can only be performed by well-trained medical staff, and is painful. Volatile Organic Compounds (VOCs) are compounds released from the human body that can be a marker of disease and based on numerous studies it also contains VOCs. An electronic nose (E-nose) is a device that can be used to identify disease. This study proposes a new approach for the detection of viral respiratory infections through sweat from the armpit using an E-nose consisting of 5 semiconductor gases and a single-board computer. Several statistical parameters are used to obtain features and the detection algorithm used is Fully Connected Deep Network (FCDN). Several sizes of hidden layers were tested to obtain the best FCDN model. This study also proposes the selection of the best FCDN model which is a trade-off between complexity and accuracy, so that the model stored in E-nose is a model that not only has good accuracy but is also not too complex. The experimental results show that using 29 statistical parameters and 2 hidden layers generate the highest accuracy of 0.940 for the detection of 2 classes, namely positive and negative, with sensitivity and specificity of 0.967 and 0.915, respectively, where the best FCDN model has a total of 90,561 parameters.

414

Authors: Ravindar Mogili, G. Narsimha

Abnormal behaviour of the heart called arrhythmia can be recorded as an electrocardiogram (ECG) signal. ECG plays a vital role in the diagnosis of heart disease. Advances in machine learning allow the development of computer-aided diagnostic models to identify heart disease type. We have proposed a hybrid model with Convolution Neural Network (CNN) to auto extract features from ECG and use XGBoost to assess the type of arrhythmia. We tested our model to diagnosis eleven kinds of arrhythmia beats from the MIT-BIH arrhythmia database and obtained overall sensitivity of 92.61%, specificity of 99.85%, positive predictive value of 95.99% and accuracy of 99.84%. The robustness of the proposed model is further confirmed by classifying the arrhythmia beats into 5 classes according to the AAMI standard and comparing the results with the state-of-the-art methods. Attained overall Sensitivity of 94.36%, specificity of 99.44%, positive predicative value of 96.40% and Accuracy of 99.69% for 5 AAMI Classes. The results demonstrated that the proposed model could be used in the diagnosis of arrhythmia.

441

Authors: Rahmad Hidayat, Agus Harjoko, Aina Musdholifah

An image retrieval system is required to provide high accuracy in a short time. Combining various features will usually increase accuracy but also increase retrieval time. This study developed a CBIR (Content-Based Image Retrieval) method based on hierarchical clustering on low-level features. Low-level features consisting of color, texture, and shape are extracted and then clustered hierarchically. The resulting clusters are then validated to obtain their optimal number. In the retrieval process, the query image features are extracted and compared with the cluster centroid on each feature. The scores of query results on each feature are normalized, and then the normalized scores are weighted to get the total score. The experiment was carried out using three datasets, namely DIKE20, Corel-1k, and Corel-10k. Based on the experimental result, the proposed method shows better performance compared to the existing state-of-the-art method. On the Corel-1k and Corel-10k datasets, the proposed method obtained precision scores of 0.81 and 0.62, respectively.

464

Authors: Aicha El Filali, El Habib Ben Lahmer, Sanaa El Filali, Mohammed Kasbouya, Mohammed Amine Ajouary, Saadia Akantous

The covid 19 has caused a strong health and economic crisis, where the business environment is turning into a great uncertainty and customer demand has become more and more fluctuating. As a result, demand forecasting as well as the task of predictive analysis, holds a great attention in the supply chain, in order to meet the needs of customers, avoid any out of stock, and at the same time avoid a waste of resources. In this paper, we propose a deep learning method based on long-term memory multilayer networks (LSTM) for demand forecasting. Using the grid search method, our method has the ability to automatically select the most optimal forecasting model, considering different combinations of LSTM hyperparameters of a time series. The proposed model has shown strength in capturing the existing nonlinear features in time series data compared to some known time series forecasting methods derived from statistical and machine learning approaches, using historical sales data of a Moroccan pharmaceutical manufacturing company. These methods include exponential smoothing (ETS), autoregressive integrated moving average (ARIMA), recurrent neural network (RNN). The evaluation of the proposed method and the comparison methods was performed using the root mean square error (RMSE) and Symmetric mean absolute percentage error (SMAPE). The comparison of the test results showed that the proposed method is the best performing with RMSE of 4487.32 and SMAPE of 0.026 much better than those obtained by the other models.

494

Authors: Asha Kethaganahalli Hanumanthaiah, Manjunathswamy Byranahalli Eraiah

Multimodal biometric systems are a natural evolution of traditional biometric authentication systems with use of multiple biometric to enhance the security level. Presentation attacks in form of photos and replay videos are becoming a threat to the biometric authentication systems and it becomes necessary to prevent from these attacks for an enhanced security level. This work proposes a challenge response based multi modal biometric authentication resilient to presentation attack. The proposed system is designed for multi modal biometric involving face and iris biometrics with challenge in form of arbitrary emotion invoking image at a random position on the screen. The changes in region of interest around the Facial landmarks and distance of iris to Facial landmarks are captured as response. The response is then matched with expected response derived based on the position of arbitrary emotion invoking image on screen to detect presentation attacks. In addition the work also proposes a hybrid deep feature which provides an addition security in recognition process to ensure authentication failure for fake samples. The error rate in leakage of spoofs is very low in proposed solution (less than 1%) in different environments compared to existing works. The recognition accuracy of face is atleast 1% higher and atleast 2% higher for IRIS in proposed solution compared to existing works.

521

Authors: Arjun Singh Saud, Subarna Shakya

Although some studies employed technical indicators as input to machine learning models to forecast stock trading signals, there was a noticeable gap between how technical analysts and machine learning professionals used technical indicators. Based on this finding, this study suggested a know sure thing based machine learning (KST-ML) technique for anticipating stock trading signals and compared its results to KST-based trading and Buy-Hold strategies. The main idea behind the KST-ML strategy is to learn and then predict stock trading signals from the relationship between KST indicators such that more accurate predictions can be made. The strategies were assessed based on their annual rate of return (ARR), Sharpe ratio (SR), and percentage of profitable trades executed. In addition, the proposed strategy's performance was compared to that of several intelligent stock trading strategies proposed in the literature. The proposed method clearly outperformed the other two strategies in terms of all three evaluation measures. With a 5.6 SR value, the strategy generated a 67.73% ARR and made 79.38% profitable trades. The results were much better than the results from the KST-based and the Buy-Hold strategies. Furthermore, we found that the annual returns generated by the KST-ML approach were significantly higher than those generated by other intelligent techniques reported in the literature.

543

Authors: Ahmed H. Mohammed, Ahmed Kareem Shibeeb, Mohammed Hussein Ahmed

Recently, the proliferation of the Internet of Things (IoT) services and applications has prompted several governments to deploy many cameras in critical sites, such as embassies, military institutions, and ministries. The images must be encrypted to protect data shared between IoT sensors/devices and embedded subsystems. Often, IoT devices are smaller and less powerful. A lot of traditional encryption methods are computationally inefficient. They require many rounds of coding, which takes up much space on a device. Despite this, this paper has developed a lightweight encryption algorithm based on chaos theory suitable for IoT devices with limited processing power. The confusion-diffusion structure is often divided into two independent components in chaotic image cryptosystems. However, it reduces the security of the cryptosystem since the independent design may be subjected to cryptanalysis individually. This paper presents an efficient chaotic image encryption algorithm based on a plaintext-associated approach. It incorporates confusion-diffusion operation to increase encryption reliability. The Zaslavsky map initial values are determined by the values and locations of an input image and random values to resist chosen-plaintext cryptanalysis. Furthermore, the proposed method uses a simultaneous confusion-diffusion process to resist any separate attack. The simulation and experimental analysis demonstrate that the proposed encryption algorithm has 0.3927 seconds as a fast encryption time. Moreover, it provides a high performance compared to several other chaotic-based image cryptosystems. as measured by the ‎histogram, entropy (‎7.9972‎‎3), ‎ pixel change rate (‎99.7194‎), unified average changing intensity (‎33.4635‎) within the critical interval, pixel ‎correlation, global and local entropy, CDR (ciphertext difference rate), and gray difference degree (GDD). It also has ‎higher security than other chaotic-based image cryptosystems.

563

Authors: Kiattisin Kanjanawanishkul

Eri silkworm pupae are well known as an alternative for a protein food source. At present, they are sold as canned food for long-term preservation. Therefore, good quality and size consistency are essential. To evaluate quality and size, an image-based grading method was proposed. The image of pupae was captured and then shape features (i.e., solidity, aspect ratio, and extent) and color features based on three color models (i.e., RGB, HSV, and L*a*b*) were extracted. Two neural networks with 10-fold cross validation were separately developed for shape evaluation and color evaluation. After misshapen and discolored pupae were identified by neural networks, remaining pupae were graded into five size numbers according to their length: very small, small, medium, large, very large. Experimental results showed that the average accuracies for shape evaluation and color evaluation were 99.64% and 99.58%, respectively. The accuracy for size evaluation was 94%. Therefore, the proposed grading method reduces sorting time and increases sorting accuracy.

584

Authors: Umar Umar, Faanzir Faanzir, Firdaus Firdaus, Indri Suryawati, Muhira Dzar Faraby, Adi Soeprijanto, Ontoseno Penangsang

The interconnection of single-phase households DG into the existing grid requires appropriate anticipation to ensure the quality and reliability of the existing system. This paper deals with the optimal placement and sizing of multi single-phase DG in unbalanced distribution systems for power loss reduction and voltage improvement while maintaining voltage unbalance and harmonic are in the acceptable limit. Optimal location and sizing of DG are determined simultaneously using the improvement version of Symbiotic Organisms Search (SOS), called Dual-Phase Parasitism SOS (DPP-SOS) with a crossover operator. DPP-SOS consists of original parasitism (OP) and random weight parasitism (RWP). DPP-SOS was programmed using MATLAB software and validated using 26 benchmark functions. To demonstrate the effectiveness of this method in reducing losses, the DPP-SOS is also tested using 7, and 25 bus test systems. Result show that DPP-SOS with a crossover operator has been successful in determining the location and rating of single-phase and three-phases DG simultaneously. DPP-SOS has an average convergence speed of 24.08% faster than the basic SOS. The simulation result also shows that multi single-phase DG is effective in reducing power loss, increasing voltage, and reducing voltage unbalance in the distribution system.