International Journal of Intelligent Engineering and Systems

14

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

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

35

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

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

64

Authors: Gouri Bompem, Nagaraju Chiluka, Dhanalakshmi Pandluri

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

92

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

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

115

Authors: Waleed Kareem. Ahmed, Rana Saad Mohammed

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

142

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

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

166

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

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

192

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

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

215

Authors: Jaafar Ahmed Abdulsaheb, Dheyaa Jasim Kadhim1

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

240

Authors: Mazin Ismael Al-Saedi

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

268

Authors: Purba Daru Kusuma, Meta Kallista

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

291

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

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

319

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

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

343

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

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

375

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

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

401

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

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

427

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

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

454

Authors: Keerthi Prasad Ganeshaiah, Vinay Hegde

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

477

Authors: Ammar Abdulsalam Al-Asadi, Mahdi Nsaif Jasim

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

497

Authors: Aruna Kumar Bananaik Thulajanaik, Naganaik Manjanaik

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

517

Authors: Kavitha Chandrashekar, Anitha Tuluvanooru Narayanreddy

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

536

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

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

561

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

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

580

Authors: Rana H. Saloom, Hussein K. Khafaji

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

607

Authors: Ajay Mamindla, Ramadevi Y

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

13

Authors: Omaima El Alaoui-Elfels, Taoufiq Gadi

Capsule Network has proved its performance against image deformation, through its ability to preserve space relations among features. However, the Capsule Network underperforms at classifying complex images datasets. Through an in-depth examination of Residual Neural Network, we present a novel Capsule Network named ER-caps to improve the model performance toward complex images classification and reconstruction. The architecture of ER-caps uses customized residual blocks to enhance feature extraction without using any pooling methods to conserve spatial information among features and preserve the Capsule Network equivariance. Our model adopts the RELU and the ELU activation function to ensure fast convergence and uses an advanced decoder to allow better reconstruction for complex images. The experiments show that the suggested model outperforms the baseline Capsule Network with dynamic routing on a variety of benchmark datasets CIFAR-10, CIFAR-100, and SVHN by approximately: 17 %, 9 %, and 4 % respectively. Moreover, ER-caps reduces the number of training parameters.

38

Authors: Sudhir Dagar, Geeta Nijhawan

Robust Principal Component Analysis (RPCA) has recently been active everywhere for dimension reduction in image processing, display and pattern recognition. Methods based on low-rank sparse representations, which make some specific significant assumptions, have recently received a lot of attention in background modelling. Meanwhile, a powerful analysis framework is needed to handle background areas or foreground motion at various scales. this paper presents a hybrid approach along with total variation L1 (TV-L1) features and reproductive RPCA model in low rank background subtraction modelling and sparse matrix with mixture of Gaussians (MoG) as foreground modelling. The hybrid structure with TV-L1 features imposes a hierarchical RPCA on the singular values of the low-rank component and MOG sparsity indicators. The proposed work was evaluated on the CDnet2014 (ChangeDetection.net) dataset, obtained result as accuracy was 92.9%, 86.7% 95.7% for Highway, Escalator, and Indoor respectively. The proposed method is compared with traditional methods and obtained relative reconstruction error is 0.01529 as a lower side.

67

Authors: Karthik Sekar, Rajdeepa Balasubramanian

Software code clones (CC) in software programs are degrading the performance of software systems. many code clones detection (CCD) methods proposed in the literature detect only individual cloned types efficiently. This paper proposes a collaborative code clones detection (CCCD) method by utilizing lexical, syntactic, semantic and structural features for effectively identifying all types of clones including type-4. Initially, a large variance mapper (LV-mapper) is utilized to identify clone pairs (CPs). Then CPs are converted into lexical features by directly applying Word2vec. The synonyms of CPs are obtained using the WordNet tool and converted as semantic features by Word2vec.Additionally, code size metrics (CZM) and object oriented metrics (OOM) are additionally measured as structural features of a program’s code blocks (CBs). The syntactic features are extracted through the abstract syntax tree (AST) from the source code. Finally, the joint feature vector is generated by combining all the features together. In order to detect CCs in any new software, the joint feature vector of known clone type source codes is generated first (training set) and then the joint feature vector of unknown clone types source codes is generated next. The Euclidean distances between training and testing of joint feature vectors determine the clone type of test features. Finally, the experimental outcome demonstrates that the proposed CCCD technique has an accuracy of 87.8%, 92.3% and 95.5% for the dataset Apache Maven 3.8.3, Appache ant 1.10.12 and Opennlp-master 1.9.1, respectively, compared to the existing LV-CCD, ES-CCD, TBCNN-CCD and CPVDetector methods.

91

Authors: Manohara M, V. C. Veera Reddy, Vijaya Kumar M

Radial distribution systems (RDSs) can work better from a technical, economic, and environmental point of view if they have the right mix of renewable energy-based distribution generation (DG) and reactive power sources like and distribution-static compensator (DSTATCOM). In this paper, the Northern Goshawk Optimization (NGO), a new meta-heuristic that mimics the hunting behaviour of the northern goshawk, is introduced as a way to figure out where and how big different types of active and reactive power DGs in RDSs should be. For the suggested multi-objective function, NGO is used to find the best places and sizes for photovoltaic systems (PVs), wind turbine systems (WTs), and DSTATCOMs. The main goals of the suggested method are to reduce distribution losses, improve the voltage profile, increase the voltage stability margin, and reduce greenhouse gas (GHG) emissions. Five different case studies are simulated to figure out how well the suggested method works with computers. Simulations use IEEE 33-bus feeder. When compared to the literature and other heuristic algorithms, the results show that NGO works. The computational efficiency of NGO is compared for four cases: (i) only PV ((ii) only WT (iii) only DSTATCOM and (vi) simultaneous PV, WT and DSTATCOM allocation. The results also show that the proposed NGO much efficient than other algorithms and also improves all techno-environmental factors and RDS performance in a big way.

110

Authors: Amit Kumar Pandey, Prashant Singh, Dhyanendra Jain, Anupam Kumar Sharma, Ashu Jain, Anjani Gupta

Network Intrusion detection performances are highly affected by imbalance data problems due to the presence of less number of attack information in the dataset. Deep learning models are applied in existing methods to improve the efficiency that has limitations of overfitting problems. The Generative Adversarial Network (GAN) – Bayesian optimization Multi-class Support Vector Machine (BMSVM) is proposed to overcome imbalance and overfitting problems in intrusion detection systems. The Min-Max Normalization method is applied to normalize the input data to reduce the differences in features. GAN model is applied to generate minority class to balance the data instances to train the model. The proposed GAN-BMSVM model is compared with the classical sampling method, optimization, and classifier in the intrusion detection model in terms of Accuracy, Detection Rate (DR), and False Alarm Rate (FAR). The classical sampling methods are Near-miss, SMOTE and Autoencoder; traditional classifiers are KNN, RF, SVM, DNN and LSTM, and classical optimizations are PSO and WOA. The existing researches such as HCRNN, HLD, DONN, FL-NIDS and CNN-LSTM are used to evaluate the efficiency of GAN-BMSVM model. The GAN-BMSVM model has achieved 99.58% and 85.38 % accuracy for NSL-KDD and UNSW-NB15 dataset respectively, which is higher than the existing CNN-LSTM model.

131

Authors: Rana Dhia'a Abdu-Aljabar, Osama A. Awad

Lung cancer is more likely to relapse in the first five years following surgery; even though the operation may have been a complete success, there remains a chance that the lung cancer could return. This return may lead the patient to die after a successful surgery. Because there are no symptoms of lung cancer in its early stage, many researchers use intelligent systems to predict the relapse of lung cancer in its early stages. The outcome of previous works considering this issue still suffers from low prediction accuracy. This study proposed a method to predict lung cancer relapse more accurately. This method has multiple stages: 1st optimization system, feature selection stage, 2nd optimization stage, and extreme gradient boost (XGBoost) classifications stage. It used two datasets (GSE8894 and GSE68465) of a gene expression microarray for NSCLC with its clinical information on relapse state. We obtained three probes (3 genes) with clinical data combinations that can get good prediction results. These genes included 225389_at (BTBD6), 220239_at (KLHL7), and 204832_s_at (BMPR1A). A comparison between the proposed model and the original XGBoost with PSO and Hyperopt as hyperparameter optimization for the XGBoost classification model is performed. Extensive comparisons with four machine learning algorithms, including Deep Forest, K-nearest neighbor (KNN), Support Vector Machine (SVM), and Naive Bayes, are conducted. The proposed model accuracies are 0.93 for the GSE8894 dataset and 0.81 for the GSE68465 dataset.

154

Authors: Ahmed Kamil Hasan Al-Ali, Fadhil Sahib Hasan

In this paper, the security of image transmission through space time block coding (STBC)-coded orthogonal frequency division multiplexing (COFDM) is enhanced using two stage algorithms. In the first-stage, the samples scrambling algorithm using one dimensional (1D) chaotic map is proposed. In the second stage, the bits scrambling algorithm using chaotic map is proposed. The sample scrambling algorithm is designed depending on three types of 1D chaotic maps that are logistic-sine-cosine (LSC), sine-tent-cosine (STC), and tent-logistic-cosine (TLC) maps. In the bit scrambling algorithm, the stream bits are XORed with a pseudo-random bit generator (PRBG) produced by these chaotic maps using threshold method. Simulation results demonstrate that the image is retrieved with high quality for STBC-COFDM system and low signal to noise ratio (SNR). The image is unrecovered when the correct key is unavailable. For one level of security, the system using TLC chaotic map has the best performance compared with other chaotic maps. The bit error rate (BER) performance of the suggested system achieved 10-4 at SNR=1, and 2 dB for (TLC, LSC) and STC, respectively. For two stages of security, the proposed system using STC chaotic map improves the BER performance with a gain of about 2 dB at BER=10-4 compared with one stage of security.

173

Authors: Maysam Alwan Hasson, Taha Mohammed Hasan, Zaidoon Kamil Maseer

A numeral of deep learning models has been suggested for COVID-19 examination in computed tomography (CT) scans as an automation tool to help in diagnosis. Although, deep learning models achieved high accuracy, but training approaches are still inefficient to detect injections due to some deep learning models did not meet the requirement of a generalization term in deep learning. Furthermore, other traditional algorithms achieved low detection for 3D CT. Therefore, is high time to develop a deep learning model to diagnose COVID-19 infections in a regularization mode. In this research, greedy learning approach (GLA) is utilized to design and implement the 3D convolutional neural network (3D CNN) model, greedy learning approach is consistings of two stages; the first stage generates many 3D CNN models based on the randomness in the layers, for providing many movements toward solving one problem which is diagnosing COVID-19.Then, the second stage selects an optimal 3D CNN model based on high accuracy of 3D CNNs obtained in the first stage, optimal 3D CNN model is to be significate solution among them. We evaluate the proposed approach on the 3D Mosmed-1110 and 2D SARS-CoV-2 CT Datasets, the best accuracy scores obtained by the present approach are 1.00% and 98.87% respectively on the said datasets in terms of metrics, such as accuracy, precision, recall, and F1. The proposed system also exhibited good generalization and robustness, when it was trained and tested using a portion of data (80%) and (20%).

202

Authors: Sumanth Yamparala, L. Lakshminarasimman, G. Sambasiva Rao

One of the wind energy industry's fastest-growing technologies is the doubly fed induction generator (DFIG). Wind energy resources (WER) must be connected to the network both during and after grid voltage disturbances, according to the most recent grid codes. For improving the low-voltage ride through (LVRT) capability of DFIG without an additional hardware equipment during voltage sag, a control strategy is developed in this research, which is based on fractional order proportional integral derivative (FoPID) controller. A sea Lion adapted grey wolf optimized (SLnO+GWO) hybrid algorithm is employed for optimal selection of controller parameters. Consequently minimizes the variations in active, reactive powers, rotor current harmonics and dc link voltage in DFIG. Furthermore the proposed method is simulated in MATLAB/SIMULNK and has 2.56% THD in rotor current whereas existing methods has more. The performance of chosen method is compared with the methods available in literature RTO, MEHRFA, GSA and is found to be superior.

224

Authors: Purba Daru Kusuma, Astri Novianty

This research introduced a novel simple metaheuristic called as total interaction algorithm (TIA). TIA is a swarm intelligence which relies on the interaction among solutions in the population. The core and distinct concept of TIA is that each solution interacts with all other solutions in every iteration to find the best possible solution. Then, this new alternative replaces the current solution if it is better than the current solution. In this research, TIA is tested to solve 23 functions that represent both unimodal and multimodal problems. TIA is benchmarked with five metaheuristics: particle swarm optimization (PSO), marine predator algorithm (MPA), golden search optimizer (GSO), guided pelican algorithm (GPA), and driving training-based optimizer (DTBO). The result indicates that TIA is superior to other benchmark metaheuristics, especially in solving the high dimension functions. TIA is better than PSO, MPA, GSO, GPA, and DTBO in 22, 21, 16, 11, 13 functions. The result also indicates that the increase of the maximum iteration improves the performance of TIA mostly in solving high dimension unimodal functions. Meanwhile, the increase of the population size is less significant to improve its performance. Overall, this research resumes that the interaction with as many as possible individuals is proven better than with only selected individuals as implemented in many other metaheuristics.

246

Authors: Hind A. majeed, Ahlam M. Kadhim, Hazim G. Daway

The enhancement of colour medical microscopy images plays an important role in biological fields. Colour medical microscopy images have the lack of contrast and lighting. In this paper, a robust algorithm for enhancing microscopy images was proposed. In the proposed method, the image was first improved by using Contrast-Limited Adaptive Histogram Equalisation (CLAHE). Then, the luminance channel in the colour space was enhanced on the basis of sigmoid transformation and fuzzy logic. Data containing 50 medical microscopy image forms were studied, and the suggested method for enhancement was compared with several other methods, such as principal component analysis using the reflection model, fuzzy logic by stretch membership function, CLAHE, modified colour histogram equalization, contrast-enhancement based on median–mean based sub image-clipped histogram equalization and retinex method by colour restoration. The collected results illustrated that the suggested method gets excellent quality averages in terms of entropy value (7.96), average gradient (14.57) and mean of standard division (62.80).

265

Authors: Tody Ariefianto Wibowo, Nana Rachmana Syambas, Hendrawan, Leanna Vidya Yovita, Ade Aditya Ramadha

Due to named data network (NDN) 's advantages as a content-based architecture, it is a strong candidate for future internet architecture. Extensive research has been done on NDN implementation in the real world. However, the method widely used has advantages and disadvantages in reaching the goal. Simulation gives simplicity but is unlikely to provide reality in node implementation. The testbed method is close to real-world performance but needs higher effort. To solve the problems, building an environment with nature closer to the implementation world is required. The method also gives ease of use and observation, like simulation/emulation. Due to these needs, we proposed a framework that combines the advantages of emulation and a testbed that occupies a UNetLab-based virtualization environment for extensive observations for the NDN researcher. We build a test case in the PNETLab and Mini-NDN for gives a perspective on how our proposed method works. PNETLab framework evaluates NDN network’s nodes performance, giving 7,59% in RTT delay, and 0,89% in nCHR deference result to Mini-NDN, and provides intensive hardware performance parameters result which is very important for NDN implementation.

290

Authors: Rana H. Saloom, Hussein K. Khafaji

Numerous variables, such as changes in the environment, toxins, spontaneous mutations, and replication mistakes, can have an impact on DNA. It is known as a gene mutation when this occurrence permanently alters the DNA sequence that forms a gene, changing it from the sequence seen in most people. Alleles, which are small variations within the same gene, are produced as a result of mutations. Each person is different due to these minute variations in their DNA sequence. In particular, some mutations affect just the carriers, whilst others affect both all children and the carrier organism's progeny. Changes in the DNA sequence (genetic mutations) are one of the reasons that lead to life-threatening disorders such as cancer and other diseases, so it has become necessary to detect these mutations early and know their types and their impact on the DNA sequence The DNA modifications developing in the cells of the following generation are what bioinformatics is most concerned about. In this paper, an efficient approach based on the architecture of long-term memory (LSTM) recurrent neural networks is presented. The method involves locating mutations using the Needleman algorithm that compares the reference DNA sequence with the mutant sequence. The deep neural network is then trained on the Cancer Cell Lines portal (CCLE) dataset to classify the different types of genetic mutations that have been identified. The algorithm reports the mutation type and locus for each pair of DNA inputs. Finally, the simulation output demonstrates the effectiveness of the algorithm. By testing the method, it was found that it was able to identify the mutation type with 100% accuracy.

314

Authors: Oger Amanuel, Yarub Alazzawi

It has been shown recently that there is a need to design smart structures, such as smart houses, in order to be controlled in different ways. That will be in high demand due to its usefulness for some people who are incapable of reaching some control units that require direct interaction with human beings. In this paper, we propose and develop a new enhanced electroencephalography (EEG)-based smart structure setup that can be utilized to assist people, with or without disorders, to control devices in an easy and comfortable way. Ten people of a wide range of ages (20–65) and both genders actively participated in this research. Consequently, eight EEG channels are employed in this study to cover most of the brain’s regions, and the protocol utilized is suitable for people with disabilities and immobility. Finding the standard or common features for the wide range of participants is a challenge. To mitigate this, reconstruction independent component analysis (RICA), which is a modified technique of the conventional independent component analysis (ICA), was used to obtain the optimum features. In addition, the proposed modified support vector machine (SVM) model classifies the selected features into different classes with the capability of removing the high noise and overlaps that cause misclassification. The identified classes are responsible for actuating the smart house’s actuators based on participant status. Real-time classification of multi-channel EEG data into brain wave components, visualization of results, and control of the devices are carried out using MATLAB and an embedded system. With the proposed model, there is only one case of overlap between the classes, compared with 74 cases with conventional SVM. Consequently, the results of misclassification reach zero, and the proposed model enables control of a smart room based on brain waves, achieving an overall accuracy of 98%. With future improvement, the acquired findings would urge the usage of the suggested EEG-based smart structure, which might be helpful for immobile individuals.

339

Authors: Komagal Yallini Shanmuga Sundaram Kanagaraj, Mahendiran Nallappan

In data engineering, multi-label learning (MLL) has emerged to classify the instances through a specific characteristic that associates with the set of class labels (CLs). Mostly, the learning design was adaptive and newer views may exist in a data stream (DS); so, MLL has to classify the features with newer CLs. To tackle this issue, the MLL with emerging new labels (MuENL) and handling high-dimensional DSs (MuENLHD) technique has been adopted which considers the CLs in the test data were similar to that in the learning data. But, it was not able to deal with the adaptive situation where multiple newer CLs exist since it can manage only a single newer CL in one iteration. Hence this article proposes an MLL with emerging multiple new labels (MuEMNL) and MuEMNLHD versions to combat the issues in a complex scenario wherein several newer CLs are found. The main idea of this technique is to split the newer CL group into many newer CLs independently for changing the complex scenario. In this technique, four different steps are executed: i) creates a linear classification model to adjust the pairwise CL sorting error and the categorization error on the given CLs, ii) develops a novel outlier identifier depending on the primary and test DS, iii) discovers the cluster for the MuEMNL and MuEMNLHD depending on the OPTICS clustering and iv) employs a classifier updating scheme to integrate newer CLs for designing a robust classifier. Finally, the experimental outcomes exhibit that these techniques on low-dimensional databases attain an overall mean precision of 67.08% and an overall F1-score of 64.02% compared to the classical MLL techniques. Similarly, these techniques on high-dimensional databases achieve mean precision of 64.6% and a mean F1-score of 63.1% compared to the classical MLL techniques.

364

Authors: Priyanka Y, Raghu Ram

The distribution systems act as the primary connecting point between the power utility and the end users of the electricity. It is vital to assess the behaviour of the network when subjected to a variety of various types of loads and any new D-FACTS devices that may be developed in the near future, in order to improve the operation and planning of electrical distribution systems. The authors of this article performed an analysis of the distribution system when unified power quality conditioner (UPQC) was present and acting as a Distribution FACTS device. The main objective of this article is to minimization of total investment cost of UPQC as well as system operating considering distribution network reconfiguration (DNR). For optimal solution, an improved whale optimization algorithm (IWOA) has been introduced. Besides, impact of voltage dependent load models has been studied. In order to evaluate the practicability and usefulness of the suggested strategy, a number of potential outcomes involving the DNR and allocation of UPQC under a voltage dependent load model have been considered. The active and reactive power losses have been reduced by 44.67 % and 37.42 %, respectively, as compared to conventional schemes (such as UPQC allocation solely). In addition, the proposed methodology results in cost reductions of around 33.68 % annually.

388

Authors: Mustafa A. Kamoona, Omer Cihan Kivanc, Oday A Ahmed

An energy management system (EMS) for hydrogen fuel cell hybrid electric vehicles (FCHEV) based on artificial intelligent (AI) technique is presented in this paper. In order to achieve a fast dynamic response and maintain high efficiency of energy storage resources, the fuzzy logic controller (FLC) and artificial neural networks (ANNs) are utilized for purpose of intelligently managing the system's power flow. Moreover, the feed-forward wavelet neural network linked with proportional-integral (PI) controller named (WNN-PI) and the recurrent wavelet neural network linked with PI controller named (RWNN-PI) are tuned by particle swarm optimization (PSO) algorithm, both are aimed at the operating of the resources at high efficiency with respect to their mechanism performance, meeting the load power demands efficiently, and reducing hydrogen usage. Finally, a comparison of the simulation outcomes is presented to choose the best of the proposed AI controllers where the results showed optimum power flow between power sources and load power of FCHEV then as consequence, the BAT and UC are run in a safe manner and extend their lifetime, also, the average efficiency of the FC stack has been increased, and the amount of usage of hydrogen fuel is reduced. The simulation of AI EMS has been carried out by MATLAB/Simulink R2022a, with various vehicle driving cycles by the advanced vehicle simulator (ADVISOR).

410

Authors: Hussein Fouad Almazini, Ku Ruhana Ku-Mahamud, Hassan Almazini

Anomaly detection deals with identification of items that do not conform to an expected pattern or items present in a dataset. The performance of the various mechanisms that are employed to execute anomaly detection is strongly dependent on the set of features that are utilized. Thus, not every feature in the dataset may be employed in the classification operation since certain characteristics may result in poor solution quality. Feature selection (FS) may reduce the size of high-dimensional datasets by eliminating unimportant features. Modified binary grey wolf optimizer (MBGWO) is a successful metaheuristic that has been used for FS in anomaly detection. Nonetheless, the MBGWO is a randomization population-based algorithm that has an issue in finding a good quality solution during the initial population procedure. Thus, this study proposes a heuristic modified binary grey wolf optimizer (heuristic MBGWO) algorithm for FS in intrusion detection to enhance the initial population of the MBGWO using a heuristic-based ant colony optimization algorithm (ACO). The heuristic MBGWO algorithm was evaluated on NSL-KDD benchmark dataset from the University California Irvine (UCI) repository against five (5) benchmark metaheuristic algorithms. experimental results of the heuristic MBGWO algorithm on the NSL-KDD dataset in terms of the number of chosen attributes and classification accuracy are superior to other benchmark optimization algorithms, where it obtained the best features with 99.85% classification accuracy. The proposed heuristic MBGWO algorithm can be used for FS in anomaly detection tasks that involve any dataset size from various application domains.

435

Authors: Sreenivasulu Ummadisetty, Madhavi Tatineni

The absence of P waves through electrocardiogram (ECG) tracing causes atrial fibrillation (AF) affecting around 1% of the global population. In recent years, wearable and portable devices have made mobile healthcare much closer to reality. The main purpose of this article is to develop an automatic AF detection system based on short single lead ECG signals. Also, AF is one kind of arrhythmia that change the rhythms in the heart and have the potential to alter the characteristics of morphology in ECG tracings. For feature extraction, heart rate variability (HRV) and frequency analysis are adopted. The novel contribution of this work is to deploy a modified Moth Flame Optimization algorithm for detecting AF in a short ECG recording. The obtained results were validated with the available public data set comprised of short ECG recordings by genetic algorithm and modified moth flame optimisation algorithm. For N versus A classification, accuracy varies from 94.2% to 97% under noise levels ranging from 0 to 30 dB. For N versus A versus O, maximum accuracy of 84.3% is obtained. The obtained experimental results suggest that HRV is efficient and robust for AF detection for relatively short ECG recording.

474

Authors: Syamala Misala, Satya Anuradha Mosa

A compact rectangular dielectric resonator antenna (RDRA) with a surface-mounted short rectangle (SMSR) having an E- a shaped microstrip feed of wide aperture slot investigated for C-band operation in applications of Radar as well as wireless. The surface-mounted short rectangle improved the gain and efficiency characteristics. The size, material, and cost are essential considerations in designing an antenna due to advancements in C-band applications. The RDRA designed on a small-size substrate is 46x46x1.6 mm3 made with FR4. It is rugged and can withstand in all pressure conditions. The proposed design utilizes copper has high conductivity and low cost for SMSR enhancement of return loss up to -47 dB, additionally gain to 9.4 dB, and observed bandwidth impedance of 19.6 % in the wide frequency of (5.75– 7.5) GHz. Furthermore, 3dB beam width attained in E – plane 108.430 whereas in H – plane is 64.360. The proposed SMSR design outcomes the conventional counterparts in all salient parameters, including return loss, impedance bandwidth, Gain, and Radiation efficiency with small size and low cost.

493

Authors: Marouane Rayyam, Malika Zazi

The design of electrical drives capable of tolerating sensor faults has recently become very popular due to their possible use in Fault Tolerant Control systems. This paper introduces a virtual speed sensor design based on a nonlinear state estimation algorithm for fault-tolerant vector control for Induction Motor Drives after speed sensor faults. The rotor speed estimator is realized by integrating the Unscented Kalman Filter (UKF) and the Sliding Mode Observer (SMO) based only on the measurements of the stator current and voltage. This proposal is dedicated to maintain the proper behaviour of the Indirect Rotor Field Oriented Control of the induction motor drive, where it is necessary to switch to the nonlinear estimators as a result of damage to the speed encoder. Simulation studies of the proposed fault tolerant design are performed in the case of total and partial losses of the speed sensor in a closed loop control structure for different operating conditions of the drive system. Fault-tolerant designs based on the UKF and the SMO estimators show good performance in the presence of speed sensor faults in terms of stability, overshoot rate (less than 10%), and trajectory tracking over low and medium speed range. However, the SMO performance is reduced for high-speed operation (⍵r >1400rpm) over the UKF which maintain good performance over the entire speed range.

518

Authors: Alaa Thamer Mahmood, Raed Kamil Naser, Sura Khalil Abd

YouTube platform provides paid/ unpaid video services for different users for promotions, education, learning, information sharing, etc. Accessing this platform's content/ channel-based advancements is based on user identification through registration/ credential sharing. In recent years, accessing information through fake accounts has increased other users' illegitimacy and security demands. The article introduces a temporal behavioral method (TBM) using the classified learning (CL) technique to address the impact of fake users on YouTube-like platforms. The proposed method eyes on user credentials, interactions, and access information under different usage series. The data used for the classification learning identifies the active, inactive, and false searches/ information accessed by the user. With the recent meetings removed, the static and inaccurate searches are segregated using the learning process from the last known access session. Rather than applying new rules to the identified account/user, false user flagging reported data sharing and illegitimacy is all avoided. Based on the different session logins and the recommendations by the learning process, the fraudulent users are restricted from communicating with other users and legitimate information. Maximizing data sharing and user detection is facilitated by the better recognizability afforded by the prolonged inactive session categorization following in succession. The suggested TBM-CL strategy addresses the impact of fake users on YouTube-like platforms and increases the maximums for user detection (14.93%), information sharing (11.3%), and classifications (15.93%) across all sessions. It decreases the erroneous ratio by 8.03% and the delay by 9.07% based on the dataset.

548

Authors: Saja A. Nahoo, AllaEldin H. Kassam

A key field of research for addressing complicated decision problems is multiple criteria decision-making (MCDM), which has grown quickly. The purpose of the paper is to explore the performance appraisal model for educational institutions. In this paper, Analytic Hierarchy Process (AHP) is used to calculate the subjective weights and Shannon’s method is used to calculate the objective weights, then VIKOR method is carried out using these weights which finally results in the ranking of the alternatives. To explore the feasibility of the developed model, the performance evaluation of twelve Iraqi medical colleges was considered as alternatives applied based on eight academic criteria. The results indicated that the sixth choice received the highest rating, while the fifth alternative received the lowest rating. Finally, sensitivity analysis (SA) is used to determine how the alternatives change depending on how important the criteria are. SA results the eighth criterion (Scientific output) was discovered to have the greatest influence on changing the priorities of the alternatives. The proposed method is receiving critical attention from higher authorities because it is considered a reliable scientific method of performance appraisal.

572

Authors: Parag Rastogi, Devendra Singh, Sarabjeet Singh Bedi

The medical technology is being evolved for enhancing the healthcare information related to medication, diseases, medical images etc., by using Electronic Healthcare Recording (EHR). The details such as gender, age, and weight of patients are sensitive, and require protection against unauthorized access. Therefore, providing a robust security for medical data is indeed a challenge. The present research work proposed a Fully Decentralized Block chain (FDBC) model with the Proxy Re-Encryption Algorithm (PReA) with an Ethereum smart contract. The proposed FDBC-PReA allows the data to be visible only for the owner and the smart contract as the model is implemented in an Ethereum based test bed. Each time when the data is transformed, the data loss is obtained for the incorrect data which is taken care by the decentralized BC. The fully decentralized BC stores and checks whether each of the entity shares or accesses the real time data. The proposed FDBC-PReA Ethereum smart contracts, and proxy re-encryption algorithms are executed with a faster rate that consumed average block time of 5.48s, which is lesser when compared to average block times of Smart-Contract Ethereum Distributed Ledger-14s, Searchable Encryption-48.125s, and Decentralized Security Architecture-Software Defined Networking (SDN)-10s.

597

Authors: Maura Widyaningsih, Tri Kuntoro Priyambodo, Moh Edi Wibowo, Muhammad Kamal

Contrast improvement and noise reduction are needed to improve aerial images prone to interference during data collection. Contrast limited adaptive histogram equalization (CLAHE) is a histogram equalization (HE) development method that is commonly used for contrast improvement. Median blur (MB) is one of the methods used for noise reduction. Combining these two techniques helps optimize the preprocessing process before semantic segmentation analysis is carried out in image maps. The results of testing experiments with U-Net VGG-16 and VGG-19 on image maps show a detailed representation of the predicted pixel class. Comparing accuracy with state-of-the-art methods shows that contrast enhancement and noise reduction are better than the previous method. The highest average result for combining CLAHE+MB with U-Net VGG-16 was 76.5 and VGG-19 was 73.8, and the highest accuracy for image sample testing was 87.94 with U-net VGG-16.

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.