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Compact Bat Algorithm with Deep Learning Model for Biomedical EEG EyeState Classification

Computers, Materials & Continua 2022年第9期72卷 4589-4601页

作者： Souad Larabi-Marie-Sainte Eatedal Alabdulkreem Mohammad Alamgeer Mohamed K Nour Anwer Mustafa Hilal Mesfer Al Duhayyim Abdelwahed Motwakel Ishfaq Yaseen Department of Computer Science College of Computer and Information SciencesPrince Sultan UniversitySaudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Information Systems College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Natural and Applied Sciences College of Community-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia

Electroencephalography(EEG)eye state classification becomes an essential tool to identify the cognitive state of humans.It can be used in several fields such as motor imagery recognition,drug effect detection,emotion categorization,seizure detection,etc.With the latest advances in deep learning(DL)models,it is possible to design an accurate and prompt EEG EyeState classification problem.In this view,this study presents a novel compact bat algorithm with deep learning model for biomedical EEG EyeState classification(CBADL-BEESC)model.The major intention of the CBADL-BEESC technique aims to categorize the presence of EEG EyeState.The CBADL-BEESC model performs feature extraction using the ALexNet model which helps to produce useful feature vectors.In addition,extreme learning machine autoencoder(ELM-AE)model is applied to classify the EEG signals and the parameter tuning of the ELM-AE model is performed using CBA.The experimental result analysis of the CBADL-BEESC model is carried out on benchmark results and the comparative outcome reported the supremacy of the CBADL-BEESC model over the recent methods.

关键词： Biomedical signals EEG EyeState classification deep learning metaheuristics

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Locomotion of Bioinspired Underwater Snake Robots Using Metaheuristic Algorithm

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Computers, Materials & Continua 2022年第7期72卷 1293-1308页

作者： Souad Larabi-Marie-Sainte Taiseer Abdalla Elfadil Eisa Fahd N.Al-Wesabi Amani Abdulrahman Albraikan Manar Ahmed Hamza Abdelwahed Motwakel Ishfaq Yaseen Mesfer Al Duhayyim Department of Computer Science College of Computer and Information SciencesPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Information Systems-Girls Section King Khalid UniversityMahayil62529Saudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid University62529Saudi Arabia Faculty of Computer and IT Sana’a UniversitySana’a1247Yemen Department of Computer Science College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman University11564Saudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz University16278Saudi Arabia Department of Natural and Applied Sciences College of Community-AflajPrince Sattam bin Abdulaziz University16278Saudi Arabia

Snake Robots(SR)have been successfully deployed and proved to attain bio-inspired solutions owing to its capability to move in harsh environments,a characteristic not found in other kinds of robots(like wheeled or legged robots).Underwater Snake Robots(USR)establish a bioinspired solution in the domain of underwater robotics.It is a key challenge to increase the motion efficiency in underwater robots,with respect to forwarding speed,by enhancing the locomotion method.At the same time,energy efficiency is also considered as a crucial issue for long-term automation of the systems.In this aspect,the current research paper concentrates on the design of effectual Locomotion of Bioinspired Underwater Snake Robots using Metaheuristic Algorithm(LBIUSR-MA).The proposed LBIUSR-MA technique derives a bi-objective optimization problem to maximize the ForwardVelocity(FV)and minimize the Average Power Consumption(APC).LBIUSR-MA technique involves the design ofManta Ray Foraging Optimization(MRFO)technique and derives two objective functions to resolve the optimization issue.In addition to these,effective weighted sum technique is also used for the integration of two objective functions.Moreover,the objective functions are required to be assessed for varying gait variables so as to inspect the performance of locomotion.A detailed set of simulation analyses was conducted and the experimental results demonstrate that the developed LBIUSR-MA method achieved a low Average Power Consumption(APC)value of 80.52W underδvalue of 50.The proposed model accomplished the minimum PAC and maximum FV of USR in an effective manner.

关键词： Bio-inspired robots underwater snake robots locomotion efficiency forwarding velocity average power consumption objective function

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Modified Harris Hawks Optimization Based Test Case Prioritization for Software Testing

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Computers, Materials & Continua 2022年第7期72卷 1951-1965页

作者： Manar Ahmed Hamza Abdelzahir Abdelmaboud Souad Larabi-Marie-Sainte Haya Mesfer Alshahrani Mesfer Al Duhayyim Hamza Awad Ibrahim Mohammed Rizwanullah Ishfaq Yaseen Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharj16278Saudi Arabia Department of Information Systems College of Science and ArtsKing Khalid UniversityMahayil Asir62529Saudi Arabia Department of Computer Science College of Computer and Information SciencesPrince Sultan UniversityP.O.Box No.66833Rafha StreetRiyadh11586Saudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Natural and Applied Sciences College of Community-AflajPrince Sattam bin Abdulaziz University16278Saudi Arabia College of Computer at Al-Gunfudah Umm Al-Qura UniversityAl-Gunfudah24382Saudi Arabia

Generally,software testing is considered as a proficient technique to achieve improvement in quality and reliability of the software.But,the quality of test cases has a considerable influence on fault revealing capability of software testing activity.Test Case Prioritization(TCP)remains a challenging issue since prioritizing test cases is unsatisfactory in terms of Average Percentage of Faults Detected(APFD)and time spent upon execution results.TCP ismainly intended to design a collection of test cases that can accomplish early optimization using preferred characteristics.The studies conducted earlier focused on prioritizing the available test cases in accelerating fault detection rate during software testing.In this aspect,the current study designs aModified Harris Hawks Optimization based TCP(MHHO-TCP)technique for software testing.The aim of the proposed MHHO-TCP technique is to maximize APFD and minimize the overall execution time.In addition,MHHO algorithm is designed to boost the exploration and exploitation abilities of conventional HHO algorithm.In order to validate the enhanced efficiency of MHHO-TCP technique,a wide range of simulations was conducted on different benchmark programs and the results were examined under several aspects.The experimental outcomes highlight the improved efficiency of MHHO-TCP technique over recent approaches under different measures.

关键词： Software testing harris hawks optimization test case prioritization apfd execution time metaheuristics

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Malware Detection Using Decision Tree Based SVM Classifier for IoT

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Computers, Materials & Continua 2022年第7期72卷 713-726页

作者： Anwer Mustafa Hilal Siwar Ben Haj Hassine Souad Larabi-Marie-Sainte Nadhem Nemri Mohamed K.Nour Abdelwahed Motwakel Abu Sarwar Zamani Mesfer Al Duhayyim Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Computer Science College of Science and ArtsKing Khalid UniversityMahayil AsirSaudi Arabia Department of Computer Science College of Computer and Information SciencesPrince Sultan UniversitySaudi Arabia Department of Computer Science College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Natural and Applied Sciences College of Community-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia

The development in Information and Communication Technology has led to the evolution of new computing and communication environment.Technological revolution with Internet of Things(IoTs)has developed various applications in almost all domains from health care,education to entertainment with sensors and smart devices.One of the subsets of IoT is Internet of Medical things(IoMT)which connects medical devices,hardware and software applications through internet.IoMT enables secure wireless communication over the Internet to allow efficient analysis of medical data.With these smart advancements and exploitation of smart IoT devices in health care technology there increases threat and malware attacks during transmission of highly confidential medical data.This work proposes a scheme by integrating machine learning approach and block chain technology to detect malware during data transmission in IoMT.The proposed Machine Learning based Block Chain Technology malware detection scheme(MLBCT-Mdetect)is implemented in three steps namely:feature extraction,Classification and blockchain.Feature extraction is performed by calculating the weight of each feature and reduces the features with less weight.Support Vector Machine classifier is employed in the second step to classify the malware and benign nodes.Furthermore,third step uses blockchain to store details of the selected features which eventually improves the detection of malware with significant improvement in speed and accuracy.ML-BCT-Mdetect achieves higher accuracy with low false positive rate and higher True positive rate.

关键词： Blockchain malware detection classification feature selection internet of medical things

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Optimal Bidirectional LSTM for Modulation Signal Classification in Communication Systems

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Computers, Materials & Continua 2022年第8期72卷 3055-3071页

作者： Manar Ahmed Hamza Siwar Ben Haj Hassine Souad Larabi-Marie-Sainte Mohamed K.Nour Fahd N.Al-Wesabi Abdelwahed Motwakel Anwer Mustafa Hilal Mesfer Al Duhayyim Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Computer Science College of Science and ArtsKing Khalid UniversityMahayil AsirSaudi Arabia Department of Computer Science College of Computer and Information SciencesPrince Sultan UniversitySaudi Arabia Department of Computer Science College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Faculty of Computer and IT Sana’a UniversitySana’aYemen Department of Natural and Applied Sciences College of Community-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia

Modulation signal classification in communication systems can be considered a pattern recognition problem.Earlier works have focused on several feature extraction approaches such as fractal feature,signal constellation reconstruction,etc.The recent advent of deep learning(DL)models makes it possible to proficiently classify the modulation signals.In this view,this study designs a chaotic oppositional satin bowerbird optimization(COSBO)with bidirectional long term memory(BiLSTM)model for modulation signal classification in communication systems.The proposed COSBO-BiLSTM technique aims to classify the different kinds of digitally modulated signals.In addition,the fractal feature extraction process takes place by the use of Sevcik Fractal Dimension(SFD)approach.Moreover,the modulation signal classification process takes place using BiLSTM with fully convolutional network(BiLSTM-FCN).Furthermore,the optimal hyperparameter adjustment of the BiLSTM-FCN technique takes place by the use of COSBO algorithm.In order to ensure the enhanced classification performance of the COSBO-BiLSTM model,a wide range of simulations were carried out.The experimental results highlighted that the COSBO-BiLSTM technique has accomplished improved performance over the existing techniques.

关键词： Complex systems communication system signal processing modulation signals classification artificial intelligence deep learning parameter tuning

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