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An Optimized Algorithm for Resource Allocation for D2D in Heterogeneous Networks

Computers, Materials & Continua 2022年第2期70卷 2923-2936页

作者： Abdul Kadir Hamid Fahd N.Al-Wesabi nadhem nemri Ammar Zahary Imran Khan Department of Electrical&Computer Engineering University of SharjahSharjahUnited Arab Emirates Department of Computer Science King Khalid UniversityMuhayel AseerKSA Faculty of Computer and IT Sana’a UniversitySanaaYemen Department of Information Systems King Khalid UniversityMayahel AseerKSA Department of Electrical Engineering University of Engineering and TechnologyPeshawarPaki

With the emergence of 5G mobile multimedia services,end users’demand for high-speed,low-latency mobile communication network access is *** them,the device-to-device(D2D)communication is one of the considerable *** D2D communication,the data does not need to be relayed and forwarded by the base station,but under the control of the base station,a direct local link is allowed between two adjacent mobile *** flexible communicationmode reduces the processing bottlenecks and coverage blind spots of the base station,and can be widely used in dense user communication scenarios such as heterogeneous ultra-dense wireless *** of the important factors which affects the quality-of-service(QoS)of D2D communications is co-channel *** order to solve this problem of co-channel interference,this paper proposes a graph coloring based *** main idea is to utilize the weighted priority of spectrum resources and enables multiple D2D users to reuse the single cellular user *** proposed algorithm also provides simpler power *** heterogeneous pattern of interference is determined using different types of interferences and UE and the priority of color is *** results show that the proposed algorithm effectively reduced the co-channel interference,power consumption and improved the system throughput as compared with existing algorithms.

关键词： 5G device-to-device communication wireless networks spectrum

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Automated Deep Learning Based Melanoma Detection and Classification Using Biomedical Dermoscopic Images

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Computers, Materials & Continua 2023年第2期74卷 2443-2459页

作者： Amani Abdulrahman Albraikan nadhem nemri Mimouna Abdullah Alkhonaini Anwer Mustafa Hilal Ishfaq Yaseen Abdelwahed Motwakel Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Systems College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Department of Computer Science College of Computer and Information SciencesPrince Sultan UniversitySaudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Melanoma remains a serious illness which is a common formof skin *** the earlier detection of melanoma reduces the mortality rate,it is essential to design reliable and automated disease diagnosis model using dermoscopic *** recent advances in deep learning(DL)models find useful to examine the medical image and make proper *** this study,an automated deep learning based melanoma detection and classification(ADL-MDC)model is *** goal of the ADL-MDC technique is to examine the dermoscopic images to determine the existence of *** ADL-MDC technique performs contrast enhancement and data augmentation at the initial ***,the k-means clustering technique is applied for the image segmentation *** addition,Adagrad optimizer based Capsule Network(CapsNet)model is derived for effective feature extraction ***,crow search optimization(CSO)algorithm with sparse autoencoder(SAE)model is utilized for the melanoma classification *** exploitation of the Adagrad and CSO algorithm helps to properly accomplish improved performance.A wide range of simulation analyses is carried out on benchmark datasets and the results are inspected under several *** simulation results reported the enhanced performance of the ADL-MDC technique over the recent approaches.

关键词： Biomedical images dermoscopic images deep learning melanoma detection machine learning

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Intelligent Deep Learning Model for Privacy Preserving IIoT on 6G Environment

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Computers, Materials & Continua 2022年第7期72卷 333-348页

作者： Anwer Mustafa Hilal Jaber SAlzahrani Ibrahim Abunadi nadhem nemri Fahd NAl-Wesabi Abdelwahed Motwakel Ishfaq Yaseen Abu Sarwar Zamani Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversitySaudi Arabia Department of Information Systems Prince Sultan UniversityRiyadh11586Saudi Arabia Department of Information Systems King King Khalid UniversityMuhayel Aseer62529Saudi Arabia Department of Computer Science King King Khalid UniversityMuhayel Aseer62529Saudi Arabia Faculty of Computer and IT Sana’a UniversitySana’a61101Yemen

In recent times,Industrial Internet of Things(IIoT)experiences a high risk of cyber attacks which needs to be *** technology can be incorporated into IIoT system to help the entrepreneurs realize Industry 4.0 by overcoming such cyber *** blockchain-based IIoT network renders a significant support and meet the service requirements of next generation network,the performance arrived at,in existing studies still needs *** this scenario,the current research paper develops a new Privacy-Preserving Blockchain with Deep Learning model for Industrial IoT(PPBDL-IIoT)on 6G *** proposed PPBDLIIoT technique aims at identifying the existence of intrusions in ***,PPBDL-IIoT technique also involves the design of Chaos Game Optimization(CGO)with Bidirectional Gated Recurrent Neural Network(BiGRNN)technique for both detection and classification of intrusions in the ***,CGO technique is applied to fine tune the hyperparameters in BiGRNN *** algorithm is applied to optimally adjust the learning rate,epoch count,and weight decay so as to considerably improve the intrusion detection performance of BiGRNN ***,Blockchain enabled Integrity Check(BEIC)scheme is also introduced to avoid the misrouting attacks that tamper the OpenFlow rules of SDN-based IIoT *** performance of the proposed PPBDL-IIoT methodology was validated using Industrial Control System Cyber-attack(ICSCA)dataset and the outcomes were analysed under various *** experimental results highlight the supremacy of the presented PPBDL-IIoT technique than the recent state-of-the-art techniques with the higher accuracy of 91.50%.

关键词： 6G networks industrial iot blockchain security intrusion detection artificial intelligence

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Optimized Stacked Autoencoder for IoT Enabled Financial Crisis Prediction Model

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Computers, Materials & Continua 2022年第4期71卷 1079-1094页

作者： Mesfer Al Duhayyim Hadeel Alsolai Fahd N.Al-Wesabi nadhem nemri Hany Mahgoub Anwer Mustafa Hilal Manar Ahmed Hamza Mohammed Rizwanullah Department of Natural and Applied Sciences College of Community-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversitySaudi Arabia Department of Computer Science King Khalid UniversityMuhayel AseerSaudi Arabia Faculty of Computer and IT Sana’a UniversitySana’aYemen Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Recently,Financial Technology(FinTech)has received more attention among financial sectors and researchers to derive effective solutions for any financial institution or *** crisis prediction(FCP)is an essential topic in business sector that finds it useful to identify the financial condition of a financial *** the same time,the development of the internet of things(IoT)has altered the mode of human interaction with the physical *** IoT can be combined with the FCP model to examine the financial data from the users and perform decision making *** paper presents a novel multi-objective squirrel search optimization algorithm with stacked autoencoder(MOSSA-SAE)model for FCP in IoT *** MOSSA-SAE model encompasses different subprocesses namely preprocessing,class imbalance handling,parameter tuning,and ***,the MOSSA-SAE model allows the IoT devices such as smartphones,laptops,etc.,to collect the financial details of the users which are then transmitted to the cloud for further *** addition,SMOTE technique is employed to handle class imbalance *** goal of MOSSA in SMOTE is to determine the oversampling rate and area of nearest neighbors of ***,SAE model is utilized as a classification technique to determine the class label of the financial *** the same time,the MOSSA is applied to appropriately select the‘weights’and‘bias’values of the *** extensive experimental validation process is performed on the benchmark financial dataset and the results are examined under distinct *** experimental values ensured the superior performance of the MOSSA-SAE model on the applied dataset.

关键词： Financial data financial crisis prediction class imbalance problem internet of things stacked autoencoder

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Intelligent Intrusion Detection System for Industrial Internet of Things Environment

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Computer Systems Science & Engineering 2023年第2期44卷 1567-1582页

作者： R.Gopi R.Sheeba K.Anguraj T.Chelladurai Haya Mesfer Alshahrani nadhem nemri Tarek Lamoudan Department of Computer Science and Engineering Dhanalakshmi Srinivasan Engineering CollegePerambalur621212India Department of Computer Science and Engineering K.Ramakrishnan College of EngineeringTiruchirappalli621112India Department of Electronics and Communication Engineering Sona College of TechnologySalem636005India Department of Electronics and Communication Engineering PSNA College of Engineering and TechnologyDindigul624622India Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Information Systems College of Science&Arts at MahayilKing Khalid UniversityMuhayel Aseer62529Saudi Arabia Department of Mathematics College of Science&Arts at MahayilKing Khalid UniversityMuhayel Aseer62529Saudi Arabia

Rapid increase in the large quantity of industrial data,Industry 4.0/5.0 poses several challenging issues such as heterogeneous data generation,data sensing and collection,real-time data processing,and high request arrival *** classical intrusion detection system(IDS)is not a practical solution to the Industry 4.0 environment owing to the resource limitations and *** resolve these issues,this paper designs a new Chaotic Cuckoo Search Optimiza-tion Algorithm(CCSOA)with optimal wavelet kernel extreme learning machine(OWKELM)named CCSOA-OWKELM technique for IDS on the Industry 4.0 *** CCSOA-OWKELM technique focuses on the design of feature selection with classiﬁcation approach to achieve minimum computation complex-ity and maximum detection *** CCSOA-OWKELM technique involves the design of CCSOA based feature selection technique,which incorpo-rates the concepts of chaotic maps with ***,the OWKELM technique is applied for the intrusion detection and classiﬁcation *** addition,the OWKELM technique is derived by the hyperparameter tuning of the WKELM technique by the use of sunﬂower optimization(SFO)*** utilization of CCSOA for feature subset selection and SFO algorithm based hyperparameter tuning leads to better *** order to guarantee the supreme performance of the CCSOA-OWKELM technique,a wide range of experiments take place on two benchmark datasets and the experimental outcomes demonstrate the promis-ing performance of the CCSOA-OWKELM technique over the recent state of art techniques.

关键词： Intrusion detection system artiﬁcial intelligence machine learning industry 4.0 internet of things

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Design of Intelligent Alzheimer Disease Diagnosis Model on CIoT Environment

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Computers, Materials & Continua 2022年第6期71卷 5979-5994页

作者： Anwer Mustafa Hilal Fahd NAl-Wesabi Mohamed Tahar Ben Othman Khaled Mohamad Almustafa nadhem nemri Mesfer Al Duhayyim Manar Ahmed Hamza Abu Sarwar Zamani Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia Department of Computer Science King King Khalid UniversityMuhayel Aseer62529Saudi Arabia Faculty of Computer and IT Sana’a UniversitySana’a61101Yemen Department of Computer Science College of ComputerQassim UniversityAl-Bukairiyah52571Saudi Arabia Department of Information Systems College of Computer and Information SystemsPrince Sultan UniversitySaudi Arabia Department of Information Systems King King Khalid UniversityMuhayel Aseer62529Saudi Arabia Department of Natural and Applied Sciences College of Community-AflajPrince Sattam bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia

Presently,cognitive Internet of Things(CIoT)with cloud computing(CC)enabled intelligent healthcare models are developed,which enables communication with intelligent devices,sensor modules,and other stakeholders in the healthcare sector to avail effective decision *** the other hand,Alzheimer disease(AD)is an advanced and degenerative illness which injures the brain cells,and its earlier detection is necessary for suitable interference by healthcare *** this aspect,this paper presents a new Oriented Features from Accelerated Segment Test(FAST)with Rotated Binary Robust Independent Elementary Features(BRIEF)Detector(ORB)with optimal artificial neural network(ORB-OANN)model for AD diagnosis and classification on the CIoT based smart healthcare *** initial pre-processing,bilateral filtering(BLF)based noise removal and region of interest(RoI)detection processes are carried *** addition,the ORBOANN model includes ORB based feature extractor and principal component analysis(PCA)based feature ***,artificial neural network(ANN)model is utilized as a classifier and the parameters of the ANN are optimally chosen by the use of salp swarm algorithm(SSA).A comprehensive experimental analysis of the ORB-OANN model is carried out on the benchmark database and the obtained results pointed out the promising outcome of the ORB-OANN technique in terms of different measures.

关键词： Cognitive internet of things machine learning parameter tuning alzheimer’s disease healthcare decision making

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Predictive Multimodal Deep Learning-Based Sustainable Renewable and Non-Renewable Energy Utilization

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Computer Systems Science & Engineering 2023年第10期47卷 1267-1281页

作者： Abdelwahed Motwakel MarwaObayya nadhem nemri Khaled Tarmissi Heba Mohsen Mohammed Rizwanulla Ishfaq Yaseen Abu Sarwar Zamani Department of Information Systems College of Business Administration in Hawtat bani TamimPrince Sattam bin Abdulaziz UniversityHowtat Bani Tamim16622Saudi Arabia Department of Biomedical Engineering College of EngineeringPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Systems College of Computer ScienceKing Khalid UniversityAbhaSaudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Recently,renewable energy(RE)has become popular due to its benefits,such as being inexpensive,low-carbon,ecologically friendly,steady,and *** RE sources are gradually combined with non-renewable energy(NRE)sources into electric grids to satisfy energy *** energy utilization is highly related to national energy policy,energy prediction using artificial intelligence(AI)and deep learning(DL)based models can be employed for energy prediction on RE and NRE power *** energy consumption of RE and NRE sources using effective models becomes *** this motivation,this study presents a new multimodal fusionbased predictive tool for energy consumption prediction(MDLFM-ECP)of RE and NRE power *** data may influence the prediction performance of the results in prediction *** proposed MDLFMECP technique involves pre-processing,fusion-based prediction,and hyperparameter *** addition,the MDLFM-ECP technique involves the fusion of four deep learning(DL)models,namely long short-termmemory(LSTM),bidirectional LSTM(Bi-LSTM),deep belief network(DBN),and gated recurrent unit(GRU).Moreover,the chaotic cat swarm optimization(CCSO)algorithm is applied to tune the hyperparameters of the DL *** design of the CCSO algorithm for optimal hyperparameter tuning of the DL models,showing the novelty of the work.A series of simulations took place to validate the superior performance of the proposed method,and the simulation outcome emphasized the improved results of the MDLFM-ECP technique over the recent approaches with minimum overall mean absolute percentage error of 3.58%.

关键词： Sustainability renewable energy power source energy prediction deep learning fusion model metaheuristics

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CryptoNight Mining Algorithm with YAC Consensus for Social Media Marketing Using Blockchain

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Computers, Materials & Continua 2022年第5期71卷 3921-3936页

作者： Anwer Mustafa Hil Fahd N.Al-Wesabi Hadeel Alsolai Ola Abdelgney Omer Ali nadhem nemri Manar Ahmed Hamza Abu Sarwar Zamani Mohammed Rizwanullah Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlkharjSaudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia&Faculty of Computer and ITSana’a UniversityYemen Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversitySaudi Arabia Department of Information Technology College of ComputerQassim UniversityBuraydahSaudi Arabia Department of Information Systems College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia

Social media is a platform in which user can create,share and exchange the knowledge/*** media marketing is to identify the different consumer’s demands and engages them to create marketing *** popular social media platforms are Microsoft,Snapchat,Amazon,Flipkart,Google,eBay,Instagram,Facebook,Pin interest,and *** main aim of social media marketing deals with various business partners and build good relationship with millions of customers by satisfying their *** technology is replacing old approaches in the social media marketing to new technology-based ***,this disruptive technology creates some issues like fake news,insecure,inconsistency,inaccuracy and so *** issues contribute economic instability in the society,diminishing the level of *** overcome these issues,this paper we present blockchain as disruptive technology for social media *** plays a vital role on social media marketing by providing secure to the company page in the *** properties of disruptive potential of blockchain on social media marketing is transparency,security,reliability and *** paper presents a new framework for disruptive technology in blockchain social media marketing using fusion of CryptoNight mining algorithm with YAC consensus algorithm[BCDSMM-CNYAC].This mining algorithm provides high CPU efficiency,high dimensionality of secure and detecting falsifying data attack in the social media *** the data analysis we proposed ANOVA analysis method regarding to the factors of age,time,frequency visiting times of social media *** reliability analysis of data Cronbach’s alpha tests are implemented.

关键词： Cryptonight disruptive technology smart contracts social media security YAC

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Modeling of Explainable Artificial Intelligence for Biomedical Mental Disorder Diagnosis

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Computers, Materials & Continua 2022年第5期71卷 3853-3867页

作者： Anwer Mustafa Hilal Imène ISSAOUI Marwa Obayya Fahd N.Al-Wesabi nadhem nemri Manar Ahmed Hamza Mesfer Al Duhayyim Abu Sarwar Zamani Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam Bin Abdulaziz UniversityAlkharj16278Saudi Arabia Department of Natural and Applied Sciences Community CollegeQassim UniversityBuraydahSaudi Arabia Department of Biomedical Engineering College of EngineeringPrincess Nourah Bint Abdulrahman UniversityRiyadh11564Saudi Arabia Department of Computer Science King Khalid UniversityMuhayel AseerSaudi Arabia&Faculty of Computer and ITSana’a UniversitySana’a31220Yemen Department of Information Systems King Khalid UniversityMuhayel Aseer62529Saudi Arabia Department of Natural and Applied Sciences College of Community-AflajPrince Sattam Bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia

The abundant existence of both structured and unstructured data and rapid advancement of statistical models stressed the importance of introducing Explainable Artificial Intelligence(XAI),a process that explains how prediction is done in AI *** mental disorder,i.e.,Autism Spectral Disorder(ASD)needs to be identified and classified at early stage itself in order to reduce health *** this background,the current paper presents XAI-based ASD diagnosis(XAI-ASD)model to detect and classify ASD *** proposed XAI-ASD technique involves the design of Bacterial Foraging Optimization(BFO)-based Feature Selection(FS)*** addition,Whale Optimization Algorithm(WOA)with Deep Belief Network(DBN)model is also applied for ASD classification process in which the hyperparameters of DBN model are optimally tuned with the help of *** order to ensure a better ASD diagnostic outcome,a series of simulation process was conducted on ASD dataset.

关键词： Explainable artificial intelligence autism spectral disorder feature selection data classification machine learning metaheuristics

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Improved Bat Algorithm with Deep Learning-Based Biomedical ECG Signal Classification Model

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Computers, Materials & Continua 2023年第2期74卷 3151-3166页

作者： Marwa Obayya nadhem nemri Lubna A.Alharbi Mohamed K.Nour Mrim M.Alnfiai Mohammed Abdullah Al-Hagery Nermin M.Salem Mesfer Al Duhayyim Department of Biomedical Engineering College of EngineeringPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Systems College of Science and Art at MahayilKing Khalid UniversitySaudi Arabia Department of Computer Science College of Computers and Information TechnologyTabuk UniversitySaudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Computer Science College of ComputerQassim UniversitySaudi Arabia Department of Electrical Engineering Faculty of Engineering and TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam Bin Abdulaziz UniversitySaudi Arabia

With new developments experienced in Internet of Things(IoT),wearable,and sensing technology,the value of healthcare services has *** evolution has brought significant changes from conventional medicine-based healthcare to real-time observation-based *** Electrocardiogram(ECG)signals are generally utilized in examination and diagnosis of Cardiovascular Diseases(CVDs)since it is quick and non-invasive in *** to increasing number of patients in recent years,the classifier efficiency gets reduced due to high variances observed in ECG signal patterns obtained from *** such scenario computer-assisted automated diagnostic tools are important for classification of ECG *** current study devises an Improved Bat Algorithm with Deep Learning Based Biomedical ECGSignal Classification(IBADL-BECGC)*** accomplish this,the proposed IBADL-BECGC model initially pre-processes the input ***,IBADL-BECGC model applies NasNet model to derive the features from test ECG *** addition,Improved Bat Algorithm(IBA)is employed to optimally fine-tune the hyperparameters related to NasNet ***,Extreme Learning Machine(ELM)classification algorithm is executed to perform ECG classification *** presented IBADL-BECGC model was experimentally validated utilizing benchmark *** comparison study outcomes established the improved performance of IBADL-BECGC model over other existing methodologies since the former achieved a maximum accuracy of 97.49%.

关键词： Data science ECG signals improved bat algorithm deep learning biomedical data data classification machine learning

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