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Optimized Stacked Autoencoder for IoT Enabled Financial Crisis Prediction Model

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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Metaheuristic Based Data Gathering Scheme for Clustered UAVs in 6G Communication Network

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Computers, Materials & Continua 2022年第6期71卷 5311-5325页

作者： ahmed S.Almasoud Siwar Ben Haj Hassine nadhem nemri Fahd N.Al-Wesabi manar ahmed hamza Anwer Mustafa Hilal Abdelwahed Motwakel Mesfer Al Duhayyim Department of Information Systems College of Computer and Information SciencesPrince Sultan UniversitySaudi Arabia Department of Computer Science College of Science&Arts at MahayilKing Khalid UniversitySaudi Arabia Faculty of Computer and IT Sana’a UniversitySana’aYemen 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

The sixth-generation(6G)wireless communication networks are anticipated in integrating aerial,terrestrial,and maritime communication into a robust system to accomplish trustworthy,quick,and low latency *** enables to achieve maximum throughput and delay for several ***,the evolution of 6G leads to the design of unmanned aerial vehicles(UAVs)in providing inexpensive and effective solutions in various application areas such as healthcare,environment monitoring,and so *** the UAV network,effective data collection with restricted energy capacity poses a major issue to achieving high quality network *** can be addressed by the use of clustering techniques forUAVs in 6G *** this aspect,this study develops a novel metaheuristic based energy efficient data gathering scheme for clustered unmanned aerial vehicles(MEEDG-CUAV).The proposed MEEDG-CUAV technique intends in partitioning the UAV networks into various clusters and assign a cluster head(CH)to reduce the overall energy ***,the quantum chaotic butterfly optimization algorithm(QCBOA)with a fitness function is derived to choose CHs and construct *** experimental validation of the MEEDG-CUAV technique occurs utilizing benchmark dataset and the experimental results highlighted the better performance over the other state of art techniques interms of different measures.

关键词： 6G network mobile communication uav networks energy efficiency clustering metaheuristics

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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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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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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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Hyperparameter Tuned Deep Hybrid Denoising Autoencoder Breast Cancer Classification on Digital Mammograms

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Intelligent Automation & Soft Computing 2023年第6期36卷 2879-2895页

作者： manar ahmed hamza Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Breast Cancer(BC)is considered the most commonly scrutinized can-cer in women worldwide,affecting one in eight women in a ***-phy screening becomes one such standard method that is helpful in identifying suspicious masses’malignancy of BC at an initial ***,the prior iden-tification of masses in mammograms was still challenging for extremely dense and dense breast categories and needs an effective and automatic mechanisms for helping radiotherapists in *** learning(DL)techniques were broadly utilized for medical imaging applications,particularly breast mass *** advancements in the DL field paved the way for highly intellectual and self-reliant computer-aided diagnosis(CAD)systems since the learning cap-ability of Machine Learning(ML)techniques was constantly *** paper presents a new Hyperparameter Tuned Deep Hybrid Denoising Autoenco-der Breast Cancer Classification(HTDHDAE-BCC)on Digital *** presented HTDHDAE-BCC model examines the mammogram images for the identification of *** the HTDHDAE-BCC model,the initial stage of image preprocessing is carried out using an average median *** addition,the deep convolutional neural network-based Inception v4 model is employed to generate feature *** parameter tuning process uses the binary spider monkey opti-mization(BSMO)*** HTDHDAE-BCC model exploits chameleon swarm optimization(CSO)with the DHDAE model for BC *** experimental analysis of the HTDHDAE-BCC model is performed using the MIAS *** experimental outcomes demonstrate the betterments of the HTDHDAE-BCC model over other recent approaches.

关键词： Digital mammograms breast cancer classification computer-aided diagnosis deep learning metaheuristics

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Energy-Efficient Routing Using Novel Optimization with Tabu Techniques for Wireless Sensor Network

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Computer Systems Science & Engineering 2023年第5期45卷 1711-1726页

作者： manar ahmed hamza Aisha Hassan Abdalla Hashim Dalia H.Elkamchouchi nadhem nemri Jaber S.Alzahrani Amira Sayed A.Aziz Mnahel ahmed Ibrahim Abdelwahed Motwakel Department of Electrical and Computer Engineering International Islamic University MalaysiaKuala Lumpur53100Malaysia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Information Technology 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 Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversitySaudi Arabia Department of Digital Media Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt College of Science and Humanities Studies in Alquwayiyah Shaqra UniversitySaudi Arabia

Wireless Sensor Network(WSN)consists of a group of limited energy source sensors that are installed in a particular region to collect data from the *** the energy-efficient data collection methods in largescale wireless sensor networks is considered to be a difficult area in the *** node clustering is a popular approach for ***,the sensor nodes are grouped to form clusters in a cluster-based WSN *** battery performance of the sensor nodes is likewise *** a result,the energy efficiency of WSNs is *** specific,the energy usage is influenced by the loads on the sensor node as well as it ranges from the Base Station(BS).Therefore,energy efficiency and load balancing are very essential in *** the proposed method,a novel Grey Wolf Improved Particle Swarm Optimization with Tabu Search Techniques(GW-IPSO-TS)was *** selection of Cluster Heads(CHs)and routing path of every CH from the base station is enhanced by the proposed *** provides the best routing path and increases the lifetime and energy efficiency of the ***-to-end delay and packet loss rate have also been *** proposed GW-IPSO-TS method enhances the evaluation of alive nodes,dead nodes,network survival index,convergence rate,and standard deviation of sensor *** to the existing algorithms,the proposed method outperforms better and improves the lifetime of the network.

关键词： Wireless sensor networks energy-efficient load balancing energy consumption network’s lifetime cluster heads grey wolf optimization tabu search particle swarm optimization

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Automated Video-Based Face Detection Using Harris Hawks Optimization with Deep Learning

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Computers, Materials & Continua 2023年第6期75卷 4917-4933页

作者： Latifah Almuqren manar ahmed hamza Abdullah Mohamed Amgad Atta Abdelmageed Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Research Centre Future University in EgyptNew Cairo11845Egypt

Face recognition technology automatically identifies an individual from image or video *** detection process can be done by attaining facial characteristics from the image of a subject *** developments in deep learning(DL)and computer vision(CV)techniques enable the design of automated face recognition and tracking *** study presents a novel Harris Hawks Optimization with deep learning-empowered automated face detection and tracking(HHODL-AFDT)*** proposed HHODL-AFDT model involves a Faster region based convolution neural network(RCNN)-based face detection model and HHO-based hyperparameter opti-mization *** presented optimal Faster RCNN model precisely rec-ognizes the face and is passed into the face-tracking model using a regression network(REGN).The face tracking using the REGN model uses the fea-tures from neighboring frames and foresees the location of the target face in succeeding *** application of the HHO algorithm for optimal hyperparameter selection shows the novelty of the *** experimental validation of the presented HHODL-AFDT algorithm is conducted using two datasets and the experiment outcomes highlighted the superior performance of the HHODL-AFDT model over current methodologies with maximum accuracy of 90.60%and 88.08%under PICS and VTB datasets,respectively.

关键词： Face detection face tracking deep learning computer vision video surveillance parameter tuning

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Metaheuristics with Deep Learning Empowered Biomedical Atherosclerosis Disease Diagnosis and Classification

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Computers, Materials & Continua 2022年第8期72卷 2859-2875页

作者： Areej A.Malibari Siwar Ben Haj Hassine Abdelwahed Motwakel manar ahmed hamza Department of Industrial and Systems Engineering College of EngineeringPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer Science College of Science and ArtsKing Khalid UniversityMahayilAsirSaudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia

Atherosclerosis diagnosis is an inarticulate and complicated cognitive *** on medical diagnosis necessitate maximum accuracy and performance to make optimal clinical *** the medical diagnostic outcomes need to be prompt and accurate,the recently developed artificial intelligence(AI)and deep learning(DL)models have received considerable attention among research *** study develops a novel Metaheuristics with Deep Learning Empowered Biomedical Atherosclerosis Disease Diagnosis and Classification(MDL-BADDC)*** proposed MDL-BADDC technique encompasses several stages of operations such as pre-processing,feature selection,classification,and parameter ***,the proposed MDL-BADDC technique designs a novel Quasi-Oppositional Barnacles Mating Optimizer(QOBMO)based feature selection ***,the deep stacked autoencoder(DSAE)based classification model is designed for the detection and classification of atherosclerosis ***,the krill herd algorithm(KHA)based parameter tuning technique is applied to properly adjust the parameter *** order to showcase the enhanced classification performance of the MDL-BADDC technique,a wide range of simulations take place on three benchmarks biomedical *** comparative result analysis reported the better performance of the MDL-BADDC technique over the compared methods.

关键词： Atherosclerosis disease biomedical data data classification machine learning disease diagnosis deep learning

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Sustainable Energy Management with Traffic Prediction Strategy for Autonomous Vehicle Systems

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Computers, Materials & Continua 2022年第8期72卷 3465-3479页

作者： manar ahmed hamza Masoud Alajmi Jaber SAlzahrani Siwar Ben Haj Hassine Abdelwahed Motwakel Ishfaq Yaseen Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversitySaudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia

Recent advancements of the intelligent transportation system(ITS)provide an effective way of improving the overall efficiency of the energy management strategy(EMSs)for autonomous vehicles(AVs).The use of AVs possesses many advantages such as congestion control,accident prevention,and ***,energy management and traffic flow prediction(TFP)still remains a challenging problem in *** complexity and uncertainties of driving situations adequately affect the outcome of the designed *** this view,this paper presents novel sustainable energy management with traffic flow prediction strategy(SEM-TPS)for *** SEM-TPS technique applies type II fuzzy logic system(T2FLS)energy management scheme to accomplish the desired engine torque based on distinct *** addition,the membership functions of the T2FLS scheme are chosen optimally using the barnacles mating optimizer(BMO).For accurate TFP,the bidirectional gated recurrent neural network(Bi-GRNN)model is used in AVs.A comprehensive experimental validation process is performed and the results are inspected with respect to several evaluation *** experimental outcomes highlighted the supreme performance of the SEM-TPS technique over the recent state of art approaches.

关键词： Sustainable energy transportation energy management traffic flow prediction soft computing deep learning

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