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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

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

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

Automated Deep Learning Driven Crop Classification on Hyperspectral Remote Sensing Images

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Computers, Materials & Continua 2023年第2期74卷 3167-3181页

作者： Mesfer Al Duhayyim Hadeel Alsolai Siwar Ben haj Hassine Jaber SAlzahrani ahmed SSalama Abdelwahed Motwakel Ishfaq Yaseen Abu Sarwar Zamani Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversitySaudi Arabia Department of Electrical Engineering Faculty of Engineering&TechnologyFuture University in EgyptNew Cairo11845Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Hyperspectral remote sensing/imaging spectroscopy is a novel approach to reaching a spectrum from all the places of a huge array of spatial places so that several spectral wavelengths are utilized for making coherent *** remote sensing contains acquisition of digital images from several narrow,contiguous spectral bands throughout the visible,Thermal Infrared(TIR),Near Infrared(NIR),and Mid-Infrared(MIR)regions of the electromagnetic *** order to the application of agricultural regions,remote sensing approaches are studied and executed to their benefit of continuous and ***,hyperspectral images(HSI)are considered the precise for agriculture as they can offer chemical and physical data on *** this motivation,this article presents a novel Hurricane Optimization Algorithm with Deep Transfer Learning Driven Crop Classification(HOADTL-CC)model onHyperspectralRemote Sensing *** presentedHOADTL-CC model focuses on the identification and categorization of crops on hyperspectral remote sensing *** accomplish this,the presentedHOADTL-CC model involves the design ofHOAwith capsule network(CapsNet)model for generating a set of useful feature ***,Elman neural network(ENN)model is applied to allot proper class labels into the input ***,glowworm swarm optimization(GSO)algorithm is exploited to fine tune the ENNparameters involved in this *** experimental result scrutiny of the HOADTL-CC method can be tested with the help of benchmark dataset and the results are assessed under distinct *** comparative studies stated the enhanced performance of the HOADTL-CC model over recent approaches with maximum accuracy of 99.51%.

关键词： Hyperspectral images remote sensing deep learning hurricane optimization algorithm crop classification parameter tuning

Depression among medical students in Tunisia:Prevalence and associated factors

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World Journal of Psychiatry 2023年第10期13卷 772-783页

作者： Badii Amamou Sondess Alouani Amjed Ben Haouala Saoussen Alouani Mohamed Ayoub Tlili ahmed Mhalla ferid Zaafrane Lotfi Gaha Department of Psychiatry Fattouma Bourguiba University Hospital of MonastirMonastir 5000Tunisia Department of Family and Community Medicine Faculty of Medicine of SousseSousse 4002Tunisia

BACKGROUND Medical school is known for its lengthy process,which is both physically and emotionally ***’mental balance would shrink as they progress in their medical training.A systematic review and meta-analysis reported that the prevalence of depressive symptoms among medical students remained relatively constant at 27.2%.AIM To assess the prevalence of depressive symptoms among Tunisian medical students and evaluate its associated *** This is a descriptive cross-sectional study that was carried out in the second semester of the academic year 2017/2018,between April 2018 and July 2018 among 1138 medical *** were collected using a socio-demographic questionnaire and the Beck Depression Inventory-II(BDI-II).RESULTS Sixty-four percent(n=728)of the participants had depressive symptoms,of which 266(23.4%)met the criteria for mild,271(23.8%)for moderate,and 191(16.8%)for severe depressive *** gender,low socio-economic level,smoking habits and history of mental disorder,performing leisure and physical activities,satisfaction toward a career choice,and happiness perception were the main prognostic factors for depression among medical *** academic grades may not be considered a prognostic factor,final-year students appeared to be less depressive than their *** These findings give insight into mental health issues and comorbidities among Tunisian medical *** is a hopeful request for decision-makers and academic authorities to set serious measures and draw effective interventions to minimize the currency of psychological distress among this subpopulation.

关键词： Depression Psychiatry Medical students Mental health Beck Depression Inventory Tunisia

Automated Multi-Document Biomedical Text Summarization Using Deep Learning Model

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Computers, Materials & Continua 2022年第6期71卷 5799-5815页

作者： ahmed S.Almasoud Siwar Ben haj Hassine Fahd N.Al-Wesabi Mohamed K.Nour Anwer Mustafa Hilal Mesfer Al Duhayyim Manar ahmed Hamza Abdelwahed Motwakel Department of Information Systems College of Computer and Information SciencesPrince Sultan UniversitySaudi Arabia Department of Computer Science College of Science and Arts at MahayilKing Khalid UniversitySaudi Arabia Faculty of Computer and IT Sana’a UniversitySana’aYemen Department of Computer Science 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

Due to the advanced developments of the Internet and information technologies,a massive quantity of electronic data in the biomedical sector has been exponentially *** handle the huge amount of biomedical data,automated multi-document biomedical text summarization becomes an effective and robust approach of accessing the increased amount of technical and medical literature in the biomedical sector through the summarization of multiple source documents by retaining the significantly informative ***,multi-document biomedical text summarization acts as a vital role to alleviate the issue of accessing precise and updated *** paper presents a Deep Learning based Attention Long Short Term Memory(DLALSTM)Model for Multi-document Biomedical Text *** proposed DL-ALSTM model initially performs data preprocessing to convert the available medical data into a compatible format for further ***,the DL-ALSTM model gets executed to summarize the contents from the multiple biomedical *** order to tune the summarization performance of the DL-ALSTM model,chaotic glowworm swarm optimization(CGSO)algorithm is *** experimentation analysis is performed to ensure the betterment of the DL-ALSTM model and the results are investigated using the PubMed *** comparative result analysis is carried out to showcase the efficiency of the proposed DL-ALSTM model with the recently presented models.

关键词： Biomedical text summarization healthcare deep learning lstm parameter tuning

Measuring Impact of Lean Manufacturing Tools for Continuous Improvement on Economic Sustainability

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Journal of Systems Science and Systems Engineering 2024年第4期33卷 452-474页

作者： Luis Javier Márquez Figueroa Jorge Luis García-Alcaraz ahmed I.Osman Alfonso Jesús Gil López Yashar Aryanfar Mika Sillanpää Mamdouh El haj Assad Department of Electrical Engineering and Computer Science Universidad Autónoma de Ciudad JuárezCiudad JuárezChihuahuaMéxico Department of Industrial Engineering and Manufacturing Universidad Autónoma de Ciudad JuárezCiudad JuárezChihuahuaMéxico School of Chemistry and Chemical Engineering Queens UniversityBelfastNorthern IrelandUK Department of Economic and Managerial Sciences University of La RiojaLa RiojaSpain Department of Chemical Engineering University of JohannesburgJohannesburgSouth Africa Department of Sustainable and Renewable Energy Engineering University of SharjahSharjahUAE

Using Lean Manufacturing(LM)tools in production processes is crucial for companies’economic,environmental,and social sustainability *** study shows a structural equation model(SEM)that shows the relationship between LM Tools like Kaizen(KAI),Gemba(GEM),Value Stream Mapping(VSM)and Key Performance Indicator(KPI)with Economic Sustainability(ECS).Seven hypotheses were evaluated with data from 179 responses to a questionnaire about the Mexican maquiladora industry,showing that these variables are *** a 95%confidence level,the model was evaluated using the partial least squares *** findings indicate that the relationships between KAI and GEM and KAI and VSM have the strongest relationship,followed by VSM and ECS;however,VSM has the strongest effect on *** on these findings,it is recommended that managers adopt a continuous improvement(KAI)approach based on working directly on the shop floor(GEM and VSM)to support their decisions regarding economic growth(ECS).

关键词： Lean manufacturing economic sustainability structural equation model quality improvement

Blockchain Driven Metaheuristic Route Planning in Secure Vehicular Adhoc Networks

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Computers, Materials & Continua 2022年第12期73卷 6461-6477页

作者： Siwar Ben haj Hassine Saud SAlotaibi Hadeel Alsolai Reem Alshahrani Lilia Kechiche Mrim M.Alnfiai Amira Sayed A.Aziz Manar ahmed Hamza Department of Computer Science College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Department of Information Systems College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Sciences and Technology Taif UniversityTaif P.O.Box 11099Taif21944Saudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaif P.O.Box 11099Taif21944Saudi Arabia Department of Digital Media Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Nowadays,vehicular ad hoc networks(VANET)turn out to be a core portion of intelligent transportation systems(ITSs),that mainly focus on achieving continual Internet connectivity amongst vehicles on the *** VANET was utilized to enhance driving safety and build an ITS in modern *** safety is a main portion of VANET,the privacy and security of these messages should be *** this aspect,this article presents a blockchain with sunflower optimization enabled route planning scheme(BCSFO-RPS)for secure *** presented BCSFO-RPSmodel focuses on the identification of routes in such a way that vehicular communication is *** addition,the BCSFO-RPS model employs SFO algorithm with a fitness function for effectual identification of ***,the proposed BCSFO-RPS model derives an intrusion detection system(IDS)encompassing two processes namely feature selection and *** detect intrusions,correlation based feature selection(CFS)and kernel extreme machine learning(KELM)classifier is *** performance of the BCSFO-RPS model is tested using a series of experiments and the results reported the enhancements of the BCSFO-RPS model over other approaches with maximum accuracy of 98.70%.

关键词： VANET sunflower optimization machine learning blockchain intrusion detection

Energy Aware Secure Cyber-Physical Systems with Clustered Wireless Sensor Networks

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Computers, Materials & Continua 2022年第9期72卷 5499-5513页

作者： Masoud Alajmi Mohamed K.Nour Siwar Ben haj Hassine Mimouna Abdullah Alkhonaini Manar ahmed Hamza Ishfaq Yaseen Abu Sarwar Zamani Mohammed Rizwanullah Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif21944Saudi 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 UniversityAbha62529Saudi 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

Recently,cyber physical system(CPS)has gained significant attention which mainly depends upon an effective collaboration with computation and physical *** greatly interrelated and united characteristics of CPS resulting in the development of cyber physical energy systems(CPES).At the same time,the rising ubiquity of wireless sensor networks(WSN)in several application areas makes it a vital part of the design of *** security and energy efficiency are the major challenging issues in CPES,this study offers an energy aware secure cyber physical systems with clustered wireless sensor networks using metaheuristic algorithms(EASCPSMA).The presented EASCPS-MA technique intends to attain lower energy utilization via clustering and security using intrusion *** EASCPSMA technique encompasses two main stages namely improved fruit fly optimization algorithm(IFFOA)based clustering and optimal deep stacked autoencoder(OSAE)based intrusion ***,the optimal selection of stacked autoencoder(SAE)parameters takes place using root mean square propagation(RMSProp)*** extensive performance validation of the EASCPS-MA technique takes place and the results are inspected under varying *** simulation results reported the improved effectiveness of the EASCPS-MA technique over other recent approaches interms of several measures.

关键词： Intrusion detection system metaheuristics stacked autoencoder deep learning cyber physical energy systems clustering wsn