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

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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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

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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

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Investigation of Extracts from Tunisian Ethnomedicinal Plants as Antioxidants,Cytotoxins,and Antimicrobials

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Biomedical and Environmental Sciences 2017年第11期30卷 811-824页

作者： Guesmi Fatma ben Hadj ahmed Sami Landoulsi ahmed Laboratory of Biochemistry and Molecular Biology Faculty of Sciences of BizerteUniversity of CarthageZarzouna 7021Tunisia Laboratory of Protein Engineering and Bioactive Molecules(LIP-MB) National Institute of Applied Sciences and Technology(INSA T)BP 6761080 Tunis CedexTunisia

Objective To determine the medicinal potential of various plants and their parts extracted with different solvents. Methods The total phenolic content of acetonitrile/water（60%-40%）（ACN/W） and aqueous（W） extract fractions was determined by high-performance liquid chromatography（HPLC）, and terpenic compounds were detected by gas chromatography/mass spectrometry（GC/MS）. Antioxidant activity of the samples was evaluated using 2,2-diphenyl-1-picrylhydrazyl（DPPH） assay and β-carotene bleaching method. Cell viability was investigated by thiazolyl blue tetrazolium bromide [3-（4,5-dimethylthiazol）-2-yl 2,5-diphenyltetrazolium bromide]（MTT） assay. The mechanisms involved in cytotoxic activity were investigated in a murine macrophage cell line（RAW 264.7） and cancer lines. Results Our findings show that 11 plant species exhibited biological activity. In addition, moderate antibacterial activity was reported against one or more of the tested bacterial strains at two concentrations： 300 μg and 3 mg/disc. Furthermore, our data reveal that among all plants investigated, some extract and hydrophobic fractions were potent scavengers of the DPPH radical（6.78 μg/m L 〈 EC50 〈 8.55 μg/mL）. Taken together, our results show that Nerium oleander（NOACN/W） and Pituranthos tortuosus（PTACN/W） were highly cytotoxic against RAW 264.7 cells with IC80 values of 0.36, and 1.55 μg/mL, respectively. In contrast, murine macrophage cell lines had low growth and were significantly sensitive to water extracts of Thymus hirtus sp. algeriensis（THW）, Lavandula multifida（LMW）, and ACN/W extract of Erica multiflora（EMACN/W） at doses 〉 400, 47.20, and 116.74 μg/m L, respectively. The current work demonstrates that RAW 264.7 cell proliferation was inhibited by samples in a dose-dependent manner. Conclusion Our findings, validated through free radical scavenging activity, agar diffusion assay, and cytotoxicity of essential oils towards cancer cells, show that ethnomedicinal plants used in this work have a novel

关键词： Phenolic compounds Cytotoxicity Antioxidant Antibacterial

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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

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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

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Feature Selection with Optimal Stacked Sparse Autoencoder for Data Mining

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Computers, Materials & Continua 2022年第8期72卷 2581-2596页

作者： Manar ahmed Hamza Siwar ben Haj Hassine Ibrahim Abunadi Fahd N.Al-Wesabi Hadeel Alsolai Anwer Mustafa Hilal Ishfaq Yaseen Abdelwahed Motwakel Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia Department of Computer Science College of Science and Arts at MahayilKing Khalid UniversityMuhayel Aseer62529Saudi Arabia Department of Information Systems Prince Sultan UniversityRiyadh11586Saudi Arabia Faculty of Computer and IT Sana’a UniversitySana’a61101Yemen Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversitySaudi Arabia

Data mining in the educational field can be used to optimize the teaching and learning performance among the *** recently developed machine learning(ML)and deep learning(DL)approaches can be utilized to mine the data *** study proposes an Improved Sailfish Optimizer-based Feature SelectionwithOptimal Stacked Sparse Autoencoder(ISOFS-OSSAE)for data mining and pattern recognition in the educational *** proposed ISOFS-OSSAE model aims to mine the educational data and derive decisions based on the feature selection and classification ***,the ISOFS-OSSAEmodel involves the design of the ISOFS technique to choose an optimal subset of ***,the swallow swarm optimization(SSO)with the SSAE model is derived to perform the classification *** showcase the enhanced outcomes of the ISOFSOSSAE model,a wide range of experiments were taken place on a benchmark dataset from the University of California Irvine(UCI)Machine Learning *** simulation results pointed out the improved classification performance of the ISOFS-OSSAE model over the recent state of art approaches interms of different performance measures.

关键词： Data mining pattern recognition feature selection data classification SSAE model

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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

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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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Optimal Deep Learning-based Cyberattack Detection and Classification Technique on Social Networks

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Computers, Materials & Continua 2022年第7期72卷 907-923页

作者： Amani Abdulrahman Albraikan Siwar ben Haj Hassine Suliman Mohamed Fati Fahd NAl-Wesabi Anwer Mustafa Hilal Abdelwahed Motwakel Manar ahmed Hamza Mesfer Al Duhayyim Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer Science College of Science and ArtsKing Khalid UniversityMahayil AsirSaudi Arabia Department of Information Systems College of Computer and Information SciencesPrince Sultan 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

Cyberbullying(CB)is a distressing online behavior that disturbs mental health *** studies have employed statistical and Machine Learning(ML)techniques for CB *** this motivation,the current paper presents an Optimal Deep Learning-based Cyberbullying Detection and Classification(ODL-CDC)technique for CB detection in social *** proposed ODL-CDC technique involves different processes such as pre-processing,prediction,and hyperparameter *** addition,GloVe approach is employed in the generation of word ***,the pre-processed data is fed into BidirectionalGated Recurrent Neural Network(BiGRNN)model for ***,hyperparameter tuning of BiGRNN model is carried out with the help of Search and Rescue Optimization(SRO)*** order to validate the improved classification performance of ODL-CDC technique,a comprehensive experimental analysis was carried out upon benchmark dataset and the results were inspected under varying aspects.A detailed comparative study portrayed the superiority of the proposed ODL-CDC technique over recent techniques,in terms of performance,with the maximum accuracy of 92.45%.

关键词： Cybersecurity cyberbullying social networks parameter tuning deep learning metaheuristics

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