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Feature Subset Selection with Artificial Intelligence-Based Classification model for Biomedical Data

Computers, materials & Continua 2022年第9期72卷 4267-4281页

作者： jaber S.Alzahrani Reem m.Alshehri mohammad Alamgeer Anwer mustafa Hilal Abdelwahed motwakel Ishfaq Yaseen Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversitySaudi Arabia Department of Computer Science College of Computing and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Information Systems College of Science&Art at MahayilKing Khalid UniversityMahayil62529Saudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharj62529Saudi Arabia

Recently,medical data classification becomes a hot research topic among healthcare professionals and research communities,which assist in the disease diagnosis and decision making *** latest developments of artificial intelligence(AI)approaches paves a way for the design of effective medical data classification *** the same time,the existence of numerous features in the medical dataset poses a curse of dimensionality *** resolving the issues,this article introduces a novel feature subset selection with artificial intelligence based classification model for biomedical data(FSS-AICBD)*** FSS-AICBD technique intends to derive a useful set of features and thereby improve the classifier ***,the FSS-AICBD technique undergoes min-max normalization technique to prevent data *** addition,the information gain(IG)approach is applied for the optimal selection of feature ***,group search optimizer(GSO)with deep belief network(DBN)model is utilized for biomedical data classification where the hyperparameters of the DBN model can be optimally tuned by the GSO *** choice of IG and GSO approaches results in promising medical data classification *** experimental result analysis of the FSS-AICBD technique takes place using different benchmark healthcare *** simulation results reported the enhanced outcomes of the FSS-AICBD technique interms of several measures.

关键词： medical data classification feature selection deep learning healthcare sector artificial intelligence

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An Efficient method for Heat Recovery Process and Temperature Optimization

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Computers, materials & Continua 2023年第4期75卷 1017-1031页

作者： Basim Kareem Naser mohammed Dauwed Ahmed Alkhayyat mustafa.m.sa jaber Shahad Alyousif mohammed Hasan Ali Dentistry Department Al-Mustaqbal University CollegeHilla51001Iraq medical Instrumentation Techniques Engineering Dijlah University CollegeBaghdad00964Iraq College of Technical Engineering The Islamic UniversityNajaf54001Iraq Department of medical Instruments Engineering Techniques Al-Turath University CollegeBaghdad10021Iraq Department of medical Instruments Engineering Techniques Al-Farahidi UniversityBaghdad10021Iraq Research Centre University of MashreqBaghdad11001Iraq College of Engineering Department of Electrical&Electronic EngineeringGulf UniversityAlmasnad4363Kingdom of Bahrain Computer Techniques Engineering Department Faculty of Information TechnologyImam Ja’afarAl-Sadiq UniversityNajaf10023Iraq

Flue gas heat loss accounts for a significant component of theoverall heat loss for coal-fired boilers in power plants. The flue gas absorbsmore heat as the exhaust gas temperature rises, which reduces boiler efficiencyand raises coal consumption. Additionally, if the exhaust gas temperatureis too high, a lot of water must be used to cool the flue gas for the wetflue gas desulfurization system to function well, which has an impact onthe power plant’s ability to operate profitably. It is consequently vital totake steps to lower exhaust gas temperatures in order to increase boilerefficiency and decrease the amount of coal and water used. Desulfurizationperformance may be enhanced and water use can be decreased by reasonableflue gas characteristics at the entry. This study analyzed the unit’s energyconsumption, investment, and coal savings while proposing four couplingstrategies for regulating flue gas temperature and waste heat recovery. Agraded flue gas conditioning and waste heat recovery plan was presentedunder the condition of ensuring high desulfurization efficiency, along withthe notion of minimizing energy loss owing to energy inflow temperaturedifference. Numerical results show that the proposed methods improved thesystem performance and reduced the water consumption and regulated theboiler temperature.

关键词： Heat exchange system boiler energy optimization temperature control

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medical Image Analysis Using Deep Learning and Distribution Pattern matching Algorithm

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Computers, materials & Continua 2022年第8期72卷 2175-2190页

作者： mustafa.m.sa jaber salman Yussof Amer S.Elameer Leong Yeng Weng Sura Khalil Abd Anand Nayyar Institute of Informatics and Computing in Energy Universiti Tenaga NasionalMalaysia Department of Computer Science Dijlah University CollageBaghdad10021Iraq Biomedical Informatics College University of Information Technology and CommunicationsBaghdadIraq Faculty of Information Technology Duy Tan UniversityDa Nang550000Viet Nam Graduate School Duy Tan UniversityDa Nang550000Viet Nam Department of Computer Science Al-turath University CollegeBaghdadIraq

Artificial intelligence plays an essential role in the medical and health *** convolution networks offer valuable services and help create automated systems to perform medical image ***,convolution networks examine medical images effectively;such systems require high computational complexity when recognizing the same disease-affected ***,an optimized deep convolution network is utilized for analyzing disease-affected regions in this *** disease-relatedmedical images are selected and examined pixel by pixel;this analysis uses the gray wolf optimized deep learning *** method identifies affected pixels by the gray wolf hunting *** convolution network uses an automatic learning function that predicts the disease affected by previous imaging *** optimized algorithm-based selected regions are further examined using the distribution pattern-matching *** pattern-matching process recognizes the disease effectively,and the system’s efficiency is evaluated using themATLAB implementation *** process ensures high accuracy of up to 99.02%to 99.37%and reduces computational complexity.

关键词： Artificial intelligence medical field gray wolf-optimized deep convolution networks distribution pattern-matching rule

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Characteristic properties of date-palm fibre/sheep wool reinforced polyester composites

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Journal of Bioresources and Bioproducts 2023年第4期8卷 430-443页

作者： mohammed Y.Abdellah mustafa Gamal sadek Hamzah Alharthi G.T.Abdel-jaber Ahmed H.Backar mechanical Engineering Department Faculty of EngineeringSouth Valley UniversityQena83523Egypt mechanical Engineering Department College of Engineering and Islamic ArchitectureUmm AL-Qura UniversityMakkah21955Saudi Arabia New Assiut Technological University(NAUT) Assiut71684Egypt Production Engineering Department Faculty of EngineeringAlexandria UniversityAlexandria21544Egypt

In this study,we attempted to characterise the effects of date-palm fibre(DPF)and a date-palm fibre/sheep wool hybrid in polyester to enhance high-performance and low-cost composite materials that can be used in insulation building systems,automotive parts,and home *** DPF was treated using 5%NaOH solution;and the sheep wool was cleaned with 50℃hot water and *** composite specimens were prepared with different fibre contents(0%,10%,20%,30%(w))using a compression moulding *** effect of fibre reinforcement was analysed in terms of the mechanical properties(tensile,flexural,impact,and hardness)and composite ***,scanning electron microscopy(SEm)was performed on the fibres before and after treatment,and the fractured surfaces of all composite specimens were examined after tensile *** results showed that the 20%DPF/sheep wool hybrid reinforced polyester produced the best *** ultimate tensile strength and modulus were 27 mPa and 3.69GPa,*** ultimate flexural strength and flexural modulus were 35.4 and 2507 mPa,*** impact strength was 39.5 kJ/m^(2)and the hardness was 64 *** density decreased to the lowest value of 1.02 g/cm^(3)with the 30%DPF/sheep wool *** SEm showed good adhesion and interfacial bonding between DPF/sheep wool hybrid fibres and the polyester matrix,particularly at 20%fibre content.

关键词： Date-palm fibre Polyester Compression moulding mechanical property

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Competitive multi-Verse Optimization with Deep Learning Based Sleep Stage Classification

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Computer Systems Science & Engineering 2023年第5期45卷 1249-1263页

作者： Anwer mustafa Hilal Amal Al-Rasheed jaber salzahrani majdy m.Eltahir mesfer Al Duhayyim Nermin m.salem Ishfaq Yaseen Abdelwahed motwakel Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam Bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversitySaudi Arabia Department of Information Systems College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam Bin Abdulaziz UniversitySaudi Arabia Department of Electrical Engineering Faculty of Engineering and TechnologyFuture University in EgyptNew Cairo11835Egypt

Sleep plays a vital role in optimum working of the brain and the *** people suffer from sleep-oriented illnesses like apnea,insomnia,*** stage classification is a primary process in the quantitative examination of polysomnographic *** stage scoring is mainly based on experts’knowledge which is laborious and time ***,it can be essential to design automated sleep stage classification model using machine learning(mL)and deep learning(DL)*** this view,this study focuses on the design of Competitive multi-verse Optimization with Deep Learning Based Sleep Stage Classification(CmVODL-SSC)model using Electroencephalogram(EEG)*** proposed CmVODL-SSC model intends to effectively categorize different sleep stages on EEG ***,data pre-processing is performed to convert the actual data into useful ***,a cascaded long short term memory(CLSTm)model is employed to perform classification *** last,the CmVO algorithm is utilized for optimally tuning the hyperparameters involved in the CLSTm *** order to report the enhancements of the CmVODL-SSC model,a wide range of simulations was carried out and the results ensured the better performance of the CmVODL-SSC model with average accuracy of 96.90%.

关键词： Signal processing EEG signals sleep stage classification clstm model deep learning cmvo algorithm

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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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manta Ray Foraging Optimization with machine Learning Based Biomedical Data Classification

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Computers, materials & Continua 2022年第11期73卷 3275-3290页

作者： Amal Al-Rasheed jaber S.Alzahrani majdy m.Eltahir Abdullah mohamed Anwer mustafa Hilal Abdelwahed motwakel Abu sarwar Zamani mohamed I.Eldesouki Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversityMecca24382Saudi Arabia Department of Information Systems College of Science&Art at MahayilKing Khalid UniversityAbha62529Saudi Arabia Research Centre Future University in EgyptNew Cairo11845Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Information System College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

The biomedical data classification process has received significant attention in recent times due to a massive increase in the generation of healthcare data from various *** developments of artificial intelligence(AI)and machine learning(mL)models assist in the effectual design of medical data classification ***,this article concentrates on the development of optimal Stacked Long Short Term memory Sequence-toSequence Autoencoder(OsaE-LSTm)model for biomedical data *** presented OsaE-LSTm model intends to classify the biomedical data for the existence of ***,the OsaE-LSTm model involves min-max normalization based pre-processing to scale the data into uniform *** by,the saE-LSTm model is utilized for the detection and classification of diseases in biomedical *** last,manta ray foraging optimization(mRFO)algorithm has been employed for hyperparameter optimization *** utilization of mRFO algorithm assists in optimal selection of hypermeters involved in the saE-LSTm *** simulation analysis of the OsaE-LSTm model has been tested using a set of benchmark medical datasets and the results reported the improvements of the OsaELSTm model over the other approaches under several dimensions.

关键词： Biomedical data classification deep learning manta ray foraging optimization healthcare machine learning artificial intelligence

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Political Optimizer with Deep Learning-Enabled Tongue Color Image Analysis model

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Computer Systems Science & Engineering 2023年第5期45卷 1129-1143页

作者： Anwer mustafa Hilal Eatedal Alabdulkreem jaber S.Alzahrani majdy m.Eltahir mohamed I.Eldesouki Ishfaq Yaseen Abdelwahed motwakel Radwa marzouk Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversityMecca24382Saudi Arabia Department of Information Systems College of Science&Art at MahayilKing Khalid UniversityAbha62529Saudi Arabia Department of Information System College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of mathematics Faculty of ScienceCairo UniversityGiza12613Egypt

Biomedical image processing is widely utilized for disease detection and classification of biomedical *** color image analysis is an effective and non-invasive tool for carrying out secondary detection at anytime and *** removing the qualitative aspect,tongue images are quantitatively inspected,proposing a novel disease classification model in an automated way is *** article introduces a novel political optimizer with deep learning enabled tongue color image analysis(PODL-TCIA)*** presented PODL-TCIA model purposes to detect the occurrence of the disease by examining the color of the *** attain this,the PODL-TCIA model initially performs image pre-processing to enhance medical image *** by,Inception with ResNet-v2 model is employed for feature ***,political optimizer(PO)with twin support vector machine(TSVm)model is exploited for image classification process,shows the novelty of the *** design of PO algorithm assists in the optimal parameter selection of the TSVm *** ensuring the enhanced outcomes of the PODL-TCIA model,a wide-ranging experimental analysis was applied and the outcomes reported the betterment of the PODL-TCIA model over the recent approaches.

关键词： Tongue color image analysis political optimizer twin support vector machine inception model deep learning

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Optimal Deep Learning Enabled Communication System for Unmanned Aerial Vehicles

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Computer Systems Science & Engineering 2023年第4期45卷 955-969页

作者： Anwer mustafa Hilal jaber S.Alzahrani Dalia H.Elkamchouchi majdy m.Eltahir Ahmed S.Almasoud Abdelwahed motwakel Abu sarwar Zamani Ishfaq Yaseen 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 Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh 11671Saudi Arabia Department of Information Systems College of Science&Art at MahayilKing Khalid UniversityMuhayel Aseer62529Saudi Arabia Department of Information Systems College of Computer and Information SciencesPrince Sultan UniversitySaudi Arabia

Recently,unmanned aerial vehicles(UAV)or drones are widely employed for several application areas such as surveillance,disaster management,*** UAVs are limited to energy,efficient coordination between them becomes essential to optimally utilize the resources and effective communication among them and base station(BS).Therefore,clustering can be employed as an effective way of accomplishing smart communication systems among multiple *** this aspect,this paper presents a group teaching optimization algorithm with deep learning enabled smart communication system(GTOADL-SCS)technique for UAV *** proposed GTOADL-SCS model encompasses a two stage process namely clustering and *** the initial stage,the GTOADL-SCS model includes a GTOA based clustering scheme to elect cluster heads(CHs)and organize ***,the GTOADL-SCS model develops a fitness function containing three input parameters as residual energy of UAVs,average neighoring distance,and UAV *** classification process,the GTOADLSCS model applies pre-trained densely connected network(DenseNet201)feature extractor with gated recurrent unit(GRU)*** ensuring the enhanced performance of the GTOADL-SCS model,a widespread simulation analysis is performed and the comparative study reported the significant outcomes over the existing approaches with maximum packet delivery ratio(PDR)of 92.60%.

关键词： Unmanned aerial vehicles energy efficiency smart communication system deep learning

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Chaotic Pigeon Inspired Optimization Technique for Clustered Wireless Sensor Networks

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Computers, materials & Continua 2022年第12期73卷 6547-6561页

作者： Anwer mustafa Hilal Aisha Hassan Abdalla Hashim sami Dhahbi Dalia H.Elkamchouchi jaber S.Alzahrani mrim m.Alnfiai Amira sayed A.Aziz Abdelwahed motwakel Department of Electrical and Computer Engineering International Islamic University Malaysia53100Kuala LumpurMalaysia 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 Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversitySaudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaifP.O.Box 11099Taif21944Saudi Arabia Department of Digital media Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt

Wireless Sensor Networks(WSN)interlink numerous Sensor Nodes(SN)to support Internet of Things(loT)*** the data gathered from SNs can be divulged,tempered,and *** WSN data processes manage the data in a centralized format at terminal *** devices are prone to attacks and the security of systems can get *** is a distributed and decentralized technique that has the ability to handle security issues in *** security issues include transactions that may be copied and spread across numerous nodes in a peer-peer network *** breaches the mutual trust and allows data immutability which in turn permits the network to go *** some instances,few nodes die or get compromised due to heavy power *** current article develops an Energy Aware Chaotic Pigeon Inspired Optimization based Clustering scheme for Blockchain assisted WSN technique abbreviated as EACPIO-CB *** primary objective of the proposed EACPIO-CB model is to proficiently group the sensor nodes into clusters and exploit Blockchain(BC)for inter-cluster communication in the *** select ClusterHeads(CHs)and organize the clusters,the presented EACPIO-CB model designs a fitness function that involves distinct input ***,BC technology enables the communication between one CH and the other and with the Base Station(BS)in the *** authors conducted comprehensive set of simulations and the outcomes were investigated under different *** simulation results confirmed the better performance of EACPIO-CB method over recent methodologies.

关键词： Blockchain wireless sensor networks clustering energy efficiency security data transmission

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