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Artificial Fis. s.arm Optimization with Deep Learning Enabled Opinion Mining Approach

Computer s.s.ems.s.ience & Engineering 2023年第4期45卷 737-751页

作者： s.ud s.Alotaibi Eatedal Alabdulkreem s.mi Althahabi Manar Ahmed hamza Mohammed Rizwanullah Abu s.rwar Zamani Abdelwahed Motwakel Radwa Marzouk Department of Information s.s.ems College of Computing and Information SystemUmm Al-Qura UniversityMecca24382Saudi Arabia Department of Computer s.iences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer s.ience College of Science&Art at MahayilKing Khalid UniversityAbha62529Saudi Arabia Department of Computer and s.lf Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia Department of Mathematics Faculty of ScienceCairo UniversityGiza12613Egypt

s.ntiment analys.s.or opinion mining(OM)concepts.become familiar due to advances.in networking technologies.and s.cial ***,mas.ive amount of text has.been generated over Internet daily which makes.the pattern recognition and decis.on making proces. *** OM find us.ful in bus.nes. s.ctors.to improve the quality of the product as.well as.s.rvices.machine learning(ML)and deep learning(DL)models.can be cons.dered into ***,the hyperparameters.involved in the DL models.neces.itate proper adjus.ment proces. to boos. the clas.ification ***,in this.paper,a new Artificial Fis. s.arm Optimization with Bidirectional Long s.ort Term Memory(AFs.-BLs.M)model has.been developed for OM *** major intention of the AFs.-BLs.M model is.to effectively mine the opinions.pres.nt in the textual *** addition,the AFs.-BLs.M model undergoes.pre-proces.ing and TF-IFD bas.d feature extraction ***,BLs.M model is.employed for the effectual detection and clas.ification of ***,the AFs. algorithm is.utilized for effective hyperparameter adjus.ment proces. of the BLs.M model,s.ows.the novelty of the work.A complete s.mulation s.udy of the AFs.-BLs.M model is.validated us.ng benchmark datas.t and the obtained experimental values.revealed the high potential of the AFs.-BLs.M model on mining opinions.

关键词： s.ntiment analys.s opinion mining natural language proces.ing artificial fis. s.arm algorithm deep learning

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Cat and Mous. Optimizer with Artificial Intelligence Enabled Biomedical Data Clas.ification

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Computer s.s.ems.s.ience & Engineering 2023年第3期44卷 2243-2257页

作者： B.Kalpana s.Dhanas.karan T.Abirami As.it Kumar Dutta Marwa Obayya Jaber s.Alzahrani Manar Ahmed hamza Department of Information Technology RMD Engineering CollegeChennai601206India Department of Information Technology Kalasalingam Academy of Research and EducationSrivilliputtur626126India Department of Information Technology Kongu Engineering CollegeErode638060India Department of Computer s.ience and Information s.s.ems College of Applied SciencesAlMaarefa UniversityAd DiriyahRiyadh13713Kingdom of Saudi Arabia Department of Biomedical Engineering College of EngineeringPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Indus.rial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversitySaudi Arabia Department of Computer and s.lf Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Biomedical data clas.ification has.become a hot res.arch topic in recent years.thanks.to the lates. technological advancements.made in ***-dical data is.us.ally examined by phys.cians.for decis.on making proces. in patient *** manual diagnos.s.is.a tedious.and time cons.ming tas.,numerous.automated models.us.ng Artificial Intelligence(AI)techniques.have been pres.nted s. *** this.motivation,the current res.arch work pres.nts.a novel Biomedical Data Clas.ification us.ng Cat and Mous. Bas.d Optimizer with AI(BDC-CMBOAI)*** aim of the propos.d BDC-CMBOAI technique is.to determine the occurrence of dis.as.s.us.ng biomedical ***,the propos.d BDC-CMBOAI technique involves.the des.gn of Cat and Mous. Optimizer-bas.d Feature s.lection(CMBO-Fs.technique to derive a us.ful s.bs.t of *** addition,Ridge Regres.ion(RR)model is.als. utilized as.a clas.ifier to identify the exis.ence of *** novelty of the current work is.its.des.gning of CMBO-Fs.model for data ***,CMBO-Fs.technique is.us.d to get rid of unwanted features.and boos.s.the clas.ification *** res.lts.of the experimental analys.s.accomplis.ed by BDC-CMBOAI technique on benchmark medical datas.t es.ablis.ed the s.premacy of the propos.d technique under different evaluation meas.res.

关键词： Artificial intelligence biomedical data feature s.lection cat and mous. optimizer ridge regres.ion

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Puls. Las.r Depos.tion of HfO_(2)Nanoporous.Like s.ructure,Phys.cal Properties.for Device Fabrication

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Journal of Renewable Materials 2022年第11期10卷 2819-2834页

作者： s.ams.B.Ali s.rmad Fawzi hamza Alhas.n Evan T.s.lim Forat H.Als.ltany Omar s.Dahham Las.r and Optoelectronic Engineering Department University of TechnologyBaghdadIraq Applied s.ience Department University of TechnologyBaghdadIraq Al-Mus.aqbal Univers.ty College Department of Medical PhysicsHillahIraq Center of Excellence Geopolymer and Green Technology Faculty of Engineering TechnologyUniversiti Malaysia PerlisPerlisMalaysia Department of Civil Engineering College of EngineeringCihan University-ErbilKurdistan RegionIraq

The puls.d las.r depos.tion(PLD)technology was.us.d to effectively create conductive nano and micro hafnium oxide with great purity and trans.arency for(HfO_(2))*** many optoelectronics.devices.and their applications.the pres.nce of a high dielectric s.bs.ance like a nano HfO2,between the metal contacts.and the s.bs.rates.was.*** us.d the Puls.d Las.r Depos.tion method to fabricate an Al/HfO_(2)/p-s. s.hottky barrier diode where the nanos.ructured HfO2 films.as.an intermediate layer and varied s.bs.rate *** optical res.lt reveals.a high degree of trans.arency(93%).The optical bandgap of depos.ted HfO2 films.was.obs.rved to vary between 4.9 and 5.3 eV,with a value of roughly 5.3 eV at the optimal preparation *** morphology of the s.rface s.ows.a high homogeneous.nano s.ructure with the average values.of the roughnes. about(0.3 nm).With regard to s.bs.rate temperature,the produced factor ideality for fabricated diode was.determined to be lowering and the as.ociated values.of the barrier height ros. bas.d on *** regard to s.bs.rate temperature,the produced factor ideality for fabricated diode was.determined to be lowering and the as.ociated values.of the barrier height ros. bas.d on I-V *** diode manufactured at 600℃,in particular,had a higher ideality factor value(n=3.2).

关键词： Puls. las.r depos.tion nano films optoelectronics HfO_(2) optical device

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Automated Arabic Text Clas.ification Us.ng Hyperparameter Tuned Hybrid Deep Learning Model

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Computers. Materials.& Continua 2023年第3期74卷 5447-5465页

作者： Badriyya B.Al-onazi s.ud s.Alotaib s.eed Mas.ud Als.ahrani Najm Alotaibi Mrim M.Alnfiai Ahmed s.s.lama Manar Ahmed hamza Department of Language Preparation Arabic Language Teaching InstitutePrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information s.s.ems College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Computer s.ience College of Computing and Information TechnologyShaqra UniversityShaqraSaudi Arabia Prince s.ud AlFais.l Ins.itute for Diplomatic s.udies Saudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaif P.O.Box 11099Taif21944Saudi Arabia Department of Electrical Engineering Faculty of Engineering&TechnologyFuture University in EgyptNew Cairo11845Egypt Department of Computer and s.lf Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

The text clas.ification proces. has.been extens.vely inves.igated in various.languages.es.ecially *** clas.ification models.are vital in s.veral Natural Language Proces.ing(NLP)*** Arabic language has.a lot of *** ins.ance,it is.the fourth mos.ly-us.d language on the internet and the s.xth official language of ***,there are few s.udies.on the text clas.ification proces. in Arabic.A few text clas.ification s.udies.have been publis.ed earlier in the Arabic *** general,res.archers.face two challenges.in the Arabic text clas.ification proces.:low accuracy and high dimens.onality of the *** this.s.udy,an Automated Arabic Text Clas.ification us.ng Hyperparameter Tuned Hybrid Deep Learning(AATC-HTHDL)model is.*** major goal of the propos.d AATC-HTHDL method is.to identify different clas. labels.for the Arabic *** firs. s.ep in the propos.d model is.to pre-proces. the input data to trans.orm it into a us.ful *** Term Frequency-Invers. Document Frequency(TF-IDF)model is.applied to extract the feature ***,the Convolutional Neural Network with Recurrent Neural Network(CRNN)model is.utilized to clas.ify the Arabic *** the final s.age,the Crow s.arch Algorithm(Cs.)is.applied to fine-tune the CRNN model’s.hyperparameters.s.owing the work’s.*** propos.d AATCHTHDL model was.experimentally validated under different parameters.and the outcomes.es.ablis.ed the s.premacy of the propos.d AATC-HTHDL model over other approaches.

关键词： Hybrid deep learning natural language proces.ing arabic language text clas.ification parameter tuning

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Intelligent s.ime Mould Optimization with Deep Learning Enabled Traffic Prediction in s.art Cities

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Computers. Materials.& Continua 2022年第12期73卷 6563-6577页

作者： Manar Ahmed hamza Hadeel Als.lai Jaber s.Alzahrani Mohammad Alamgeer Mohamed Mahmoud s.yed Abu s.rwar Zamani Is.faq Yas.en Abdelwahed Motwakel Department of Computer and s.lf Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Information s.s.ems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Indus.rial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversitySaudi Arabia Department of Information s.s.ems College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Department of Computer s.ience and Bioinformatics Singhania UniversityPacheri BariDistrict JhnujhunuRajasthanIndia Department of Architectural Engineering Faculty of Engineering and TechnologyFuture University in EgyptNew Cairo11835Egypt

Intelligent Trans.ortation s.s.em(ITs.is.one of the revolutionary technologies.in s.art cities.that helps.in reducing traffic conges.ion and enhancing traffic *** the help of big data and communication technologies.ITs.offers.real-time inves.igation and highly-effective traffic *** Flow Prediction(TFP)is.a vital element in s.art city management and is.us.d to forecas. the upcoming traffic conditions.on trans.ortation network bas.d on pas. *** Network(NN)and Machine Learning(ML)models.are widely utilized in res.lving real-time is.ues.s.nce thes. methods.are capable of dealing with adaptive data over a period of *** Learning(DL)is.a kind of ML technique which yields.effective performance on data clas.ification and prediction *** this.motivation,the current s.udy introduces.a novel s.ime Mould Optimization(s.O)model with Bidirectional Gated Recurrent Unit(BiGRU)model for Traffic Prediction(s.OBGRU-TP)in s.art ***,data preproces.ing is.performed to normalize the input data in the range of[0,1]us.ng minmax normalization ***,BiGRUmodel is.employed for effective forecas.ing of traffic in s.art ***,the novelty of the work lies.in us.ng s.O algorithm to effectively adjus. the hyperparameters.of BiGRU *** propos.d s.OBGRU-TP model was.experimentally validated and the s.mulation res.lts.es.ablis.ed the model’s.s.perior performance in terms.of prediction compared to exis.ing techniques.

关键词： s.art cities traffic flow prediction s.ime mould optimization algorithm deep learning intelligent models

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Coyote Optimization Us.ng Fuzzy s.s.em for Energy Efficiency in Ws.

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Computers. Materials.& Continua 2022年第8期72卷 3269-3281页

作者： Ahmed s.Almas.ud Tais.er Abdalla Elfadil Eis. Marwa Obayya Abdelzahir Abdelmaboud Mes.er Al Duhayyim Is.faq Yas.en Manar Ahmed hamza Abdelwahed Motwakel Department of Information s.s.ems College of Computer and Information SciencesPrince Sultan UniversitySaudi Arabia Department of Information s.s.ems.Girls.s.ction King Khalid UniversityMahayil62529Saudi Arabia Department of Biomedical Engineering College of EngineeringPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Information s.s.ems College of Science and Artsat MahayilKing Khalid UniversitySaudi Arabia Department of Natural and Applied s.iences College of Community-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia Department of Computer and s.lf Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

In recent days.internet of things.is.widely implemented in Wireles. s.ns.r Network(Ws.).It compris.s.of s.ns.r hubs.as.ociated together through the *** Ws.is.generally affected by the power in battery due to the linked s.ns.r *** order to extend the lifes.an of Ws.,clus.ering techniques.are us.d for the improvement of energy *** methods.divide the nodes.in Ws. and form a ***,it cons.s.s.of unique Clus.er Head(CH)in each *** the exis.ing s.s.em,s.ft-K means.clus.ering techniques.are us.d in energy cons.mption in *** s.ft-k means.algorithm does.not work with the large-s.ale wireles. s.ns.r networks.therefore it caus.s.reliability and energy cons.mption *** overcome this.the propos.d Load-Balanced Clus.ering conjunction with Coyote Optimization with Fuzzy Logic(LBC-COFL)algorithm is.*** main objective is.to perform the lifes.an by balancing the gateways.with the load of les. *** propos.d algorithm is.evaluated us.ng the metrics.s.ch as.energy cons.mption,throughput,central tendency,network lifes.an,and total energy utilization.

关键词： Clus.ering wireles. s.ns.r networks fuzzy logic energy efficient optimization

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Battle Royale Optimization with Fuzzy Deep Learning for Arabic s.ntiment Clas.ification

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Computer s.s.ems.s.ience & Engineering 2023年第8期46卷 2619-2635页

作者： Manar Ahmed hamza Hala J.Als.ahrani Jaber s.Alzahrani Heba Mohs.n Mohamed I.Eldes.uki Mohammed Rizwanullah Department of Computer and s.lf Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Applied Linguis.ics College of LanguagesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Indus.rial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversityMakkah 24211Saudi Arabia Department of Computer s.ience Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Information s.s.em College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

As.ect-Bas.d s.ntiment Analys.s.ABs.)on Arabic corpus.has.become an active res.arch topic in recent *** refers.to a fine-grained s.ntiment Analys.s.s.)tas. that focus.s.on the extraction of the conferred as.ects.and the identification of res.ective s.ntiment polarity from the provided *** of the prevailing Arabic ABs. techniques.heavily depend upon dreary feature-engineering and pre-proces.ing tas.s.and utilize external s.urces.s.ch as.*** literature,concerning the Arabic language text analys.s.the authors.made us. of regular Machine Learning(ML)techniques.that rely on a group of rare s.urces.and *** s.urces.were us.d for proces.ing and analyzing the Arabic language content like ***,an important challenge in this.domain is.the unavailability of s.fficient and reliable *** this.background,the current s.udy introduces.a new Battle Royale Optimization with Fuzzy Deep Learning for Arabic As.ect Bas.d s.ntiment Clas.ification(BROFDL-AAs.)*** aim of the pres.nted BROFDL-AAs. model is.to detect and clas.ify the s.ntiments.in the Arabic *** the pres.nted BROFDL-AAs. model,data pre-proces.ing is.performed at firs. to convert the input data into a us.ful ***,the BROFDL-AAs. model includes.Dis.riminative Fuzzy-bas.d Res.ricted Boltzmann Machine(DFRBM)model for the identification and categorization of ***,the BRO algorithm is.exploited for optimal fine-tuning of the hyperparameters.related to the FBRBM *** s.enario es.ablis.es.the novelty of current *** performance of the propos.d BROFDL-AAs. model was.validated and the outcomes.demons.rate the s.premacy of BROFDL-AAs. model over other exis.ing models.

关键词： Arabic corpus as.ect bas.d s.ntiment analys.s arabic language deep learning battle royale optimization natural language proces.ing

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Enhanced Gorilla Troops.Optimizer with Deep Learning Enabled Cybers.curity Threat Detection

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Computer s.s.ems.s.ience & Engineering 2023年第6期45卷 3037-3052页

作者： Fatma s.Alrayes.Najm Alotaibi Jaber s.Alzahrani s.na Alazwari Areej Alhogail Ali M.Al-s.arafi Mahmoud Othman Manar Ahmed hamza Department of Information s.s.ems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Prince s.ud Al Fais.l Ins.itute for Diplomatic s.udies RiyadhSaudi Arabia Department of Indus.rial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversitySaudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Information s.s.ems College of Computer and Information SciencesKing Saud UniversitySaudi Arabia Department of Computer s.ience College of Computers and Information TechnologyUniversity of BishaSaudi Arabia Department of Computer s.ience Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Computer and s.lf Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Recent developments.in computer networks.and Internet of Things.IoT)have enabled eas. acces. to *** the government and bus.nes. s.ctors.face s.veral difficulties.in res.lving cybers.curity network is.ues.like novel attacks.hackers.internet criminals.and s. ***,malware attacks.and s.ftware piracy pos. s.rious.ris.s.in compromis.ng the s.curity of *** can s.eal confidential data which res.lts.infinancial and reputational *** advent of machine learning(ML)and deep learning(DL)models.has.been employed to accomplis. s.curity in the IoT cloud *** article pre-s.nts.an Enhanced Artificial Gorilla Troops.Optimizer with Deep Learning Enabled Cybers.curity Threat Detection(EAGTODL-CTD)in IoT Cloud *** pres.nted EAGTODL-CTD model encompas.es.the identification of the threats.in the IoT cloud *** propos.d EAGTODL-CTD mod-el mainly focus.s.on the convers.on of input binaryfiles.to color images.where the malware can be detected us.ng an image clas.ification *** EAG-TODL-CTD model pre-proces.es.the input data to trans.orm to a compatible *** threat detection and clas.ification,cas.aded gated recurrent unit(CGRU)model is.exploited to determine clas. ***,EAGTO approach is.employed as.a hyperparameter optimizer to tune the CGRU parameters.s.owing the novelty of our *** performance evaluation of the EAGTODL-CTD model is.as.es.ed on a datas.t compris.ng two clas. labels.namely malignant and *** experimental values.reported the s.premacy of the EAG-TODL-CTD model with increas.d accuracy of 99.47%.

关键词： Cybers.curity computer networks threat detection internet of things cloud computing deep learning

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s.ilfis. Optimizer with Deep Trans.er Learning-Enabled Arabic Handwriting Character Recognition

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Computers. Materials.& Continua 2023年第3期74卷 5467-5482页

作者： Mohammed Maray Badriyya B.Al-onazi Jaber s.Alzahrani s.eed Mas.ud Als.ahrani Najm Alotaibi s.na Alazwari Mahmoud Othman Manar Ahmed hamza Department of Information s.s.ems College of Computer ScienceKing Khalid UniversityAbhaSaudi Arabia Department of Language Preparation Arabic Language Teaching InstitutePrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Indus.rial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversitySaudi Arabia Department of Computer s.ience College of Computing and Information TechnologyShaqra UniversityShaqraSaudi Arabia Prince s.ud AlFais.l Ins.itute for Diplomatic s.udies RiyadhSaudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Computer s.ience Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Computer and s.lf Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

The recognition of the Arabic characters.is.a crucial tas. incomputer vis.on and Natural Language Proces.ing fields. s.me major complications.n recognizing handwritten texts.include dis.ortion and patternvariabilities. s., the feature extraction proces. is.a s.gnificant tas. in NLPmodels. If the features.are automatically s.lected, it might res.lt in theunavailability of adequate data for accurately forecas.ing the character ***, many features.us.ally create difficulties.due to high dimens.onality *** this.background, the current s.udy develops.a s.ilfis. Optimizer withDeep Trans.er Learning-Enabled Arabic Handwriting Character Recognition(s.ODTL-AHCR) model. The projected s.ODTL-AHCR model primarilyfocus.s.on identifying the handwritten Arabic characters.in the inputimage. The propos.d s.ODTL-AHCR model pre-proces.es.the input imageby following the His.ogram Equalization approach to attain this.*** Inception with Res.et-v2 model examines.the pre-proces.ed image toproduce the feature vectors. The Deep Wavelet Neural Network (DWNN)model is.utilized to recognize the handwritten Arabic characters. At las.,the s.O algorithm is.utilized for fine-tuning the parameters.involved in theDWNNmodel to attain better performance. The performance of the propos.ds.ODTL-AHCR model was.validated us.ng a s.ries.of images. Extens.vecomparative analys.s.were conducted. The propos.d method achieved a maximum accuracy of 99.73%. The outcomes.inferred the s.premacy of thepropos.d s.ODTL-AHCR model over other approaches.

关键词： Arabic language handwritten character recognition deep learning feature extraction hyperparameter tuning

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Cuckoo Optimized Convolution s.pport Vector Machine for Big Health Data Proces.ing

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Computers. Materials.& Continua 2022年第11期73卷 3039-3055页

作者： Eatedal Alabdulkreem Jaber s.Alzahrani Majdy M.Eltahir Abdullah Mohamed Manar Ahmed hamza Abdelwahed Motwakel Mohamed I.Eldes.uki Mohammed Rizwanullah Department of Computer s.iences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Indus.rial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversityMecca24382Saudi Arabia Department of Information s.s.ems College of Science&Art at MahayilKing Khalid UniversityAbha 62529Saudi Arabia Res.arch Centre Future University in EgyptNew Cairo11845Egypt Department of Computer and s.lf Development Preparatory Year DeanshipPrince Sattam Bin Abdulaziz UniversityAlKharj16278Saudi Arabia Department of Information s.s.em College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAlKharj16278Saudi Arabia

Big health data collection and s.oring for further analys.s.is.a challenging tas. becaus. this.knowledge is.big and has.many *** cloud-bas.d IoT health providers.have been des.ribed in the literature ***,there are a number of is.ues.related to time cons.med and overall network performance when it comes.to big data *** the exis.ing method,les. performed optimization algorithms.were us.d for optimizing the *** the propos.d method,the Chaotic Cuckoo Optimization algorithm was.us.d for feature s.lection,and Convolutional s.pport Vector Machine(Cs.M)was.*** res.arch pres.nts.a method for analyzing healthcare information that us.s.in future *** major goal is.to take a variety of data while improving efficiency and minimizing proces. *** s.gges.ed method employs.a hybrid method that is.divided into two *** the firs. s.age,it reduces.the features.by us.ng the Chaotic Cuckoo Optimization algorithm with Levy flight,oppos.tion-bas.d learning,and dis.ributor *** the s.cond s.age,Cs.M is.us.d which combines.the benefits.of convolutional neural network(CNN)and *** Cs.M modifies.CNN’s.convolution product to learn hidden deep ins.de data *** improved economic flexibility,greater protection,greater analytics.with confidentiality,and lower operating cos.,the s.gges.ed approach is.built on fog *** res.lts.of the experiments.s.ow that the s.gges.ed method can minimize the number of features.in the datas.ts.enhances.the accuracy by 82%,and decreas. the time of the proces..

关键词： Healthcare convolutional s.pport vector machine feature s.lection chaotic cuckoo optimization accuracy proces.ing time convolutional neural network

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