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Chaotic Metaheuristics with Multi-spiking Neural Network Based Cloud Intrusion Detection

Computers, Materials & Continua 2023年第3期74卷 6101-6118页

作者： Mohammad Yamin s.leh bajaba Zenah Mahmoud AlKubaisy Department of Management Information systems Faculty of Economics and AdministrationKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Business Administration Faculty of Economics and AdministrationKing Abdulaziz UniversityJeddah21589Saudi Arabia

Cloud Computing(CC)provides data storage options as well as computing services to its users through the *** the other hand,cloud users are concerned about security and privacy issues due to the increased number of *** protection has become an important issue since the users’information gets exposed to third *** networks are exposed to different types of attacks which have extensively grown in addition to the novel intrusion methods and hacking *** Detection systems(IDss)can be used in a network to manage suspicious *** IDss monitor the activities of the CC environment and decide whether an activity is legitimate(normal)or malicious(intrusive)based on the established system’s confidentiality,availability and integrity of the data *** the current study,a Chaotic Metaheuristics with Optimal Multi-spiking Neural Network-based Intrusion Detection(CMOMsNN-ID)model is proposed to secure the cloud *** presented CMOMsNNID model involves the Chaotic Artificial Bee Colony Optimization-based Feature selection(CABC-Fs)technique to reduce the curse of *** addition,the Multi-spiking Neural Network(MsNN)classifier is also used based on the simulation of brain *** is applied to resolve pattern classification *** order to fine-tune the parameters relevant to theMsNN model,theWhale Optimization Algorithm(WOA)is employed to boost the classification *** demonstrate the superiority of the proposed CMOMsNN-ID model,a useful set of simulations was *** simulation outcomes inferred that the proposed CMOMsNN-ID model accomplished a superior performance over other models with a maximum accuracy of 99.20%.

关键词： Cloud computing security intrusion detection feature selection multi-spiking neural network

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Biometric Finger Vein Recognition Using Evolutionary Algorithm with Deep Learning

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Computers, Materials & Continua 2023年第6期75卷 5659-5674页

作者： Mohammad Yamin Tom Gedeon s.leh bajaba Mona M.Abusurrah Department of Management Information systems Faculty of Economics and AdministrationKing Abdulaziz UniversityJeddah21589Saudi Arabia Optus Centre for AI Curtin UniversityPerth6102Australia Department of Business Administration Faculty of Economics and AdministrationKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Management Information systems College of Business AdministrationTaibah UniversityAl-Madinah42353Saudi Arabia

In recent years,the demand for biometric-based human recog-nition methods has drastically increased to meet the privacy and security *** prints,palm veins,finger veins,fingerprints,hand veins and other anatomic and behavioral features are utilized in the development of different biometric recognition *** the available biometric recognition techniques,Finger Vein Recognition(FVR)is a general technique that analyzes the patterns of finger veins to authenticate the *** Learning(DL)-based techniques have gained immense attention in the recent years,since it accomplishes excellent outcomes in various challenging domains such as computer vision,speech detection and Natural Language Processing(NLP).This technique is a natural fit to overcome the ever-increasing biomet-ric detection problems and cell phone authentication issues in airport security *** current study presents an Automated Biometric Finger Vein Recognition using Evolutionary Algorithm with Deep Learning(ABFVR-EADL)*** presented ABFVR-EADL model aims to accomplish bio-metric recognition using the patterns of the finger ***,the presented ABFVR-EADL model employs the histogram equalization technique to pre-process the input *** feature extraction,the salp swarm Algorithm(ssA)with Densely-connected Networks(DenseNet-201)model is exploited,showing the proposed method’s ***,the Deep-stacked Denoising Autoencoder(DsAE)is utilized for biometric *** proposed ABFVR-EADL method was experimentally validated using the benchmark databases,and the outcomes confirmed the productive performance of the proposed ABFVR-EADL model over other DL models.

关键词： Biometric authentication finger vein recognition deep learning evolutionary algorithm security privacy

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Biometric Verification system UsingHyperparameter Tuned Deep Learning Model

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Computer systems science & Engineering 2023年第7期46卷 321-336页

作者： Mohammad Yamin s.leh bajaba sarah B.Basahel E.Laxmi Lydia Department of Management Information systems Faculty of Economics and AdministrationKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Business Administration Faculty of Economics and AdministrationKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Management Information systems Faculty of Economics and AdministrationKing Abdulaziz UniversityJeddahSaudi Arabia Department of Computer science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India

Deep learning(DL)models have been useful in many computer vision,speech recognition,and natural language processing tasks in recent *** models seem a natural fit to handle the rising number of biometric recognition problems,from cellphone authentication to airport security *** approaches have recently been utilized to improve the efficiency of various biometric recognition *** recognition was considered the more reliable and accurate biometric detection method *** recognition has been an active research region in the last few decades due to its extensive applications,from security in airports to homeland security border *** article presents a new Political Optimizer with Deep Transfer Learning Enabled Biometric Iris Recognition(PODTL-BIR)*** presented PODTL-BIR technique recognizes the iris for biometric *** the presented PODTL-BIR model,an initial stage of pre-processing is carried *** addition,the MobileNetv2 feature extractor is utilized to produce a collection of feature *** PODTL-BIR technique utilizes a bidirectional gated recurrent unit(BiGRU)model to recognise iris for biometric ***,the political optimizer(PO)algorithm is used as a hyperparameter tuning strategy to improve the PODTL-BIR technique’s recognition ***-ranging experimental investigation was executed to validate the enhanced performance of the PODTL-BIR *** experimental outcome stated the promising performance of the PODTL-BIR system over other existing algorithms.

关键词： Biometric verification iris recognition political optimizer deep learning feature extraction

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Teamwork Optimization with Deep Learning Based Fall Detection for IoT-Enabled smart Healthcare system

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Computers, Materials & Continua 2023年第4期75卷 1353-1369页

作者： sarah B.Basahel s.leh bajaba Mohammad Yamin sachi Nandan Mohanty E.Laxmi Lydia Department of Management Information systems Faculty of Economics and AdministrationKing Abdulaziz UniversityJeddahSaudi Arabia Department of business Administration Faculty of Economics and AdministrationKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Management Information systems Faculty of Economics and AdministrationKing Abdulaziz UniversityJeddah21589Saudi Arabia school of Computer science&Engineering(sCOPE) VIT-AP UniversityAmaravatiAndhra PradeshIndia Department of Computer science and Engineering GMR Institute of TechnologyAndhra PradeshRajam 532127India

The current advancement in cloud computing,Artificial Intelligence(AI),and the Internet of Things(IoT)transformed the traditional healthcare system into smart *** services could be enhanced by incorporating key techniques like AI and *** convergence of AI and IoT provides distinct opportunities in the medical *** is regarded as a primary cause of death or post-traumatic complication for the ageing ***,earlier detection of older person falls in smart homes is required to improve the survival rate of an individual or provide the necessary ***,the emergence of IoT,AI,smartphones,wearables,and so on making it possible to design fall detection(FD)systems for smart home *** article introduces a new Teamwork Optimization with Deep Learning based Fall Detection for IoT Enabled smart Healthcare systems(TWODLFDsHs).The TWODL-FDsHs technique’s goal is to detect fall events for a smart healthcare ***,the presented TWODL-FDsHs technique exploits IoT devices for the data collection ***,the TWODLFDsHs technique applies the TWO with Capsule Network(CapsNet)model for feature *** last,a deep random vector functional link network(DRVFLN)with an Adam optimizer is exploited for fall event detection.A wide range of simulations took place to exhibit the enhanced performance of the presentedTWODL-FDsHs *** experimental outcomes stated the enhancements of the TWODL-FDsHs method over other models with increased accuracy of 98.30%on the URFD dataset.

关键词： Internet of things smart healthcare deep learning team work optimizer capsnet fall detection

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