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Deep Learning Enabled Autoencoder Architecture for Collaborative Filtering Recommendation in IoT Environment

Computers, Materials & Continua 2021年第7期68卷 487-503页

作者： thavavel vaiyapuri College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

The era of the Internet of things(IoT)has marked a continued exploration of applications and services that can make people’s lives more convenient than ever ***,the exploration of IoT services also means that people face unprecedented difficulties in spontaneously selecting the most appropriate ***,there is a paramount need for a recommendation system that can help improve the experience of the users of IoT services to ensure the best quality of *** of the existing techniques—including collaborative filtering(CF),which is most widely adopted when building recommendation systems—suffer from rating sparsity and cold-start problems,preventing them from providing high quality *** by the great success of deep learning in a wide range of fields,this work introduces a deep-learning-enabled autoencoder architecture to overcome the setbacks of CF *** proposed deep learning model is designed as a hybrid architecture with three key networks,namely autoencoder(AE),multilayered perceptron(MLP),and generalized matrix factorization(GMF).The model employs two AE networks to learn deep latent feature representations of users and items respectively and in ***,MLP and GMF networks are employed to model the linear and non-linear user-item interactions respectively with the extracted latent user and item ***,the rating prediction is performed based on the idea of ensemble learning by fusing the output of the GMF and MLP *** conducted extensive experiments on two benchmark datasets,MoiveLens100K and MovieLens1M,using four standard evaluation *** experiments were conducted to confirm the validity of the proposed model and the contribution of each of its components in achieving better recommendation *** analyses were also carried out to demonstrate the potential of the proposed model in gaining better accuracy than the existing CF methods with resistance to rat

关键词： Neural collaborative filtering cold-start problem data sparsity multilayer perception generalized matrix factorization autoencoder deep learning ensemble learning top-K recommendations

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Enhanced Deep Autoencoder Based Feature Representation Learning for Intelligent Intrusion Detection System

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Computers, Materials & Continua 2021年第9期68卷 3271-3288页

作者： thavavel vaiyapuri Adel Binbusayyis College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz UniversityAlKharjSaudi Arabia

In the era of Big data,learning discriminant feature representation from network traffic is identified has as an invariably essential task for improving the detection ability of an intrusion detection system(IDS).Owing to the lack of accurately labeled network traffic data,many unsupervised feature representation learning models have been proposed with state-of-theart ***,these models fail to consider the classification error while learning the feature ***,the learnt feature representation may degrade the performance of the classification *** the first time in the field of intrusion detection,this paper proposes an unsupervised IDS model leveraging the benefits of deep autoencoder(DAE)for learning the robust feature representation and one-class support vector machine(OCSVM)for finding the more compact decision hyperplane for intrusion ***,the proposed model defines a new unified objective function to minimize the reconstruction and classification error *** unique contribution not only enables the model to support joint learning for feature representation and classifier training but also guides to learn the robust feature representation which can improve the discrimination ability of the classifier for intrusion *** set of evaluation experiments are conducted to demonstrate the potential of the proposed ***,the ablation evaluation on benchmark dataset,NSL-KDD validates the design decision of the proposed ***,the performance evaluation on recent intrusion dataset,UNSW-NB15 signifies the stable performance of the proposed ***,the comparative evaluation verifies the efficacy of the proposed model against recently published state-of-the-art methods.

关键词： Cybersecurity network intrusion detection deep learning autoencoder stacked autoencoder feature representational learning joint learning one-class classifier OCSVM

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Improved Metaheuristic Based Failure Prediction with Migration Optimization in Cloud Environment

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Computer Systems Science & Engineering 2023年第5期45卷 1641-1654页

作者： K.Karthikeyan Liyakathunisa Eman Aljohani thavavel vaiyapuri Department of Computer Applications Anna UniversityRegional CampusMaduraiMaduraiIndia Department of Computer Science College of Computer Science and EngineeringTaibah UniversityMadinahSaudi Arabia College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia

Cloud data centers consume high volume of energy for processing and switching the servers among different *** Machine(VM)migration enhances the performance of cloud servers in terms of energy efficiency,internal failures and *** the other end,energy utilization can be minimized by decreasing the number of active,underutilized sources which conversely reduces the dependability of the *** VM migration process,the VMs are migrated from underutilized physical resources to other resources to minimize energy utilization and optimize the *** this view,the current study develops an Improved Metaheuristic Based Failure Prediction with Virtual Machine Migration Optimization(IMFP-VMMO)model in cloud *** major intention of the proposed IMFP-VMMO model is to reduce energy utilization with maximum performance in terms of failure *** accomplish this,IMFPVMMO model employs Gradient Boosting Decision Tree(GBDT)classification model at initial stage for effectual prediction of VM *** the same time,VMs are optimally migrated using Quasi-Oppositional Artificial Fish Swarm Algorithm(QO-AFSA)which in turn reduces the energy *** performance of the proposed IMFP-VMMO technique was validated and the results established the enhanced performance of the proposed *** comparative study outcomes confirmed the better performance of the proposed IMFP-VMMO model over recent approaches.

关键词： Cloud computing energy efficiency virtual machine migration failure prediction energy optimization metaheuristics

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Optimal IoT Based Improved Deep Learning Model for Medical Image Classification

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Computers, Materials & Continua 2022年第11期73卷 2275-2291页

作者： Prasanalakshmi Balaji B.Sri Revathi Praveetha Gobinathan Shermin Shamsudheen thavavel vaiyapuri Department of Computer Science King Khalid UniversitySaudi Arabia School of Electrical Engineering Vellore Institute of TechnologyIndia Department of Computer Science Jazan UniversitySaudi Arabia College of Computer Engineering and Science Prince Sattam bin Abdulaziz UniversitySaudi Arabia

Recently medical image classification plays a vital role in medical image retrieval and computer-aided diagnosis *** deep learning has proved to be superior to previous approaches that depend on handcrafted features;it remains difficult to implement because of the high intra-class variance and inter-class similarity generated by the wide range of imaging modalities and clinical *** Internet of Things(IoT)in healthcare systems is quickly becoming a viable alternative for delivering high-quality medical treatment in today’s e-healthcare *** recent years,the Internet of Things(IoT)has been identified as one of the most interesting research subjects in the field of health care,notably in the field of medical image *** medical picture analysis,researchers used a combination of machine and deep learning techniques as well as artificial *** newly discovered approaches are employed to determine diseases,which may aid medical specialists in disease diagnosis at an earlier stage,giving precise,reliable,efficient,and timely results,and lowering death *** on this insight,a novel optimal IoT-based improved deep learning model named optimization-driven deep belief neural network(ODBNN)is proposed in this *** context,primarily image quality enhancement procedures like noise removal and contrast normalization are *** the preprocessed image is subjected to feature extraction techniques in which intensity histogram,an average pixel of RGB channels,first-order statistics,Grey Level Co-Occurrence Matrix,Discrete Wavelet Transform,and Local Binary Pattern measures are *** extracting these sets of features,the May Fly optimization technique is adopted to select the most relevant *** selected features are fed into the proposed classification algorithm in terms of classifying similar input images into similar *** proposed model is evaluated in terms of accuracy,precision,recall,and f-

关键词： Deep belief neural network mayfly optimization gaussian filter contrast normalization grey level variance local binary pattern discrete wavelet transform

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Automatic Vehicle License Plate Recognition Using Optimal Deep Learning Model

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Computers, Materials & Continua 2021年第5期67卷 1881-1897页

作者： thavavel vaiyapuri Sachi Nandan Mohanty M.Sivaram Irina V.Pustokhina Denis A.Pustokhin K.Shankar College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz UniversityAl-KharjSaudi Arabia Department of Computer Science&Engineering IcfaiTechICFAI Foundation for Higher EducationHyderabadIndia Assistant professor Research Research CenterLebanese French UniversityErbil44001Iraq Department of Entrepreneurship and Logistics Plekhanov Russian University of EconomicsMoscow117997Russia Department of Logistics State University of ManagementMoscow109542Russia Department of Computer Applications Alagappa UniversityKaraikudiIndia

The latest advancements in highway research domain and increase in the number of vehicles everyday led to wider exposure and attention towards the development of efficient Intelligent Transportation System(ITS).One of the popular research areas i.e.,Vehicle License Plate Recognition(VLPR)aims at determining the characters that exist in the license plate of the *** VLPR process is a difficult one due to the differences in viewpoint,shapes,colors,patterns,and non-uniform illumination at the time of capturing *** current study develops a robust Deep Learning(DL)-based VLPR model using Squirrel Search Algorithm(SSA)-based Convolutional Neural Network(CNN),called the SSA-CNN *** presented technique has a total of four major processes namely preprocessing,License Plate(LP)localization and detection,character segmentation,and *** Transform(HT)is applied as a feature extractor and SSA-CNN algorithm is applied for character recognition in *** SSA-CNN method effectively recognizes the characters that exist in the segmented image by optimal tuning of CNN *** HT-SSA-CNN model was experimentally validated using the Stanford Car,FZU Car,and HumAIn 2019 Challenge *** experimentation outcome verified that the presented method was better under several *** projected HT-SSA-CNN model implied the best performance with optimal overall accuracy of 0.983%.

关键词： Deep learning license plate recognition intelligent transportation segmentation

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Intelligent Feature Selection with Deep Learning Based Financial Risk Assessment Model

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Computers, Materials & Continua 2022年第8期72卷 2429-2444页

作者： thavavel vaiyapuri K.Priyadarshini A.Hemlathadhevi M.Dhamodaran Ashit Kumar Dutta Irina V.Pustokhina Denis A.Pustokhin College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia Department of Electronics and Communication Engineering K.Ramakrishnan College of EngineeringTiruchirappalli621112India Department of Computer Science and Engineering Panimalar Engineering CollegeChennai600123India Department of Electronics and Communication Engineering M.Kumarasamy College of EngineeringKarur639113India Department of Computer Science and Information System College of Applied SciencesAlMaarefa UniversityRiyadh11597Kingdom of Saudi Arabia Department of Entrepreneurship and Logistics Plekhanov Russian University of Economics117997MoscowRussia Department of Logistics State University of Management109542MoscowRussia

Due to global financial crisis,risk management has received significant attention to avoid loss and maximize profit in any *** the financial crisis prediction(FCP)process is mainly based on data driven decision making and intelligent models,artificial intelligence(AI)and machine learning(ML)models are widely *** article introduces an intelligent feature selection with deep learning based financial risk assessment model(IFSDL-FRA).The proposed IFSDL-FRA technique aims to determine the financial crisis of a company or *** addition,the IFSDL-FRA technique involves the design of new water strider optimization algorithm based feature selection(WSOA-FS)manner to an optimum selection of feature ***,Deep Random Vector Functional Link network(DRVFLN)classification technique was applied to properly allot the class labels to the financial ***,improved fruit fly optimization algorithm(IFFOA)based hyperparameter tuning process is carried out to optimally tune the hyperparameters of the DRVFLN *** enhancing the better performance of the IFSDL-FRA technique,an extensive set of simulations are implemented on benchmark financial datasets and the obtained outcomes determine the betterment of IFSDL-FRA technique on the recent state of art approaches.

关键词： Financial risks intelligent models financial crisis prediction deep learning feature selection metaheuristics

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Intelligent Deep Learning Based Multi-Retinal Disease Diagnosis and Classification Framework

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Computers, Materials & Continua 2022年第12期73卷 5543-5557页

作者： thavavel vaiyapuri S.Srinivasan Mohamed Yacin Sikkandar T.S.Balaji Seifedine Kadry Maytham N.Meqdad Yunyoung Nam College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia Saveetha School of Engineering Saveetha Institute of Medical and Technical SciencesSaveetha NagarThandalamChennai602105India Department of Medical Equipment Technology College of Applied Medical SciencesMajmaah UniversityAl Majmaah11952Saudi Arabia Saveetha School of Engineering Saveetha Institute of Medical and Technical SciencesChennai602105India Department of Electronics and Communication Engineering College of Engineering&TechnologySRM Institute of Science and TechnologyVadapalani CampusChennai600026India Department of Applied Data Science Noroff University CollegeKristiansand4608Norway Department of Computer Techniques Engineering Al-Mustaqbal University CollegeHillahBabil51001Iraq Department of Computer Science and Engineering Soonchunhyang UniversityAsan31538Korea

In past decades,retinal diseases have become more common and affect people of all age grounds over the *** examining retinal eye disease,an artificial intelligence(AI)based multilabel classification model is needed for automated *** analyze the retinal malady,the system proposes a multiclass and multi-label arrangement ***,the classification frameworks based on features are explicitly described by ophthalmologists under the application of domain knowledge,which tends to be time-consuming,vulnerable generalization ability,and unfeasible in massive ***,the automated diagnosis of multi-retinal diseases becomes essential,which can be solved by the deep learning(DL)*** this motivation,this paper presents an intelligent deep learningbased multi-retinal disease diagnosis(IDL-MRDD)framework using fundus *** proposed model aims to classify the color fundus images into different classes namely AMD,DR,Glaucoma,Hypertensive Retinopathy,Normal,Others,and Pathological ***,the artificial flora algorithm with Shannon’s function(AFA-SF)basedmulti-level thresholding technique is employed for image segmentation and thereby the infected regions can be properly *** addition,SqueezeNet based feature extractor is employed to generate a collection of feature ***,the stacked sparse Autoencoder(SSAE)model is applied as a classifier to distinguish the input images into distinct retinal *** efficacy of the IDL-MRDD technique is carried out on a benchmark multi-retinal disease dataset,comprising data instances from different *** experimental values pointed out the superior outcome over the existing techniques with the maximum accuracy of 0.963.

关键词： Multi-retinal disease computer aided diagnosis fundus images deep learning segmentation intelligent models

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