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Hybrid Swarm Intelligence Based QoS Aware Clustering with Routing Protocol for WSN

Computers, Materials & Continua 2021年第9期68卷 2995-3013页

作者： M.S.Maharajan T.Abirami irina v.pustokhina Denis A.Pustokhin K.Shankar Department of Computer Science and Engineering GRT Institute of Engineering and TechnologyTiruttani631209India Department of Information Technology Kongu Engineering CollegeErode638060India Department of Entrepreneurship and Logistics Plekhanov Russian University of EconomicsMoscow117997Russia Department of Logistics State University of ManagementMoscow109542Russia Department of Computer Applications Alagappa UniversityKaraikudi630001India

Wireless Sensor Networks(WSN)started gaining attention due to its wide application in the fields of data collection and information *** recent advancements in multimedia sensors demand the Quality of Service(QoS)be maintained up to certain *** restrictions and requirements in QoS management completely depend upon the nature of target *** of the major QoS parameters in WSN are energy efficiency,network lifetime,delay and *** this scenario,clustering and routing are considered as the most effective techniques to meet the demands of *** they are treated as NP(Non-deterministic Polynomial-time)hard problem,Swarm Intelligence(SI)techniques can be *** current research work introduces a new QoS aware Clustering and Routing-based technique using Swarm Intelligence(QoSCRSI)*** proposed QoSCRSI technique performs two-level clustering and proficient ***,the fuzzy is hybridized with Glowworm Swarm Optimization(GSO)-based clustering(HFGSOC)technique for optimal selection of Cluster Heads(CHs).Here,Quantum Salp Swarm optimization Algorithm(QSSA)-based routing technique(QSSAR)is utilized to select the possible routes in the *** order to evaluate the performance of the proposed QoSCRSI technique,the authors conducted extensive simulation analysis with varying node *** experimental outcomes,obtained from the proposed QoSCRSI technique,apparently proved that the technique is better compared to other state-of-the-art techniques in terms of energy efficiency,network lifetime,overhead,throughput,and delay.

关键词： Quality of service clustering routing energy efficiency wireless sensor networks swarm intelligence

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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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Hybrid BWO-IACO Algorithm for Cluster Based Routing in Wireless Sensor Networks

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Computers, Materials & Continua 2021年第10期69卷 433-449页

作者： R.Punithavathi Chinnarao Kurangi S.P.Balamurugan irina v.pustokhina Denis A.Pustokhin K.Shankar Department of Information Technology M.Kumarasamy College of EngineeringKarur639113India Department of Computer Science Pondicherry UniversityPuducherry605014India Department of Computer and Information Science Faculty of ScienceAnnamalai UniversityChidambaram608002India Department of Entrepreneurship and Logistics Plekhanov Russian University of EconomicsMoscow117997Russia Department of Logistics State University of ManagementMoscow109542Russia Department of Computer Applications Alagappa UniversityKaraikudi630001India

Wireless Sensor Network(WSN)comprises a massive number of arbitrarily placed sensor nodes that are linked wirelessly to monitor the physical parameters from the target *** the nodes in WSN operate on inbuilt batteries,the energy depletion occurs after certain rounds of operation and thereby results in reduced network *** enhance energy efficiency and network longevity,clustering and routing techniques are commonly employed in *** paper presents a novel black widow optimization(BWO)with improved ant colony optimization(IACO)algorithm(BWO-IACO)for cluster based routing in *** proposed BWO-IACO algorithm involves BWO based clustering process to elect an optimal set of cluster heads(CHs).The BWO algorithm derives a fitness function(FF)using five input parameters like residual energy(RE),inter-cluster distance,intra-cluster distance,node degree(ND),and node *** addition,IACO based routing process is involved for route selection in inter-cluster *** IACO algorithm incorporates the concepts of traditional ACO algorithm with krill herd algorithm(KHA).The IACO algorithm utilizes the energy factor to elect an optimal set of routes to BS in the *** integration of BWO based clustering and IACO based routing techniques considerably helps to improve energy efficiency and network *** presented BWO-IACO algorithm has been simulated using MATLAB and the results are examined under varying aspects.A wide range of comparative analysis makes sure the betterment of the BWO-IACO algorithm over all the other compared techniques.

关键词： Clustering routing wireless sensor network energy efficiency black widow optimization

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An Optimal Big Data Analytics with Concept Drift Detection on High-Dimensional Streaming Data

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Computers, Materials & Continua 2021年第9期68卷 2843-2858页

作者： Romany F.Mansour Shaha Al-Otaibi Amal Al-Rasheed Hanan Aljuaid irina v.pustokhina Denis A.Pustokhin Department of Mathematics Faculty of ScienceNew Valley UniversityEl-Kharga72511Egypt Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh84428Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh84428Saudi Arabia Department of Entrepreneurship and Logistics Plekhanov Russian University of EconomicsMoscow117997Russia Department of Logistics State University of ManagementMoscow109542Russia

Big data streams started becoming ubiquitous in recent years,thanks to rapid generation of massive volumes of data by different *** is challenging to apply existing data mining tools and techniques directly in these big data *** the same time,streaming data from several applications results in two major problems such as class imbalance and concept *** current research paper presents a new Multi-Objective Metaheuristic Optimization-based Big Data Analytics with Concept Drift Detection(MOMBD-CDD)method on High-Dimensional Streaming *** presented MOMBD-CDD model has different operational stages such as pre-processing,CDD,and ***-CDD model overcomes class imbalance problem by Synthetic Minority Over-sampling Technique(SMOTE).In order to determine the oversampling rates and neighboring point values of SMOTE,Glowworm Swarm Optimization(GSO)algorithm is ***,Statistical Test of Equal Proportions(STEPD),a CDD technique is also ***,Bidirectional Long Short-Term Memory(Bi-LSTM)model is applied for *** order to improve classification performance and to compute the optimum parameters for Bi-LSTM model,GSO-based hyperparameter tuning process is carried *** performance of the presented model was evaluated using high dimensional benchmark streaming datasets namely intrusion detection(NSL KDDCup)dataset and ECUE spam *** extensive experimental validation process confirmed the effective outcome of MOMBD-CDD *** proposed model attained high accuracy of 97.45%and 94.23%on the applied KDDCup99 Dataset and ECUE Spam datasets respectively.

关键词： Streaming data concept drift classification model deep learning class imbalance data

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Hybrid Deep Learning Enabled Air Pollution Monitoring in ITS Environment

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Computers, Materials & Continua 2022年第7期72卷 1157-1172页

作者： Ashit Kumar Dutta Jenyfal Sampson Sultan Ahmad T.Avudaiappan Kanagaraj Narayanasamy irina v.pustokhina Denis A.Pustokhin Department of Computer Science and Information Systems College of Applied SciencesAlMaarefa UniversityAd DiriyahRiyadh13713Kingdom of Saudi Arabia Department of Electronics and Communication Engineering Kalasalingam Academy of Research and EducationKrishnankoil626128India Department of Computer Science College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAl-Kharj 11942Saudi Arabia Department of Computer Science and Engineering K.Ramakrishnan College of TechnologyTiruchirappalli621112India Department of Computer Applications J.J College of Arts and Science(Autonomous)Affiliated to Bharathidasan UniversityPudukkottai622422India Department of Entrepreneurship and Logistics Plekhanov Russian University of Economics117997MoscowRussia Department of Logistics State University of Management109542MoscowRussia

Intelligent Transportation Systems(ITS)have become a vital part in improving human lives and modern *** aims at enhancing road safety and environmental *** is a tremendous increase observed in the number of vehicles in recent years,owing to increasing *** vehicle has its own individual emission rate;however,the issue arises when the emission rate crosses a standard *** to the technological advances made in Artificial Intelligence(AI)techniques,it is easy to leverage it to develop prediction approaches so as to monitor and control air *** current research paper presents Oppositional Shark Shell Optimization with Hybrid Deep Learning Model for Air Pollution Monitoring(OSSOHDLAPM)in ITS *** proposed OSSO-HDLAPM technique includes a set of sensors embedded in vehicles to measure the level of *** addition,hybridized Convolution Neural Network with Long Short-Term Memory(HCNN-LSTM)model is used to predict pollutant level based on the data attained earlier by the *** HCNN-LSTM model,the hyperparameters are selected and optimized using OSSO *** order to validate the performance of the proposed OSSO-HDLAPM technique,a series of experiments was conducted and the obtained results showcase the superior performance of OSSO-HDLAPM technique under different evaluation parameters.

关键词： Deep learning air pollution environment monitoring internet of things intelligent transportation systems oppositional learning LSTM model

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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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An Optimal Classification Model for Rice Plant Disease Detection

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Computers, Materials & Continua 2021年第8期68卷 1751-1767页

作者： R.Sowmyalakshmi T.Jayasankar v.Ayyem PiIllai Kamalraj Subramaniyan irina v.pustokhina Denis A.Pustokhin K.Shankar Department of Electronics and Communication Engineering University College of EngineeringBIT CampusAnna UniversityTiruchirappalli620024India Department of Electronics and Communication Engineering Gokaraju Rangaraju Institute of Engineering TechnologyHyderabad500090India Department of Electronics and Communication Engineering Karpagam Academy of Higher EducationCoimbatore641021India Department of Entrepreneurship and Logistics Plekhanov Russian University of EconomicsMoscow117997Russia Department of Logistics State University of ManagementMoscow109542Russia Department of Computer Applications Alagappa UniversityKaraikudi630001India

Internet of Things(IoT)paves a new direction in the domain of smart farming and precision *** farming is an upgraded version of agriculture which is aimed at improving the cultivation practices and yield to a certain *** smart farming,IoT devices are linked among one another with new technologies to improve the agricultural *** farming makes use of IoT devices and contributes in effective decision *** is the major food source in most of the ***,it becomes inevitable to detect rice plant diseases during early stages with the help of automated tools and IoT *** development and application of Deep Learning(DL)models in agriculture offers a way for early detection of rice diseases and increase the yield and *** study presents a new Convolutional Neural Network-based inception with ResNset v2 model and Optimal Weighted Extreme Learning Machine(CNNIR-OWELM)-based rice plant disease diagnosis and classification model in smart farming *** proposed CNNIR-OWELM method involves a set of IoT devices which capture the images of rice plants and transmit it to cloud server via *** CNNIROWELM method uses histogram segmentation technique to determine the affected regions in rice plant *** addition,a DL-based inception with ResNet v2 model is engaged to extract the ***,in OWELM,the Weighted Extreme Learning Machine(WELM),optimized by Flower Pollination Algorithm(FPA),is employed for classification *** FPA is incorporated into WELM to determine the optimal parameters such as regularization coefficient C and kernelγ.The outcome of the presented model was validated against a benchmark image dataset and the results were compared with one *** simulation results inferred that the presented model effectively diagnosed the disease with high sensitivity of 0.905,specificity of 0.961,and accuracy of 0.942.

关键词： Agriculture internet of things smart farming deep learning rice plant diseases

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An Intelligent Deep Learning Based Xception Model for Hyperspectral Image Analysis and Classification

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Computers, Materials & Continua 2021年第5期67卷 2393-2407页

作者： J.Banumathi A.Muthumari S.Dhanasekaran S.Rajasekaran irina v.pustokhina Denis A.Pustokhin K.Shankar Department of Information Technology University College of EngineeringNagercoil629004India Department of Computer Science and Engineering University College of EngineeringRamanathapuram623513India Department of Computer Science and Engineering Kalasalingam Academy of Research and EducationKrishnankoil626128India Department of Electrical Engineering PSN College of EngineeringTirunelveli627152India Department of Entrepreneurship and Logistics Plekhanov Russian University of EconomicsMoscow117997Russia Department of Logistics State University of ManagementMoscow109542Russia Department of Computer Applications Alagappa UniversityKaraikudi630001India

Due to the advancements in remote sensing technologies,the generation of hyperspectral imagery(HSI)gets significantly *** classification of HSI becomes a critical process in the domain of hyperspectral data *** massive availability of spectral and spatial details of HSI has offered a great opportunity to efficiently illustrate and recognize ground ***,deep learning(DL)models particularly,convolutional neural networks(CNNs)become useful for HSI classification owing to the effective feature representation and high *** this view,this paper introduces a new DL based Xception model for HSI analysis and classification,called Xcep-HSIC ***,the presented model utilizes a feature relation map learning(FRML)to identify the relationship among the hyperspectral features and explore many features for improved classifier ***,the DL based Xception model is applied as a feature extractor to derive a useful set of features from the FRML *** addition,kernel extreme learning machine(KELM)optimized by quantum-behaved particle swarm optimization(QPSO)is employed as a classification model,to identify the different set of class *** extensive set of simulations takes place on two benchmarks HSI dataset,namely Indian Pines and Pavia University *** obtained results ensured the effective performance of the XcepHSIC technique over the existing methods by attaining a maximum accuracy of 94.32%and 92.67%on the applied India Pines and Pavia University dataset respectively.

关键词： Hyperspectral imagery deep learning xception kernel extreme learning map parameter tuning

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High Security for De-Duplicated Big Data Using Optimal SIMON Cipher

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Computers, Materials & Continua 2021年第5期67卷 1863-1879页

作者： A.Muthumari J.Banumathi S.Rajasekaran P.vijayakarthik K.Shankar irina v.pustokhina Denis A.Pustokhin Department of Computer Science and Engineering University College of EngineeringRamanathapuram623513India Department of Information Technology University College of EngineeringNagercoil629004India Department of EEE PSN College of Engineering(Anna University)Tirunelveli627152India Department of Information Science and Engineering Sir M.Visvesvaraya Institute of TechnologyBangalore56215India Department of Computer Applications Alagappa UniversityKaraikudi63003India Department of Entrepreneurship and Logistics Plekhanov Russian University of EconomicsMoscow117997Russia Department of Logistics State University of ManagementMoscow109542Russia

Cloud computing offers internet location-based affordable,scalable,and independent *** computing is a promising and a cost-effective approach that supports big data analytics and advanced applications in the event of forced business continuity events,for instance,pandemic *** handle massive information,clusters of servers are required to assist the equipment which enables streamlining the widespread quantity of data,with elevated velocity and modified *** deduplication model enables cloud users to efficiently manage their cloud storage space by getting rid of redundant data stored in the *** deduplication also saves network *** this paper,a new cloud-based big data security technique utilizing dual encryption is *** clustering model is utilized to analyze the Deduplication process hash *** kernel Fuzzy C means(MKFCM)was used which helps cluster the data stored in cloud,on the basis of confidence data encryption *** confidence finest data is implemented in homomorphic encryption data wherein the Optimal SIMON Cipher(OSC)technique is *** security process involving dual encryption with the optimization model develops the productivity *** this paper,the excellence of the technique was confirmed by comparing the proposed technique with other encryption and clustering *** results proved that the proposed technique achieved maximum accuracy and minimum encryption time.

关键词： Cloud security deduplication clustering optimization big data dual encryption

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Design of QoS Aware Routing Protocol for IoT Assisted Clustered WSN

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Computers, Materials & Continua 2022年第5期71卷 3785-3801页

作者： Ashit Kumar Dutta S.Srinivasan Bobbili Prasada Rao B.Hemalatha irina v.pustokhina Denis A.Pustokhin Gyanendra Prasad Joshi Department of Computer Science and Information Systems College of Applied SciencesAlmaarefa UniversityRiyadh13713Kingdom of Saudi Arabia Saveetha School of Engineering Saveetha Institute of Medical and Technical SciencesSaveetha NagarThandalamChennai602105India Department of Electrical and Electronics Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Electronics and Communication Engineering Thirumalai Engineering CollegeKancheepuram631551India Department of Entrepreneurship and Logistics Plekhanov Russian University of Economics117997MoscowRussia Department of Logistics State University of Management109542MoscowRussia Department of Computer Science and Engineering Sejong UniversitySeoul05006Korea

In current days,the domain of Internet of Things(IoT)and Wireless Sensor Networks(WSN)are combined for enhancing the sensor related data transmission in the forthcoming networking *** and routing techniques are treated as the effective methods highly used to attain reduced energy consumption and lengthen the lifetime of the WSN assisted IoT *** this view,this paper presents an Ensemble of Metaheuristic Optimization based QoS aware Clustering with Multihop Routing(EMOQoSCMR)Protocol for IoT assisted *** proposed EMO-QoSCMR protocol aims to achieve QoS parameters such as energy,throughput,delay,and *** proposed model involves two stage processes namely clustering and ***,the EMO-QoSCMR protocol involves crossentropy rain optimization algorithm based clustering(CEROAC)technique to select an optimal set of cluster heads(CHs)and construct ***,oppositional chaos game optimization based routing(OCGOR)technique is employed for the optimal set of routes in the IoT assisted *** proposed model derives a fitness function based on the parameters involved in the IoT nodes such as residual energy,distance to sink node,*** proposed EMOQoSCMR technique has resulted to an enhanced NAN of 64 nodes whereas the LEACH,PSO-ECHS,E-OEERP,and iCSHS methods have resulted in a lesser NAN of 2,10,42,and 51 *** performance of the presented protocol has been evaluated interms of energy efficiency and network lifetime.

关键词： Internet of things wireless sensor networks clustering routing metaheuristics cluster head selection QoS parameters

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