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Intelligent recognition system for viewpoint variations on gait and speech using CNN-CapsNet

International Journal of Intelligent Computing and Cybernetics 2022年第3期15卷 363-382页

作者： G.merlin Linda N.V.S.Sree Rathna Lakshmi N.Senthil murugan Rajendra Prasad mahapatra V.muthukumaran m.sivaram Department of Computer Science and Engineering SRM Institute of Science and Technology–Vadapalani CampusChennaiIndia Department of Electronics and Communication Engineering Agni College of TechnologyChennaiIndia School of Computer Science and Engineering VIT-AP UniversityAmaravatiIndia Department of Computer Science and Engineering SRM Institute of Science and TechnologyGhaziabadIndia Department of mathematics School of Applied SciencesREVA UniversityBangaloreIndia Faculty of Information Technology Ton Duc Thang UniversityHo Chi Minh CityVietnam

Purpose-The paper aims to introduce an intelligent recognition system for viewpoint variations of gait and *** proposes a convolutional neural network-based capsule network(CNN-CapsNet)model and outlining the performance of the system in recognition of gait and speech *** proposed intelligent system mainly focuses on relative spatial hierarchies between gait features in the entities of the image due to translational invariances in sub-sampling and speech ***/methodology/approach-This proposed work CNN-CapsNet is mainly used for automatic learning of feature representations based on CNNand used capsule vectors as neurons to encode all the spatial information of an image by adapting equal variances to change in *** proposed study will resolve the discrepancies caused by cofactors and gait recognition between opinions based on a model of ***-This research work provides recognition of signal,biometric-based gait recognition and sound/speech *** evaluations are conducted on three aspects of scenarios,namely fixed-view,cross-view and multi-view *** main parameters for recognition of gait are speed,change in clothes,subjects walking with carrying object and intensity of *** limitations/implications-The proposed CNN-CapsNet has some limitations when considering for detecting the walking targets from surveillance videos considering multimodal fusion approaches using hardware sensor *** can also act as a pre-requisite tool to analyze,identify,detect and verify the malware *** implications-This research work includes for detecting the walking targets from surveillance videos considering multimodal fusion approaches using hardware sensor *** can also act as a pre-requisite tool to analyze,identify,detect and verify the malware ***/value-This proposed research work proves to be performing better for the recognition of gait and speech when

关键词： Intelligent system Gait recognition Convolutional neural network Deep learning Capsule network Viewpoint variations

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Shape, Color and Texture Based CBIR System Using Fuzzy LogicClassifier

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Computers, materials & Continua 2019年第6期59卷 729-739页

作者： D.Yuvaraj m.sivaram B.Karthikeyan Jihan Abdulazeez Department of Computer Science Cihan University-Duhok-42001Kurdistan Region-Iraq Department of Computer Networking Lebanese French UniversityErbil-44001Kurdistan Region-Iraq School of Computing SASTRA Deemed UniversityThanjavur-613401India

The perfect image retrieval and retrieval time are the two major challenges inCBIR systems. To improve the retrieval accuracy, the whole database is searched basedon many image characteristics such as color, shape, texture and edge information whichleads to more time consumption. This paper presents a new fuzzy based CBIR method,which utilizes colour, shape and texture attributes of the image. Fuzzy rule based systemis developed by combining color, shape, and texture feature for enhanced image *** this approach, DWT is used to pull out the texture characteristics and the region basedmoment invariant is utilized to pull out the shape features of an image. Color similarityand texture attributes are extorted using customized Color Difference Histogram (CDH).The performance evaluation based on precision and BEP measures reveals the superiorityof the proposed method over renowned obtainable approaches.

关键词： Image retrieval color histogram texture edge orientation retrieval accuracy

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Nature Inspired Improved Firefly Algorithm for Node Clustering in WSNs

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Computers, materials & Continua 2020年第8期64卷 753-776页

作者： V.manikandan m.sivaram Amin Salih mohammed V.Porkodi Department of Computer Networking College of Engineering and Computer ScienceLebanese French UniversityErbil44001Iraq College of Engineering and Computer Science Lebanese French UniversityErbil44001Iraq Department of Information Technology College of Engineering and Computer ScienceLebanese French UniversityErbil44001Iraq

Wireless Sensor Networks(WSNs)comprises low power devices that are randomly distributed in a geographically isolated *** energy consumption of nodes is an essential factor to be ***,an improved energy management technique is designed in this investigation to reduce its consumption and to enhance the network’s *** can be attained by balancing energy clusters using a meta-heuristic Firefly algorithm model for network *** improved technique is based on the cluster head selection technique with measurement of the tour length of *** Division multiple Access(TDmA)scheduler is also improved with the characteristics/behavior of fireflies and also *** last,the development approach shows the progression of the network lifetime,the total number of selected Cluster Heads(CH),the energy consumed by nodes,and the number of packets *** approach is compared with Ad hoc On-Demand Distance Vector(AODV),Dynamic Source Routing(DSR)and Low Energy Adaptive Clustering Hierarchy(LEAH)*** is performed in mATLAB with the numerical outcomes showing the efficiency of the proposed *** energy consumption of sensor nodes is reduced by about 50%and increases the lifetime of nodes by 78%more than AODV,DSR and LEACH *** parameters such as cluster formation,end to end delay,percentage of nodes alive and packet delivery ratio,are also evaluated...The anticipated method shows better trade-off in contrast to existing techniques.

关键词： Cluster head wireless sensor network LEAH TDmA firefly AODV DSR

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Secure Localization Based Authentication (SLA) Strategy for Data Integrity in WNS

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Computers, materials & Continua 2021年第6期67卷 4005-4018页

作者： V.manikandan m.sivaram Amin Salih mohammed V.Porkodi K.Shankar Assistant Professor Research Research CenterLebanese French UniversityErbil44001Iraq Vice President Lebanese French UniversityErbil44001Iraq Department of Information Technology College of Engineering and Computer ScienceLebanese French UniversityErbil44001Iraq Department of Computer Applications Alagappa UniversityKaraikudi630003India

Wireless Sensor Networks(WSN)has been extensively utilized as a communication model in Internet of Things(IoT).As well,to offer service,numerous IoT based applications need effective transmission over unstable *** ensure reliability,prevailing investigations exploit multiple candidate forwarders over geographic opportunistic routing in ***,these models are affected by crucial denial of service(DoS)attacks,where huge amount of invalid data are delivered intentionally to the receivers to disturb the functionality of ***,secure localization based authentication(SLA)is presented to fight against DoS attack,and to fulfil the need of reliability and *** examining state information,SLA projects a trust model to enhance efficacy of data ***,of the prevailing opportunistic protocols,SLA guarantees data integrity by modelling a trust based authentication,providing protection against DoS attackers and diminishing computational ***,this model acts as a verification strategy to accelerate?attackers and to handle *** strategy helps SLA in eliminating duplicate transmission and by continuous verification that results from conventional opportunistic *** is performed in a mATLAB environment that offers authentic and reliable delivery by consuming approximately 50%of the cost in contrast to other *** anticipated model shows better trade off in comparison to the prevailing ones.

关键词： Wireless sensor networks opportunistic routing secure localization based authentication denial of service computational cost

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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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