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

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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Metaheuristic Clustering Protocol for Healthcare DataCollection in MobileWireless Multimedia Sensor Networks

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computers, Materials & Continua 2021年第3期66卷 3215-3231页

作者： G G.Kadiravan P.Sujatha T.Asvany R.Punithavathi Mohamed Elhoseny Irina V.Pustokhina Denis A.Pustokhin K.Shankar Department of Computer Science Pondicherry UniversityPuducherry605014India Department of Information Technology M.Kumarasamy College of EngineeringKarur639113India Faculty of Computers and Information Mansoura UniversityEgypt Department of Entrepreneurship and Logistics Plekhanov Russian University of EconomicsMoscow117997Russia Department of Logistics State University of ManagementMoscow109542Russia Department of Computer Applications Alagappa UniversityKaraikudiIndia

Nowadays,healthcare applications necessitate maximum volume of medical data to be fed to help the physicians,academicians,pathologists,doctors and other healthcare *** in the domain of Wireless Sensor Networks(WSN)andMultimediaWireless Sensor Networks(MWSN)are tremendous.M-WMSN is an advanced form of conventional Wireless Sensor Networks(WSN)to networks that use multimedia *** compared with traditional WSN,the quantity of data transmission in M-WMSN is significantly high due to the presence of multimedia ***,clustering techniques are deployed to achieve low amount of energy *** current research work aims at introducing a new Density Based Clustering(DBC)technique to achieve energy efficiency *** DBC technique is mainly employed for data collection in healthcare environment which primarily depends on three input parameters namely remaining energy level,distance,and node *** addition,two static data collector points called Super Cluster Head(SCH)are placed,which collects the data from normal CHs and forwards it to the Base Station(BS)*** supports multi-hop data transmission that assists in effectively balancing the available *** simulation analysiswas conducted to showcase the superior performance of DBC technique and the results were examined under diverse *** simulation outcomes concluded that the proposed DBC technique improved the network lifetime to a maximum of 16,500 rounds,which is significantly higher compared to existing methods.

关键词： Smart sensor environment healthcare data multimedia big data processing clustering mobility energy efficiency

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Unified View of Damage leaves Planimetry & Analysis Using Digital Images Processing Techniques

Unified View of Damage leaves Planimetry & Analysis Using Di...

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2023 International Conference on Computational Intelligence and Sustainable Engineering Solution, CISES 2023

作者： Kumar, Pijush Kanti Munjal, DeepKiran Rani, Sunita Dutta, Anurag Voumik, Liton Chandra Ramamoorthy, A. Government College of Engineering & Textile Technology Department of Information Technology Serampore Calcutta India G. L. Bajaj Institute of Technology and Management Department of Computer Applications Uttar Pradesh Greater Noida India Noida Institute of Engineering and Technology Department of Engineering Administration Uttar Pradesh Greater Noida India Noakhali Science and Technology University Department of Economics Noakhali Bangladesh Velammal Engineering College Anna University Department of Mathematics Tamil Nadu Chennai India

ISBN: (纸本)9798350323917

The detection of leaf diseases in plants generally involves visual observation of patterns appearing on the leaf surface. However, there are many diseases that are distinguished based on very subtle changes in these visually observable patterns. This paper attempts to identify plant leaf diseases using image processing techniques. The focus of this study is on the detection of citrus leaf canker disease. Canker is a bacterial infection of leaves. Symptoms of citrus cankers include brown spots on the leaves, often with a watery or oily appearance. The spots (called lesions in botany) are usually yellow. It is surrounded by a halo of the leaves and is found on both the top and bottom of the leaf. This paper describes various methods that have been used to detect citrus leaf canker disease. The methods used are histogram comparison and k-means clustering. Using these methods, citrus canker development was detected based on histograms generated based on leaf patterns. The results thus obtained can be used, after consultation with experts in the field of agriculture, to identify suitable treatments for the processes used. © 2023 IEEE.

关键词： Graphic methods

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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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An Exploratory analysis of Machine Learning adaptability in Big Data Analytics Environments: A Data Aggregation in the age of Big Data and the Internet of Things 2

An Exploratory analysis of Machine Learning adaptability in ...

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2nd International Conference on Innovative Practices in Technology and management, ICIPTM 2022

作者： Rajendran, Ratchana Sharma, Priyanka Saran, Nitin Kumar Ray, Samrat Alanya-Beltran, Joel Tongkachok, Korakod International School Of Management Excellence Bengaluru India Mriirs Faculty Of Computer Applications Faridabad India Central Electronics Limited SPV-M-Project Execution Division Uttar Pradesh Ghaziabad India Economics The Institute Of Industrial Management Economics And Trade Peter The Great Saint Petersburg Polytechnic University Russia Universidad Tecnológica del Perú Electronic Department Peru Thaksin University Faculty Of Law Thailand

ISBN: (纸本)9781665466431

The paper discusses a new concept combining the potentialities of Big Data processing as well as machine learning developed for security monitoring of mobile Internet of Things. The structure of the security monitoring system is considered as a most effective and useful element to create a new viewpoint of mobile IoT. This article focuses implementation of machine learning in online education. Thus mobile IoT has found successful application in few areas such as security monitoring in public places, transport management, medicine, smart houses, industrial production, electrical consumption, and robotics. All the mathematical foundations along with issues related to this have been considered in this study. In order to solve the classification task, several machine learning mechanisms have been mentioned in this paper. Large organizations are incorporating data-driven actions, and decision making in organizational function. The role of data aggregation is effective here achieving the business objectives. Vast amount of raw data can be processed linearly through data aggregation. This article describes the interaction of data aggregation through wireless networking assuming its effectiveness in online education. Data aggregation in machine learning is highlighted based on evidence based data. The purpose of this research article is to investigate the machine learning adaptability in big data analytics environments with the approach of IoT. In order to collect accurate data, the researcher has taken the help of a secondary data collection method. It has helped the researcher to find out the valid information about mobile IoT. In addition, qualitative methods have been adapted to malaise the collected data within a systematic way. Moreover, this study will help the readers to understand the value of mobile IoT helping in machine learning adaptability in big data analytics. © 2022 IEEE.

关键词： Data Analytics

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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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Using Machine Learning and Data Mining to Evaluate Modern Financial management Techniques 2nd

Using Machine Learning and Data Mining to Evaluate Modern Fi...

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2nd International Conference on Mechanical and Energy Technologies , ICMET 2021

作者： Al Ayub Ahmed, Alim Rajesh, Sudha Lohana, Sarika Ray, Samrat Maroor, Jnaneshwar Pai Naved, Mohd School of Accounting Jiujiang University 551 Qianjindonglu Jiangxi Jiujiang China Department of Computer Applications BSA Crescent Institute of Science and Technology Chennai Vandalur India State Bank Institute of Leadership Kolkata India The Institute of Industrial Management Economics and Trade Peter the Great Saint Petersburg Polytechnic University Saint Petersburg Russia NMAM Institute of Technology Nitte India Department of Business Analytics Jagannath University Delhi-NCR India

ISBN: (纸本)9789811901072

It is the question of determining patterns in big data sets that correlate to helpful data. It involves techniques that are at the confluence of machine learning, statistics, and legacy system, and it is also known as data mining. Machine learning is a branch of artificial intelligence that emerged from the areas of object recognition and artificial intelligence. It is concerned with the research and development of methods that can understand from assessment tools. The study shows financial institutions use of financial data performance and ensure precise management of consumer data in order to identify defaulters, to reduce the number of equipment failures associated, to process transactions quickly and efficiently, to reduce the number of incorrect judgments, to categorize potential customers, and to minimize the wastage of the financial organizations. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Machine learning

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Hyper-heuristic Algorithm based on ε-Greedy for Solving the Online Seru Scheduling Problem *

Hyper-heuristic Algorithm based on ε-Greedy for Solving the...

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Chinese Control and Decision Conference, CCDC

作者： Fan Tu Yang Liu Qi Lou Fajun Liu Yang Yu Department of Intelligent Data and Systems Engineering Northeastern University Shenyang China Beijing Institute of Computer Technology and Applications Beijing China Shenyang City University Shenyang China Shanghai Science and Technology Museum Shanghai China School of Economics and Management Dalian University of Technology Dalian China

ISBN: (数字)9798350387780

ISBN: (纸本)9798350387797

We address an online seru scheduling problem with the uncertainty of new batch arrivals. First, the decision-making process for online seru scheduling is delineated. Subsequently, drawing on this process, we design three simple and effective scheduling rules that have different advantages. To optimize their inherent benefits, an ε-greedy based hyper-heuristic algorithm is designed to adaptively select the appropriate rule for online seru scheduling. Within this algorithmic framework, the three scheduling rules serve as low-level heuristics, while ε-greedy acts as a high-level strategy. The extensive experiments are conducted to demonstrate the performance of the proposed algorithm.

关键词： Uncertainty Heuristic algorithms Decision making Scheduling

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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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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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