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Fuzzy Based Ant Colony Optimization Scheduling in Cloud Computing

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Computer Systems Science & Engineering 2022年第2期40卷 581-592页

作者： K.Rajakumari m.vinoth kumar Garima Verma S.Balu Dilip kumar Sharma Sudhakar Sengan Department of Computer Science and Engineering School of EngineeringAvinashlingam Institute for Home Science and Higher Education for WomenCoimbatore641043Tamil NaduIndia Department of Information Science and Engineering Dayananda Sagar Academy of Technology and ManagementBangalore560082India School of Computing DIT UniversityDehradun248009UttarakhandIndia Department of Computer Science and Engineering Paavai Engineering CollegePachal637018Tamil NaduIndia Department of mathematics Jaypee University of Engineering and TechnologyGuna473226M.P.India Department of Computer Science and Engineering PSN College of Engineering and TechnologyTirunelveli627152Tamil NaduIndia

Cloud computing is an Information Technology deployment model established on *** scheduling states the set of rules for task allocations to an exact virtual machine in the cloud computing ***,task scheduling challenges such as optimal task scheduling performance solutions,are addressed in cloud ***,the cloud computing performance due to task scheduling is improved by proposing a Dynamic Weighted Round-Robin *** recommended DWRR algorithm improves the task scheduling performance by considering resource competencies,task priorities,and ***,a heuristic algorithm called Hybrid Particle Swarm Parallel Ant Colony Optimization is proposed to solve the task execution delay problem in DWRR based task *** the end,a fuzzy logic system is designed for HPSPACO that expands task scheduling in the cloud environment.A fuzzy method is proposed for the inertia weight update of the PSO and pheromone trails update of the ***,the proposed Fuzzy Hybrid Particle Swarm Parallel Ant Colony Optimization on cloud computing achieves improved task scheduling by minimizing the execution and waiting time,system throughput,and maximizing resource utilization.

关键词： Cloud Computing scheduling ant colony optimization fuzzy logic

An Improved multi-Objective Particle Swarm Optimization Routing on mANET

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Computer Systems Science & Engineering 2023年第2期44卷 1187-1200页

作者： G.Rajeshkumar m.vinoth kumar K.Sailaja kumar Surbhi Bhatia Arwa mashat Pankaj Dadheech Department of Computer Science and Engineering Erode Sengunthar Engineering CollegePerunduraiErode638057Tamil NaduIndia Department of Information Science and Engineering Dayananda Sagar Academy of Technology and ManagementBangalore560082India Department of master of Computer Applications M.S.Ramaiah Institute of TechnologyBangalore560054India Department of Information Systems College of Computer Science and Information TechnologyKing Faisal UniversityRiyadh11533Saudi Arabia Faculty of Computing and Information Technology King Abdulaziz UniversityRabigh21911Saudi Arabia Department of Computer Science and Engineering Swami Keshvanand Institute of TechnologyManagement&GramothanJaipur302017RajasthanIndia

A mobile Ad hoc Network(mANET)is a group of low-power con-sumption of wireless mobile nodes that conﬁgure a wireless network without the assistance of any existing infrastructure/centralized *** primary aim of mANETs is to extendﬂexibility into the self-directed,mobile,and wireless domain,in which a cluster of autonomous nodes forms a mANET routing *** Intrusion Detection System(IDS)is a tool that examines a network for mal-icious behavior/policy violations.A network monitoring system is often used to report/gather any suspicious attacks/*** IDS is a software program or hardware system that monitors network/security trafﬁc for malicious attacks,sending out alerts whenever it detects malicious *** impact of Dynamic Source Routing(DSR)in mANETs challenging blackhole attack is investigated in this research *** Cluster Trust Adaptive Acknowledgement(CTAA)method is used to identify unauthorised and malfunctioning nodes in a mANET *** system is active and provides successful delivery of a data packet,which implements Kalman Filters(KF)to anticipate node ***,KF is used to eliminate synchronisation errors that arise during the sending and receiving *** order to provide an energy-efﬁcient solution and to minimize network trafﬁc,route optimization in mANET by using multi-Objective Particle Swarm Optimization(mOPSO)technique to determine the optimal num-ber of clustered mANET along with energy dissipation in *** to the researchﬁndings,the proposed CTAA-mPSO achieves a Packet Delivery Ratio(PDR)of 3.3%.In mANET,the PDR of CTAA-mPSO improves CTAA-PSO by 3.5%at 30%malware.

关键词： mANET intrusion detection system cluster kalmanﬁlter dynamic source routing multi-objective particle swarm optimization packet delivery ratio

Hybrid GLFIL Enhancement and Encoder Animal migration Classification for Breast Cancer Detection

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Computer Systems Science & Engineering 2022年第5期41卷 735-749页

作者： S.Prakash m.vinoth kumar R.Saravana Ram miodrag Zivkovic Nebojsa Bacanin milos Antonijevic Department of Computer Science and Engineering Sri Shakthi Institute of Engineering and TechnologyCoimbatore641062India Department of Computer Science and Engineering Anna UniversityUniversity College of Engineering DindigulDindigul624622India Department of Electronics and Communication Engineering Anna UniversityUniversity College of Engineering Dindigul624622India Singidunum University Belgrade11000Serbia

Breast cancer has become the second leading cause of death among women *** India,a woman is diagnosed with breast cancer every four *** has been no known basis behind it,and detection is extremely challenging among medical scientists and researchers due to unknown *** India,the ratio of women being identified with breast cancer in urban areas is 22:*** for this disease are micro calcification,lumps,and masses in mammogram *** sources are mostly used for early *** mammography is used for breast cancer *** this study,we introduce a new hybrid wavelet filter for accurate image *** main objective of enhancement is to produce quality images for detecting cancer sections in *** enhancement is the main step where the quality of the input image is improved to detect cancer *** this study,we use a combination of two filters,namely,Gabor and *** edges are detected using the Canny detector to smoothen the ***-quality enhanced image is obtained through the Gabor-Legendre filter(GLFIL)*** image is used by classification *** migration optimization with neural network is implemented for classifying the *** output is compared to existing filter ***,the accuracy achieved by the proposed technique is 98%,which is higher than existing algorithms.

关键词： Breast cancer Gabor filter Legendre filter GLFIL algorithm animal migration optimization neural networks

Stress corrosion cracking behaviour of gas tungsten arc welded super austenitic stainless steel joints

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Defence Technology（防务技术） 2015年第3期11卷 282-291页

作者： m.vinoth kumar V.BALASUBRAmANIAN S.RAJAkumar SHAJU K.ALBERT Department of manufacturing Engineering Annamalai University Center for materials Joining and Research (CEmAJOR) Department of Manufacturing Engineering materials Technology Division Indira Gandhi Centre for Atomic Research (IGCAR)

Super 304 H austenitic stainless steel with 3% of copper posses excellent creep strength and corrosion resistance, which is mainly used in heat exchanger tubing of the boiler. Heat exchangers are used in nuclear power plants and marine vehicles which are intended to operate in chloride rich offshore environment. Chloride stress corrosion cracking is the most likely life limiting failure with austenitic stainless steel tubing. Welding may worsen the stress corrosion cracking susceptibility of the material. Stress corrosion cracking susceptibility of Super 304 H parent metal and gas tungsten arc(GTA) welded joints were studied by constant load tests in 45% boiling mg Cl2 solution. Stress corrosion cracking resistance of Super 304 H stainless steel was deteriorated by GTA welding due to the formation of susceptible microstructure in the HAZ of the weld joint and the residual stresses. The mechanism of cracking was found to be anodic path cracking, with transgranular nature of crack propagation. Linear relationships were derived to predict the time to failure by extrapolating the rate of steady state elongation.

关键词：奥氏体不锈钢管应力腐蚀裂纹钨极氩弧焊开裂行为钢接头开裂敏感性焊接接头应力腐蚀开裂

Enhanced Long Short Term memory for Early Alzheimer's Disease Prediction

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Intelligent Automation & Soft Computing 2023年第2期35卷 1277-1293页

作者： m.vinoth kumar m.Prakash m.Naresh kumar H.Abdul Shabeer Department of Information Science and Engineering Dayananda Sagar Academy of Technology&ManagementKanakapura Main Road560082BangaloreIndia Department of Data Science and Business Systems School of Computing SRM Institute of Science and TechnologyKattankulathurChengalpattu603203TamilnaduIndia Department of ECE Vardhaman College of EngineeringHyderabad501218India IBm India Pvt Ltd. DLF IT ParkChennai600125India

The most noteworthy neurodegenerative disorder nationwide is appar-ently the Alzheimer's disease(AD)which ha no proven viable treatment till date and despite the clinical trials showing the potential of preclinical therapy,a sen-sitive method for evaluating the AD has to be developed *** to the correla-tions between ocular and brain tissue,the eye(retinal blood vessels)has been investigated for predicting the ***,en enhanced method named Enhanced Long Short Term memory(E-LSTm)has been proposed in this work which aims atﬁnding the severity of AD from ocular ***ﬁnd the level of disease severity,the new layer named precise layer was introduced in E-LSTm which will help the doctors to provide the apt treatments for the patients *** avoid the problem of overﬁtting,a dropout has been added to *** the existing work,boundary detection of retinal layers was found to be inaccurate during the seg-mentation process of Optical Coherence Tomography(OCT)image and to over-come this issue;Particle Swarm Optimization(PSO)has been *** the best of our understanding,this is theﬁrst paper to use Particle Swarm *** compared with the existing works,the proposed work is found to be per-forming better in terms of F1 Score,Precision,Recall,training loss,and segmen-tation accuracy and it is found that the prediction accuracy was increased to 10%higher than the existing systems.

关键词： Alzheimer's disease enhanced LSTm particle swarm optimization OCT image boundary detection

Industrial Centric Node Localization and Pollution Prediction Using Hybrid Swarm Techniques

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Computer Systems Science & Engineering 2022年第8期42卷 545-560页

作者： R.Saravana Ram m.vinoth kumar N.Krishnamoorthy A.Baseera D.mansoor Hussain N.Susila Department of Electronics and Communication Engineering Anna UniversityUniversity College of Engineering DindigulDindigul624622India Department of Computer Science and Engineering Anna UniversityUniversity College of Engineering DindigulDindigul624622India Department of Computer Science and Engineering Kongu Engineering CollegePerundurai638060India School of Computing Science and Engineering VIT Bhopal UniversityBhopal466114India Department of Computer Science and Engineering Sri Krishna College of Engineering and TechnologyCoimbatore641008India Department of Information Technology Sri Krishna College of Engineering and TechnologyCoimbatore641008India

major fields such as military applications,medical fields,weather forecasting,and environmental applications use wireless sensor networks for major computing *** play a vital role in emerging technologies of the 20th *** of sensors in needed locations is a very serious *** environment is home to every living being in the *** growth of industries after the industrial revolution increased pollution across the *** to recent uncontrolled growth and development,sensors to measure pollution levels across industries and surroundings are *** interesting and challenging task is choosing the place to fit the *** meta-heuristic techniques have been introduced in node *** intelligent algorithms have proven their efficiency in many studies on localization *** this article,we introduce an industrial-centric approach to solve the problem of node localization in the sensor ***,our work aims at selecting industrial areas in the sensed *** use random forest regression methodology to select the polluted ***,the elephant herding algorithm is used in sensor node *** two algorithms are combined to produce the best standard result in localizing the sensor *** check the proposed performance,experiments are conducted with data from the KDD Cup 2018,which contain the name of 35 stations with concentrations of air pollutants such as Pm,SO_(2),CO,NO_(2),and O_(3).These data are normalized and tested with *** results are comparatively analyzed with other swarm intelligence algorithms such as the elephant herding algorithm,particle swarm optimization,and machine learning algorithms such as decision tree regression and multi-layer *** can indicate our proposed algorithm can suggest more meaningful locations for localizing the sensors in the *** proposed method achieves a lower root mean square value with 0.06 to 0.08 for lo

关键词： Wireless sensor networks node localization industrial-centric approach random forest regression elephant herding optimization swarm intelligence pollution

Deep Fake Detection Using Computer Vision-Based Deep Neural Network with Pairwise Learning

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Intelligent Automation & Soft Computing 2023年第2期35卷 2449-2462页

作者： R.Saravana Ram m.vinoth kumar Tareq m.Al-shami mehedi masud Hanan Aljuaid mohamed Abouhawwash Department of Electronics and Communication Engineering Anna UniversityUniversity College of EngineeringDindigul624622India Department of Computer Science and Engineering Anna UniversityUniversity College of EngineeringDindigul624622India Department of mathematics Faculty of ScienceSana’a UniversitySana’a 13509Yemen Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Computer Sciences Department College of Computer and Information SciencesPrincess Nourah bint Abdulrahman University(PNU)Riyadh11671Saudi Arabia Department of mathematics Faculty of ScienceMansoura UniversityMansoura35516Egypt Department of Computational mathematics Scienceand Engineering(CMSE)Michigan State UniversityEast LansingMI48824USA

Deep learning-based approaches are applied successfully in manyﬁelds such as deepFake identiﬁcation,big data analysis,voice recognition,and image *** is the combination of deep learning in fake creation,which states creating a fake image or video with the help of artiﬁcial intelligence for political abuse,spreading false information,and *** artiﬁcial intel-ligence technique has a wide demand,increasing the problems related to privacy,security,and *** paper has analyzed the features related to the computer vision of digital content to determine its *** method has checked the computer vision features of the image frames using the fuzzy clustering feature extraction *** the proposed deep belief network with loss handling,the manipulation of video/image is found by means of a pairwise learning *** proposed approach has improved the accuracy of the detection rate by 98%on various datasets.

关键词： Deep fake deep belief network fuzzy clustering feature extraction pairwise learning

Spatial Attention Integrated EfficientNet Architecture for Breast Cancer Classification with Explainable AI

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Computers, materials & Continua 2024年第9期80卷 5029-5045页

作者： Sannasi Chakravarthy Bharanidharan Nagarajan Surbhi Bhatia Khan vinoth kumar Venkatesan mahesh Thyluru Ramakrishna Ahlam Almusharraf Khursheed Aurungzeb Department of Electronics and Communication Engineering Bannari Amman Institute of TechnologySathyamangalam638402India School of Computer Science Engineering and Information Systems(SCORE) Vellore Institute of TechnologyVellore632014India School of Science Engineering and EnvironmentUniversity of SalfordManchesterM54WTUK Department of Computer Science&Engineering Faculty of Engineering and TechnologyJAIN(Deemed-to-be University)Bengaluru562112India Department of management College of Business AdministrationPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh11543Saudi Arabia Adjunct Research Faculty Centre for Research Impact&OutcomeChitkara UniversityRajpura140401India

Breast cancer is a type of cancer responsible for higher mortality rates among *** cruelty of breast cancer always requires a promising approach for its earlier *** light of this,the proposed research leverages the representation ability of pretrained EfficientNet-B0 model and the classification ability of the XGBoost model for the binary classification of breast *** addition,the above transfer learning model is modified in such a way that it will focus more on tumor cells in the input ***,the work proposed an EfficientNet-B0 having a Spatial Attention Layer with XGBoost(ESA-XGBNet)for binary classification of *** this,the work is trained,tested,and validated using original and augmented mammogram images of three public datasets namely CBIS-DDSm,INbreast,and mIAS *** accuracy of 97.585%(CBISDDSm),98.255%(INbreast),and 98.91%(mIAS)is obtained using the proposed ESA-XGBNet architecture as compared with the existing ***,the decision-making of the proposed ESA-XGBNet architecture is visualized and validated using the Attention Guided GradCAm-based Explainable AI technique.

关键词： EfficientNet mammograms breast cancer Explainable AI deep-learning transfer learning

Effects of Flower Buds Extract of Tussilago farfara on Focal Cerebral Ischemia in Rats and Inflammatory Response in BV2 microglia

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Chinese Journal of Integrative medicine 2018年第11期24卷 844-852页

作者： Ji Hye Hwang vinoth kumar R Seok Yong Kang Hyo Won Jung Yong-Ki Park Department of Acupuncture and moxibustion medicine College of Korean Medicine Gachon University Department of Herbology College of Korean Medicine Dongguk University

Objective： To investigate the effects of the flower buds extract of Tussilago farfara Linné（Farfarae Flos; FF） on focal cerebral ischemia through regulation of inflammatory responses in activated microglia. methods： Brain ischemia was induced in Sprague-Dawley rats by a transient middle cerebral artery occlusion（t mCAO） for 90 min and reperfusion for 24 h. Twenty rats were randomly divided into 4 groups（n=5 per group）： normal, t mCAO-induced ischemic control, t mCAO plus FF extract 300 mg/kg-treated, and t mCAO plus mK-801 1 mg/kg-treated as reference drug. FF extract（300 mg/kg, p.o.） or mK-801（1 mg/kg, i.p.） was administered after reperfusion. Brain infarction was measured by 2,3,5,-triphenyltetrazolium chloride staining. Neuronal damage was observed by haematoxylin eosin, Nissl staining and immunohistochemistry using anti-neuronal nuclei（Neu N）, anti-glial fibril ary acidic protein（GFAP）, and antiCD11 b/c（OX42） antibodies in ischemic brain. The expressions of inducible nitric oxide synthase（i NOS）, tumor necrosis factor（TNF-α）, and hypoxia-inducible factor-1 a（HIF-1α） were determined by Western blot. BV2 microglial cel s were treated with FF extract or its main bioactive compound, tussilagone with or without lipopolysaccharide（LPS）. Nitric oxide（NO） production was measured in culture medium by Griess assay. The expressions of i NOS, COX-2 and pro-inflammatory cytokines mRNA were analyzed by reverse transcription-polymerase chain reaction. The expression of i NOS, and COX-2 proteins, the phosphorylation of ERK1/2, JNK, and p38 mAPK and the nuclear expression of NF-κB p65 in BV2 cells were determined by Western blot. Results： FF extract significantly decreased brain infarctions in ischemic rats（P〈0.01）. The neuronal death and the microglia/astrocytes activation in ischemic brains were inhibited by FF extract. FF extract also suppressed i NOS, TNF-α, and HIF-1α expression in ischemic brains. FF extract（0.2 and 0.5 mg/m L, P〈0.01） and tussilagone 20 and 50 μmol/L, P〈0

关键词： Tussilago farfara focal cerebral ischemia inflammation microglia tussilagone