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| 1 | Fuzzy Based Ant Colony Optimization Scheduling in Cloud Computing显示文摘Cloud computing is an Information Technology deployment model established on virtualization.Task scheduling states the set of rules for task allocations to an exact virtual machine in the cloud computing environment.However,task scheduling challenges such as optimal task scheduling performance solutions,are addressed in cloud computing.First,the cloud computing performance due to task scheduling is improved by proposing a Dynamic Weighted Round-Robin algorithm.This recommended DWRR algorithm improves the task scheduling performance by considering resource competencies,task priorities,and length.Second,a heuristic algorithm called Hybrid Particle Swarm Parallel Ant Colony Optimization is proposed to solve the task execution delay problem in DWRR based task scheduling.In 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 PACO.Thus,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. | K.Rajakumari M.Vinoth Kumar Garima Verma S.Balu Dilip Kumar Sharma Sudhakar Sengan | 2022 | Computer Systems Science & Engineering2022，40，2： | 0 |
| 2 | An Improved Multi-Objective Particle Swarm Optimization Routing on MANET显示文摘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 organization.The 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 system.An 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/violations.An IDS is a software program or hardware system that monitors network/security traffic for malicious attacks,sending out alerts whenever it detects malicious nodes.The impact of Dynamic Source Routing(DSR)in MANETs challenging blackhole attack is investigated in this research article.The Cluster Trust Adaptive Acknowledgement(CTAA)method is used to identify unauthorised and malfunctioning nodes in a MANET environment.MANET system is active and provides successful delivery of a data packet,which implements Kalman Filters(KF)to anticipate node trustworthiness.Furthermore,KF is used to eliminate synchronisation errors that arise during the sending and receiving data.In 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 nodes.According 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. | G.Rajeshkumar M.Vinoth Kumar K.Sailaja Kumar Surbhi Bhatia Arwa Mashat Pankaj Dadheech | 2023 | Computer Systems Science & Engineering2023，44，2： | 0 |
| 3 | Enhanced Long Short Term Memory for Early Alzheimer's Disease Prediction显示文摘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 yet.Due to the correla-tions between ocular and brain tissue,the eye(retinal blood vessels)has been investigated for predicting the AD.Hence,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 biomarkers.Tofind 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 rapidly.To avoid the problem of overfitting,a dropout has been added to LSTM.In 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 utilized.To the best of our understanding,this is thefirst paper to use Particle Swarm Optimization.When 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. | M.Vinoth Kumar M.Prakash M.Naresh Kumar H.Abdul Shabeer | 2023 | Intelligent Automation & Soft Computing2023，，2： | 0 |
| 4 | Stress corrosion cracking behaviour of gas tungsten arc welded super austenitic stainless steel joints显示文摘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. | M.VINOTH KUMAR V.BALASUBRAMANIAN S.RAJAKUMAR SHAJU K.ALBERT | 2015 | Defence Technology(防务技术)2015，11，3： | 0 |
| 5 | Hybrid GLFIL Enhancement and Encoder Animal Migration Classification for Breast Cancer Detection显示文摘Breast cancer has become the second leading cause of death among women worldwide.In India,a woman is diagnosed with breast cancer every four minutes.There has been no known basis behind it,and detection is extremely challenging among medical scientists and researchers due to unknown reasons.In India,the ratio of women being identified with breast cancer in urban areas is 22:1.Symptoms for this disease are micro calcification,lumps,and masses in mammogram images.These sources are mostly used for early detection.Digital mammography is used for breast cancer detection.In this study,we introduce a new hybrid wavelet filter for accurate image enhancement.The main objective of enhancement is to produce quality images for detecting cancer sections in images.Image enhancement is the main step where the quality of the input image is improved to detect cancer masses.In this study,we use a combination of two filters,namely,Gabor and Legendre.The edges are detected using the Canny detector to smoothen the images.High-quality enhanced image is obtained through the Gabor-Legendre filter(GLFIL)process.Further image is used by classification algorithm.Animal migration optimization with neural network is implemented for classifying the image.The output is compared to existing filter techniques.Ultimately,the accuracy achieved by the proposed technique is 98%,which is higher than existing algorithms. | S.Prakash M.Vinoth Kumar R.Saravana Ram Miodrag Zivkovic Nebojsa Bacanin Milos Antonijevic | 2022 | Computer Systems Science & Engineering2022，41，5： | 0 |
| 6 | Deep Fake Detection Using Computer Vision-Based Deep Neural Network with Pairwise Learning显示文摘Deep learning-based approaches are applied successfully in manyfields such as deepFake identification,big data analysis,voice recognition,and image recognition.Deepfake 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 pornography.The artificial intel-ligence technique has a wide demand,increasing the problems related to privacy,security,and ethics.This paper has analyzed the features related to the computer vision of digital content to determine its integrity.This method has checked the computer vision features of the image frames using the fuzzy clustering feature extraction method.By the proposed deep belief network with loss handling,the manipulation of video/image is found by means of a pairwise learning approach.This proposed approach has improved the accuracy of the detection rate by 98%on various datasets. | R.Saravana Ram M.Vinoth Kumar Tareq M.Al-shami Mehedi Masud Hanan Aljuaid Mohamed Abouhawwash | 2023 | Intelligent Automation & Soft Computing2023，，2： | 0 |
| 7 | A Hybrid Approach for COVID-19 Detection Using Biogeography-Based Optimization and Deep Learning显示文摘The COVID-19 pandemic has created a major challenge for countries all over the world and has placed tremendous pressure on their public health care services.An early diagnosis of COVID-19 may reduce the impact of the coronavirus.To achieve this objective,modern computation methods,such as deep learning,may be applied.In this study,a computational model involving deep learning and biogeography-based optimization(BBO)for early detection and management of COVID-19 is introduced.Specifically,BBO is used for the layer selection process in the proposed convolutional neural network(CNN).The computational model accepts images,such as CT scans,X-rays,positron emission tomography,lung ultrasound,and magnetic resonance imaging,as inputs.In the comparative analysis,the proposed deep learning model CNNis compared with other existingmodels,namely,VGG16,InceptionV3,ResNet50,and MobileNet.In the fitness function formation,classification accuracy is considered to enhance the prediction capability of the proposed model.Experimental results demonstrate that the proposed model outperforms InceptionV3 and ResNet50. | K.Venkatachalam Siuly Siuly M.Vinoth Kumar Praveen Lalwani Manas Kumar Mishra Enamul Kabir | 2022 | Computers, Materials & Continua2022，，2： | 0 |
| 8 | Industrial Centric Node Localization and Pollution Prediction Using Hybrid Swarm Techniques显示文摘Major fields such as military applications,medical fields,weather forecasting,and environmental applications use wireless sensor networks for major computing processes.Sensors play a vital role in emerging technologies of the 20th century.Localization of sensors in needed locations is a very serious problem.The environment is home to every living being in the world.The growth of industries after the industrial revolution increased pollution across the environment.Owing to recent uncontrolled growth and development,sensors to measure pollution levels across industries and surroundings are needed.An interesting and challenging task is choosing the place to fit the sensors.Many meta-heuristic techniques have been introduced in node localization.Swarm intelligent algorithms have proven their efficiency in many studies on localization problems.In this article,we introduce an industrial-centric approach to solve the problem of node localization in the sensor network.First,our work aims at selecting industrial areas in the sensed location.We use random forest regression methodology to select the polluted area.Then,the elephant herding algorithm is used in sensor node localization.These two algorithms are combined to produce the best standard result in localizing the sensor nodes.To 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 algorithms.The 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 perceptron.Results can indicate our proposed algorithm can suggest more meaningful locations for localizing the sensors in the topology.Our proposed method achieves a lower root mean square value with 0.06 to 0.08 for localizing with Stations 1 to 5. | R.Saravana Ram M.Vinoth Kumar N.Krishnamoorthy A.Baseera D.Mansoor Hussain N.Susila | 2022 | Computer Systems Science & Engineering2022，42，8： | 0 |
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