检索结果-南通市图书馆

MCWOA Scheduler:Modified Chimp-Whale Optimization Algorithm for Task Scheduling in Cloud Computing

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Computers, Materials & Continua 2024年第2期78卷 2593-2616页

作者： Chirag Chandrashekar Pradeep Krishnadoss Vijayakumar Kedalu Poornachary balasundaram Ananthakrishnan School of Computer Science and Engineering Vellore Institute of TechnologyChennai600127India Center for Cyber Physical Systems School of Computer Science and EngineeringVellore Institute of TechnologyChennai600127India

Cloud computing provides a diverse and adaptable resource pool over the internet,allowing users to tap into various resources as *** has been seen as a robust solution to relevant challenges.A significant delay can hamper the performance of IoT-enabled cloud ***,efficient task scheduling can lower the cloud infrastructure’s energy consumption,thus maximizing the service provider’s revenue by decreasing user job processing *** proposed Modified Chimp-Whale Optimization Algorithm called Modified Chimp-Whale Optimization Algorithm(MCWOA),combines elements of the Chimp Optimization Algorithm(COA)and the Whale Optimization Algorithm(WOA).To enhance MCWOA’s identification precision,the Sobol sequence is used in the population initialization phase,ensuring an even distribution of the population across the solution ***,the traditional MCWOA’s local search capabilities are augmented by incorporating the whale optimization algorithm’s bubble-net hunting and random search mechanisms into MCWOA’s position-updating *** study demonstrates the effectiveness of the proposed approach using a two-story rigid frame and a simply supported beam *** outcomes reveal that the new method outperforms the original MCWOA,especially in multi-damage detection *** excels in avoiding false positives and enhancing computational speed,making it an optimal choice for structural damage *** efficiency of the proposed MCWOA is assessed against metrics such as energy usage,computational expense,task duration,and *** simulated data indicates that the new MCWOA outpaces other methods across all *** study also references the Whale Optimization Algorithm(WOA),Chimp Algorithm(CA),Ant Lion Optimizer(ALO),Genetic Algorithm(GA)and Grey Wolf Optimizer(GWO).

关键词： Cloud computing scheduling chimp optimization algorithm whale optimization algorithm

U-Net Inspired Deep Neural Network-Based Smoke Plume Detection in Satellite Images

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Computers, Materials & Continua 2024年第4期79卷 779-799页

作者： Ananthakrishnan balasundaram Ayesha Shaik Japmann Kaur Banga Aman Kumar Singh Centre for Cyber Physical Systems Vellore Institute of Technology(VIT)ChennaiTamil Nadu600127India School of Computer Science and Engineering Vellore Institute of Technology(VIT)ChennaiTamil Nadu600127India

Industrial activities, through the human-induced release of Green House Gas (GHG) emissions, have beenidentified as the primary cause of global warming. Accurate and quantitative monitoring of these emissions isessential for a comprehensive understanding of their impact on the Earth’s climate and for effectively enforcingemission regulations at a large scale. This work examines the feasibility of detecting and quantifying industrialsmoke plumes using freely accessible geo-satellite imagery. The existing systemhas so many lagging factors such aslimitations in accuracy, robustness, and efficiency and these factors hinder the effectiveness in supporting timelyresponse to industrial fires. In this work, the utilization of grayscale images is done instead of traditional colorimages for smoke plume detection. The dataset was trained through a ResNet-50 model for classification and aU-Net model for segmentation. The dataset consists of images gathered by European Space Agency’s Sentinel-2 satellite constellation from a selection of industrial sites. The acquired images predominantly capture scenesof industrial locations, some of which exhibit active smoke plume emissions. The performance of the abovementionedtechniques and models is represented by their accuracy and IOU (Intersection-over-Union) *** images are first trained on the basic RGB images where their respective classification using the ResNet-50model results in an accuracy of 94.4% and segmentation using the U-Net Model with an IOU metric of 0.5 andaccuracy of 94% which leads to the detection of exact patches where the smoke plume has occurred. This work hastrained the classification model on grayscale images achieving a good increase in accuracy of 96.4%.

关键词： Smoke plume ResNet-50 U-Net geo satellite images early warning global monitoring

MDCN:Modified Dense Convolution Network Based Disease Classification in Mango Leaves

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Computers, Materials & Continua 2024年第2期78卷 2511-2533页

作者： Chirag Chandrashekar K.P.Vijayakumar K.Pradeep A.balasundaram School of Computer Science and Engineering Vellore Institute of Technology(VIT)ChennaiIndia Center for Cyber Physical Systems School of Computer Science and EngineeringVellore Institute of Technology(VIT)ChennaiIndia

The most widely farmed fruit in the world is *** the production and quality of the mangoes are hampered by many *** diseases need to be effectively controlled and ***,a quick and accurate diagnosis of the disorders is *** convolutional neural networks,renowned for their independence in feature extraction,have established their value in numerous detection and classification ***,it requires large training datasets and several parameters that need careful *** proposed Modified Dense Convolutional Network(MDCN)provides a successful classification scheme for plant diseases affecting mango *** model employs the strength of pre-trained networks and modifies them for the particular context of mango leaf diseases by incorporating transfer learning *** data loader also builds mini-batches for training the models to reduce training ***,optimization approaches help increase the overall model’s efficiency and lower computing *** employed on the MangoLeafBD Dataset consists of a total of 4,000 *** the experimental results,the proposed system is compared with existing techniques and it is clear that the proposed algorithm surpasses the existing algorithms by achieving high performance and overall throughput.

关键词： Leaf disease detection deep convolutional neural networks transfer learning optimization MangoLeafBD Dataset

Comparative Assessment of Low-Concentration Ethanol and Waste Fish Oil Biodiesel Blends on Emission Reduction and Performance Improvement in Variable Compression Ratio Engine

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Journal of Thermal Science 2023年第3期32卷 1306-1319页

作者： Vasanthaseelan sathiyaseelan Savadamuthu LAKSHMANA GOWDER Ravishankar SATHYAMURTHY Department of Mechanical Engineering KPR Institute of Engineering and TechnologyArasurCoimbatore-641407Tamil NaduIndia Department of Mechanical Engineering SNS College of TechnologyCoimbatoreTamil NaduIndia Mechanical Engineering Department King Fahd University of Petroleum and MineralsDhahranSaudi Arabia

The costs of conventional fuels are rising on a daily basis as a result of technical limits,a misallocation of resources between demand and supply,and a shortage of conventional *** use of crude oil contributes to increased environmental contamination,and as a result,there is a pressing need to investigate alternate fuel sources for car *** is a renewable fuel that is derived chemically by reacting with the sources of *** present research is based on analyzing the effect of fish oil biodiesel-ethanol blend in variable compression engine for variable compression ratio(VCR).The processed fish oil was procured and subjected to a transesterification process to convert fatty acids into methyl *** obtained methyl esters(biodiesel)were blended with ethanol and diesel to obtain a ternary *** ternary blend was tested for its stability,and a stable blend was obtained and tested in VCR engines for its performance,combustion,and emission *** the second phase,experiments are conducted in the diesel engine by fueling the fish oil methyl ester and ethanol blended with diesel fuel in the concentration of 92.5 vol%of Diesel+7.5 vol%of Fish oil+1.25vol%ethanol,92.5 vol%of Diesel+7.5 vol%of Fish oil+5 vol%ethanol,87.5 vol%of Diesel+12.5 vol%of Fish oil+1.25 vol%ethanol,87.5 vol%of Diesel+12.5 vol%of Fish oil+5 vol%ethanol,82.5 vol%of Diesel+17.5vol%of Fish oil+1.25 vol%ethanol,82.5 vol%of Diesel+17.5 vol%of Fish oil+5 vol%ethanol to find out the performance parameters and *** the alternative fuel performs better in terms of engine performance and pollution management,the percentage chosen is considered the best *** results showed that the use of a lower concentration of ethanol in the fish oil biodiesel blend improved the engine thermal efficiency by 5.23%at a higher compression ***,the engine operated with a higher compression ratio reduced the formation of HC and CO emissions,whereas the NOxa

关键词： biodiesel VCR alternate fuels fish oil performance combustion emission injection timing crank angle

Deep Learning Based Face Detection and Identification of Criminal Suspects

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

Computers, Materials & Continua 2023年第2期74卷 2331-2343页

作者： S.Sandhya A.balasundaram Ayesha Shaik School of Computer Science and Engineering Vellore Institute of Technology(VIT)Chennai600127India School of Computer Science and Engineering Center for Cyber Physical SystemsVellore Institute of Technology(VIT)Chennai600127India

Occurrence of crimes has been on the constant rise despite the emerging discoveries and advancements in the technological field in the past *** of the most tedious tasks is to track a suspect once a crime is *** most of the crimes are committed by individuals who have a history of felonies,it is essential for a monitoring system that does not just detect the person’s face who has committed the crime,but also their ***,a smart criminal detection and identification system that makes use of the OpenCV Deep Neural Network(DNN)model which employs a Single Shot Multibox Detector for detection of face and an auto-encoder model in which the encoder part is used for matching the captured facial images with the criminals has been *** detection and extraction of the face in the image by face cropping,the captured face is then compared with the images in the *** comparison is performed by calculating the similarity value between each pair of images that are obtained by using the Cosine Similarity *** plotting the values in a graph to find the threshold value,we conclude that the confidence rate of the encoder model is 0.75 and above.

关键词： Deep learning opencv deep neural network single shot multi-box detector auto-encoder cosine similarity

Analysis of surface roughness of rock dust reinforced AA6061-Mg matrix composite in turning

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Journal of Magnesium and Alloys 2021年第5期9卷 1686-1693页

作者： R.Balachandhar R.balasundaram M.Ravichandran Department of Mechanical Engineering Pavender Barathidasan College of Engineering and TechnologyTiruchirappalli620024Tamil NaduIndia Department of Mechanical Engineering K.Ramakrishnan College of EngineeringTiruchirappalli621112Tamil NaduIndia Department of Mechanical Engineering SRM Institute of Science and TechnologyTiruchirappalli Campus621105Tamil NaduIndia

This work,examines the Surface Roughness(SR)of composite consisting Aluminium alloy(AA6061),Magnesium and Rock dust during turning *** study the performance,three different test specimens with different constituents of Al 6061-T6,AZ31 and Rock dust were prepared by stir casting *** experiments were carried out using MTAB Siemens-CNC *** input parameters for machining are speed,depth of cut&feed and output response is surface roughness For each test specimen,there are 15 turning operations were performed using Box-Ben hen Design *** analyze the process parameters for SR,the models of ANOVA and Decision Tree(DT)algorithms were *** algorithms are confirmed that,speed is the most significant factor for *** addition of AZ 31 with 1%and rock dust of 2%in AA6061 produced better surface *** models of linear regression,multilayer perception and support vector regression from data science were formulated to find the relationship between *** these models multi layer perception produced minimum root mean square error.

关键词： Composite Turning Surface roughness Anova Decision tree Regression

A HYBRID APPROACH FOR MINIMIZING MAKESPAN IN PERMUTATION FLOWSHOP SCHEDULING

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

Journal of Systems Science and Systems Engineering 2017年第1期26卷 50-76页

作者： Kannan Govindan R'balasundaram N.Baskar e.Asokan Center for Sustainable Engineering Operations Management Department of Technology and Innovation University of Southern Denmark Odense M Denmark-5230 Department of Mechanical Engineering K.Ramakrishnan College of Engineering Tiruchirappalli Tamilnadu India Pin-621112 Department of Mechanical Engineering Saranathan College of Engineering Tiruchirappalli Tamilnadu India Pin 620012 Department of Production Engineering National Institute of Technology Tiruchirappalli Tamilnadu India Pin-620015

This work proposes a hybrid approach for solving traditional flowshop scheduling problems to reduce the makespan （total completion time）. To solve scheduling problems, a combination of Decision Tree （DT） and Scatter Search （SS） algorithms are used. Initially, the DT is used to generate a seed solution which is then given input to the SS to obtain optimal / near optimal solutions of makespan. The DT used the entropy function to convert the given problem into a tree structured format / set of rules. The SS provides an extensive investigation of the search space through diversification. The advantages of both DT and SS are used to form a hybrid approach. The proposed algorithm is tested with various benchmark datasets available for flowshop scheduling. The statistical results prove that the proposed method is competent and efficient for solving flowshop problems.

关键词： Flowshop scheduling makespan decision tree algorithm scatter search algorithm hybrid algorithm

Breast Mammogram Analysis and Classification Using Deep Convolution Neural Network

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Computer Systems Science & Engineering 2022年第10期43卷 275-289页

作者： V.Ulagamuthalvi G.Kulanthaivel A.balasundaram Arun Kumar Sivaraman Department of Computer Science and Engineering Sathyabama UniversityChennai600119India Department of Electrical Electronics and Communication EngineeringNITTTRChennai600113India Centre for Cyber Physical Systems School of Computer Science and EngineeringVellore Institute of Technology(VIT)Chennai600127India School of Computer Science and Engineering Vellore Institute of Technology(VIT)Chennai600127India

One of the fast-growing disease affecting women’s health seriously is breast *** is highly essential to identify and detect breast cancer in the earlier *** paper used a novel advanced methodology than machine learning algorithms such as Deep learning algorithms to classify breast cancer *** learning algorithms are fully automatic in learning,extracting,and classifying the features and are highly suitable for any image,from natural to medical *** methods focused on using various conventional and machine learning methods for processing natural and medical *** is inadequate for the image where the coarse structure matters *** of the input images are downscaled,where it is impossible to fetch all the hidden details to reach accuracy in *** deep learning algorithms are high efficiency,fully automatic,have more learning capability using more hidden layers,fetch as much as possible hidden information from the input images,and provide an accurate *** this paper uses AlexNet from a deep convolution neural network for classifying breast cancer in mammogram *** performance of the proposed convolution network structure is evaluated by comparing it with the existing algorithms.

关键词： Medical image processing deep learning convolution neural network breast cancer feature extraction classification

CE-EEN-B0:Contour Extraction Based Extended EfficientNet-B0 for Brain Tumor Classification Using MRI Images

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Computers, Materials & Continua 2023年第3期74卷 5967-5982页

作者： Abishek Mahesh Deeptimaan Banerjee Ahona Saha Manas Ranjan Prusty A.balasundaram School of Computer Science and Engineering Vellore Institute of TechnologyChennai600127India Centre for Cyber-Physical Systems Vellore Institute of TechnologyChennai600127India

A brain tumor is the uncharacteristic progression of tissues in the *** are very deadly,and if it is not diagnosed at an early stage,it might shorten the affected patient’s life ***,their classification and detection play a critical role in *** Brain tumor detection is done by biopsy which is quite *** is usually not preferred at an early stage of the *** detection involvesMagneticResonance Imaging(MRI),which is essential for evaluating the *** paper aims to identify and detect brain tumors based on their location in the *** order to achieve this,the paper proposes a model that uses an extended deep Convolutional Neural Network(CNN)named Contour Extraction based Extended EfficientNet-B0(CE-EEN-B0)which is a feed-forward neural network with the efficient net layers;three convolutional layers and max-pooling layers;and finally,the global average pooling *** site of tumors in the brain is one feature that determines its effect on the functioning of an ***,this CNN architecture classifies brain tumors into four categories:No tumor,Pituitary tumor,Meningioma tumor,andGlioma *** network provides an accuracy of 97.24%,a precision of 96.65%,and an F1 score of 96.86%which is better than already existing pre-trained networks and aims to help health professionals to cross-diagnose an MRI *** model will undoubtedly reduce the complications in detection and aid radiologists without taking invasive steps.

关键词： Brain tumor image preprocessing contour extraction disease classification transfer learning