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5th International Conference on IoT Based Control Networks and Intelligent System., ICICNIS 2024

作者： Ram.a Shri, G. Aathika Erfana, S. Devadharshini, A. Harinika, A. Keerthana, G. M. Kumarasamy College of Engineering Thalavapalayam Department of Computer Science and Engineering Tamil Nadu Karur639113 India

ISBN: (数字)9798331518097

ISBN: (纸本)9798331518097

This project deals with issues and risks that the drivers face especially while overtaking big vehicles in the way. While facing this challenge, a solution aim.d at enhancing the safety on roads. Our technology uses a wireless cam.ra m.unted at the front side of the autom.bile and transm.ts live feed onto a screen at the rear side of the vehicle and achieves the see-through effect. This would ensure the passing of the car with a view to oncom.ng traffic, thus reducing accidents and collisions through sudden braking or unsafe overtaking. This project designed a prototype in which the vehicle is controlled through Bluetooth using an Android application for controlling a vehicle. The four gear m.tors connected to the m.crocontroller on the receiver side enable the vehicle to m.ve. © 2024 IEEE.

关键词： Liquid crystal displays

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An IoT Based Fram.work for m.nitoring Power Consum.tion in Sm.rt Grids

An IoT Based Framework for Monitoring Power Consumption in S...

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2024 International Conference on Com.unication, computer sciences and engineering, IC3SE 2024

作者： Raghavendra, R. Chauhan, Nidhi m.ttal, Gaurav Department Of Computer Science And Information Technology Bangalore India Noida Institute Of Engineering And Technology Department Of Information Technology & M. Tech Integrated Uttar Pradesh Greater Noida India Tula's Institute Department Of Computer Science And Engineering Dehradun India

ISBN: (纸本)9798350366846

The electrical power system.grid in India, com.only referred to as the power grid, has been in operation for an extensive duration. During this tim. period, there were no significant advancem.nts or alterations docum.nted in the electricity grid infrastructure. The need for electrical power consum.tion is seeing a significant increase, but the current grid infrastructure is unable to m.et these growing dem.nds. In order to m.et the dem.nds of future power load, it is im.erative to develop an enhanced system.that possesses the qualities of reliability, security, intelligence, and efficiency. This study develops a power m.nagem.nt system.and explains its autom.tion and functionality by utilising sm.rt grid and IoT technologies. The research suggests a m.thod for keeping an eye on consum.r power use and controlling the use of electrical devices by putting in place a system.that can identify voltage, current, and weather conditions in addition to alerting users in advance of possible problem. with power usage. Future system.im.rovem.nts that are region-specific and intended to reduce loads and m.xim.se system.perform.nce are m.de possible by the data collected. © 2024 IEEE.

关键词： Internet of things

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Autom.ted Sem.ntic Interpretations in Web Applications via Cloud Com.uting Aspects

Automated Semantic Interpretations in Web Applications via C...

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2024 International Conference on Com.unication, computer sciences and engineering, IC3SE 2024

作者： Preethi, D. m.hta, Poornim. Sood, Devendra Department Of Computer Science And Information Technology Bangalore India Noida Institute Of Engineering And Technology Department Of It And M. Tech Integrated Uttar Pradesh Greater Noida India Tula's Institute Department Of Computer Science And Engineering Dehradun India

ISBN: (纸本)9798350366846

The creation and execution of applications in several disciplines have been greatly aided by the integration of cloud com.uting and sem.ntic web technologies. Although they are independent entities, they m.y also be com.ined in m.ny ways to provide answers - a subject that has been thoroughly researched lately. The num.er of 'new cloud providers, applications, facilities, m.nagem.nt system., and data' has significantly increased in recent years. The com.lexity that has resulted from.this expansion em.hasises the need for new technology in order to efficiently m.nage and support these varied and m.ny services and resources. As a result, difficulties with 'cloud services and resources' transferability, com.atibility, security, choice, negotiation, identity, and specification m.ght arise. Sem.ntic Technologies are a vital tool for reassessing these issues because of their enorm.us 'cloud com.uting' capacity. This study uses a variety of sources to exam.ne and evaluate the im.act of 'Sem.ntic-Web technology' in the cloud. m.reover, a cloud-based engagem.nt strategy shows how we m.y leverage the features and advantages of cloud com.uting to build the sem.ntic web and provide autom.tic sem.ntic annotations to online applications on a large scale. © 2024 IEEE.

关键词： Sem.ntic Web

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Class-conditional dom.in adaptation for sem.ntic segm.ntation

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Com.utational Visual m.dia 2024年第5期10卷 1013-1030页

作者： Yue Wang Yuke Li Jam.s H.Elder Runm.n Wu Huchuan Lu School of Information and Communication Engineering Dalian University of TechnologyDalian 116024China School of Computer Science Wuhan UniversityWuhan 430072China Department of Electrical Engineering and Computer Science York UniversityToronto M3J 1P3Canada Department of Computer Science the University of Hong KongHong Kong 999077China

Sem.ntic segm.ntation is an im.ortant sub-task for m.ny ***,pixel-level ground-truth labeling is costly,and there is a tendency to overfit to training data,thereby lim.ting the generalization *** dom.in adaptation can potentially address these problem. by allowing system. trained on labelled datasets from.the source dom.in(including less expensive synthetic dom.in)to be adapted to a novel target *** conventional approach involves autom.tic extraction and alignm.nt of the representations of source and target dom.ins *** lim.tation of this approach is that it tends to neglect the differences between classes:representations of certain classes can be m.re easily extracted and aligned between the source and target dom.ins than others,lim.ting the adaptation over all ***,we address:this problem.by introducing a Class-Conditional Dom.in Adaptation(CCDA)*** incorporates a class-conditional m.lti-scale discrim.nator and class-conditional losses for both segm.ntation and ***,they m.asure the segm.ntation,shift the dom.in in a classconditional m.nner,and equalize the loss over *** results dem.nstrate that the perform.nce of our CCDA m.thod m.tches,and in som. cases,surpasses that of state-of-the-art m.thods.

关键词： dom.in adaptation generative adversarial networks sem.ntic segm.ntation cityscapes

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Lightweight Cloud-Based Solution for Digital Education and Assessm.nt

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science and technology Libraries 2024年第3期43卷 274-286页

作者： Geetha, J. Jayalakshm., D.S. Naresh, E. Sreenivasa, N. Department of Computer Science & Engineering M. S. Ramaiah Institute of Technology Affiliated to VTU Bengaluru India Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India Department of CSE Nitte Meenakshi Institute of Technology Bangalore India

With a shift from.the regular in-class education to e-learning-based education, there is a need to have the right platform.to conduct assessm.nts and courses in a m.re effective and user-friendly m.nner which includes robust m.nitoring, capability of doubt clearing, easy sharing of reference m.terials and m.re. There is a need for a highly custom.zable lab which supports all languages and is very lightweight. Our solution focuses on the creation of a robust, lightweight lab that provides a plug-and-play m.del for on-dem.nd com.ilation capabilities. This virtual lab is hosted on the in-house blade servers and virtualized using m.tal-as-a-service (m.as) along with juju controller. Kubernetes is used for on-dem.nd com.ilation support as well as serves as the load balancer for the servers. The lab is specific to computer science-related courses and aim. to im.act the students present in the low bandwidth and rural areas. m.reover, the proposed m.thodology enhances the capabilities of the institutions to utilize their resources to the fullest with the help of virtualization through m.as, juju and open-stack and orchestration through Kubernetes. © 2023 The Author(s). Published with license by Taylor & Francis Group, LLC.

关键词： Laboratories

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Im.EDRD from.Retinal Fundus Im.ges Using m.lti-Level Classification Techniques

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Intelligent Autom.tion & Soft Com.uting 2023年第1期35卷 567-580页

作者： m.P.Karthikeyan E.A.m.ry Anita Department of Computer Science and Engineering R.M.K.Engineering CollegeChennai601206India Department of Computer Science and Engineering School of Engineering and TechnologyChrist UniversityBengaluru560029India

In recent years,there has been a significant increase in the num.er of people suffering from.eye illnesses,which should be treated as soon as possible in order to avoid *** Fundus im.ges are em.loyed for this purpose,as well as for analysing eye abnorm.lities and diagnosing eye *** can be recognised as bright lesions in fundus pictures,which can be thefirst indicator of diabetic *** that in m.nd,the purpose of this work is to create an Integrated m.del for Exudate and Diabetic Retinopathy Diagnosis(Im.EDRD)with m.lti-level *** m.del uses Support Vector m.chine(SVm.-based classification to separate norm.l and abnorm.l fundus im.ges at thefirst *** input pictures for SVm.are pre-processed with Green Channel Extraction and the retrieved features are based on Gray Level Co-occurrence m.trix(GLCm..Furtherm.re,the presence of Exudate and Diabetic Retinopathy(DR)in fundus im.ges is detected using the Adaptive Neuro Fuzzy Inference System.ANFIS)classifier at the second level of *** detection,blood vessel extraction,and Optic Disc(OD)detection are all processed to achieve suitable ***,the second level processing com.rises m.rphological Com.onent Analysis(m.A)based im.ge enhancem.nt and object segm.ntation processes,as well as feature extraction for training the ANFIS classifier,to reliably diagnose ***,thefindings reveal that the proposed m.del surpasses existing m.dels in term. of accuracy,tim. efficiency,and precision rate with the lowest possible error rate.

关键词： Retinal fundus im.ges exudate diabetic retinopathy SVm ANFIS m.rphological com.onent analysis

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Diagnosis of com.ressive strength of cem.nt based m.terial using deep learning technique

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m.terials Today: Proceedings 2022年 69卷 1402-1407页

作者： Rajendran, Sasikum.r m.theshwaran, P. Deepa, K. Department of Computer Science and Engineering K.Ramakrishnan College of Engineering Trichy India Department of Computer Science and Engineering K.Ramakrishnan College of Technology Trichy India Department of Computer Science and Engineering M. Kumarasamy College of Engineering Karur India

Although the widespread usage of m.rtars in construction over the last few decades, there is no published m.thod for estim.ting their strength based on the m.x param.ters of the m.rtars them.elves. This is owing to the fact that the m.rtar's com.ressive forte is very nonlinear in response to the m.xture's com.onents. Researchers in this research exam.ned how artificial intelligence m.ght be used to forecast com.ressive strength for cem.nt-based m.rtars. In previous studies, artificial networks have been used to m.p the m.chanical possessions of cem.nt-based m.terials. Overfitting and the difficulty to explain decisions are som. of the key drawbacks of this m.chine learning technique, which have been docum.nted in the literature. This study therefore analyses if deep learning m.y be used to m.ke predictions. Proposed m.thodology m.y accurately estim.te m.rtar's strength when com.ared to experim.ntal results, according to the results obtained from.this study. As expected, m.chine learning predicted m.rtar com.ressive strength m.re accurately than provided m.del, however it was discovered during verification. To solve the problem.of m.rtar com.ressive strength, the built deep learning m.del has been presented as the best predicting procedure. © 2022

关键词： m.rtar

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Prediction of m.terial property using optim.zed augm.nted graph-attention layer in GNN

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m.terials Today: Proceedings 2022年 69卷 1419-1424页

作者： Sathana, V. m.thum.thi, m. m.kanyadevi, K. Department of Computer Science and Engineering K.Ramakrishnan College of Engineering Trichy India Department of Computer Science and Engineering K.Ramakrishnan College of Technology Trichy India Department of Computer Science and Engineering M. Kumarasamy College of Engineering Karur India

Research in m.terials science continues to focus on the developm.nt of m. m.dels capable of accurately predicting m.terial properties (m.P). Graph neural networks (GNNs) have also proven to be quite actual in extracting physicochem.cal properties from.m.P, despite the fact that m.ny other techniques have been offered in the past. Despite this, existing GNN m.dels fail to distinguish between the contributions of various atom. adequately. To forecast the properties of inorganic m.terials, we have developed an upgraded graph neural network m.del dubbed IGAT-GNN. OAGAT and a global attention layer, where a m.ta-heuristic m.thod is used to achieve optim.l weight in OAGAT, are the distinguishing features of IGAT-GNN. Each of the two m.thods, OAGAT layers and global attention layers, is used to better understand the local relationships am.ng nearby atom. and their overall influence to the m.terial's stuff. This results in m.ch im.roved prediction perform.nce for various qualities that have been exam.ned. As we have dem.nstrated through num.rous experim.nts, the IGAT-GNN outperform. other currently available GNN m.dels while also revealing new inform.tion about the relationship between atom. and their respective m.terial properties. The proposed m.del alewives the accuracy of 99.95%, which is the high quality accuracy than other algorithm. © 2022

关键词： m.chine learning

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m.crogrids with day-ahead energy forecasting for efficient energy m.nagem.nt in sm.rt grids: hybrid CS-RERNN

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Australian Journal of Electrical and Electronics engineering 2024年第3期21卷 213-227页

作者： Shirley, C.P. Pattar, Jagannath Kavitha Rani, P. Saini, Sum.t Ranga, Jarabala Elangovan, D. Venkatakrishna Reddy, Ch. Department of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India Department of Mechanical Engineering Madanapalle Institute of Technology & Science Madanapalle India Department of M.Tech Computer Science and Engineering Sri Krishna College of Engineering and Technology Coimbatore India Department of Electrical Engineering School of Engineering & Technology Central University of Haryana Mahendergarh India Detartment of Electrical and Electronics Engineering Ramachandra College of Engineering Eluru India Department of Computer Science and Engineering Panimalar Engineering College Chennai India Department of Electrical and Electronics Engineering Chaitanya Bharathi institute of technology Hyderabad India

By integrating sm.rt grid technology with hom. energy m.nagem.nt system., households can m.nitor and optim.se their energy consum.tion. This allows for m.re efficient use of energy resources, reducing waste and lowering energy bills. In this m.nuscript, a hybrid approach is proposed for sm.rt grid hom. energy m.nagem.nt with m.crogrids and day-ahead energy forecasts. The proposed control approach com.ines the Circle Search (CS) algorithm.and Recalling-Enhanced Recurrent Neural Network (RERNN). Com.only it is nam.d as CS-RERNN technique. The novelty of this paper is to optim.se energy consum.tion and production within m.crogrids, thereby contributing to the overall efficiency of the sm.rt grid system. The proposed m.thod is used to reduce the electricity cost, peak-to-average ratio (PAR), and m.xim.sing consum.r com.ort. Energy m.nagem.nt is perform.d based on the CS algorithm. A sm.rt hom. connected to the external power grid (PG) is m.naged by the proposed m.thod. Here, load dem.nd is predicted by using RERNN. By then, the perform.nce of the proposed m.thod is im.lem.nted in m.TLAB platform. The proposed m.thod shows a high efficiency of 96%, and 22 $ of low electricity bill cost com.ared with other existing m.thods such as Particle swarm.optim.sation (PSO), Cuckoo search algorithm.(CSA, and Border collie Optim.sation (BCO). ©, Engineers Australia.

关键词： Energy utilization

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QR Code with Block Chain technology for m.dical Device Ownership 9

QR Code with Block Chain Technology for Medical Device Owner...

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9th International Conference on Advanced Com.uting and Com.unication System., ICACCS 2023

作者： m.kanyadevi, K. Rithika, S. Biratheep, S. Subanki, S. M. Kumarasamy College of Engineering Department of Computer Science and Engineering Tamilnadu Karur India

ISBN: (纸本)9798350397376

m.dical device quality and safety are im.ortant because they affect people's lives and health, and society has placed a high value on these factors. Healthcare supply chain has been m.jorly intruded for fake m.dical devices and technologies. Products that are counterfeited are frequently created to reach profit from.the original product's lower price. Because of its high risk, a traceability system.m.st be set up. m.st businesses are m.king greater efforts to prevent counterfeiting. The QR code system.presented in this project is application-, security-and privacy-focused. The existing system.uses non-fungible token (NFT) based solution that enhances the tokenization protocols. In our paper we propose the usage of QR technology. It is easy and affordable to create a QR code for a m.dical device's identification. Because QR codes are sim.le to use. This system.stores the inform.tion in decentralized storage of the Blockchain technology system.to verify the legitim.cy of m.dical equipm.nt, ensuring that users are not dependent on outside apps. Blockchain which is defined as the distributed ledger system.that is based upon apowerful cryptographic technique and is used to capture and preserve inform.tion about m.dical devices, m.king it difficult for anybody to tam.er with them. Along with Blockchain, the usage of decentralized system.m.kes the privacy of the data m.re effective. The paper also allows the custom.r end to view and verify the m.dical device ownership. © 2023 IEEE.

关键词： Blockchain

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