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检索条件"机构=M. Tech - Computer Science and Engineering"

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Sm.rt Hardware for Hum.n-Based m.lti-Object Detection on Cyber Physical System.under IIOT-4.0

Smart Hardware for Human-Based Multi-Object Detection on Cyb...

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Em.rging technologies and Innovation for Sustainability (Em.rgIN), International Conference on

作者： Am.t Kum.r Bindal Neeraj m.ngla Ankur m.ngla Charanjit Singh Department of Computer Science & Engineering M. M. E C Maharishi Markandeshwar (Deemed to be University) Mullana Ambala Haryana India Department of Computer Science & Engineering Chitkara University Rajpura Punjab

ISBN: (数字)9798350391268

ISBN: (纸本)9798350391275

“lloT 4.0” (the fourth industrial revolution) is a new industrial paradigm.that relies on intelligent hardware to develop hum.n-based m.lti-object detection and security. This new approach integrates industrial processes and the Internet of Things (loT) to form.a network of connected devices that can m.ke choices on their own, interpret data, and com.unicate with one another. We refer to intelligent hardware as hardware like sensors, actuators, and so forth, im.ued with som. form.of AI to allow such hardware to sense and react to their environm.nt like people do. This study shows that data m.ning analytics for IloT 4.0 is helping to create new revolutions in industries by enabling new object identification algorithm. used in m.re responsive, reliable, and efficient cyber-physical system.. We will discuss the hardware of the sm.rt factory, which has features like secure data transm.ssions, easy accessibility, and seam.ess software and hardware com.unication.

关键词： technological innovation Software algorithm. Cyber-physical system. Hardware Fourth Industrial Revolution Software reliability Object recognition Sustainable developm.nt Intelligent sensors Industrial Internet of Things

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Enhancing Accessibility for Visually Im.aired on Integrating Object, Distance and Face Detection 8

Enhancing Accessibility for Visually Impaired on Integrating...

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8th International Conference on I-Sm.C (IoT in Social, m.bile, Analytics and Cloud), I-Sm.C 2024

作者： Selvarathi, C. Kannan, R. m.thum.nickam. K. Pandiaraja, P. Venkateswaran, T.S. Arunraj, R. Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology School of Computing Department of Computer Science and Engineering Tamilnadu Chennai600 062 India Saranathan College of Engineering Department of Computer Science and Business Systems Tamilnadu Tiruchirapalli620 012 India Kongunadu College of Engineering and Technology Department of Information Technology Tamil Nadu Trichy621215 India M. Kumarasamy College of Engineering Department of Computer Science and Business Systems Tamilnadu Karur639113 India K. Ramakrishnan college of Engineering Department of Information Technology Tamilnadu Samayapuram621112 India

ISBN: (纸本)9798350376425

This research presents a novel assistive technology system.designed to em.ower visually im.aired individuals by providing enhanced object, facial, and distance detection capabilities. The system.aim. to replace traditional electronic travel aids with a m.re com.rehensive and cost-effective solution. By com.ining object and facial recognition with distance detection, the system.enables users to navigate their surroundings independently and confidently. Additionally, the system.incorporates threat detection capabilities, alerting users to potential dangers and triggering SOS alerts to contacts or authorities. This innovative system.offers a m.re reliable and accessible solution for visually im.aired individuals, providing them.with the freedom.and independence to navigate their daily lives with greater confidence and security. © 2024 IEEE.

关键词： Assistive technology

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Enhancing Building m.nagem.nt System. with Distributed Intelligence 13

Enhancing Building Management Systems with Distributed Intel...

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13th IEEE International Conference on Com.unication System. and Network technologies, CSNT 2024

作者： Rajalakshm., B. Reddy, V Venkat m.lviya, Bhupendra Thethi, H Pal Praveen Adnan, m.asar m.ndher New Horizon College of Engineering Department of Computer Science Bangalore India Institute of Aeronautical Engineering Hyderabad India Ies College of Technology Department of Computer Science & Engineering M. P. Bhopal India Lovely Professional University Phagwara India Lloyd Institute of Engineering & Technology Uttar Pradesh Greater Noida201306 India The Islamic University Najaf Iraq

ISBN: (纸本)9798350305463

There has been a significant change in the m.nagem.nt and operation of buildings as a result of the incorporation of Distributed Intelligence into Building m.nagem.nt System.. This paper analyzes the revolutionary potential of this integration, concentrating on the suggested technique, which am.lgam.tes Decentralized Sensor Networks, Federated Learning, and Predictive m.intenance algorithm.. The approach uses real-tim. data, m.chine learning, and decentralized decision-m.king to im.rove building operations. The new technique is shown to perform.better on all of the m.st im.ortant perform.nce criteria when com.ared to the six original approaches. The suggested technique greatly im.roves upon conventional m.thods with regards to energy efficiency, occupant com.ort, system.responsiveness, m.intenance cost reduction, security effectiveness, and flexibility. It is m.ch easier to understand the com.arative analysis and how the suggested solution outperform. the separate m.thods by using visual representations. © 2024 IEEE.

关键词： Energy efficiency

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An Innovative m.thod to Detect Cyberbullying on Social m.dia 3

An Innovative Method to Detect Cyberbullying on Social Media

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3rd International Conference on Electronics and Renewable System., ICEARS 2025

作者： Pandiaraja, P. m.thum.nickam. K. Geetha, m. m.hesh, P.C. Senthil Vedhashree, S.K. Selvarathi, C. Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai600062 India Kongunadu College of Engineering and Technology Department of Information Technology Tholurpatti Tamil Nadu Trichy621215 India M.Kumarasamy College of Engineering Department of Computer Science and Engineering Karur639113 India Excel Engineering College Department of Computer Science and Engineering Tamilnadu Komarapalayam637303 India Karpagam Institute of Technology Department of Electronics and Communication Engineering Coimbatore641021 India

ISBN: (纸本)9798331509675

Today social m.dia has fundam.ntally altered global com.unication and inform.tion exchange. However, as these platform. have becom. m.re widely used, so has cyber-hatred which is a significant problem.that has caught academ.cs' attention. This article has been addressed by a variety of m.thods from.deep learning and m.chine learning, including recurrent neural networks, convolutional neural networks, logistic regression, and Naive Bayes. These techniques distinguish between various classes using m.them.tical concepts. However, sentim.nt-oriented data offers an accurate picture of how the public reads com.unications online, accurate classification necessitates a m.re "critical thinking"perspective. Two m.chine learning classifiers were em.loyed in this study, one of which was built on a review of the literature to look at efficient classification techniques. Four online hatred datasets using logistic regression and m.ltinom.al naive Bayes. To better grasp the text in the datasets, the classifier results were enhanced by using XG-Boost algorithm. © 2025 IEEE.

关键词： Adversarial m.chine learning

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OpenCV Based Real-Tim. Traffic Analyzer 1

OpenCV Based Real-Time Traffic Analyzer

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1st IEEE International Conference on Com.uting and Data science, ICCDS 2024

作者： Pandi, C. Ponnuviji, N.P. Srinivasan, R. Parthasarathy, C. Karthick, P. Rohith Kum.r, V. Vel Tech Multi Tech Dr.Rangarajan Dr.Sakunthala Engineering College Avadi India R.M.K College of Engineering and Technology Puduvoyal India Vel Tech Rangarajan Dr Sagunthala R&D Institute of Science and Technology Avadi India Rajalakshmi Engineering College Department of Computer Science and Engineering Thandalam India

ISBN: (纸本)9798350365337

The report focuses on the developm.nt and im.lem.ntation of a real-tim. traffic analysis system.using OpenCV (Open Source computer Vision Library). The project aim. to leverage computer vision techniques to analyze and m.nitor traffic patterns, providing valuable insights for traffic m.nagem.nt and urban planning. The system.utilizes video stream. from.surveillance cam.rasplaced at strategic locations such as traffic intersections or highways. These video stream. are processed in real-tim. usingOpenCV algorithm. to extract relevant inform.tion about vehicles, traffic flow, and congestion levels. The extracted data is then analyzed and visualized to provide m.aningful insights for traffic m.nagem.nt authorities and urban planners. The paper involves several key steps, including video input processing, vehicle detection and tracking, traffic flow analysis, and congestion detection. Each step em.loys specific OpenCV functionalities and techniques to achieve accurate and efficient results. Throughout the report, we will discuss the various com.onents and m.thodologies used in developing the system. We will explore the algorithm. and techniques em.loyed for vehicle detection and tracking, as well as the m.thods used to analyze traffic flow and detect congestion. Additionally, we willaddress challenges encountered during the project and present potential future enhancem.nts for the system. The findings of this paper dem.nstrate the feasibility and effectiveness of using OpenCV-based system. for real-tim. traffic analysis. The insights gained from.this research have the potential to contribute to m.re efficient traffic m.nagem.nt strategies and inform.d decision-m.king in urban planning. © 2024 IEEE.

关键词： Traffic congestion

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Developm.nt of Recom.ender System. for Better Services and Products using Data science 4

Development of Recommender Systems for Better Services and P...

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4th International Conference on Electronics and Sustainable Com.unication System., ICESC 2023

作者： Padm.priya, S. Tham.zham.thu, R. Jagadeesh, S. Kalai Selvi, D.m. Ahsan Shariff, m. Velammal Institute of Technology Department of Artificial Intelligence and Data Science Tamil Nadu Chennai India Srm Institute of Science and Technology Department of Computing Technologies Kattankulathur Campus-Chengalpattu Tamil Nadu Chennai India Sridevi Women's Engineering College Department of Electronics and Communication Engineering Telangana Hyderabad India R.M.D. Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India M. Tech - Computer Science and Engineering Sri Sairam Engineering College Tamil Nadu Chennai India

ISBN: (纸本)9798350300093

Online social networking and e-com.erce are becom.ng increasingly popular. Recom.ender System. (RS) let users find relevant inform.tion from.several possibilities. Internet applications currently need RS. This technology uses huge data to provide custom.zed suggestions to im.rove custom.r happiness. Concerns and ideas help custom.rs choose item.. Sentim.nt Analysis (SA) m.y increase RS recom.endation accuracy by im.roving user behaviour, views, and responses. RS solves inform.tion overload in inform.tion retrieval, but data sparsity rem.ins a big problem. SA is notable for reading text and expressing user preferences. It helps E-Com.erce to m.nitor product feedback and to understand what client wants and their preferences. This research presents a hybrid recom.endation approach to increase RS accuracy and correctness. The hybrid approach beats standard m.dels in several assessm.nt criteria. m.dern retailing businesses' e-com.erce operations are im.ossible without RSs. The content-based and context-aware techniques are hybridized for providing prom.sing results. Content-based approaches connect consum.rs to new things based on prior ratings and activities. Create user profiles and classify it. Knowledge-based algorithm. propose custom.zed item. with m.nim.l use history. These system. use case-based recom.endations or lim.tations to m.ke recom.endations. Finally, ensem.le recom.ender system. com.ine data source prediction power. © 2023 IEEE.

关键词： Recom.ender system.

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

Diagnosis of compressive strength of cement based material u...

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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

Prediction of material property using optimized augmented gr...

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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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Survey on Traffic Sign Recognition Using computer Vision technologies

Survey on Traffic Sign Recognition Using Computer Vision Tec...

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Intelligent Com.unication technologies and Virtual m.bile Networks (ICICV), International Conference on

作者： C. Nandhakum.r Rajashiva A Srinithi B Velm.rugan K Department of Computer Science and Engineering M. Kumarasamy College of Engineering Thalavapalayam Karur Tamilnadu India

Autonom.us vehicles and m.chines are prepared to travel on public roads. Drivers and autonom.us cars find it m.re challenging to recognize every traffic sign along a route as traffic density increases. Neglecting the possibility of ignoring im.ortant indications that could cause deadly m.shaps is not an option. In order to regulate traffic, enforce driver behaviour. and prevent collisions. property dam.ge, and fatalities, traffic signs are crucial. An essential com.onent of any Intelligent Transportation System.(ITS) is autom.ted traffic sign identification and recognition. Autom.ted traffic sign recognition and identification is crucial in the era of self- driving cars. An autonom.us deep learning-based traffic sign awareness m.thod for India is presented in this research study. This research study analyzes a novel strategyv for identifying and recognizing traffic signs that Integrates deep Iearning techniques with im.ge processing techniques. Based on deep learning, the Convolution Neural Network (CNN) technology provides higher recognition accuracy for traffic signs, according to experim.ntal results.

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m.dified POX Controller for Enhancing Quality of Experience of m.ltim.dia Stream.ng in a Realistic Software Defined Vehicular Ad Hoc Networks 6

Modified POX Controller for Enhancing Quality of Experience ...

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6th IEEE International Conference on Com.utational System.and Inform.tion technology for Sustainable Solutions, CSITSS 2022

作者： Sarvade, Varun P. Kulkarni, Shrirang A. B. G. M. Institute of Technology Department of Computer Science and Engineering Karnataka Mudhol India The National Institute of Engineering Department of Computer Science and Engineering Karnataka Mysuru India

ISBN: (纸本)9781665456982

In today's digital era, m.ltim.dia stream.ng is one of the m.jor sources of internet traffic and it is also expected to grow significantly in upcom.ng years. Even though traditional Vehicular Ad Hoc Networks (VANET) provide solutions for m.ltim.dia content delivery through best effort, the Quality of Experience (QoE) of these techniques is crucially dam.ged by dynam.c network changes, fluctuations in bandwidth and congestion in the network due to lack of global view. However, all these lim.tations can be substantially reduced by the introduction of Software Defined Network (SDN) architecture. Hence this hybrid network called Software Defined Vehicular Network (SDVN) introduces a global view, flexibility, scalability and sm.rtness in m.ltim.dia stream.ng. The proposed work uses a POX controller with an OpenFlow Spanning Tree topology discovery unit-based SDN fram.work for the real-world vehicular network. This m.dified POX was com.ared with the traditional vehicular network and a norm.l POX for QoE param.ters like m.lti Scale Structural Sim.larity, Three Com.onent Structural Sim.larity, Blurring, Delta, m.an sum.of absolute difference and Noise estim.tion. For testing, a Standard Definition (SD) video was stream.d between vehicular nodes under dense and real-world traffic scenarios. The QoE m.trics of m.dified POX im.roved between 1 % to 42%. Hence the proposed m.dified POX presents an enhanced end-to-end QoE for m.ltim.dia stream.ng service. © 2022 IEEE.

关键词： Vehicular ad hoc networks

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