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5th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2021

作者： Poonkodi, M. Vadivu, G. SRM Institute of Science and Technology Department of Computer Science and Engineering Chennai India Big Data Analytics School of Computing SRM Institute of Science and Technology Chennai India

ISBN: (纸本)9781665435246

Computer Vision is playing aremarkable role right from essentials to entertainment and thus trying to turn computer as a 'seeing' machine. Having widespread applications in most of the real world domain like healthcare, surveillance., space and social media, yet it is still challenging to match human intelligence in understanding the underlying context. Recognition of a task performed by human is important to learn the happenings, but predicting it, from the activities' initial subset becomes an aid to overcome adversities in near future. Numerous algorithms and techniques are present, yet a gap exists due to the environment in which it is recorded, illumination condition,overlapping of men and materials on captured scenes *** lead to the requirement of high power computing devices for recognition, which is considerably *** this work, Dense OPnet (Dense optical flow and Convolution neural Net) is proposed which takes in the RGB input video, converted to dense optical flow frames and then fed to three layered CNN for recognition and early detection of an activity using N/2 frames of the total input video. This method is evaluated on two benchmark datasetlike J-HMDB21 and UCF50 giving an accuracy of 93.4% and 74.3% on recognition, 68.2% and 50.24% on early action detection respectively. © 2021 IEEE.

关键词： Computer vision

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Multi-Agent Systems for Autonomous IoT Network Management Using Distributed Reinforcement Learning 3

Multi-Agent Systems for Autonomous IoT Network Management Us...

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3rd International Conference on Intelligent Systems, Advanced computing, and Communication, ISACC 2025

作者： Neelamegam, G. Venkatesan, Rajaram Ramya, S.R. Ramya, R.S. Akshya, J. Sundarrajan, M. Choudhry, Mani Deepak VSB College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Coimbatore India Builders Engineering College Department of Artificial Intelligence and Data Science Tamil Nadu Tirupur India Dr NGP Institute of Technology Department of Computer Science and Engineering Tamil Nadu Coimbatore India Sri Ramakrishna Engineering College Department of Computer Science and Engineering Tamil Nadu Coimbatore India SRM Institute of Science and Technology Department of Computational Intelligence Tamil Nadu Chennai India SRM Institute of Science and Technology Department of Networking and Communications Tamil Nadu Chennai India SRM Institute of Science and Technology Department of Computing Technologies Tamil Nadu Chennai India

ISBN: (纸本)9798331523893

The ever-growing complexity of IoT networks ignited by their wide scale adoption in applications such as smart cities, the industrial automation, and health care, compelled to develop sophisticated yet resource efficient and scalable network management solution. Centralized systems are traditional in architecture, which leads to higher latency and bottlenecks, while decentralized systems can distribute workloads however the lack of adaptability to real time, along with the difficulty of extracting maximum resource usage, are limitations. In recent years, reinforcement learning (RL) based approaches have taken shape as a promising alternative to facilitate the automated learning and optimisation of task management. However, there are two challenges for existing RL based methods: slow convergence, inefficient priority assignment of tasks, and poor coordination amongst agents in the case of dynamic, large scale IoT environment. To solve these challenges, in this research we propose a distributed reinforcement learning based multi agent framework for managing IoT network. The framework also integrates a task prioritization mechanism to dynamically choose an optimal task allocation solution. Further validation was performed in a simulated IoT environment where real world inspired datasets were used. The proposed framework demonstrated 30% more task throughput than centralized, 25% less latency than decentralized, and 40% more energy efficiency than current RL based systems. The system was further validated using advanced visualizations such as anomaly detection maps and resource allocation efficiency graphs, demonstrating its capacity to manage dynamic load of tasks and resource allocation. The proposed framework addresses the critical limitations of current methods, through scalable, adaptive, and energy efficient framework for managing IoT network, and accordingly helps advancing the intelligent and autonomous IoT systems. © 2025 IEEE.

关键词： Resource allocation

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Analysis and Detection of Fake News Using Machine Learning 3

Analysis and Detection of Fake News Using Machine Learning

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3rd International Conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Sathish Kumar, P. Suthanthiradevi, P. Arul Stephen, C. Ebenezer Abishek, B. Sivakumar, S. Mathiyarasu, M. Computer and Communication Engineering Rajalakshmi institute of technology Chennai124 India Data Science and Business Systems School of Computing SRM institute of Science and Technology Kattankulathur Chennai603203 India Electronics and Communication Engineering Vels Institute of science Technology and Advanced studies Chennai117 India Electronics and Communication Engineering Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College Avadi Chennai600062 India Electronics and Communication Engineering BIHER Chennai73 India Computer and Communication Engineering Rajalakshmi institute of technology Chennai India

ISBN: (纸本)9798350372120

The Fake news becomes a more critical issue nowadays, it may reduce the trust of the public about the particular information. This project focuses on the advancement of fake news detection model using machine learning. The main goal of this project is to develop, analyze a model that can effectively classify the news articles based on their Subject or contents. Natural preprocessing Algorithm and features such as Stemming, Tokenization, Stop word and TF-IDF Vectorization are used to train the machine learning model. Then the Machine learning Algorithms likeLogistic Regression and Random Forest are used to classifying the dataset which gives 98% accuracy and 99% of accuracy score. For this Project we have taken a dataset of 22413 Real news and 23481 Fake News from the online resources. This model has a potential to reduce the impact of fake news by grant the promotion of accurate and reliable information in a society. © 2024 IEEE.

关键词： Machine learning

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Battery Health Prediction Using Deep Hybrid Learning

Battery Health Prediction Using Deep Hybrid Learning

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Pervasive computing and Social Networking (ICPCSN), International Conference on

作者： Rahul Anil Nair Dev Karan Suresh Rajeswari D Department of Data Science and Business Systems School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Tamil Nadu India

The type and condition of the road the vehicle travels; how long the battery is used for are just a few of the variables that affect the battery life of electric cars (EVs). These elements might affect the battery's lifespan by causing it to drain more quickly than usual. By understanding the various factors that impact battery life, work can be done to mitigate these issues and extend the useful life of the battery. To find the best circumstances for increasing the battery's longevity, this research paper is concentrated on examining the impacts of road conditions on battery life. The previous article classified routes with an accuracy of 83.56% based on battery health while arriving at the destination [1]. This article examines EV routed and categorises them on the basis of the number of brakes applied upon arrival at the destination. By doing so, it aims to develop recommendations for drivers on how to optimize their driving habits to extend the battery's lifespan. Observations indicate that bad routes generally involve a lot more brakes as compared to good routes. Vehicle speed has a negative correlation with heater power in a bad route and a positive correlation in a good route. Battery voltage and Battery state- of-charge have opposing correlations in both categories. In this research, a prediction of the route category was attempted by using Deep Hybrid Learning to integrate three pre-trained models., VGGI6, Resnet50 and VGG19 with two boosting algorithms:- XGBoost and AdaBoost. The accuracy of the six deep hybrid learning models in predicting the category of these routes are as follows:-0.87625, 0.84875, 0.80125, 0.7925, 0.8425 and 0.855.

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Strengthening Authentication: The Role of Multi-Level Hashing in OSPA Protocol 5

Strengthening Authentication: The Role of Multi-Level Hashin...

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

作者： Hariharasudhan, S. Varghese, Vineetha Malarvizhi, K. Santhoshkumar, S.P. Nalini, T. Vikram, D. Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Chennai Avadi India Rathinam Technical Campus Department of Computer Science and Engineering Coimbatore India Jct College of Engineering and Technology Department of Computer Science Al Business Systems Coimbatore India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology School of Computing Department of Computer Science and Engineering Chennai Avadi India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Saveetha University Department of Computer Science and Engineering Chennai India Rathinam Technical Campus Department of Artificial Intelligence Data Science Coimbatore India

ISBN: (纸本)9798331518097

Authentication is a crucial step in the cyber security process that confirms user identities. Even though they are widely used, traditional password based techniques are frequently vulnerable to attacks like guessing and password theft. This study presents the Optimal Strong Password Authentication (OSPA) protocol, which uses USB sticks to provide safe user authentication in order to address these issues. The Multi-Level Hashing Algorithm (MLHA), which dynamically picks hash functions during authentication to generate many layers of hashed passwords, is a key component of OSPA's effectiveness. By preventing eavesdropping efforts and reducing the possibility of unwanted access through guessing attacks, this method strengthens security. This protocol provides strong authentication procedures, protecting confidential data and improving overall cyber security posture by integrating MLHA into the OSPA architecture. © 2024 IEEE.

关键词： Authentication Protocol

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Failures in the Loop: Human Leadership in AI-Based Decision-Making

IEEE Transactions on Technology and Society

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IEEE Transactions on Technology and Society 2024年第1期5卷 2-13页

作者： Michael, Katina Schoenherr, Jordan Richard Vogel, Kathleen M. Arizona State University School for the Future of Innovation in Society TempeAZ85287 United States Arizona State University School of Computing and Augmented Intelligence TempeAZ85287 United States Applied Ai Institute Concordia University Department of Psychology MontrealQCH3G 1M8 Canada Institute for Data Science Carleton University Department of Psychology OttawaONK1S 5B6 Canada

The dark side of AI has been a persistent focus in discussions of popular science and academia (Appendix A), with some claiming that AI is 'evil' [1]. Many commentators make compelling arguments for their concerns. Techno-elites have also contributed to the polarization of these discussions, with ultimatums that in this new era of industrialized AI, citizens will need to '[join] with the AI or risk being left behind' [2]. With such polarizing language, debates about AI adoption run the risk of being oversimplified. Discussion of technological trust frequently takes an all-or-nothing approach. All technologies - cognitive, social, material, or digital - introduce tradeoffs when they are adopted, and contain both 'light and dark' features [3]. But descriptions of these features can take on deceptively (or unintentionally) anthropomorphic tones, especially when stakeholders refer to the features as 'agents' [4], [5]. When used as an analogical heuristic, this can inform the design of AI, provide knowledge for AI operations, and potentially even predict its outcomes [6]. However, if AI agency is accepted at face value, we run the risk of having unrealistic expectations for the capabilities of these systems. © 2020 IEEE.

关键词： Decision making

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Iterative Utility Judgment Framework via LLMs Inspired by Relevance in Philosophy

arXiv

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arXiv 2024年

作者： Zhang, Hengran Bi, Keping Guo, Jiafeng Cheng, Xueqi CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences China School of Computer Science and Technology University of Chinese Academy of Sciences China

Utility and topical relevance are critical measures in information retrieval (IR), reflecting system and user perspectives, respectively. While topical relevance has long been emphasized, utility is a higher standard of relevance and is more useful for facilitating downstream tasks, e.g., in Retrieval-Augmented Generation (RAG). When we incorporate utility judgments into RAG, we realize that the topical relevance, utility, and answering in RAG are closely related to the three types of relevance that Schutz discussed from a philosophical perspective. They are topical relevance, interpretational relevance, and motivational relevance, respectively. Inspired by the dynamic iterations of the three types of relevance, we propose an Iterative utiliTy judgmEnt fraMework (ITEM) to promote each step of the cycle of RAG. We conducted extensive experiments on multigrade passage retrieval and factoid questionanswering datasets (i.e., TREC DL, WebAP, and NQ). Experimental results demonstrate significant improvements in utility judgments, ranking of topical relevance, and answer generation upon representative baselines, including multiple single-shot utility judging approaches. Our code and benchmark can be found at https://anonymous.4open. science/r/ITEM-B486/. Copyright © 2024, The Authors. All rights reserved.

关键词： Search engines

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A robust deep structured prediction model for petroleum reservoir characterization using pressure transient test data

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Petroleum Research 2022年第2期7卷 204-219页

作者： Rakesh Kumar Pandey Anil Kumar Ajay Mandal Department of Petroleum and Energy Studies School of Engineering and TechnologyDIT UniversityDehradun248009India Data Science Research Group School of ComputingDIT UniversityDehradun248009India Department of Petroleum Engineering Indian Institute of Technology(Indian School of Mines)Dhanbad826004India

A robust deep learning model consisting of long short-term memory and fully connected neural net-works has been proposed to automatically interpret homogeneous petroleum reservoirs having infinite,no flow,and constant pressure outer boundary *** pressure change data recorded during the well test operation along with its derivative is input into the model to perform the classification for identifying the reservoir model and,further,regression to estimate output *** noise was added to analytical models while generating the synthetic training *** hyperparameters were regulated to perform model optimization,resulting in a batch size of 64,Adam optimization algorithm,learning rate of 0.01,and 80:10:10 data split ratio as the best choices of *** perfor-mance accuracy also increased with an increase in the number of samples during *** classification and regression metrics have been used to evaluate the performance of the *** paper also demonstrates the prediction performance of the optimized model using simulated and actual oil well pressure drawdown test *** proposed model achieved minimum and maximum relative errors of 0.0019 and 0.0308,respectively,in estimating output for the simulated test cases and relative error of 0.0319 for the real test case.

关键词： Well test Reservoir characterization Automatic interpretation Prediction model Hyperparameter tuning Performance indicator

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Freeze the Backbones: a Parameter-Efficient Contrastive Approach to Robust Medical Vision-Language Pre-Training

Freeze the Backbones: a Parameter-Efficient Contrastive Appr...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Jiuming Qin Che Liu Sibo Cheng Yike Guo Rossella Arcucci Department of Computing Imperial College London Data Science Institute Imperial College London Department of Earth Science and Engineering Imperial College London Department of Computer Science and Engineering The Hong Kong University of Science and Technology

Modern healthcare often utilises radiographic images alongside textual reports for diagnostics, encouraging the use of Vision-Language Self-Supervised Learning (VL-SSL) with large pre-trained models to learn versatile medical vision representations. However, most existing VL-SSL frameworks are trained end-to-end, which is computation-heavy and can lose vital prior information embedded in pre-trained encoders. To address both issues, we introduce the backbone-agnostic Adaptor framework, which preserves medical knowledge in pre-trained image and text encoders by keeping them frozen, and employs a lightweight Adaptor module for cross-modal learning. Experiments on medical image classification and segmentation tasks across three datasets reveal that our framework delivers competitive performance while cutting trainable parameters by over 90% compared to current pre-training approaches. Notably, when fine-tuned with just 1% of data, Adaptor outperforms several Transformer-based methods trained on full datasets in medical image segmentation.

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Dual-Functional Waveform Design for STAR-RIS Aided ISAC via Deep Reinforcement Learning 35

Dual-Functional Waveform Design for STAR-RIS Aided ISAC via ...

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35th IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC 2024

作者： Zhang, Jifa Gong, Shiqi Lu, Weidang Xing, Chengwen Zhao, Nan Ng, Derrick Wing Kwan Niyato, Dusit Dalian University of Technology School of Information and Communication Engineering Dalian China Beijing Institute of Technology School of Cyberspace Science and Technology Beijing China Zhejiang University of Technology College of Information Engineering Hangzhou China Beijing Institute of Technology School of Information and Electronics Beijing China University of New South Wales School of Electrical Engineering and Telecommunications Sydney Australia Nanyang Technological University College of Computing and Data Science Singapore

ISBN: (纸本)9798350362244

Integrated sensing and communication (ISAC) technology effectively enables spectrum and hardware sharing between radar and communication. This paper investigates the dual-functional (DF) constant modulus waveform design for simultaneously transmitting and reconfigurable intelligent surface (STAR-RIS)-aided ISAC, in which the channel information can be used as semantic information. To investigate the performance trade-off, the weighted sum of multi-user interference (MUI) energy and waveform discrepancies is minimized via jointly optimizing the transmit waveform and the reflection and transmission coefficient matrices at STAR-RIS. Furthermore, a practical case of coupled phase shifts at STARRIS is investigated. We first formulate the optimization problem as a Markov decision process, employing a twin delayed deep deterministic policy gradient (TD3)-based deep reinforcement learning approach to address it. Simulation results verify the effectiveness of the proposed scheme. © 2024 IEEE.

关键词： Deep reinforcement learning

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