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2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Muthukumar, D. Surendiran Chowdary, Bommineni Sreekar Kumar, D. Senthil Jagadeesan, P. Mythili, B. Swapna, B. Kalasalingam Academy of Research and Education Department of computer science and Engineering Tamilnadu Srivilliputhur India Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamil Nadu Virudhunagar India Rmk College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India R.M.D. Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India Vel tech Multi tech Dr.Rangarajan Dr. Sakunthala Engineering college Department of Computer Science and Engineering Tamil Nadu Chennai India Dr.M.G.R Educational and Research Institute Department of Electronics and communication engineering Chennai India

ISBN: (纸本)9798331509828

Pneumonia continues to pose a substantial global health challenge, necessitating prompt and precise diagnosis to enhance patient outcomes. In order to detect pneumonia and classify its severity into mild, moderate, and severe phases, the web-based program uses deep learning algorithms to analyse chest X-ray pictures. A Convolutional Neural Network (CNN) model that has been trained on publicly accessible datasets and achieved high accuracy is integrated into the application. Furthermore, it provides personalized health recommendation, downloadable PDF reports, and a local hospital database to facilitate users in locating nearby treatment facilities. The system utilizes Fast API for backend processing and user-friendly interfaces to enhance accessibility and efficiency. This solution underscores the potential of artificial intelligence in supporting early diagnosis and effective pneumonia management. © 2025 IEEE.

关键词： Convolutional neural networks

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Smart Home Automation with IoT Sensor Systems for Energy Conservation using Zigbee and Wifi 5

Smart Home Automation with IoT Sensor Systems for Energy Con...

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5th International Conference on Electronics and Sustainable Communication Systems, ICESC 2024

作者： Jagannathan, Srinivasan Deepa, V. Sakthi Karthi Durai, B. Rajan, M. Sundar Celin, J. Jeya A. Dhamodaran, Sasikala Madanapalle Institute of Technology & Science Department of Computer Applications Madanapalle India Tiruppur Kumaran College for Women Pg and Research Department of Computer Science Tamil Nadu Tiruppur India Kalasalingam Academy of Research and Education Computer Science and Engineering India Arbaminch Institute of Technology Arbaminch University Faculty of Electrical and Computer Engineering Ethiopia Kalasalingam Academy of Research and Education Department of Information Technology Viruthunagar Krishnankoil626126 India Rajasthan Jaipur India

ISBN: (纸本)9798350379945

This study presents a comprehensive study on smart home automation systems utilizing Internet of Things (IoT) sensor technology to achieve efficient energy conservation. The proposed system integrates ZigBee and WiFi communication protocols for effective and reliable connectivity across various smart devices within a home. Through the deployment of interconnected sensors and intelligent controllers, the system allows real-time monitoring and management of household energy usage, optimizing energy consumption and reducing waste. The combination of ZigBee for low-power, long-range sensor networks and WiFi for high-speed data transmission enables seamless interaction between smart appliances, lighting, heating, and cooling systems. Proposesd approach offers a scalable, cost-effective solution for enhancing home automation with an emphasis on energy conservation and sustainability. Also discussed the implementation, challenges, and future potential of IoT-based smart home systems for improved energy efficiency. © 2024 IEEE.

关键词： Smart homes

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A novel recursive privacy-preserving information retrieval approach for private retrieval

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International Journal of Intelligent Information and Database Systems 2022年第3期15卷 269-274页

作者： Bhat, Radhakrishna Kumar, K.M. Mahesh Sunitha, N.R. Department of Computer Science and Engineering Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Manipal576104 India Department of Information Science and Engineering Nitte Meenakshi Institute of Technology Karnataka Bangalore560064 India Department of Computer Science and Engineering Siddaganga Institute of Technology Karnataka Tumkur572102 India

The major drawback of existing information retrieval schemes in preserving user privacy is that they either exhibit computationally bounded privacy with intractability assumptions or perfect privacy with high bandwidth utilisation. Today, the essential requirement is to have a bandwidth efficient perfect privacy preserving information retrieval scheme in order to provide effective and guaranteed service to the information retrieving user. Therefore, in this paper, we have constructed a new single database perfect privacy preserving private block retrieval scheme called ‘pepperPBR’ using quadratic residuosity as the underlying primitive where private block retrieval (PBR) is a natural extension to private information retrieval (PIR). In this paper, user generates O(6k) bit query and server generates O(2ku + o(n)) bit response where n is the database size, o(n) is the non-trivial server communication cost, u is the number of database blocks, k is the security parameter. Copyright © 2022 Inderscience Enterprises Ltd.

关键词： Information retrieval

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MahitiBandhu: A Solar-Powered Information Kiosk System for Rural Community Empowerment

MahitiBandhu: A Solar-Powered Information Kiosk System for R...

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2024 IEEE International Conference on Information technology, Electronics and Intelligent Communication Systems, ICITEICS 2024

作者： Arakeri, Megha P. Lakshmana Jain, Ayush Hemachandra, G. Mohan Gowda, V. Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Department of Computer Science and Engineering Manipal India Seamovations Labs Pvt. Ltd Bangalore India Ramaiah Institute of Technology Department of Information Science and Engineering Bangalore India Ramaiah Institute of Technology Department of Electrical and Electronics Engineering Bangalore India Gitam School of Technology Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350382693

A major portion of Karnataka's population lives in rural areas. Most of the rural people are illiterate and poor due to lack of proper services and infrastructure. There are various rural development schemes run by the government of Karnataka/India for the benefit of villagers. However, most people in the villages of Karnataka are unaware of these schemes due to lack of education, poverty, inaccessibility, and lack of technological knowledge. Hence, the government is trying to render services and information to the public using ICT tools. While there are few examples of achievements of ICT in rural development, there are many failures due to lack of connectivity, bandwidth, language problems, and power supply. Also, Gram Panchayat of the village does not have proper facilities to give widespread publicity about government services and many people do not have smartphones to access information online. Thus, this paper focuses on the development of a solar-powered interactive touch-screen based kiosk system for the rural region. This single system can provide information about all rural schemes for education, agriculture, healthcare, and employment. It also gives information on other services like banking, vaccination drives, and fertilizers for farmers. This is an end-to-end user application with a text-free user interface and interacts based on audio, video, and image display irrespective of the user's literacy level. © 2024 IEEE.

关键词： Touch screens

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Fine-Grained Multivariate Time Series Anomaly Detection in IoT

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computers, Materials & Continua 2023年第6期75卷 5027-5047页

作者： Shiming He Meng Guo Bo Yang Osama Alfarraj Amr Tolba Pradip Kumar Sharma Xi’ai Yan School of Computer&Communication Engineering Changsha University of Science&TechnologyChangsha410114China Computer Science Department Community CollegeKing Saud UniversityRiyadh11437Saudi Arabia Department of Computing Science University of AberdeenAberdeenAB243FXUK Hunan Provincial Key Laboratory of Network Investigational Technology Hunan Police AcademyChangsha410138China

Sensors produce a large amount of multivariate time series data to record the states of Internet of Things(IoT)*** time series timestamp anomaly detection(TSAD)can identify timestamps of attacks and ***,it is necessary to determine which sensor or indicator is abnormal to facilitate a more detailed diagnosis,a process referred to as fine-grained anomaly detection(FGAD).Although further FGAD can be extended based on TSAD methods,existing works do not provide a quantitative evaluation,and the performance is ***,to tackle the FGAD problem,this paper first verifies that the TSAD methods achieve low performance when applied to the FGAD task directly because of the excessive fusion of features and the ignoring of the relationship’s dynamic changes between ***,this paper proposes a mul-tivariate time series fine-grained anomaly detection(MFGAD)*** avoid excessive fusion of features,MFGAD constructs two sub-models to independently identify the abnormal timestamp and abnormal indicator instead of a single model and then combines the two kinds of abnormal results to detect the fine-grained *** on this framework,an algorithm based on Graph Attention Neural Network(GAT)and Attention Convolutional Long-Short Term Memory(A-ConvLSTM)is proposed,in which GAT learns temporal features of multiple indicators to detect abnormal timestamps and A-ConvLSTM captures the dynamic relationship between indicators to identify abnormal *** simulations on a real-world dataset demonstrate that the proposed algorithm can achieve a higher F1 score and hit rate than the extension of existing TSAD methods with the benefit of two independent sub-models for timestamp and indicator detection.

关键词： Multivariate time series graph attention neural network fine-grained anomaly detection

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An Efficient Deep Learning Approach for Automatic License Plate Detection with Novel Feature Extraction 2

An Efficient Deep Learning Approach for Automatic License Pl...

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2nd International Conference on Machine Learning and Data engineering, ICMLDE 2023

作者： Kothai, G. Povammal, E. Amutha, S. Deepa, V. KPR Institute of Engineering and Technology Coimbatore641407 India Department of Computing Technologies SRM Institute of Science and Technology Kattankulathur60203 India Department of Computer Science and Engineering Kalasalingam Academy of Research and Education Krishnankoil626126 India

In the domain of traffic management, road toll collection, and parking lot systems, vehicle number plate detection and identification play a pivotal role. Unlike conventional methods that treat license plate detection and character recognition as separate tasks, the system simultaneously addresses both challenges within a single neural network. Our Proposed methodology capitalizes on the efficiency and accuracy of the one-stage object detection algorithm known as YOLO (You Only Look Once) to locate license plates under diverse and challenging conditions. To augment the quality of input images with low resolution or poor clarity, we employ super-resolution generative adversarial networks (SRGANs). The image enhancement process substantially improves the visual quality of captured license plate images, facilitating more precise character recognition. Quantitative assessment of propounded system reveals compelling results. The license plate detection component achieves an outstanding average accuracy rate of 98.5%, surpassing previous methods by 15.2%. This comprehensive approach not only reduces the dependency on manual labour but also elevates processing precision. It seamlessly integrates into existing transportation infrastructure, resulting in heightened operational efficiency, reduced traffic congestion, and enhanced security measures. The rapid evolution of neural networks and deep learning techniques has streamlined the deployment of such applications, revolutionizing the field of traffic monitoring and management with unprecedented ease and precision. One-stage object detector, widely referred to as YOLO, is used to find licence plates in difficult circumstances. © 2024 Elsevier B.V.. All rights reserved.

关键词： Image enhancement

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ChatGPT: Opportunities, Features and Future Prospects 7

ChatGPT: Opportunities, Features and Future Prospects

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7th International Conference on Trends in Electronics and Informatics, ICOEI 2023

作者： Santhosh, R. Abinaya, M. Anusuya, V. Gowthami, D. Karpagam Academy of Higher Education Department of Computer Science and Engineering Tamil Nadu Coimbatore India Ramco Institute of Technology Department of Computer Science and Engineering Tamil Nadu Rajapalayam India

ISBN: (纸本)9798350397284

ChatGPT is an AI-based Natural Language Generation (NLG) system developed by Microsoft that enables users to converse with virtual agents in a conversational manner. ChatGPT is based on the transformer-based architecture, a type of deep learning algorithm that can be used to generate text directly from raw inputs. ChatGPT is designed to enable natural conversations, with the goal of making it easier for people to interact with virtual agents in a conversational manner. ChatGPT has the potential to revolutionize the way people interact with virtual agents, as it can be used to generate natural language responses to userinput. The system could also be used to create virtual agents that can respond to user queries in a more natural manner, allowing for more efficient customer service interactions. In the future, ChatGPT could be used to create more sophisticated virtual agents that can understand user intent and provide more accurate responses. Additionally, ChatGPT could be used to generate more engaging content for virtual agents, such as stories, jokes, and conversations. This paper provides the power of ChatGPT with its various features, opportunities and impact of ChatGPT in various sectors. © 2023 IEEE.

关键词： Learning algorithms

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negSPUC: Trees-Based Single-Phase High-Utility Itemset Mining Algorithm with Negative Profit Values 3rd

negSPUC: Trees-Based Single-Phase High-Utility Itemset Minin...

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3rd International Conference on Machine Learning, Image Processing, Network Security and Data sciences, MIND 2021

作者： Anup Bhat, B. Harish, S.V. Geetha, M. Department of Computer Science and Engineering Manipal Institute of Technology Manipal Academy of Higher Education Manipal India

ISBN: (纸本)9789811958670

High-utility itemset mining (HUIM) extracts novel, non-trivial itemsets by incorporating the revenue generated by the purchased items from voluminous customer transaction databases. Although, most of the tree-based algorithms in the literature are two-phased, recently a single-phase algorithm called single-phase utility computation (SPUC) has been proposed. However, such conventional algorithms mine only a subset of HUIs when certain items bear negative profit values in the database. In this study, negSPUC algorithm has been proposed where the two compact tree structures—Utility Count Tree and String Utility Tree—are restructured to handle items bearing negative profits. negSPUC with path-based and overestimated utility pruning strategies can mine a complete set of HUIs when transaction database contains both positive and/or negative profit values. Further, experimental evaluation on synthetic and real data sets demonstrates the efficiency of negSPUC over HUINIV-Mine algorithm. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Profitability

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Numerical Predictions of Laminar Forced Convection Heat Transfer with and without Buoyancy Effects from an Isothermal Horizontal Flat Plate to Supercritical Nitrogen

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Frontiers in Heat and Mass Transfer 2024年第3期22卷 889-917页

作者： K.S.Rajendra Prasad Sathya Sai T.R.Seetharam Adithya Garimella Department of Mechanical and Industrial Engineering Manipal Institute of Technology BengaluruManipal Academy of Higher EducationManipal576104India Sustainable Energy Technology Faculty of Electrical EngineeringMathematics and Computer ScienceDelft University of TechnologyDelft2600 AANetherlands Department of Mechanical Engineering PES UniversityBengaluru560085India

Numerical predictions are made for Laminar Forced convection heat transfer with and without buoyancy effects for Supercritical Nitrogen flowing over an isothermal horizontal flat plate with a heated surface facing *** are performed by varying the value ofΔT from5 to 30 K and P_(∞)/P_(cr)ratio from1.1 to *** of all the thermophysical properties of supercritical Nitrogen is *** wall temperatures are chosen in such a way that two values of Tw are less than T∗(T*is the temperature at which the fluid has a maximum value of Cp for the given pressure),one value equal to T∗and two values greater than T∗.Three different values of U∞are used to obtain Re∞range of 3.6×10_(4)to 4.74×10^(5)for forced convection without buoyancy effects and Gr_(∞)/Re^(2)_(∞)range of 0.011 to 3.107 for the case where buoyancy effects are *** different forms of correlations are proposed based on numerical predictions and are compared with actual numerical *** has been found that in all six forms of correlations,the maximum deviations are found to occur in those cases where the pseudocritical temperature TT∗lies between the wall temperature and bulk fluid temperature.

关键词： Supercritical nitrogen laminar flow numerical methods forced convection heat transfer isothermal horizontal surface

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Building a portable parallel asynchronous PDE solver using Kokkos 31

Building a portable parallel asynchronous PDE solver using K...

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31st IEEE International Conference on High Performance Computing, Data, and Analytics Workshops, HiPCW 2024

作者： Bhat, Ranjan Aditya, Konduri Department of Computer Science and Engineering Manipal Institute of Technology Manipal Academy of Higher Education Manipal India Department of Computational and Data Sciences Indian Institute of Science Bengaluru India

ISBN: (纸本)9798331509118

In this study, we outline the design and implementation of a portable massively parallel asynchronous solver for time-dependent partial differential equations (PDEs). The solver is implemented using Kokkos library for portability across different node architectures and is coupled with the communication-avoiding algorithm that relaxes the communication across nodes, thus improving the scalability. Asynchrony-tolerant finite difference schemes for spatial derivatives along with the low storage Runge-Kutta scheme for time integration are used to achieve high-order accuracy. The efficacy of the implementation is demonstrated using a mini-app that solves the three-dimensional diffusion problem. © 2024 IEEE

关键词： Scalability

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