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2nd International Conference on Smart Technologies for Smart Nation, SmartTechCon 2023

作者： Selvakanmani, S. Yadav, C. Hazarathaiah Singh, Brijesh Manigandan, E. Kumar, Satheesh S. Karthik, B. Department of Information Technology RMK Engineering College RSM Nagar Chennai India Department of Chemistry Vel Tech Rangarajan Dr.Sagunthala R&D institute of Science and Technology Chennai India Department of MBA CMR Institute Of Technology Bangalore India Department of Computer Applications Saveetha College of Liberal Arts and Sciences Thandalam Tamilnadu Chennai602105 India Department of Artificial Intelligence and Data Science Bannari Amman Institute of Technology Sathyamangalam Tamilnadu Erode India Department of Electrical and Electronics Engineering Sona college of technology Salem India

ISBN: (纸本)9798350305418

Because of India's growing urbanization, coupled with dwindling urban land supply and rising land costs, the construction of high-rise structures has emerged as the dominant method to urban planning and development. The rapid growth of India's economy and the rapid pace of urbanization have resulted in an increasing urban population, raising the requirement for effective land usage. High-rise structures have gradually been the main center of urban construction projects in response to these *** primary goal of this study is to look into the use of Building Information Modeling (BIM) in building projects with the goal of optimizing the use of information received from it. Numerous graphs and algorithmic formulas are developed during the study to examine and illustrate these features. According to the findings of the study, a significant amount, accounting for 28.2% of the total, is related to various focused research procedures. As a result, the interest and significance of this field of investigation are expected to endure and develop in the coming years.. © 2023 IEEE.

关键词： BIM and decide tree Algorithms Multiple target maximize of construction progress

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dynamic Task Allocation Method for Heterogenous Multiagent System in Uncertain Scenarios of Agricultural Field Operation

Dynamic Task Allocation Method for Heterogenous Multiagent S...

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2022 International Conference on Electrical, Electronics and Information Engineering, EEIE 2022

作者： Liang, Yajie Zhou, Kun Wu, Caicong College of Information and Electrical Engineering China Agricultural University BeijingCO 100083 China Key Laboratory of Agricultural Machinery Monitoring and Big Data Applications Ministry of Agriculture and Rural Affairs Beijing100083 China R&D Department AGCO Corp. RandersDK 8930 Denmark

This study focuses on the problem of dynamic task allocation for a heterogeneous multiagent system (MAS) in uncertain scenarios and its application in agricultural field operation. Previous studies lacked robustness or efficiency for uncertain environments especially in agricultural field, such as agent removal, agent inclusion, changes of agent capabilities, and task changes. We present herein a novel concept of the potential field of capability influence (PFCI), and based on which, we can estimate potential overloaded tasks. This provides an opportunity to improve the allocation of the remaining tasks. Then, we propose a heuristic-based clustering auction (HBCA) method by introducing PFCI into the auction mechanism to achieve an effective and efficient task allocation. The whole method consists of the three following phases based on the auction mechanism: 1) auctioneer preprocessing phase that adopts a capability priority strategy to select the preferable agent;2) bidder preprocessing phase that introduces the PFCI to obtain a list of clustered tasks heuristically;and 3) adaptive auction phase that applies the auction mechanism to achieve an appropriate match between agents and tasks. Numerical simulations with different uncertain field operation scenarios validate the effectiveness of the proposed method. The results show that the proposed HBCA can dramatically reduce the total assignment cost and outperform state-of-the-art methods, especially in the case of obvious variances in agent capabilities. © Published under licence by IOP Publishing Ltd.

关键词： Agriculture

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A Hybrid Blockchain Framework for Secure, Scalable, and Energy-Efficient digital Identity Management with Token-based Selective disclosure 3

A Hybrid Blockchain Framework for Secure, Scalable, and Ener...

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3rd International Conference on Electronics and renewable Systems, ICEArS 2025

作者： Velmurugan, M. Shinzeer, C.K. Maya, G. ramya, r. Kanimozhi, r. Lathif, Syed Ismail Abdul Vel Tech Rangarajan Dr Sagunthala R&d Institute of Science And Technology Department Of Computer Science & Engineering Chennai600 062 India Lovely Professional University Department of School of Computer Applications Punjab Phagwara144411 India Srm Institute of Science and Technology Faculty of Management Kattankulathur603 203 India K.Ramakrishnan College of Engineering Department of Electronics and Communication Engineering Samayapuram621112 India Muthayammal Engineering College Computer Science and Engineering Rasipuram637408 India Srm Institute of Science and Technology Department of Data Science and Business Systems Chennai603203 India

ISBN: (纸本)9798331509675

data breaches, identity theft, and the lack of user control within traditional digital identity management systems, it begs a more secure and decentralized alternative. In this study we propose a blockchain based digital identity management framework that implements privacy, security and transparency by employing distributed ledger technology. To provide tamper proof identity storage and automated verification, the proposed model uses cryptographic hashing and smart contracts. The use of a novel token-based access mechanism enables users to safely share their identity data with third parties, while keeping sensitive information out of sight. Security metrics are enhanced by 30% for data integrity with 45% reduction in unauthorized access attempts all through simulations. The framework also achieves improved efficiency reducing identity verification time by 40 percent compared to traditional systems. It offers a promising approach to address important vulnerabilities in centralized identity management in line with the development of marketable and user-friendly digital identity solutions. © 2025 IEEE.

关键词： data integrity

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BAA: A Novel decentralized Authorization System for Privacy-Sensitive Medical data

BAA: A Novel Decentralized Authorization System for Privacy-...

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IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Cong Zha Yulei Wu Zexun Jiang Wenqian Zhao Hao Yin The Network and Big Data Technology R&D Center Tsinghua University Beijing China The Department of Computer Science Faculty of Environment Exeter University Exeter UK School of Data Science and Intelligent Media Communication University of China Beijing China Department of Information Science University of North Texas Texas United States

ISBN: (数字)9798350381993

ISBN: (纸本)9798350382006

data authorization is the basis for the orderly sharing of medical data. Most of the applied decentralized authorization mechanisms rely on blockchain, but face the problems of privacy leakage and low efficiency. To solve these problems, we design a policy-driven decentralized authorization system named BAA, which protects user's behavior privacy in medical data sharing and improves efficiency of both on-chain and off-chain. In order to achieve these goals, BAA uses authorization tokens to represent permissions, protects privacy of the authorization process through hiding user behaviors, realizes batch data accessing by proposing a two-tier Merkle tree, and saves authorization data in a two-tier blockchain to improve on-chain efficiency. Extensive experimental results show that the overhead of cryptographic operations in BAA is acceptable compared to that in traditional authorization systems. In addition, throughput and latency of each operation in BAA can meet the efficiency needs of medical data authorization. The results also show that the preset authorization in blockchain is effective for reducing data user's waiting time and improving the efficiency of authorization.

关键词： Authorization data privacy Privacy Throughput Blockchains Protection Faces

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Enhanced Condition Monitoring of Low Voltage Motors in Harsh Environments with Ladybug Beetle Optimization and Context-Aware Graph Convolutional Networks 4

Enhanced Condition Monitoring of Low Voltage Motors in Harsh...

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4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： rajasekar, P. Tiwari, Praveen Prakash Sharmila, L. Garg, Akriti Srinivas, rayudu Tallapragada, V. V. Satyanarayana Srm Institute of Science and Technology Department of Data Science and Business System Tamil Nadu Kattankulathur603203 India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai600062 India Mahatma Jyotiba Phule Rohilkhand University Department of Electrical Engineering Uttar Pradesh Bareilly243006 India Aditya College of Engineering & Technology Department of Electrical and Electronics Engineering Andhra Pradesh Surampalem533437 India School of Engineering Mohan Babu University Department of Electronics and Communication Engineering Andhra Pradesh Tirupati517102 India

ISBN: (纸本)9798331540364

Low voltage motors are essential in many applications in contemporary industrial settings, as they power machines and guarantee efficient operations. However, these motors often operate in harsh environments where they are exposed to factors such as extreme temperatures, moisture, dust, and vibrations, which can lead to premature wear and failure. This paper proposes a hybrid method for low voltage motor condition monitoring in challenging industrial environments. The proposed method is the combined execution of Ladybug Beetle Optimization algorithm (LBOA) and Heterogeneous Context-Aware Graph Convolutional Network (HCAGCN). Hence it is named as LBOA-HCAGCN technique. Initially, the input data is collected from Triboelectric vibration sensors. Then, the collected data is fed into preprocessing utilizing Generalized Multi-kernel Maximum Correntropy Kalman Filter (GMKMCKF). This filter normalizes and cleans the data by effectively removing noise. recursive Hilbert Transform (rHT) is used to extract time domain features such as mean, root Mean Square (rMS), Peak-to-Peak Value, Skewness, and Kurtosis. Then the extracted features are given to HCAGCN for classify the motor health conditions such as normal, broken-bar, bowed-bar, bowed-rotor, faulty bearing, and voltage imbalance. In general, HCAGCN does not express adapting optimization strategies to determine optimal parameters to classify motor conditions. Hence, the LBOA is used to optimize the weight parameter of the HCAGCN which accurately classify motor conditions. The proposed LBOA-HCAGCN is implemented in MATLAB. Performance indicators such as Mean Absolute Error (MAE), Precision, and Accuracy were used to analyze the effectiveness of the proposed approach. Comparing the proposed LBOA-HCAGCN methodology to other existing techniques like deep residual Neural Network (drNN), Convolutional Neural Network (CNN), and recursive Neural Network (rNN), it achieves 25.8%, 26.4%, and 24.7% greater accuracy, 26.7%, 29.4%, and

关键词： Mean square error

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Inbuilt Framework Using IoT and Computer Vision Technique for Automatic Visitor Management 7

Inbuilt Framework Using IoT and Computer Vision Technique fo...

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7th International Conference on Electronics, Communication and Aerospace technology, ICECA 2023

作者： Yedukondalu, Gangolu rajesh, Nichenametla Kumar, B Krishna Gowd, G.Sudha Suguna, r. Gupta, K.Gurnadha Anurag University Department of Data Science Hyderabad India KL Deemed to Be University Dept of AI&DS AP Vaddeswaram India Nalla Malla Reddy Engineering College Department of CSE Hyderabad India Srinivasa Ramanujan Institute of Technology CSE Department Anantapuram India Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Department of CSE Chennai India K L Deemed to Be University Department of CSE AP Vaddeswaram India

ISBN: (纸本)9798350340600

The Visitor Monitoring (VM) concept is based on the use of systems and gadgets to improve occupant comfort, energy efficiency, privacy, and safety of its inhabitants. Visitor monitoring system is a system through which we can monitor the people who are entering into the workplaces or offices in a digital way. These highly developed electronic dwellings are multifaceted, intricate systems that require research to improve upon those procedures. However, there is lack of consensus in the existing VM concept. So, there is a requirement to research and innovate more effective VM systems to countermeasure new and emerging processes. This research work proposes an automated visitor management tool to manage and coordinate the visitors by using IoT and computer vision techniques. Experiments were carried out on 100 visitors by using putty platform and observed noticeable result. © 2023 IEEE.

关键词： Computer vision

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Sustainable Smart Agriculture Advisor through Artificial Intelligence Approach

Sustainable Smart Agriculture Advisor through Artificial Int...

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2024 International Conference on distributed Systems, Computer Networks and Cybersecurity, ICdSCNC 2024

作者： Hariharan, Shanmugasundaram Srivani, Pogula Gogula, Sreenivasulu Venkateswaran, N. Kukreja, Vinay Jose, P. Vardhaman College of Engineering Dept. of Artificial Intelligence and Data Science Hyderabad India Cmr Technical Campus Department of H&s Hyderabad India Vardhaman College of Engineering Department of Computer Science and Engineering Hyderabad India Jyothishmathi Institute of Technology & Science Nustulapur Department of Computer Science and Engineering Karimnagar India Centre for Research Impact & Outcome Chitkara University Rajpura India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Cse Chennai India

ISBN: (纸本)9798350375442

Agriculture, constituting the backbone of India's economy and employing 70% of its population, faces critical challenges such as water scarcity, soil degradation, and unpredictable crop yields. Traditional farming methods lack efficiency, contributing to environmental impact and resource inefficiency. To address these issues on smart crop cultivation with artificial intelligence enabled crop advisor sustainable agriculture generation integrates machine learning and data analytic to offer real-time insights and recommendations. This advanced agricultural approach aims to optimize resource utilization, minimize environmental impact, and adapt to changing climatic conditions. By empowering farmers with actionable insights, Smart Crop seeks to revolutionize agriculture, promoting sustainable practices for increased resource efficiency and enhanced resilience against challenges like limited precision farming, climate variability, and yield maximization. This innovative solution endeavors to bridge the gap between traditional farming methods and modern challenges, fostering a more sustainable and productive future for Indian agriculture. The methods analyzed leads to promising results as compared with well known metrics. © 2024 IEEE.

关键词： Smart agriculture

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IoT-based trusted wireless communication framework by machine learning approach

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Measurement: Sensors 2024年 34卷

作者： Chakaravarthi, S. Saravanan, S. Jagadeesh, M. Nandhini, S. Department of Artificial Intelligence and Data Science Panimalar Engineering College India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Tamilnadu Chennai India Department of Computing Technologies SRM Institute of Science and Technology Tamilnadu Kattankulathur603203 India Department of Data Science and Business Systems SRM Institute of Science and Technology Tamilnadu Kattankulathur603203 India

The traditional radio-Frequency Systems (rFS) authentication methods, designed to ensure secure data transmission on the web, may not always effectively prevent adversaries from gaining access to concealed Ids or asymmetric cryptography through infiltrative, side-channel, training, and computer attacks. In contrast, Unaccounted Information (UAI) has the potential to exploit irregularities in production systems to automatically identify microchips, offering a highly robust and cost-effective security solution. This approach introduces rFS-UAI, a deep neural network-based system that efficiently manages wireless node identification by leveraging synthetic rFS characteristics of remote controls (Tx) learned through supervised methods in Wireless Sensor Networks (WSN). Unlike traditional methods that require the development of specialized transistors for UAI or semantic segmentation, this approach utilizes the existing asymmetrical rFS communication networks. Similar to the way the human brain processes information, rx handles the entire device identification process at the gateway. According to test results, which include assessing process capability at a specified 65 nm threshold voltage and characteristics such as Local Oscillator (LO) misalignment and I-Q disparity using a probabilistic model with 52 hidden units, the system can distinguish up to 4800 transmitters with a remarkable 99.9 % accuracy under various channel conditions, all without the need for regular preambles. This recommended method can serve as a standalone security measure or be integrated into a biometric identification system. © 2024 The Authors

关键词： Authentication

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Artificial Intelligence Model for Classification of Sensitivity data Utilized at Industrial Applications

Artificial Intelligence Model for Classification of Sensitiv...

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2022 International Conference on Electronic Systems and Intelligent Computing, ICESIC 2022

作者： Mohammad, Ashraf Sree, Mupparaju Kavya dheeraj Pedapalli, Saam Prasanth raveendra, Muddineni Kumar, T. Yaswanth Pavan National Ilan University Department of Electronic Engineering Yilan City Taiwan Vel Tech Rangarajan Dr Sagunthala R&d Institute of Science and Technology Department of Cse Chennai India Wistron Corporation Failure Data Analysis Engineer Hsinchu City Taiwan

ISBN: (纸本)9781665483858

Every electronic gadget in the structure has the potential to cause ignite, whether through a short circuit or another means which is also the primary cause of fire accidents. Smoke detectors are used to save the lives of causalities during hazards. In the manufacturing industry of smoke detectors, around 12 percent of products are getting failed due to the parameters of sensitivity criteria. This paper provides an approach to improve the success rate sensitivity data of the products as an industrial application using a sequential model provided by the tensor flow. Model accuracy and loss were results compared at different epochs. The final artificial intelligence model was developed with a minimum number of layers and requires less computational time. This paper helps the industry to improve product quality and efficiency. © 2022 IEEE.

关键词： Tensors

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An Enhanced Traffic Incident detection using Factor Analysis and Weighted random Forest Algorithm 5

An Enhanced Traffic Incident Detection using Factor Analysis...

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

作者： Kanna, P. rajesh Vanithamani, S. Karunakaran, P. Pandiaraja, P. Tamilarasi, N. Nithin, P. Bannari Amman Institute of Technology Department of Computer Science and Engineering Tamil Nadu Erode India M. Kumarasamy College of Engineering Department of Master of Computer Applications Tamil Nadu Karur India Nandha Engineering College Department of Artificial Intelligence and Data Science Tamil Nadu Erode India Vel Tech Rangarajan Dr. Sagunthala Department of Computer Science and Engineering R&D Institute of Science and Technology Tamil Nadu Chennai India Nehru Institute of Technology Department of Electronics and Communication Engineering Tamil Nadu Coimbatore India Bannari Amman Institute of Technology Department of Artificial Intelligence and Machine Learning Tamil Nadu Erode India

ISBN: (纸本)9798331518097

Efficient and precise traffic incident detection is essential to reduce casualties and property damage. To address the issue of unbalanced event data, this work offers a novel methodology known as FA-WrF (Factor Analysis and Weighted random Forest). This approach combines dimensionality reduction through factor analysis with classification using weighted random forests, data preparation through the Synthetic Minority Over-Sampling Technique (SMOTE), and data preparation with SMOTE. The included feature of severity detection is highlighted, as are the performance metrics of the FA-WrF model using well-established metrics such as detection level rate, number of false count alarm rate, classification type rate, and area specification under the receiver operating characteristic curve (AUC). The superiority of the FA-WrF model is illustrated using real-world expressway traffic data represented by imbalanced incidents through thorough comparisons with various machine learning-based algorithms such as Support Vector Machine, k-nearest neighbours algorithms, Logistic regression algorithms, and decision-making trees. In addition to advancing incident detection, this technique has encouraging prospects for enhancing traffic management procedures and well-informed decision-making procedures in transportation networks. © 2024 IEEE.

关键词： dimensionality reduction

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