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International Conference on Smart Technologies For Smart Nation (SmartTechCon)

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

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..

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The role of Edge Computing in Enhancing Cloud Performance and reducing Latency

The Role of Edge Computing in Enhancing Cloud Performance an...

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： Puneet Sharma J Shyamala devi K Manikandan r Ani Minisha r Harini S. Karthik School of CS & AI SR University Warangal Telangana India Rajalakshmi Institute of Technology Chennai School of Computer Science & Engineering (SCOPE) Vellore Institute of Technology (VIT) Vellore Tamilnadu India Department of Artificial Intelligence and Data Science R.M. K. Engineering College Department of Computer Science and Engineering Veltech Multitech Dr. Rangarajan Dr. Sakunthala Engineering College Department of Mathematics Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Chennai Tamilnadu India

ISBN: (数字)9798350350067

ISBN: (纸本)9798350350074

The flexibility of implementing edge computing over cloud infrastructures has become essential for responding to the need for reducing latency and improving data processing in a distributed environment. This research aims to determine the use of edge computing in enhancing the effectiveness of cloud utilization through conducting machine learning investigations utilizing random Forest in the determination of the optimum contribution of resources. The work also set up and applied real-world datasets from Kaggle to illustrate that edge computing can alleviate the latency issue mainly by offloading computationally intensive tasks to edge nodes away from the central cloud. The results show that edge computing with the help of Machine learning is the best solution for cutting-edge cloud environments as it is fast and does not aggravate the problems of excessive use of resources. Therefore, this research will be a great addition to the existing literature on the integration of cloud and edge computing and is hoped to offer useful information for future developments in the field of cloud computing.

关键词： Cloud computing Machine learning algorithms Job shop scheduling Smart cities Processor scheduling Security reliability Low latency communication random forests Edge computing

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detection of Malfunctioning Modules in Photovoltaic Power Plants using Unsupervised Feature Clustering Segmentation Algorithm

arXiv

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arXiv 2022年

作者： dwivedi, divyanshi Yemula, Pradeep Kumar Pal, Mayukha Data Science Research Intern ABB Ability Innovation Center Hyderabad500084 India Department of Electrical Engineering Indian Institute of Technology Hyderabad502205 India Global R&D Leader Data Science at ABB Ability Innovation Center Hyderabad500084 India

The energy transition towards photovoltaic solar energy has evolved to be a viable and sustainable source for the generation of electricity. It has effectively emerged as an alternative to the conventional mode of electricity generation for developing countries to meet their energy requirement. Thus, many solar power plants have been set up across the globe. However, in these large-scale or remote solar power plants, monitoring and maintenance persist as challenging tasks, mainly identifying faulty or malfunctioning cells in photovoltaic (PV) panels. In this paper, we use an unsupervised deep-learning image segmentation model for the detection of internal faults such as hot spots and snail trails in PV panels. Generally, training or ground truth labels are not available for large solar power plants, thus the proposed model is highly recommended as it does not require any prior learning or training. It extracts the features from the input image and segments out the faults in the image. Here we use infrared thermal images of the PV panel as input, passed to a convolutional neural network which assigns cluster labels to the pixels. Further, optimize the pixel labels, features and model parameters using backpropagation based on iterative stochastic gradient descent. Then, we compute similarity loss and spatial continuity loss to assign the same label to the pixel with similar features and spatial continuity to reduce noises in the image segmentation process. The effectiveness of the proposed approach was examined on an online available dataset for the recognition of snail trails and hot spot failures in monocrystalline solar panels. © 2022, CC BY-NC-Nd.

关键词： Solar energy

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Advanced dual Module Weapon detection for Public Safety and Surveillance System

Advanced Dual Module Weapon Detection for Public Safety and ...

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Sustainable Expert Systems (ICSES), International Conference on

作者： Manikanta Sirigineedi M. Sowmiya HemavathI. U V.C. Bharathi Sayali S Karmode r. Sathya Department of Information Technology Vishnu Institute of Technology Vishnupur Bhimavaram Andhra Pradesh India Department of Artificial Intelligence and Data Science Sri Eshwar College of Engineering Coimbatore India Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology AVADI School of Computer Science and Engineering VIT-AP University Amaravati Andhra Pradesh India Information Technology Mahatma Gandhi Mission's College of Engineering & Technology Kamothe Navi Mumbai Maharashtra India Department of IT Kongunadu College of Engineering and Technology Trichy Tamil Nadu India

ISBN: (数字)9798331540364

ISBN: (纸本)9798331540371

This research presents a novel weapon detection system that combines state-of-the-art technologies for enhanced security. The system integrates a Convolutional Neural Network (CNN) for precise image-based weapon classification and the YOLO algorithm for real-time detection in live camera feeds. The proposed system generates email alerts to promptly inform users of detected weapons, enabling timely action to mitigate risks. By combining these advanced modules, the proposed system offers an effective solution for addressing weapon-related violence and improving security measures in various environments.

关键词： YOLO Weapons Surveillance real-time systems Public security Classification algorithms Security Convolutional neural networks Feeds Expert systems

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

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

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International conference of Electronics, Communication and Aerospace technology (ICECA)

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

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.

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Hard Sample Matters a Lot in Zero-Shot Quantization

arXiv

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arXiv 2023年

作者： Li, Huantong Wu, Xiangmiao Lv, Fanbing Liao, daihai Li, Thomas H. Zhang, Yonggang Han, Bo Tan, Mingkui South China University of Technology China Changsha Hisense Intelligent System Research Institute Co. Ltd China Hong Kong Baptist University Hong Kong Key Laboratory of Big Data and Intelligent Robot Ministry of Education China PengCheng Laboratory China Information Technology R&D Innovation Center of Peking University China School of Electronic and Computer Engineering Peking University Shenzhen Graduate School Shenzhen China

Zero-shot quantization (ZSQ) is promising for compressing and accelerating deep neural networks when the data for training full-precision models are inaccessible. In ZSQ, network quantization is performed using synthetic samples, thus, the performance of quantized models depends heavily on the quality of synthetic samples. Nonetheless, we find that the synthetic samples constructed in existing ZSQ methods can be easily fitted by models. Accordingly, quantized models obtained by these methods suffer from significant performance degradation on hard samples. To address this issue, we propose HArd sample Synthesizing and Training (HAST). Specifically, HAST pays more attention to hard samples when synthesizing samples and makes synthetic samples hard to fit when training quantized models. HAST aligns features extracted by full-precision and quantized models to ensure the similarity between features extracted by these two models. Extensive experiments show that HAST significantly outperforms existing ZSQ methods, achieving performance comparable to models that are quantized with real data. Copyright © 2023, The Authors. All rights reserved.

关键词： deep neural networks

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Autonomous Vehicle Traffic Flow Optimization Using AI and 5G Communication Networks 3

Autonomous Vehicle Traffic Flow Optimization Using AI and 5G...

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3rd IEEE International Conference on device Intelligence, Computing and Communication Technologies, dICCT 2025

作者： Shekhar, Jayant rama Prabha, K.P. Prashanth, S. Umamaheswari, K. rahul, S.G. Gayathri, K. Sharda School of Engineering and Technology Sharda University Department of Cse UP Greater Noida India School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Chennai India Excel Engineering College Department of Aeronautical Engineering Tamil Nadu Namakkal India V.S.B. Engineering College Department of Information Technology Tamilnadu Karur India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India St. Joseph's Institute of Technology Department of Artificial Intelligence and Data Science Tamil Nadu Chennai India

ISBN: (纸本)9798331543358

real-time optimization of autonomous vehicle (AV) traffic flow using 5G communication networks and AI algorithms could overcome the limitations of static traffic enforcement, which uses outdated technologies to manage fluctuating traffic demands. It allows dynamic flow. Such commodities cannot adapt in real time because to their rigorous handling requirements, which disrupts traffic dynamics and creates construction delays and traffic congestion. The study suggests transmitting real-time vehicle status data to nearest intelligent crossings via vehicular wireless networks (VWNs). These intersections enhance traffic flow and reduce 5G network congestion using AI algorithms. Predictive analytics will help AVs change routes while on the road to maximize travel efficiency. It finds that peak congestion, average route duration, and fuel use reduce vehicle CO2 emissions by 66.7%, 22%, and 15%, respectively. It determined that the proposed system was a viable design route for a deployable smart city transportation solution because it outperformed traditional techniques in economic and environmental criteria. © 2025 IEEE.

关键词： Traffic congestion

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deep Learning Based Intrusion detection System with Integration of Blockchain technology and Internet of Things

Deep Learning Based Intrusion Detection System with Integrat...

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Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES), International Conference on

作者： G. Uganya d. rajalakshmi K. Ashok Kumar S. ravi Neeraj Sunheriya Mohammad Kanan Department of ECE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of Data Science and Business Systems SRM Institute of Science and Technology Kattankulathur Chennai Department of Information Technology St Joseph's college of Engineering OMR Chennai Department of VLSI Microeelctronics Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Chennai Department of Mechanical Engineering Yeshwantrao Chavan College of Engineering Nagpur Department of Industrial Engineering College of Engineering University of Business and Technology Jeddah Saudi Arabia Department of Mechanical Engineering College of Engineering Zarqa University Zarqa Jordan

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543631

The number of people and objects linked to the Internet of Things (IoT) has expanded dramatically due to its fast development, giving consumers more opportunities to interact with and manage their physical surroundings. IoT is susceptible to a number of security risks, because of its openness, connectedness, and constrained device resources. It is essential to guarantee the dependability and security of data transmitted in IoT networks. In order to solve these security problems, this work provides intrusion detection system that combines IoT with and Blockchain technology. As well as, deep learning method is integrated to improve the security. This work uses the SHA-256 algorithm to secure the data, the Adaptive Optimization algorithm for generating the keys, rivest, Shamir, and Adleman (rSA) algorithm for encryption. and deep Neural Network (dNN) for identifying intrusions in Internet of Things. Through identification and mitigation of possible risks, this framework successfully protects IoT networks and ensures the integrity of communications, as demonstrated by a comparison analysis with existing models.

关键词： deep learning Sensitivity Adaptive systems Accuracy Intrusion detection Artificial neural networks Blockchains Internet of Things Security Optimization

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Autonomous Vehicle Traffic Flow Optimization Using AI and 5G Communication Networks

Autonomous Vehicle Traffic Flow Optimization Using AI and 5G...

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device Intelligence, Computing and Communication Technologies, (dICCT), International Conference on

作者： Jayant Shekhar rama Prabha K. P S. Prashanth Umamaheswari K rahul S G K. Gayathri Department of CSE Sharda School of Engineering and Technology Sharda University Greater Noida UP India School of Computer Science and Engineering Vellore Institute of Technology Chennai Tamil Nadu India Department of Aeronautical Engineering Excel Engineering College Namakkal Tamil Nadu India Department of Information Technology V.S.B. Engineering College Karur Tamilnadu India Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Artificial Intelligence and Data Science St. Joseph's Institute of Technology Chennai Tamil Nadu India

ISBN: (数字)9798331543358

ISBN: (纸本)9798331543365

关键词： 5G mobile communication Smart cities Heuristic algorithms real-time systems Artificial intelligence Vehicle dynamics Autonomous vehicles Optimization Traffic congestion Intelligent sensors

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Secure Audio Steganography with reversible data Hiding and Encryption

Secure Audio Steganography with Reversible Data Hiding and E...

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Mobile Computing and Sustainable Informatics (ICMCSI), International Conference on

作者： P. S. Arthy K. Sanjai Kumar S. Vinson Joshua G. Sahaana r. Kalpana d. david Neels Ponkumar S. Siamala devi S. Jeya Lakshmi Department of Electronics and Communication Engineering Sri Sai Ram Institute of Technology Chennai Tamil Nadu India Department of Artificial Intelligence and Data Science Dhanalakshmi College of Engineering Chennai Tamil Nadu India Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Computer Science and Engineering (Cyber Security) Sri Eshwar College of Engineering Coimbatore Tamil Nadu India Iconix Software Solution Tirunelveli Tamil Nadu India

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522674

This research study enhances secure data transmission by embedding sensitive information within audio files. Using Python for the frontend and PHP with MySQL as the backend, this approach employs reversible data hiding (rdH) to ensure that the original content of the audio file can be fully restored. Audio steganography is used to conceal encrypted messages within a WAV file, leveraging its varying compression rates for optimal steganographic performance. The Least Significant Bit (LSB) technique, a widely used method, embeds the hidden message into the audio stream without noticeable distortion. In this system, the secret message is scrambled, divided into segments, and embedded in even-value bytes of the audio file, with the first bit position chosen randomly for enhanced security. A permutation codebook is employed to rearrange and decrypt the message during recovery, ensuring both data confidentiality and complete reversibility of the audio file. This method provides a secure, efficient solution for transmitting sensitive information through audio channels.

关键词： Steganography data privacy distortion Encryption data communication Informatics Mobile computing

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