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4th International Conference on Power, Energy, Control and Transmission Systems, ICPECTS 2024

作者： Divya, B. Ramya, R. Hariharan, S. Abishek, B. Thangamuthu, E. Harikrishna, K.L. Sri Sai Ram Institute of Technology Dept of Computer Science and Engineering India Chennai Institute of Technology Dept of Electronics and Communication Engineering India

ISBN: (数字)9798331508845

ISBN: (纸本)9798331508845

Attendance management is an important task for an educational institution to cater to faculty accountability and correct tracking of the same. Traditional technique is enhanced by introducing a novel system that uses three separate modules;namely face recognition, geofencing, and real-time notifications. The system can identify when faculty members enter the institution's appointed area, and it embraces them with a request to complete the attendance process. The faculty members are encouraged to log onto to face detection software, where in they have a set time frame for them to log on and log their attendance using their individual identification number. The mobile application software has additional options - check-in and check-out options to precisely monitor the working hours accurately. It ensures the attendance is not falsified, and only registered within the designated area. The bank offers an elegant interface with sections of home, reports, user details, and announcement. The report sect provides a clear overview of the attendance records, and the user detail section provides comprehensive information about the faculty members, however the announcement section is relevant for the announcement section is the latest updates about the institution. Additionally, with the advanced security on this system, the data privacy and the data integrity is secure and trustworthy to use in this contemporary educational institution. It has not only increased the efficiency of workplace, but has also improved the traditional attendance logging system. © 2024 IEEE.

关键词： Application programs

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Smart Retail: Utilizing Machine Learning for Demand Prediction, Price Strategy, and Inventory Management 16

Smart Retail: Utilizing Machine Learning for Demand Predicti...

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16th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2024

作者： Singhal, Krish Singh, Vaibhav Kaul, Amrita Computer Science Engineering Jaypee Institute of Information Technology Noida India Electronics and Communication Engineering Jaypee Institute of Information Technology Noida India

ISBN: (纸本)9798331505264

This research demonstrates the ability of Artificial Intelligence (AI) to enhance real-time demand forecasting, price prediction, and inventory management by analyzing key factors such as competitor prices, customer preferences, and stock levels. The Random Forest Regressor (RFR) model has been implemented to achieve a Mean Squared Error (MSE) of 8.15% for demand forecasting and 1.11% for price prediction, highlighting its accuracy. These AI-driven insights allow businesses to make smarter, faster decisions, optimize pricing strategies, and adjust inventory levels to meet market demands. Additionally, the model helps avoid surplus or deficit, ensuring seamless inventory flow, reducing operational costs, and improving overall profitability. AI integration into retail processes allows companies to respond dynamically to real-time changes and maintain a competitive edge in a fast-paced market environment. © 2024 IEEE.

关键词： Sales

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Falcon Optimization Algorithm-Based Energy Efficient Communication Protocol for Cluster-Based Vehicular Networks

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computers, Materials & Continua 2024年第3期78卷 4243-4262页

作者： Youseef Alotaibi B.Rajasekar R.Jayalakshmi Surendran Rajendran Department of Software Engineering College of ComputingUmm Al-Qura UniversityMakkah21955Saudi Arabia Department of Electronics and Communication Engineering Sathyabama Institute of Science and TechnologyChennai600119India Department of Computer Science and Engineering Panimalar Engineering CollegeChennai600123India Department of Computer Science and Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical ScienceChennai602105India

Rapid development in Information technology(IT)has allowed several novel application regions like large outdoor vehicular networks for Vehicle-to-Vehicle(V2V)*** networks give a safe and more effective driving experience by presenting time-sensitive and location-aware *** communication occurs directly between V2V and Base Station(BS)units such as the Road Side Unit(RSU),named as a Vehicle to Infrastructure(V2I).However,the frequent topology alterations in VANETs generate several problems with data transmission as the vehicle velocity differs with ***,the scheme of an effectual routing protocol for reliable and stable communications is *** research demonstrates that clustering is an intelligent method for effectual routing in a mobile ***,this article presents a Falcon Optimization Algorithm-based Energy Efficient Communication Protocol for Cluster-based Routing(FOA-EECPCR)technique in *** FOA-EECPCR technique intends to group the vehicles and determine the shortest route in the *** accomplish this,the FOA-EECPCR technique initially clusters the vehicles using FOA with fitness functions comprising energy,distance,and trust *** the routing process,the Sparrow Search Algorithm(SSA)is derived with a fitness function that encompasses two variables,namely,energy and distance.A series of experiments have been conducted to exhibit the enhanced performance of the FOA-EECPCR *** experimental outcomes demonstrate the enhanced performance of the FOA-EECPCR approach over other current methods.

关键词： Vehicular networks communication protocol clustering falcon optimization algorithm routing

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Comparative Study and Performance Analysis of Deep Neural Networks for Sign Language Recognition using Transfer Learning 3

Comparative Study and Performance Analysis of Deep Neural Ne...

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3rd International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2023

作者： Khurana, Soumya Sreemathy, R. Turuk, Mousami Jagdale, Jayashree Pune Institute of Computer Technology Department of Electronics and Telecommunication Engineering India

ISBN: (纸本)9781665494007

Sign Language is the primary source of communication for people with hearing disabilities. Communicating with people with such a disability is a challenge. An Automatic Sign Language Recognition system will reduce this communication gap and facilitate ease of conversing with people with hearing disabilities. In this work, we have compared the performance of 6 popular and recent deep neural network architectures for sign language recognition, namely DarkNet19, DarkNet53, InceptionResNetv2, ResNet50, DenseNet201 and VGG-19. Among these, DarkNet53 performed the best with our self-created dataset of 16,000 images from 60 classes with an average accuracy of 98.02%. © 2023 IEEE.

关键词： Deep neural networks

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Synergistic applications of nanostructured boron nitride in photocatalysis and targeted drug delivery

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Nanotechnology for Environmental engineering 2024年第2期9卷 179-188页

作者： Mali, Priyadarshani Shivakumar Jagtap, Bhagyashree Krishnarao Gowda V, Dankan Ashreetha, B. Vasanthakumar, G.U. kaur, Mandeep Hariram, Venkatesan Department of Electronics and Telecommunications Engineering Bharati Vidyapeeth’s college of Engineering Kolhapur Maharashtra416013 India Department of Electronics and Computer Engineering Sharad Institute of Technology college of Engineering Yadrav Maharashtra Ichalkaranji Kolhapur416115 India Department of Electronics and Communication Engineering BMS Institute of Technology and Management Bangalore Karnataka560064 India Department of Electronics and Communication Engineering School of Engineering Mohan Babu University Andhra Pradesh Tirupati India Department of Computer Science and Engineering Nitte Meenakshi Institute of Technology Yelahanka Bengaluru560064 India Department of Electronics and Communication Engineering Punjabi University Punjab Patiala147002 India Department of Practice of Medicine Vinayaka Mission’s Homoeopathic Medical College and Hospital A Constituent College of Vinayaka Mission’s Research Foundation Deemed to be University Tamilnadu Salem636 308 India

This study explores the applications of nanostructured boron nitride (BN) in photocatalysis and targeted drug delivery, highlighting its notable thermal conductivity, chemical stability, and wide bandgap. Through the synthesis and detailed characterization of BN nanoparticles, employing Scanning Electron Microscopy (SEM) and Transmission Electron Microscopy (TEM), the material’s efficacy in light-induced chemical reactions and potential as a nanocarrier for drug delivery systems are investigated. Photocatalytic experiments reveal an enhanced ability of BN to absorb light and facilitate charge separation, suggesting a promising approach for environmental detoxification and sustainable energy generation. In parallel, investigations into BN’s drug loading capacity, release kinetics, and targeting efficiency, particularly in the context of cancer therapy, are conducted. Findings indicate that BN nanoparticles significantly enhance photocatalytic performance and offer a safe, effective platform for controlled drug release. This research underscores the dual utility of BN in addressing critical challenges in environmental engineering and medical treatment, pointing towards novel interdisciplinary applications of this versatile nanomaterial. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Photocatalysis

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Insurance Amount Prediction Based on Accidental Car Damage Level using AI 8

Insurance Amount Prediction Based on Accidental Car Damage L...

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8th International Conference on Communication and electronics Systems, ICCES 2023

作者： Kumar, D. Manoj Reddy, Chadipriala Nithin Kumar Sai, Gunisetty Venkata Chowdary, Puligundla Sujan Srm Institute of Science and Technology Electronics and Computer Engineering Chennai India

ISBN: (纸本)9798350396638

This document presents a machine learning-based approach for predicting the insurance amount for a car damage claim based on the level of damage caused by an accident. The proposed model uses a dataset of past car damage claims, including the level of damage and the corresponding insurance amount paid out. The dataset is subjected to the application of a number of machine learning algorithms, such as decision trees, random forests, and neural networks. The effectiveness of these algorithms is assessed using a variety of measures, including accuracy, precision, recall, and F1-score. The results show that the model achieves high accuracy and F1-score, indicating its effectiveness in predicting the insurance amount based on the level of damage. This proposed approach has the potential to improve the accuracy and efficiency of the insurance claims process, ultimately benefiting both insurance companies and their clients. Insurance companies use a variety of factors to determine the amount of compensation paid out to a customer for a car damage claim. One of the most important factors is the level of damage caused by the accident. However, accurately assessing the level of damage can be a difficult undertaking, as it requires a high level of expertise and experience. To address this challenge, machine learning techniques can be used to build models that can predict the insurance amount based on the level of damage. In order to do this, a model must be trained using past data to identify trends and connections between the severity of the harm and the associated insurance payouts. Overall, insurance amount prediction based on accidental car damage level using AI is a promising area of research and has the potential to benefit both insurance companies and their clients. © 2023 IEEE.

关键词： Insurance

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Lung Cancer Classification Using Deep Learning: A Comprehensive Approach with Modified Convolutional Neural Networks 2

Lung Cancer Classification Using Deep Learning: A Comprehens...

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2nd International Conference on Computational Intelligence and Network Systems, CINS 2024

作者： Lad, Saket Chafekar, Bhavisha Bide, Pramod Department of Electronics and Telecommunication Engineering Mumbai India Department of Computer Engineering Sardar Patel Institute of Technology Mumbai India

ISBN: (纸本)9798331504106

This paper presents a strong deep learning technique to identify lung cancer through computed tomography (CT) imagery, using the IQ-OTH/NCCD lung cancer set of data. We have come up with a convolutional neural network (CNN) that is to classify CT scans in normal, benign and malignant *** CNN architecture consists of multiple convolutional and pooling layers, dropout mechanisms as a regularizer and fully connected layers for feature extraction. We perform comprehensive data pre-processing which involves image resizing in order to improve model performance. The proposed model having achieved excellent accuracy during testing *** results reveal how deep learning could revolutionize the way we diagnose and classify lung cancer accurately with CT scans.T By arguing for the application of deep learning in clinical work to increase accuracy of diagnosis, the research shows just how significant use of artificial intelligence has become in the analysis of images taken during medical procedures. This proposed CNN based approach achieved a test accuracy of 99.55%, precision of 100%, recall of 99% and a f1-score of 98%. This approach might help radiologists make diagnoses sooner and plan treatments better, so patients do well because doctors can treat them quickly and accurately. ©2024 IEEE.

关键词： Lung cancer

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PSO-DBNet for Peak-to-Average Power Ratio Reduction Using Deep Belief Network

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computer Systems Science & engineering 2023年第5期45卷 1483-1493页

作者： A.Jameer Basha M.Ramya Devi S.Lokesh P.Sivaranjani D.Mansoor Hussain Venkat Padhy Department of Computer Science and Engineering Hindusthan Institute of TechnologyCoimbatore641032India Department of Computer Science and Engineering Hindusthan College of Engineering and TechnologyCoimbatore641032India Department of Electronics and Communication Engineering Kongu Engineering CollegeErode638060India Department of Computer Science and Engineering Sri Krishna College of Engineering and TechnologyCoimbatore641008India School of Computing Science&Engineering VIT Bhopal UniversityBhopal466114India

Data transmission through a wireless network has faced various signal problems in the past *** orthogonal frequency division multiplexing(OFDM)technique is widely accepted in multiple data transfer patterns at various frequency bands.A recent wireless communication network uses OFDM in longterm evolution(LTE)and 5G,among *** main problem faced by 5G wireless OFDM is distortion of transmission signals in the *** transmission loss is called peak-to-average power ratio(PAPR).This wireless signal distortion can be reduced using various *** study uses machine learning-based algorithm to solve the problem of PAPR in 5G wireless *** transmit sequence(PTS)helps in the fast transfer of data in wireless *** is merged with deep belief neural network(DBNet)for the efficient processing of signals in wireless 5G *** indicates that the proposed system outperforms other existing ***,PAPR reduction in OFDM by DBNet is optimized with the help of an evolutionary algorithm called particle swarm ***,the specified design supports in improving the proposed PAPR reduction architecture.

关键词： 5G wireless network orthogonal frequency division multiplexing signal distortion peak to average power ratio partial transmit sequence deep belief network

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FaLa: feature-augmented location-aware transformer network for graph representation learning

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Neural Computing and Applications 2025年 1-28页

作者： Morshed, Md Golam Sultana, Tangina Lee, Young-Koo Computer Science and Engineering Kyung Hee University Yongin17104 Korea Republic of Computer Science and Engineering International University of Business Agriculture and Technology Dhaka1230 Bangladesh Electronics and Communication Engineering Hajee Mohammad Danesh Science & Technology University Dinajpur5200 Bangladesh

Data augmentation is a technique that improves the ability of neural networks to make accurate predictions by increasing the size of the training dataset. However, it is still uncertain how to properly use data augmentation on graph data in order to boost the performance of graph neural networks (GNNs). While the majority of current graph regularizers primarily concentrate on modifying the topological structures of graphs by adding or deleting nodes and edges, we propose a technique to augment node and edge features in order to improve efficiency. More precisely, we use cosine similarity to perform cross-functions on the existing features. This allows us to generate new graph features, such as node and edge attributes, as well as new graph structures. We then merge these new features together to provide specific pairs of inputs for graph transformer networks. We next propose feature-augmented location-aware transformer (FaLa) networks that generalize well across several graph learning problems by combining the positional and structural encoding of nodes. Additionally, in order to avoid collecting duplicate data from other features, we apply a disparity restriction during training on these latent node embeddings. Experimental findings across eleven real-world datasets demonstrate that our methodology enhances node classification accuracy by a significant margin of 2.8% to 22.7%, link prediction by 1.6% to 8.5%, and graph classification by 7.0% to 39.8% compared to the state-of-the-art graph convolutional network (GCN) and graph transformer (GT) models. This clearly shows the importance of augmenting graph features and location encoding for efficient graph representation learning. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Graph neural networks

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Enhancing fault detection and predictive maintenance of rotating machinery with Fiber Bragg Grating sensor and machine learning techniques

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International Journal of Information technology (Singapore) 2025年第2期17卷 1225-1234页

作者： Nayak, Narayan Mohapatra, Ambarish G. Khanna, Ashish Talukdar, Jaideep Satapathy, Satyapriya Nayak, Dipak Ranjan Ghuge, Nilam N. Electronics Engineering Silicon University Odisha Bhubaneswar India Basic Sciences & amp Humanities Silicon University Odisha Bhubaneswar India Computer Science and Engineering Maharaja Agrasen Institute of Technology Delhi India Electrical Engineering University of Technology Rajasthan Jaipur India Electrical and Electronics Engineering Silicon University Odisha Bhubaneswar India Electronics and Communication Engineering University of Technology Rajasthan Jaipur India

The reliability and operational efficiency of rotating machinery have become increasingly vital across various industries. Using Fibre Bragg Grating (FBG) vibration sensors, this study investigates the use of Machine Learning (ML) techniques for fault detection and predictive maintenance. The primary goal is to enhance early fault detection and optimize maintenance schedules, thereby reducing both downtime and operational costs in any industrial settings. The research utilizes sophisticated ML models to analyze vibration data acquired from FBG sensors, which are recognized for their exceptional sensitivity and precision in detecting vibrational anomalies that frequently occur prior to mechanical failures. Following the pre-processing phase, which aims to minimize noise and improve signal quality, essential features are extracted to identify critical parameters that reflect the machine’s health. A range of ML models, such as Random Forest (RF) and Radial Basis Function Neural Networks (RBFNN), are trained and validated using this set of features. The results demonstrate that these machine learning models are effective in accurately detecting faults and predicting maintenance (PM) needs based on real-time sensor data. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Fault detection Fiber Bragg Grating (FBG) sensors Machine learning (ML) Predictive maintenance (PM) Rotating machinery

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