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Intervention of Digital Twin for Smart Monitoring of Battery Management System 14

Intervention of Digital Twin for Smart Monitoring of Battery...

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14th International Conference on Computing Communication and networking Technologies, ICCCNT 2023

作者： Joshi, Kapil Gupta, Amit Kaushik, Vishal Anandaram, Harishchander Gupta, Himanshu Kumar, Anil Uttaranchal University Uttaranchal Institute of Technology Department of Cse Dehradun India Graphic Era Hill University Department of Computer Science and Engineering Uttarakhand Dehradun India Upes School of Computer Science Dehradun India Amrita Vishwa Vidyapeetham Centre for Computational Engineering and Networking Tamil Nadu Coimbatore India Coer University Roorkee India Dit University School of Computing Dehradun India

ISBN: (纸本)9798350335095

The use of digital twins (DT) has made important improvements not just from the beginning of the process of automobile development through the final phases of vehicle building, but additionally during its usage, collecting essential information from its everyday activities to make traveling more joyful, relaxed, and secure. This study organizes the DT development history, fundamental principles, and essential technologies, as well as summarizing current research methodologies and problems in battery simulation, state estimate, remaining useful life (RUL) projection, battery protection, and management. In addition, study provide methods for batteries digital simulation, real-time status calculation, variable charging supervision, dynamic heat leadership, and energetic leveling regulation in the smart battery management system (BMS) using DT. The study also provides prospects for DT growth in the industry of battery. Lastly, the article concludes with an examination of the technological economic effects of DT technology, which will revolutionize conventional automobile manufacturing, as well as the barriers to continued growth. © 2023 IEEE.

关键词： Blockchain

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Internet of things (IoT) applications and Challenges: A Study 7

Internet of things (IoT) applications and Challenges: A Stud...

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7th International Conference on Computing, Communication and Security, ICCCS 2022 and 2022 4th International Conference on Big Data and computational Intelligence, ICBDCI 2022

作者： Kumar, Rajesh Anandaram, Harishchander Joshi, Kapil Kumar, Vivek Reshi, Jadeep Saini, Rakesh Kumar Meerut Institute of Technology Department of CSE Meerut India Amrita School of Engineering Coimbatore Amrita Vishwa Centre for Computational Engineering and Networking Vidyapeetham India Uttaranchal Institute of Technology Uttaranchal University Department of CSE Dehradun India DIT University School of Computing Uttarakhand Dehradun India

ISBN: (纸本)9798350309904

Internet of Things (IoT) transformed finished the general unlimited wrapper of persons, keen devices, intelligent effects, statistics, and arguments of attention. It is no unidentified that as continually accomplishes important additional to the network, the experiments of protective the archives that they inform and the transferences that they effort have acquired progressively heedful. Through the centuries, we've obvious about pouring in Internet of Things (IoT) devices, expansively in two domains-in homes and in accumulating. Since those are balanced and shut structures, the responsibilities of protective the devices regulator with the performance residence whole salers. In this chapter, we will state-owned arithmetic nonviolent protection in hypothesis and supplementary governments. Administrations which contribute current, oil and petrol, riddling, treatment drugs, eating times and imbuement, water cure, and a lot more are constantly bidding to comprise the accurate covers of protection, as they permit on increasingly more device and devices on the network. Apparatus discoverers and bloom crown movements administrators constantly expressions exercise to viewpoint their bodily things from numerical hazards. It is also for every one of these activities, the background of the data, topologies of IoT applications, and problems of vulnerability controller and hopeful consistence worldly normally. © 2022 IEEE.

关键词： Internet of things

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CNN based End to End Learning Steering Angle Prediction for Autonomous Electric Vehicle 4

CNN based End to End Learning Steering Angle Prediction for ...

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4th International Conference on Electrical, Computer and Communication Technologies, ICECCT 2021

作者： Prasad Mygapula, D.V. Adarsh, S. Sajith Variyar, V.V. Soman, K.P. Amrita Vishwa Vidyapeetham Amrita School of Engineering Department of Electronics and Communication Engineering Coimbatore641112 India Amrita Vishwa Vidyapeetham Amrita School of Engineering Department of Computational Engineering and Networking Coimbatore641112 India

ISBN: (纸本)9781665414807

Self driving vehicle is capable of sense the surrounding environment with the help of sensors and fulfill the transportation without any human control. It is a promising technology to reduce the accidents, increase the reliability and improve the transportation in our society. Steering angle prediction is an important feature for self driving vehicles. The normal approach for self driving vehicle is dividing into several steps such as road marking detection, vehicle detection, path planning and motor drive control. These human selected features are poor reliable to the high traffic environment and hence End to End learning method is implemented to overcome this drawback. This work explains the End to End learning method to predict the steering angle to keep up the self driving car in the lane by using the Convolutional Neural Network. The CNN architecture takes input images and predicts the steering angles consequently. This system is automatically learns inside representations of the imperative steps such as identifying beneficial road features with the driving steering angle along with road images as the training input. This work shows the ability of the convolutional neural networks to control the self driving vehicle by processing the input images. This work achieved a test loss of 0.0354 and R2 score of 0.819 using CNN architecture with different hyper parameters. © 2021 IEEE.

关键词： Forecasting

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An Informal Introduction to Semirings for GraphBLAS using Matlab

An Informal Introduction to Semirings for GraphBLAS using Ma...

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Intelligent Technologies (CONIT), International Conference on

作者： Aadharsh Aadhithya A Anirudh Edpuganti Vinith Rejathalal Sachin Kumar S Soman K. P Prabhaharan Poornachandran Center for Computational Engineering and Networking (CEN) Amrita Vishwa Vidyapeetham Coimbatore India Centre for Internet Studies and Artificial Intelligence Amrita Vishwa Vidyapeetham Amritapuri Kerala India

This paper presents an informal introduction to semirings and their application in GraphBLAS using Matlab. GraphBLAS is a powerful and versatile tool for working with graph algorithms and linear algebra, and its use in education is also discussed. The paper simplifies the mathematical complexity typically associated with semirings and provides an accessible entry point for those who are new to the field of GraphBLAS. The use of Matlab as a tool for implementing GraphBLAS is also highlighted, making it user-friendly and easy to understand. Overall, this paper serves as a useful starting point for anyone interested in learning more about GraphBLAS and its potential applications.

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Humidity Prediction in Crop Monitoring System through Machine Learning and web based application in finance and Smart Agriculture 4.0 4

Humidity Prediction in Crop Monitoring System through Machin...

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International Conference on 4th Industrial Revolution Based Technology and Practices, ICFIRTP 2022

作者： Kaur, Sukhdeep Jeevan, R.N. Joshi, Kapil Anandaram, Harishchander Kanti, Puneet Pandey, Richa Uttaranchal Institute of Technology Uttaranchal University Department of CSE Dehradun India M S Ramaiah Institute of Technology Bangalore India Amrita School of Engineering Coimbatore Amrita Vishwa Centre for Computational Engineering and Networking Vidyapeetham India Graphic Era Hill University Department of CSE Haldwani India

ISBN: (纸本)9798350345919

In India, approximately two-Third people of the total population depend on farming for their livelihood. Agriculture has given people food, shelter and a way to earn their livelihood. In sustainable goals, smart agriculture 4.0 is the major concern. The world is leading towards new heights with the advancement in technology. Farming has also been evolved in many aspects. Using technology, Farmers also has increased their yields and they are earning more money. Farmer Friend is a java based web application that will be useful for farmers as well as other people who want to gain knowledge about the crops, fertilizers and other farm related tools. And not only gaining the knowledge, they can use it practically. The main objective of this project is to help farmers by providing the essential information for the better yield. This is a web based application to provide all the information at one place. This project will help farmers by letting them know about the soils, crops, fertilizers, etc. All areas vary in terms of soil and other factors like humidity, temperature, rainfall, etc that are required for good crop production. This app will help farmers by providing them the information about which crop to grow in which season according to their area to increase their productivity. So, this application will help them in making more profit. This application will also be helpful to them because it will also provide the information about the latest schemes that government has launched for their welfare. © 2022 IEEE.

关键词： Database Humidity Smart Agriculture 4.0 Temperature Web Application

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Metaheuristic Optimization for Path Planning in UAV Networks for Long-Distance Inspection Tasks 13

Metaheuristic Optimization for Path Planning in UAV Networks...

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13th International Conference on System Modeling and Advancement in Research Trends, SMART 2024

作者： Akshya, J. Manochitra, M. Indu Poornima, R. Jeevitha, T. Saravanakumar, M. Sundarrajan, M. Choudhry, Mani Deepak Srm Institute of Science and Technology Department of Computational Intelligence Chennai India Coimbatore India Institute of Technology Department of Information Technology KGiSL Coimbatore India Nehru Institute of Engineering and Technology Department of Artificial Intelligence and Data Science Coimbatore India Srm Institute of Science and Technology Department of Networking and Communications Chennai India Srm Institute of Science and Technology Deportment of Computing Technologies Chennai India

ISBN: (纸本)9798350380583

With broad applicability in surveillance, delivery, and environmental monitoring, multi-drone path planning has received a great deal of attention recently. So far, wide applicability has been explored by various applications of Genetic Algorithms, Particle Swarm Optimization, and Ant Colony Optimization, which have already established reliable solutions to navigation and coverage through drone path optimization. However, in dynamic, obstacle-dense environments, these algorithms fail to adapt in real-time with energy efficiency. Traditional GA and PSO approaches can be effective for generating an initial path but fail to respond to collision avoidance and efficient resource management. In complex and unpredictable conditions, ACO fails to converge rapidly, which leads to suboptimal paths and increased energy consumption. To overcome such barriers, we present here an HGARL for enhanced multi-drone path optimization. The proposed algorithm thus combines the population-based search capability of GA with RL's adaptive learning ability, therefore providing an algorithm that in addition to finding optimal paths adjusts the paths according to changing environmental situations. HGARL presents better performance compared to existing methods. In path convergence, it reaches up to 15%, and in energy consumption, it reduces up to 20% with safe collision-free navigation. The strong potential of HGARL for robust, efficient, and adaptive solutions of real-time multi-drone operation in complex and dynamic environments has set a new benchmark for UAV path planning. © 2024 IEEE.

关键词： Genetic algorithms

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AI4BCancer: Breast Cancer Classification using AutoML - TPOT with PCA

AI4BCancer: Breast Cancer Classification using AutoML - TPOT...

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Artificial Intelligence in Education and Industry 4.0 (IDICAIEI), DMIHER International Conference on

作者： Charvi Jain Shaurya Singh Srinet Tarush Kumar Goyal Karpagam M Department of Computational Intelligence SRM Institute of Science and Technology Chennai Tamil Nadu India Department of Networking and Communications SRM Institute of Science and Technology Chennai Tamil Nadu India Department of Computational Intelligence Faculty of Engineering and Technology SRM Institute of Science and Technology Chennai Tamil Nadu India

ISBN: (数字)9798331528713

ISBN: (纸本)9798331528720

Breast cancer is the most common cancer diagnosed in women and is a significant cause of cancer-related deaths among women worldwide. It demands accurate and timely detection methods for improved patient outcomes. This study proposes a novel machine learning approach to enhance breast cancer classification. Emphasizing the urgency of early diagnosis, we compare existing models and find AutoML - TPOT with PCA achieving the highest accuracy among all models tested. This proposed model integrates advanced algorithms and PCA to improve accuracy and computational efficiency. Through rigorous evaluation on the Wisconsin Breast Cancer Dataset, this model demonstrates superior performance, achieving an accuracy of 98.6%. This highlights its potential to significantly impact breast cancer diagnosis and treatment planning, contributing to ongoing efforts to improve patient care. Future work will focus on integrating Explainable AI to enhance model transparency and clinical trust.

关键词： Industries Accuracy Machine learning algorithms Explainable AI computational modeling Medical services Automated machine learning Breast cancer Planning Principal component analysis

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Integrating Instruction-based Learning with Bidirectional LSTM for Autonomous Vehicle Pedestrian Detection

Integrating Instruction-based Learning with Bidirectional LS...

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

作者： Shoba, B. Raghunathan, T. Peter, S.Sam Karthick, K. Akshya, J. Sundarrajan, M. Choudhry, Mani Deepak Psn College of Engineering and Technology Department of Electronics and Communication Engineering Tirunelveli India Dr.NG.p.Institute of Technology Department of Artificial Intelligence and Data Science Coimbatore India Sri Krishna College of Technology Department of Computer Science and Engineering Coimbatore India Sona College of Technology Department of Information Technology Salem India Srm Institute of Science and Technology Department of Computational Intelligence Chennai India Srm Institute of Science and Technology Department of Networking and Communications Chennai India Srm Institute of Science and Technology Department of Computing Technologies Chennai India

ISBN: (纸本)9798331509828

Pedestrian detection is a crucial aspect of autonomous vehicle (A V) safety, as it enables accurate identification and tracking of individuals in an A V environment. Effective pedestrian detection supports essential A V functions like braking, steering, and speed control to prevent accidents in urban settings. Traditional computer vision methods, such as Support Vector Machines and Random Forests, rely on pre-designed features like the Histogram of Oriented Gradients. While the existing techniques offer some performance, it face significant challenges in dynamic environments, especially under varying lighting conditions, occlusions, and diverse pedestrian poses. Traditional models also require extensive manual feature engineering and tuning, which limits scalability and adaptability. Recent advances in deep learning, particularly with Convolutional Neural Networks (CNNs), have allowed for automatic feature learning, yielding practical improvements over classical methods. However, CNNs can struggle in low light and when facing adversarial attacks, as they mainly capture spatial features. To address these limitations, this research proposes a new methodology combining CNNs for spatial feature extraction with Bidirectional Long Short-Term Memory (BiLSTM) networks to capture temporal dependencies. This hybrid approach enhances detection capabilities by learning both static and dynamic patterns. Our proposed CNN-BiLSTM model achieves a 92 % detection accuracy and an F1 score of 88.5% across diverse conditions, outperforming existing models by up to 15%. By integrating temporal features, the model reduces missed detections and false positives by up to 20%, providing a more reliable and adaptable solution for pedestrian detection in autonomous vehicles. © 2025 IEEE.

关键词： Convolutional neural networks

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Classification of Dermoscopy Textures with an Ensemble Feedback of Multilayer Perceptron 1st

Classification of Dermoscopy Textures with an Ensemble Feedb...

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1st International Conference on Deep Sciences for Computing and Communications, IconDeepCom 2022

作者： Prabhu Chakkaravarthy, A. Saravanan, T.R. Udayakumar, Sridhar Subasini, C.A. Department of Networking and Communications School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur India Department of Computational Intelligence SRM Institute of Science and Technology Kattankulathur Campus Chennai India Department of Information Technology Mettu University Mettu Ethiopia St. Joseph’s Institute of Technology Old Mamallapuram Road Chennai India

ISBN: (纸本)9783031276217

Aim: To build a perfection classification of skin cancer dataset in the form of textured parameters. The input images are taken from ISIC 2019 dataset. The images are converted into textures like contrast, homogeneity, energy and correlation using GLCM feature extraction. Materials and method: Multilayer perceptron’s, or MLPs, can approximate any continuous function with a single hidden layer. They can be used in supervised learning to model the correlation of inputs and outputs between pairs of inputs and outputs. To optimize performance, models are trained to minimize error by adjusting their parameters, or weights and biases. Result: The input classification elements like contrast, correlation, energy and homogeneity are taken for classification, the initial with normal Multi-layer perceptron classification generates the sample input of 65.5% but the object is carried over ensemble classification again with Multi-layer perceptron generates 93.5% classification accuracy. Conclusion: On comparing the outcomes from the normal classification and the ensemble classification with Multi-Layer Perceptron, the ensemble model generates the maximum classification accuracy without breaking the sample of classification dataset. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Classification (of information)

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Automatic Hatching System by designing IoT-based Egg Incubator 3

Automatic Hatching System by designing IoT-based Egg Incubat...

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3rd International Conference on Smart Electronics and Communication, ICOSEC 2022

作者： Jaichandran, R. Shobana, R. Mohamed Tharick, K. Raja, L. Anandaram, Harishchander Vijaipriya, K. AarupadaiVeedu Institute of Technology Department of Computer Science and Engineering Tamil Nadu Paiyanoor603104 India Sri Eshwar College of Engineering Department of Electronics and Communication Engineering Tamil Nadu Coimbatore641202 India Centre for Excellence in Computational Engineering and Networking Amrita VishwaVidyapeetham Tamil Nadu Coimbatore641112 India Sri SaiRanganathan Engineering College Department of Electronics and Communication Engineering Tamil Nadu Thondamuthur641109 India

ISBN: (数字)9781665497640

ISBN: (纸本)9781665497640

Poultry is the kind of bird farming in which birds like geese, ducks, chickens, and turkeys are domesticated to produce eggs and meat for the food. Of these birds, chickens are the most consumed bird by the people. The chickens which are grown up for eggs are called layers and chickens for meat are known as broilers. Hatching eggs naturally by the hens is considered a big problem for poultry farmers. Hatching the eggs fast by using the technology can produce more chicks. To hatch the eggs by technology, a hatching incubator is implemented by using the Internet of Things (IoT), temperature sensor, humidity sensor, NodeMCU, servo motor, and Blynk cloud. Egg hatching manually by hens takes approximately 20 to 21 days. An automatic hatching system will reduce the number of days required for hatching. The cloud displays the data of the incubator like temperature, degree of rotation of egg tray, humidity, and the ON or OFF conditions of the light, fan, and humidifier. © 2022 IEEE.

关键词： Birds

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