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共 11827 条记录，以下是151-160 订阅

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intelligent Battery Management System for Electric Vehicle with IoT-based Monitoring, AI-Driven Multiple Fault Diagnosis and Energy optimization 3

Intelligent Battery Management System for Electric Vehicle w...

引用

3rd International conference on Electronics and Renewable Systems, ICEARS 2025

作者： Arun Kumar, P. Shalini, S. Sneha, R. Vasunthra, P. Suganyadevi, S. M Kumarasamy College of Engineering Department of Ece Karur India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509675

Battery related issues have a significant effect on both the performance and safety of electric vehicle (EV). Overheating Battery Lifespan is meant to be reduced by simply the dangers of overheating, as they can even cause thermal runaway, as well as from over and deep revolution. Besides, EV batteries have poor performance even in extreme temperatures, capacity loss, and internal defects. In order to cope with these problems, a proposed IoT based EV battery monitoring and fault detection system provides the real time analysis to promptly notify the battery health. Built in IoT sensors are integrated in this system which monitors critical parameters constantly i.e. temperature, voltage, current and charge levels. For 95-98% battery parameters monitoring accuracy, the collected data is transferred to a centralized cloud platform for real time cloud processing. The system utilizes advanced edge computing techniques and continuous monitoring capable of detecting a fault with 95-98% probability. Electrochemical processes are analyzed with 90-95% accuracy using predictive analytics and machine learning algorithms to ensure optimal battery performance. Moreover, the system achieves energy efficiency of more than 95% through power management and control mechanisms. In 95% of the cases, intelligent charging algorithms will prevent overcharging. This cutting-edge technology dramatically improves EV battery safety, longevity as well as efficiency, assuring reliability and sustainability. © 2025 IEEE.

关键词： Predictive analytics

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Machine learning Integrated Health Advisory Chatbot System 3

Machine Learning Integrated Health Advisory Chatbot System

引用

3rd International conference on Electronics and Renewable Systems, ICEARS 2025

作者： Jikar, Aman A. Mendhe, Samir Wardha India

ISBN: (纸本)9798331509675

The project aims to develop an intelligent health advisory system capable of predicting diseases based on user-provided symptoms. Utilizing a dataset-driven approach and advanced machine learning algorithms, the system integrates tools such as Decision Trees and Support Vector Machines (SVM) to deliver precise diagnoses. It employs labelled training datasets for symptom-prognosis mapping, enabling real-time identification of potential health conditions. The application further enhances user interaction with a conversational chatbot interface, guiding users through symptom entry, severity assessment, and personalized precautionary advice. Additionally, the model incorporates severity and description dictionaries to refine its predictive capabilities. This innovation is designed to empower individuals by providing early disease detection and advisory support, bridging gaps in access to immediate healthcare. © 2025 IEEE.

关键词： Random forests

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intelligent Classification and Sorting optimization of Transported Items Combined with BiLSTM Model 3

Intelligent Classification and Sorting Optimization of Trans...

引用

3rd International conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： Fu, Guodong Hou, Huijuan Shi, Sansheng Song, Shanshan Marketing Service Center State Grid Henan Electric Power Company Henan Zhengzhou China

ISBN: (纸本)9798331508456

In view of the low efficiency and large error of the traditional method of transport item classification and sorting, this paper proposes an optimization method based on the bidirectional long short-term memory network (BiLSTM) model. First, the multi-dimensional feature data of the items, including size, weight, shape, material, and historical location information, are collected to construct a comprehensive feature vector. Then, the BiLSTM model is used to train the time series data, and the time dependency is captured through forward and backward propagation. Finally, the proximal policy optimization (PPO) strategy is combined to optimize the transportation path and sequence to improve the system adaptability. This method can significantly reduce the transportation time (such as from 38.87 minutes to 18.58 minutes in Experiment 1) and the path error (from 7.16 meters to 1.13 meters in Experiment 3). This study provides an efficient and reliable optimization solution for intelligent warehousing systems. © 2025 IEEE.

关键词： Travel time

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intelligent Question Generator System using Natural Language Processing 3

Intelligent Question Generator System using Natural Language...

引用

3rd International conference on intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Sangeetha, V. Pani, Ishwariya Sharma, Ankit Vishwanatha Kanchana, C. Siddarth, R. Srikar Ramaiah Institute of Technology Department of Cse Bangalore India Ramaiah Institute of Technology Department of Iem Bangalore India

ISBN: (纸本)9798331515911

Question generation system in most educational institutes is not automated. There are various methods in which objective and subjective question generation is existing, but not those algorithms are not very accurate. Automation of question generation systems can assist learners to discover the levels of their expertise and increase their knowledge by helping them in clarifying their queries. It can also be widely used in various other fields such as for placement activities. In this paper, the proposed model main objective is to design and implement an automated question generator using Natural Language Processing(NLP) techniques. The proposed system will use T5 transformer and Bidirectional Encoder Representations from Transformers (BERT) technique. The performance of the automated question generator is evaluated using accuracy, relevance, and fluency of the generated questions. The results of the system will Incorporate user feedback to improve the performance and effectiveness of the question generator. © 2025 IEEE.

关键词： Adversarial machine learning

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Performance optimization in Communication Systems Using Deep Reinforcement learning with Elite Reverse learning Strategy - Arctic Puffin optimization 3

Performance Optimization in Communication Systems Using Deep...

引用

3rd International conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： Zhang, Jiaxiao Xidian University Xi'an China

ISBN: (纸本)9798331508456

The communication system involved in the various networking it becomes decentralized and automatic, network articles is to make decisions for increasing network performance in uncertainty network environment. The challenges of handle the large-scale network it making complex, state and action spaces are generally huge and reinforcement learning unable to identify optimum policy in reasonable time. In this research, Deep Reinforcement learning with Elite Reverse learning Strategy - Arctic Puffin optimization (DRL with ERL-APO) is developed for the effective performance optimization in communication. The strategy of reverse learning strategy is incorporated with traditional APO algorithm which improves the convergence rate of traditional APO algorithm for efficient communication performance optimization. For ensuring the high rate for less channel gained the users who needs much energy in terms of deprived energy share. Therefore, outcomes on substantial minimize in sum rate. The proposed DRL-APO with ELS obtained execution time of 8 on 10 SNR, 9 of 20 SNR, 10 of 30 SNR, 11 of 40 SNR and 12 of 50 SNR which is better while compared with existing algorithms. © 2025 IEEE.

关键词： Signal systems

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Fusion learning Framework for Malignant and Benign Classification in Histopathology Images 3

Fusion Learning Framework for Malignant and Benign Classific...

引用

3rd International conference on intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Subramaniyam, P. Hari Krishnan, G. Department of ECE School of Engineering Mohan Babu University Tirupati India Department of EEE School of Engineering Mohan Babu University Tirupati India

ISBN: (纸本)9798331527549

This work presents a robust ensemble-based deep learning approach for classifying breast cancer histopathology images from the BreakHis dataset into Malignant and Benign categories. This study implements three ensemble strategies efficientNetB0 and ResNet50, EfficientNetB0 and VGG16, and VGG16 and ResNet50—the study highlights the potential of combining complementary deep learning architectures for improved classification performance. Each ensemble incorporates modifications to optimize feature extraction and generalization for the specific dataset, including dense layers, dropout, and feature concatenation techniques. The proposed methodology achieves high accuracy, precision, recall, and F1-scores across all methods, with the EfficientNetB0 and VGG16 ensemble demonstrating superior performance. Comparative analysis with state-of-the-art methods with robustness and adaptability of the proposed approach, offering a scalable and efficient framework for medical image classification. This work provides valuable insights into the application of ensemble learning for reliable cancer diagnosis. © 2025 IEEE.

关键词： Contrastive learning

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intelligent Hotel Recommendation System Using Dynamic Graph learning 3

Intelligent Hotel Recommendation System Using Dynamic Graph ...

引用

3rd International conference on intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Kiruthiga, G. Kamalesh, S. Jegadeesan, S. Manojkumar, S. Kavinesh, K.K. Ruban, A.S. Karpagam College of Engineering Department of Artificial Intelligence And Data Science Coimbatore India Velammal College of Engineering and Technology Department of Information Technology Tamilnadu Madurai India

ISBN: (数字)9798331515911

ISBN: (纸本)9798331515911

Inherently, the framework of Inn Suggestion presents an entirely hybrid approach to making hotel recommendations. The framework encompasses content-based and collaborative filtering techniques into a single system by analyzing user behaviors, including past bookings and reviews, besides features, which are generally associated with hotels, such as location and price, to get relevant suggestions. At the heart of the framework lies the Adaptive Meta-learning Hybrid algorithm that coordinates several machine learning techniques, like RNN, CNN, KNN, and Node2Vec. This meta-learning layer can recalculate the selection of algorithms dynamically based on performance in targeted subsets of users. The feature affords rapid adaptation to shifts in the user behavior and underlying data distributions, thus significantly enhancing both accuracy and diversity of recommendations over time. Key features include real-time updates, an intuitive user interface, and a robust feedback system. The technologies which architecture is based on including ***, Python, ***, and cloud services, with which scalability and security are the essence of their execution. Introducing these methodologies with AMLH, the Brilliantly Inn Suggestion Framework aims to change the entire experience of booking hotels. Which means a more dynamic personalized experience through booking in respect of each user's needs and overall satisfaction toward travel planning. © 2025 IEEE.

关键词： Collaborative filtering

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Online intelligent dispatching strategy of housekeeping order based on multi-agent reinforcement learning 24

Online intelligent dispatching strategy of housekeeping orde...

引用

3rd International conference on Artificial Intelligence and Education, ICAIE 2024

作者： Hou, Guoqiang College of Intelligent Science and Engineering Harbin Engineering University Heilongjiang Harbin China

ISBN: (纸本)9798400712692

With the rise of internet services and higher living standards, online home services are booming. Online housekeeping order dispatch is often modeled as Multi-Depot Vehicle Routing with Time Windows (MDVRPTW), yet existing solutions are mostly offline and lack real-world optimization considerations. This paper formulates the online order-aunt matching problem as a dynamic mixed-integer programming model, incorporating service score, average travel time, and distance per order. It introduces ML2CO, an online matching strategy using multi-agent reinforcement learning (MARL). MARL elements, including reward mechanisms, state transition, and actions, are designed based on service state and location. The strategy uses a masked pointer network and is trained with the parameter-sharing MADDPG algorithm to enhance performance. ML2CO's generalization and effectiveness are validated through data analysis and experiments on "58 Home"platform data. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Reinforcement learning

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Design of a Real-Time Crime Monitoring System using Machine learning 3

Design of a Real-Time Crime Monitoring System using Machine ...

引用

3rd International conference on intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Krishnaveni, A. Manikandan, C. Department ofComputer Science Karpagam Academy of Higher Education Coimbatore India

ISBN: (纸本)9798331527549

Crime in public areas poses risks to individuals and society at large. Traditional crime prevention methods face significant hurdles in detecting and tracking crime. Furthermore, the influx of large volumes of data overwhelms security systems and personnel making security enforcement harder. However, technological innovations have positively influenced different fields, including public safety and crime prevention. This study introduces an innovative system for crime prediction and analysis using machine learning, designed to assist law enforcement in identifying criminals in public spaces. The system enables authorities to input detailed information about criminals, including facial data, which is used to train an advanced recognition model. The system’s ability to provide accurate and timely alerts enhances the efficiency of law enforcement operations. This framework leverages state-of-the-art face recognition technology to offer a scalable and robust solution for crime prevention. By integrating machine learning algorithms with intelligent surveillance systems, the proposed approach addresses the growing need for proactive measures to ensure public safety. Further, this system also highlights the potential of technology to transform traditional crime detection methods, paving the way for smarter and more responsive law enforcement practices. Finally, the proposed system attained a 92% face detection accuracy score which was higher than existing approaches. © 2025 IEEE.

关键词： Crime prediction exact location facial data machine learning public surveillance recognition model

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Strategic Innovations in Network Security: A Machine learning Approach to Detect and Mitigate Backdoor Attacks 3

Strategic Innovations in Network Security: A Machine Learnin...

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3rd International conference on intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Khan, Aadil Gupta, Deepali Dutta, Monica Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798331515911

A backdoor attack is a malicious act in which a hacker takes advantage of holes in the system to enter without authorization. This requires changing the network and authentication processes in order to insert malicious code and provide an entry point for unwanted activities. Early detection of backdoor attacks is essential to preventing significant damage. Proactive defence searches for irregularities in network traffic before dubious behaviours become widespread. Prompt innovation boosts the robustness of a cybersecurity system, protecting information and infrastructure against illegal access and possible breaches. This paper emphasizes proactive security measures and offers a methodical approach to identifying backdoor attacks. Strong early detection might be achieved at the network-database interface with ensemble machine learning. This helps to ensure that SQL queries are corrected correctly and prevents unwanted access. Our work improves cybersecurity resilience by deepening our knowledge of how to successfully defend against backdoor attacks. Producing datasets for machine learning algorithms of superior quality is essential to the backdoor incursion detection procedure. The variety of conditions it offers makes accurate testing of detecting systems possible. The use of ensemble machine learning on datasets like UNSW-NB15 improves the accuracy of backdoor attack detection, hence supporting the development of an all-encompassing and efficient cybersecurity plan. © 2025 IEEE.

关键词： Adversarial machine learning

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