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检索条件"任意字段=3rd International Conference on Intelligent Data Engineering and Automated Learning"

共 20248 条记录，以下是141-150 订阅

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Alzheimer's Disease Prediction using VGG16 and Transfer learning 3

Alzheimer's Disease Prediction using VGG16 and Transfer Lear...

引用

3rd international conference on intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Uprit, Maharth Monish Khattar, Pranav Brahmbhatt, Malhar Preethiya, T. Department of Networking and Communications SRM Institute of Science and Technology Kattankulathur Chennai India

ISBN: (纸本)9798331527549

Alzheimer’s Disease (AD) prediction is essential for early detection and effective treatment. In this paper, a hybrid deep learning model has been proposed for predicting Alzheimer’s Disease in a patient in their early years. The dataset consists of Magnetic Resonance Imaging (MRI) scans that have four classes namely, non-demented, very mild demented, mildly demented, and moderately demented. In this hybrid model, fine-tuned VGG16 and transfer learning models were used with hyperparameter tuning to adapt it to Alzheimer's Disease prediction. The simulation results show that the proposed hybrid model outperforms the existing VGG16 model with an accuracy of 97.23% © 2025 IEEE.

关键词： Prediction models

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A Comprehensive Survey of IoT Applications in Remote Patient Monitoring, Chronic Disease Management, and Smart Healthcare Infrastructure 3

A Comprehensive Survey of IoT Applications in Remote Patient...

引用

3rd international conference on Sentiment Analysis and Deep learning, ICSADL 2024

作者： Kavitha, V.P. Theivanathan, G. Magesh, V. Jayandhi, G. SRM Institute of Science and Technology Department of ECE Chennai India Velammal Engineering College Department of ECE Chennai India Saveetha School of Engineering Saveetha University Department of ECE Chennai India

ISBN: (纸本)9798350396157

This extensive study explores the wide range of Internet of Things (IoT) applications in the areas of remote patient monitoring, chronic disease management, and smart healthcare infrastructure. The review examines the changing role of IoT technology in revolutionizing healthcare paradigms and enhancing patient outcomes. The remote patient monitoring area explores the incorporation of wearable devices and sensors to track health parameters in real-time, promoting continuous and proactive healthcare management. The study investigates the utilization of Internet of Things (IoT) solutions, such as intelligent drug dispensers and interconnected inhalers, to improve adherence to treatment and maximize therapeutic interventions in the management of chronic diseases. The research delves into the advancement of intelligent healthcare infrastructure by integrating hospital equipment and leveraging IoT technology to optimize healthcare workflows. The focus is on the integration of data analytics, machine learning, and artificial intelligence to extract practical insights from the data produced by IoT-enabled healthcare systems. © 2024 IEEE.

关键词： Internet of things

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Construction Method of intelligent Analysis Rule Library for Monitoring Information Under Power Supply Reliability 3

Construction Method of Intelligent Analysis Rule Library for...

引用

3rd IEEE international conference on Electrical engineering, Big data and Algorithms, EEBDA 2024

作者： Xie, Jinlong Song, Youle He, Bo Yunnan Power Grid Co. Ltd. Yuxi Xinping Power Supply Bureau Yunnan Yuxi China Yunnan Power Grid Co. Ltd. Electric Power Science Research Institute Yunnan Kunming China Yunnan Power Grid Co. Ltd. Production and Operation Monitoring Center Yunnan Kunming China

ISBN: (纸本)9798350380989

In order to build a real-time intelligent analysis rule base of distribution network monitoring information, a construction method of intelligent analysis rule base of distribution network monitoring information based on machine learning is proposed. All the monitoring rule heads in the rule base are sorted and set as the main chain, and the rules are imported into the linked list to generate the rule set, so as to ensure that each monitoring information packet has an analysis rule;the machine learning based fault data classification method of distribution network is used to identify the fault data in the monitoring information of distribution network, and extract the frequent item sets of fault data. The parallel incremental updating algorithm of association rules based on MapReduce is used to update the information intelligent analysis rules in the analysis rule based, so as to ensure the real-time performance of the information intelligent analysis rules in the analysis rule base. The experimental results show that the accuracy and the average detection rate of the proposed method are greater than 0.97, and the false positive rate is 0.01. The proposed method can identify the real-time fault information of the distribution network monitoring system in time, and ensure the real-time updating of information intelligent analysis rules. © 2024 IEEE.

关键词： association rule updating distribution network intelligent analysis machine learning monitoring information rule base

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Recyclable and Non-Recyclable Waste Recognition using Image Processing with Machine learning: Recycle Mate 3

Recyclable and Non-Recyclable Waste Recognition using Image ...

引用

3rd IEEE international conference on intelligent Technologies, CONIT 2023

作者： Nara, Amit Panchal, Smit Dange, Prof. Suchita A.P. Shah Institute of Technology Department of Computer Engineering Thane India

ISBN: (纸本)9798350338607

Waste Management is the effort needed to make our world a better place. As solid waste may cause a problem for our surroundings. Nowadays many people don't even know how to distinguish between recyclable and non-recyclable objects, we know only some general things which are recyclable like paper, plastic, cardboard and drinking cans. To deal with this problem a deep machine-learning Convolution Neural Network (CNN) was created to help us all with an achievable waste management task. The automated task works for everyone as it lets down the burden of work from many people's shoulders. Our model is built using the Keras API. The model categorizes the data into a binary class. It has been tested with many objects which gave an acceptable prediction accuracy. © 2023 IEEE.

关键词： Waste management

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An AI-driven Approach for Tailoring Java Programming Instructions 3

An AI-driven Approach for Tailoring Java Programming Instruc...

引用

3rd international conference on intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Thiyagarajan, Gomathi Vijayalakshmi, V. Department of Computer Applications CMR Institute of Technology Bengaluru India Department of Data Science and Business Systems SRM Institute of Science and Technology Tamil Nadu Kattankulathur603203 India

ISBN: (纸本)9798331527549

AI and virtual assistants are transforming higher education by using digital tools to enhance teaching and learning in ways that go beyond traditional methods. These digital tools are not merely supplementary aids but are increasingly gaining popularity in educationalsetting offering innovative approaches that transcend conventional methods. In this research we introduce ByteBuddy a virtual assistant which assists instructors on Java ***'s framework is designed in a way to integrate easily into educational context. This study reveals the architecture of ByteBuddy, the process model that captured the behavior of students in the learning environment and the observation of its application in java instructions. By applying this study, we provide profound insights on how the effective use of ByteBuddy supplements the teaching practices and measure learning outcomes. © 2025 IEEE.

关键词： Java programming language

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Advancing Fake News Detection: From Traditional Machine learning to Transformer-based Approaches 3

Advancing Fake News Detection: From Traditional Machine Lear...

引用

3rd international conference on intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Abhinand, I.V. Ajal Krishna, K.P. Krishna, Ajay Jayadev, J. Reji Rahmath, K. Divya, T. Dept. of Computer Science and Engineering Sreepathy Institute of Management and Technology Vavannur A. P. J. Abdul Kalam Technological University kerala Thiruvananthapuram India

ISBN: (纸本)9798331527549

Fake news on online platforms is spreading at a rapid rate, posing great threats to social, political, and economic stability in linguistically diverse regions like Kerala, India. This paper reviews comprehensively the techniques of fake news detection from traditional machine learning methods to transformer-based models. Special attention is given to the detection of fake news in regional languages like Malayalam, characterized by complex morphology and syntax. The study investigates applications of ensemble-based learning as well as state-of-the-art transformer models, BERT, and XLNet, focusing on their ability to capture nuanced linguistic features and contextual relationships. Evaluation results show how significantly better the transformer-based models fare than traditional methods with respect to accuracy and robustness. This review also captures current limitations - such as resource constraints for regional languages - as well as future opportunities in developing scalable, multilingual fake news detection systems. © 2025 IEEE.

关键词： Adversarial machine learning

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Lung Diseases Detection Based on ResNet and Deep learning Method 3

Lung Diseases Detection Based on ResNet and Deep Learning Me...

引用

3rd international conference on Computer, Control and Robotics, ICCCR 2023

作者： Zhang, Yiqian Ding, Yan Zhang, Yifei School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9781665492126

Usually, a doctor can judge whether a patient has lung disease by X-ray. With the spread of the COVID-19 virus, the number of patients continues to increase while manual work increases. Thus, it's a challenge now to detect what kind of lung diseases the patient has with radiography., in an accurate and fast way. With the development of deep learning and the emergence of CNN (Convolutional Neural Network), the devices equipped with neural network can become more intelligent and automated. In this article, we adopted ResNet to be our network, and used 3 data augmentation methods and data regularization methods to process the data and get higher accuracy. In this article, we reached 93.39% accuracy based on the experiments. © 2023 IEEE.

关键词： COVID-19

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Credit Card Fraud Detection(CFD) using GNN and Ensemble Techniques 3

Credit Card Fraud Detection(CFD) using GNN and Ensemble Tech...

引用

3rd international conference on intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Vinay, B.V.S.N.S. Radha, D. Kirthika Devi, V.S. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidhyapeetham Bengaluru India Department of Electrical and Electronics Engineering Amrita School of Engineering Amrita Vishwa Vidhyapeetham Bengaluru India

ISBN: (纸本)9798331527549

Credit card fraud is an essential problem in the economic industry;thus, its detection is solved with the help of the developed methods in order to minimize the overall loses and to improve the confidence of clients. Towards this end, this paper presents a novel framework that applies GNNs in combination with ensemble learning to accurately identify credit card fraud. GNNs are used for cardinality, representing various relationships between transactions, cardholders, and merchants where complicated types of forgery are identified and are beyond the capability of conventional approaches. Ensemble learning further improves the accuracy of the system in order to reduce high false positive rates by using multiple models to solve the problem of imbalanced classes that are characteristic of the COVID-19 data. The effectiveness of the proposed framework is thus compared with several state-of-the-art malicious account detection techniques and various highly relevant real credit card transaction datasets show the proposed framework attains better precision, recall, F1-score, scalability. Thus, this strategy is much more suitable for ongoing and unspecific problem addressing in an unobstructed and ambiguous environment, such as credit card fraud. © 2025 IEEE.

关键词： Crime

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Artificial Intelligence in Mobile Computing for English Language Testing: Redefining automated Language Assessment 3

Artificial Intelligence in Mobile Computing for English Lang...

引用

3rd international conference on intelligent Systems, Advanced Computing, and Communication, ISACC 2025

作者： Muththamizh Selvi, S.I. Senthamarai, T. Kumar, S. Karthik Sheik Hameed, N. Vijayakumar, S. Raj, I Infant B.S. Abdur Rahman Crescent Institute of Science and Technology Chennai India Chennai India Annamalai University Chidambaram India B. S Abdur Rahman Crescent Institute of Science and Technology Chennai India K. Ramakrishnan College of Engineering Department of CSE Tamil Nadu Trichy India

ISBN: (纸本)9798331523893

Artificial intelligence's (AI) quick development has created new opportunities in a number of domains, including education. Language evaluation is one such field that has experienced a great deal of innovation. The utilization of auto encoders to redefine automated language assessment is the focus of this work, which investigates the integration of artificial intelligence (AI) in mobile computing for English language evaluation. Traditional language assessment approaches are being challenged by AI's capability to offer real-time, scalable, and personalized evaluations. By leveraging autoencoders deep learning models optimized for unsupervised learning and feature extraction - the proposed system evaluates diverse linguistic attributes such as grammar, spelling, pronunciation, fluency, and comprehension. Autoencoders transform high-dimensional input data into low-dimensional latent spaces, enabling efficient assessment of nuanced language patterns. The methodology incorporates the LibriSpeech ASR Corpus for data preprocessing, feature extraction, and training. Key innovations include robust handling of diverse input types (text and audio), accurate reconstruction of linguistic features, and scalable evaluation of proficiency. Performance metrics such as accuracy, reconstruction error, and fluency scores demonstrate the model's superior learning and assessment capabilities. Compared to other deep learning methods like LSTMs, CNNs, and Transformers, the autoencoder-based approach excels in accuracy and adaptability, offering impartial and precise assessments. The study "concludes"with a discussion of the benefits and challenges of AI-driven automated testing, providing insights into its potential to revolutionize educational assessment and reduce the burden on instructors by automating routine evaluations © 2025 IEEE.

关键词： Deep learning

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Multilayered Asynchronous Consensus-Based Federated learning (MACoFL) 25th

Multilayered Asynchronous Consensus-Based Federated Learning...

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25th international conference on intelligent data engineering and automated learning

作者： Rebollo, Miguel Carrascosa, Carlos Univ Politec Val encia Valencian Res Inst Artificial Intelligence VRAIN Valencia Spain

ISBN: (纸本)9783031777370;9783031777387

Federated learning is one of the main research lines in the last years about distributed learning, where participating nodes share their models but maintain the privacy of the data used to learn such models. Consensus is a way of calculating a mean value between a set of agents using only information from the local neighbors. This paper presents a new approach based on Asynchronous Consensus, called Multi-layered Asynchronous Consensus-based Federated learning (MACoFL). It randomly chooses a neighbor and a layer from the neural model and interchanges it with him. This new algorithm is presented and tested using the MNIST dataset.

关键词： Federated learning multi-agent systems consensus

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