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检索条件"任意字段=3rd International Conference on Machine Learning and Data Engineering, ICMLDE 2024"

共 6841 条记录，以下是121-130 订阅

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Stability Enhancement for PV and Wind Penetrated Power System Using machine learning Prediction 3

Stability Enhancement for PV and Wind Penetrated Power Syste...

引用

3rd IEEE international conference on Power Electronics, Intelligent Control and Energy Systems, ICPEICES 2024

作者： Sivaraman, P.R. Sivakumar, P. Amirthavarshini, A. Ashika, P.G. Hemanth Kumar, R. Jagan Balaji, T. Rajalakshmi Engineering College Department of Electrical and Electronics Engineering Chennai India

ISBN: (数字)9798350387193

ISBN: (纸本)9798350387193

In modern power systems, the integration of distributed generation (DG) has introduced both opportunities and challenges. It has also gained significant attention due to its potential benefits in terms of renewable energy utilization, improved system reliability and reduced transmission losses. As PV and wind are intermittent variables of the power system, stability problems arise which can be recovered using conventional PID controllers through the advanced tuning methods which incorporate the AI based tuning parameter. The process of tuning of the parameter is approached through proper artificial intelligence based trained data. This approach will enhance the power angle stability of the existing system. © 2024 IEEE.

关键词： Adversarial machine learning

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Improving the accuracy of pine wilt disease detection through the integration of multispectral imaging and machine learning 3

Improving the accuracy of pine wilt disease detection throug...

引用

3rd international conference on Image Processing and Media Computing (ICIPMC)

作者： Chu, Shiwei Bao, Wenxia Anhui Univ Sch Elect Informat Engn Hefei Peoples R China

ISBN: (纸本)9798350386660;9798350386677

In this study, the accuracy of pine wood nematode detection was significantly improved by combining a UAV-mounted multispectral imaging system with machine learning algorithms, particularly the deep learning models SSD and YOLO v5. The study employed image preprocessing techniques, such as orthorectification and atmospheric correction, to ensure the accuracy and consistency of the data. On this basis, subtle differences in the health status of pine trees were revealed by analysing the spectral features extracted from the multispectral images. Traditional algorithms such as random forest and support vector machine, as well as deep learning models were used for classification and recognition, in which SSD and YOLO v5 demonstrated high accuracy in processing complex image data, reaching 0.92 and 0.93, respectively. This achievement not only improves the detection efficiency of pine wilt disease, but also provides a new technological pathway for forestry health management, which has an early diagnosis and treatment of the disease. important significance for early disease diagnosis and treatment.

关键词： Multispectral imaging machine learning pine wilt disease

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Optimizing Diabetes Prediction Accuracy: A Comprehensive Approach with Advanced Preprocessing and Diverse machine learning Classifiers 3

Optimizing Diabetes Prediction Accuracy: A Comprehensive App...

引用

3rd international conference on Advancement in Electrical and Electronic engineering, ICAEEE 2024

作者： Talukder, Md Simul Hasan Nur, Abdullah Hafez Zaman, Snigdha Noor, Moshiur Rahman Khan, Mohammad Aman Ullah Amir, Fatima Bangladesh Atomic Energy Regulatory Authority Nuclear Safety Security and Safeguards Divison Dhaka Bangladesh International Islamic University Chittagong Department of Electronic and Telecommunication Engineering Chittagong Bangladesh International Islamic University Chittagong Department of Computer Science and Engineering Chittagong Bangladesh Eastern University Department of Electrical and Electronic Engineering Dhaka Bangladesh BRAC University Department of Computer Science and Engineering Dhaka Bangladesh Shanto Mariam University of Creative Technology Department of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350388282

Diabetes is a prevalent chronic health disease affecting millions of people over the world. Early detection and effective management contribute in preventing its complications. This study investigates the use of machine learning algorithms to predict the risk of acquiring diabetes based on multiple clinical parameters (Indian PIMA dataset). In the study, advanced data preprocessing techniques like missing data handling, outlier rejection, robust scaling and SOMTE oversampling are consolidated with eight traditional machine learning. Effectiveness of those eight models such as Gradient Boosting (GB), XGBoost (EGB), Random Forest (RF), Light GBM (LGBM), Cat Boost (CB), Ada Boost (AB), Decision Tree (DT) and K-Nearest Neighbor (KNN) are analyzed and compared. The prediction outcomes of each model are contrasted using accuracy, roc score and F1 score. After evaluating the model's performances, GB has provided the highest accuracy of 90.25%, ROC score of 96.38% and F1 score of 86.48%. © 2024 IEEE.

关键词： Forecasting

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Federated Client Selection and Attention-Based Aggregation Algorithm on Non-IID data 3

Federated Client Selection and Attention-Based Aggregation A...

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3rd international conference on Cloud Computing, Big data Application and Software engineering, CBASE 2024

作者： Shan, Ziqiang University of Science and Technology of China School of Software Engineering Hefei China

ISBN: (纸本)9798331506582

Federated learning is a decentralized machine learning approach that enables multiple devices to collaboratively train a global model while keeping their data localized, thus preserving user privacy. However, the presence of non-independent and identically distributed (Non-IID) data across clients presents significant challenges, including performance degradation and difficulties in model optimization. This paper proposes a novel Federated Client Selection and Attention Aggregation (FCSA) algorithm, designed to address these issues. FCSA improves client selection by considering factors such as training time, data diversity, and historical performance, thereby prioritizing clients that can contribute the most to the global model. On the server side, an attention-based aggregation mechanism is introduced to assign different weights to client updates based on their contributions, optimizing the model aggregation process. Experimental evaluations conducted on the MNIST dataset under Non-IID conditions demonstrate that FCSA outperforms traditional methods like FedAvg, improving model accuracy by approximately 17.5%. Future work will focus on dynamically adjusting weighting strategies to further enhance efficiency across various training stages. © 2024 IEEE.

关键词： Federated learning

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Multilevel Product Classification Using machine learning Algorithms Sowjanya 3

Multilevel Product Classification Using Machine Learning Alg...

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3rd international conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Sowjanya Arjunan, R. Vijaya Babu, D. Vijendra Manipal Institute of Technology Manipal Academy of Higher Education Computer Science and Engineering Karnataka Manipal India Vellore Institute of Technology School of Electronics Engineering Tamil Nadu Vellore India

ISBN: (纸本)9798350372120

Platforms for e-commerce commonly employ a preestablished, organized structure of product classifications. Traditional product categorization methods have primarily relied on text-based data, which can occasionally fall short of providing a comprehensive understanding of a product's characteristics. However, employing image data in classification models has several advantages. The pictures offer a wealth of visual knowledge by capturing details such as color, shape, texture, and size that text explanations may miss. By incorporating text and image data, the classifier gains greater knowledge of each product, resulting in higher categorization reliability. Furthermore, the inclusion of image data enables the machine learning model to discover aesthetic patterns and features that are tricky to convey through text alone, expanding its ability to precisely assign items to their corresponding categories. The current study validates a new classifier for predicting categories using both textual and image data. This suggested approach is being attempted to enhance the accuracy and fullness of product classification. © 2024 IEEE.

关键词： Image classification

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CautionSuicide: A Deep learning Based Approach for Detecting Suicidal Ideation in Real Time Chatbot Conversation 3

CautionSuicide: A Deep Learning Based Approach for Detecting...

引用

3rd international conference on Computing and machine Intelligence (ICMI)

作者： Elsayed, Nelly ElSayed, Zag Ozer, Murat Univ Cincinnati Sch Informat Technol Cincinnati OH 45221 USA

ISBN: (纸本)9798350372977;9798350372984

Suicide is recognized as one of the most serious concerns in the modern society. Suicide causes tragedy that affects countries, communities, and families. There are many factors that lead to suicidal ideations. Early detection of suicidal ideations can help to prevent suicide occurrence by providing the victim with the required professional support, especially when the victim does not recognize the danger of having suicidal ideations. As technology usage has increased, people share and express their ideations digitally via social media, chatbots, and other digital platforms. In this paper, we proposed a novel, simple deep learning-based model to detect suicidal ideations in digital content, mainly focusing on chatbots as the primary data source. In addition, we provide a framework that employs the proposed suicide detection integration with a chatbot-based support system.

关键词： Suicide deep learning chatbot natural language processing detection

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Design and Application of Public Risk Perception Early Warning System Based on machine learning Algorithm 24

Design and Application of Public Risk Perception Early Warni...

引用

3rd international conference on Cyber Security, Artificial Intelligence and Digital Economy (CSAIDE)

作者： Xu, Binghui Taizhou Vocat & Tech Coll Taizhou 318000 Zhejiang Peoples R China

ISBN: (纸本)9798400718212

As a negative event with group effect, public risk may be caused by a variety of reasons, but the final result is basically social or environmental turbulence, chaos, or even destruction. In order to avoid this phenomenon, it is necessary to design a public risk perception early warning system. In this paper, machine learning algorithms are used to give the perception and warning system the learning ability, so as to strengthen its perception and warning ability of risks. In addition, this paper also puts forward the perspective of using big data to analyze Internet text information, so as to strengthen the system's assessment ability of risks. Finally, the system based on the proposed method was compared with the conventional system in this paper. The average accuracy of the experimental group was 92.33%, while the average accuracy of the control group was 86.52%. The experimental results show that the proposed method can improve the performance of the risk perception early warning system, and also reflect the role of machine learning algorithms.

关键词： machine learning Algorithm Public Risk Perception Early Warning System Big data Analysis

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A machine learning based approach for Bird Migration Detection and feature analysis with SHAP 3

A Machine Learning based approach for Bird Migration Detecti...

引用

3rd IEEE North Karnataka Subsection Flagship international conference, NKCon 2024

作者： Kudari, Medha Nandeppanavar, Anupama S. Thotad, Puneeth Jatti, Sahana Koti, Sahana Hubballi India

ISBN: (纸本)9798350364569

Bird migration is vital for preserving ecological balance and biodiversity. Tracking migration patterns is very important to understand ecosystem health and predicting environmental changes. The use of machine learning methods helps analyze and predict the migration patterns of birds. In-depth evaluations of machine learning methods used in bird migration detection are provided in this research, which also discusses different models for assessing bird movement data, including Convolutional Neural Networks, K-nearest neighbors, Support Vector machines, Random Forests, and Decision Trees. © 2024 IEEE.

关键词： Adversarial machine learning

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A Comparative Analysis of Asthma Outcome Prediction with Improved Accuracy using Linear Discriminant Classifier and Narrow Neural Network Classifier 3

A Comparative Analysis of Asthma Outcome Prediction with Imp...

引用

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

作者： Viswanathan, C. Sivakumar, Vidhya Lakshmi Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Biomedical Engineering Chennai India Saveetha School of Engineering Chennai India

ISBN: (纸本)9798331509675

There are significant regional and national variations in the prevalence of childhood asthma and related risk factors. This study uses data from a prospective study with 202 children, both with and without asthma, to build machine learning models to predict childhood asthma. Narrow neural network classifiers and linear discriminant are used in the analysis. For this study, Kaggle datasets were used. The normal and abnormal values of (n=20), Excel datasets are used for the machine learning algorithm. MATLAB 2021 is used to obtain the machine learning algorithm for the normal and abnormal data sets. The linear discriminant and narrow neural network machine learning algorithm are used for the normal and abnormal datasets. The statistical analysis was done by using the latest version of IBM SPSS 22. There was a statistically significant difference in machine learning algorithms (p=0.001, p © 2025 IEEE.

关键词： Contrastive learning

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Research on a Bridge Health Monitoring System Based on machine learning 24

Research on a Bridge Health Monitoring System Based on Machi...

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3rd international conference on Algorithms, data Mining, and Information Technology, ADMIT 2024

作者： Zhang, DaYong Changjiang Survey Planning Design and Research Limited Company Chengdu China

ISBN: (纸本)9798400718120

This study designs and implements a bridge health monitoring system based on machine learning. The system adopts a four-layer architecture, including data acquisition, processing, analysis and decision-making, and user interaction layers. Core functions encompass data preprocessing, feature extraction, model training, health assessment, and early warning. It integrates algorithms such as Support Vector machines, Random Forests, and Deep Neural Networks to enhance the accuracy of damage identification and status prediction. The development utilizes modern tools to improve efficiency. Test results show that the system is stable, responsive, and has strong processing capabilities. This system provides an intelligent solution for bridge health monitoring, improving the automation level and management efficiency of monitoring, and offers strong support for bridge safety management. © 2024 Copyright held by the owner/author(s).

关键词： Damage detection

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