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

共 6844 条记录，以下是561-570 订阅

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Effective machine learning-Based Predictive Analytics for Loan Default Prediction in Banking Sector 3

Effective Machine Learning-Based Predictive Analytics for Lo...

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3rd international conference on Communication, Control, and Intelligent Systems, CCIS 2024

作者： Kumar Jain, Yogesh Mannepalli, Praveen Kumar Kaur, Kirat Maheshwari, Abhishek Singh, Jagriti Ranka, Monica Finance Dr.D.Y.Patil Vidyapeeth Centre for Online Learning Maharashtra Pune India Chandigarh University Department of Computer Science and Engineering Mohali India Chandigarh University Department of Computer Science and Engineering Mohali India University School of Commerce Finance and Accountancy Ghaziabad India Manipal University Jaipur India Dayananda Sagar College of Arts Science and Commerce Bengaluru India

ISBN: (纸本)9798331528201

These days, a crucial topic is the early prediction of loan failure in banks. Customer loan default may be predicted using a variety of statistical and AI classification methods. This paper introduces a machine learning-based predictive analytics model aimed at improving loan default prediction in the banking sector by leveraging open P2P loan data from Lending Club. To improve the precision and consistency of predictions, the model uses state-of-the-art ML methods, namely RF. By analyzing critical borrower information such as credit history, loan purpose, and debt status, the model identifies high-risk borrowers with greater precision. The study evaluates a performance of different ML models, including LR, DT, and SVM, but identifies RF as the most effective, demonstrating superior metrics with 89% accuracy, 99.5% sensitivity, and 80.3% specificity. These results highlight the model's ability to not only predict defaults accurately but also reduce false positives and negatives. © 2024 IEEE.

关键词： Prediction models

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Early-Stage Diabetes Risk Prediction using machine learning Techniques 3

Early-Stage Diabetes Risk Prediction using Machine Learning ...

引用

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

作者： Raj, Aparajita Jena, Junali Jasmine Ghosh, Debolina Jain, Sonal Sardar, Tanvir Habib Gourisaria, Mahendra Kumar School of Computer Engineering KIIT Deemed to be University Odisha Bhubaneswar India Department of Information Technology Manipal University Jaipur Rajasthan Jaipur India School of Economics and Commerce KIIT Deemed to be University Odisha Bhubaneshwar India CSE School of Engineering Dayanand Sagar University Karnataka Bangalore India

ISBN: (纸本)9798331527549

Diabetes is a chronic and metabolic disease caused by an increment in glucose level in the blood, which leads overtime to major serious damage to the heart and kidneys. Millions of people are affected by diabetes in the world. The main aim of this study is the prediction of the risk of diabetes at an early stage. The data set contains information on the symptoms of newly diagnosed diabetic patients, those who are at risk of developing diabetes. The data was collected through direct questionnaire's administer to patients at the hospital in Bangladesh. In this work, 10 supervised machine learning algorithms such as Random Forest (RF), Naive Bayes (NB), etc. are implemented by taking accuracy, precision, F1 score etc. as evaluation criteria. This diabetic risk prediction model has a higher accuracy of 99.12% with Random Forest and Gradient boosting than other models. © 2025 IEEE.

关键词： Self-supervised learning

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Alzheimer's Disease Detection using machine learning Models 3

Alzheimer's Disease Detection using Machine Learning Models

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3rd international conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2024

作者： Nayaki, A. Periya Arrchit Ramana, M.S. Prithivi Raj, S.D. Santhosh, L. Swathi, B. Abinesh, N. Thanabal, M.S. Velammal College of Engineering and Technology Department of Information Technology Madurai India Psna College of Engineering and Technology Department of Computer Science and Engineering Dindigul India

ISBN: (纸本)9798350369083

Alzheimer's disease is the most common cause of dementia in older persons, with its incidence rising rapidly. This neurodegenerative disorder affects brain function, impacting memory and cognition, and presents significant social, financial, and economic challenges. Early prediction of Alzheimer's disease is essential for effective treatment and reducing harm. This study utilizes machine learning algorithms to predict Alzheimer's disease using the "MRI and Alzheimer's"dataset from the Open Access Series of Imaging Studies (OASIS). The models evaluated include Logistic Regression, Support Vector machine, Decision Tree, Random Forest, AdaBoost, LightGBM, XGBRF Classifier, and a Voting Classifier. Their respective accuracies are 84.21 percent for Logistic Regression, 84.21 percent for Support Vector machine, 81.58 percent for Decision Tree, 94.74 percent for Random Forest, 94.74 percent for AdaBoost, 52.63 percent for LightGBM, 89.47 percent for XGBRF Classifier, and 98.17 percent for the Voting Classifier. The Voting Classifier yielded the highest accuracy, indicating its effectiveness in predicting Alzheimer's disease. © 2024 IEEE.

关键词： Random forests

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Research on Lightweight Unsafe State Recognition Algorithm Based on Dual-teacher Distillation Network 3

Research on Lightweight Unsafe State Recognition Algorithm B...

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

作者： Feng, Hao Zhang, Haochen Zhang, Chaoyu Hu, Peng Feng, Jiangzhe New Energy Engineering Technology Co. Ltd. Beijing China Center Xi'an China

ISBN: (纸本)9798331506582

This research is dedicated to the development of lightweight unsafe state recognition algorithms for new energy stations to optimize safety monitoring and real-time risk assessment. Given that traditional deep learning models are computationally redundant, have large parameters, and are not suitable for resource-constrained environments, we have constructed a lightweight CNN model by innovatively integrating integrating multi-scale modules and knowledge distillation techniques. The model accurately locates anomalies through multi-scale feature modules, and adopts a dual-teacher distillation strategy to reduce the number of parameters and storage requirements, ensuring recognition accuracy while realizing a lightweight model. This algorithm not only meets the needs of edge computing, but also follows the principle of data confidentiality, providing an efficient solution for the safety monitoring of new energy stations. © 2024 IEEE.

关键词： Deep learning

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A Study of Race Momentum Based on machine learning Methods and Pearson's Correlation Coefficient 3

A Study of Race Momentum Based on Machine Learning Methods a...

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3rd international conference on data Analytics, Computing and Artificial Intelligence, ICDACAI 2024

作者： Ma, Ruofei Hu, Mingjie Wang, Xiaoyang Beihang University Beijing China

ISBN: (纸本)9798331519032

In this study, we developed two models to deeply investigate and predict momentum effects in tennis matches. First, a momentum assessment system model was constructed to categorise momentum effects into past momentum, serving situations, psychological and physical factors, and to identify the effects through data processing and correlation analysis, defining the momentum function and applying it to the 2023 Wimbledon tournament;second, we carried out hypothesis testing to demonstrate the significant effects of momentum on match progression and player scoring. Additionally, we developed the Match Swing Prediction Model, which uses a machine learning approach to predict lead and swing points in a match, providing practical advice to players. By exploring the model's extension to women's tennis and table tennis matches, we found that the momentum effect for women's matches was slightly less significant than for men's matches, but that the model performed well in predicting women's matches. These findings provide useful insights into understanding and predicting momentum changes in tennis matches and provide practical references for players and coaches. © 2024 IEEE.

关键词： Prediction models

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Forecasting Bitcoin Price Trends: Integrated machine learning with Market Trends 3

Forecasting Bitcoin Price Trends: Integrated Machine Learnin...

引用

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

作者： Samir Ahamed, 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

Bitcoin price prediction remains a complicated task in financial markets since price fluctuations are characterized by high variability, non-linear and dependent on different trends. The common models of forecasting can be disruptive because they do not readily accommodate the breadth of the data and the dynamics of the marketplace. The paper presents a new approach to predicting Bitcoin price shifts based on state-of-the-art data structures and both machine learning and technical analysis. The formative historical price datasets and large scale features are organized and manipulated in an efficient manner with the aid of data structures as well as the temporal and non-linear trends in prices are captured by ARIMA,Exponential smoothing and Random. Also, trends of moving averages are used to boost the model’s competency to analyze the sentiments of the market. The proposed framework was put through a test on real world Bitcoin price data and it was shown to provide better results as compared to the traditional methods with better accuracy and visualization. Besides that, our method enhances the accuracy of the forecast, while the deployment of the method allows developing a realtime Bitcoin price prediction model that can be useful for traders and investors. The integration of machine learning and technical analysis gives a powerful and reliable tool to forecast the Bitcoins price trends. © 2025 IEEE.

关键词： Prediction models

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Decoding Tumor Gene Expression for Multiclass Cancer Classification 3

Decoding Tumor Gene Expression for Multiclass Cancer Classif...

引用

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

作者： Jyothi, V. Srujana, Medipally Himasreeja, Yelam Sanjana, Sevana Sowmya Fatima, Bushra Gokaraju Rangaraju Institute of Engineering and Technology Cse Department Nizampet Road Bachupally Kukatpally Telangana Hyderabad500090 India

ISBN: (纸本)9798331509675

Cancer, a multifactorial and complex disease, is caused by genetic mutations and environmental influences leading to uncontrolled cell proliferation and the possibility of tumor spread. Recent developments in genomic analysis with the use of next-generation sequencing technologies like Whole Genome Sequencing (WGS) and Whole Exome Sequencing (WES) have greatly enhanced detection and classification of cancer. machine learning (ML) has become a useful tool in the diagnosis of cancer, providing better accuracy in the classification of cancer types from high-level genomic and histopathological information. The following is a review of the existing machine learning methods used in cancer classification, comprising deep learning (DL) techniques, and addresses some of the major challenges like data imbalance, feature selection, and model interpretability. The paper also points towards the possibility of combining multi-modal data sources, federated learning, and explainable AI (XAI) to further improve cancer classification systems. In addition, the solution proposed in this review describes a machine learning-based framework for cancer classification, involving different ML algorithms, cross-validation methods, and sophisticated feature selection techniques for robust and reliable results. © 2025 IEEE.

关键词： Federated learning

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Study on the label generation model of power suppliers' bad behavior based on supervised machine learning 3

Study on the label generation model of power suppliers' bad ...

引用

2023 3rd international conference on Intelligent Traffic Systems and Smart City, ITSSC 2023

作者： Shen, Mengru Zhang, Hongchun Pi, Te Wang, Xinkai Xu, Nan Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport Beijing Jiaotong University Beijing China Beijing Jiaotong University Beijing China The University Of Sheffield Sheffield United Kingdom

ISBN: (纸本)9781510672963

The occurrence of bad behavior of power enterprise suppliers is serious, and it is difficult to obtain and reuse a large amount of effective information. This has caused a certain impact on the subsequent supplier evaluation work, especially for the work of the bidding and procurement department. In order to extract the core information in the bad behavior data, the business department can take targeted management countermeasures in the follow-up bidding, procurement and cooperation process. Based on the characteristics of the bad behavior data of power enterprise suppliers, the framework design, model construction and experimental evaluation of the bad behavior label model based on supervised machine learning are conducted. © 2024 SPIE. All rights reserved.

关键词： Supervised learning

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Voice AI-Intelligence Based Voice Assistant 3

Voice AI-Intelligence Based Voice Assistant

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3rd international conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Garai, Pooja Nikam, Aditya Kerkar, Suyash Deshmukh, Samruddhi Mane, Atharva Bhise, Suvarna Vasantdada Patil College Of Engineering & Visual Arts Dept. of Artificial Intelligence & Data Science Mumbai India

ISBN: (纸本)9798331515911

Voice assistant applications have become integral parts of modern technology ecosystems, offering users convenient and efficient ways to interact with their devices. This paper introduces Voice Ai, a versatile voice assistant application developed using Android Studio and Google's machine learning Kit machine learning(MLKit) for Optical Character Recognition (OCR) functionalities. The app includes features like setting alarms, making calls, opening applications, and conducting question-answer sessions powered by G4F. A comparative analysis between G4F and Google's backend is performed, assessing their performance and user satisfaction during question-answer interactions. Voice Ai simplifies daily tasks and boosts productivity, providing insights for developers, researchers, and end-users interested in AI-based voice assistant applications. © 2025 IEEE.

关键词： Adversarial machine learning

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AI-Powered Trash Classification System for Lakes and Water Bodies Using Transfer learning 3

AI-Powered Trash Classification System for Lakes and Water B...

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3rd international conference on Power, Control and Computing Technologies, ICPC2T 2024

作者： Kundu, Sumit Sharma, Mehul Pillai, Anju S Amrita School of Engineering Department of Electrical and Electronics Engineering Amrita Vishwa Vidyapeetham Coimbatore India

ISBN: (纸本)9798350349207

The growing accumulation of rubbish in lakes and bodies of water poses a huge environmental concern that necessitates creative solutions. To address this issue, the study proposes an artificial intelligence (AI) powered trash classification system based on transfer learning. This system seeks to revolutionize waste management in lakes and water bodies by leveraging AI and machine learning (ML) technology. The methodology integrates computer vision, advanced ML algorithms, and robotics to identify, classify, and collect various sorts of trash in bodies of water. The system incorporates a pre-Trained residual network (ResN et-50) model. Extensive simulations are carried out, and the experiments show that transfer learning using ResN et50 can obtain great results in classifying garbage photos into numerous categories. With an accuracy of 95.6%, the model offers a promising deep-learning option for automated waste management systems. The model's strong precision and recall scores demonstrate its ability to classify various waste types in real-world contexts correctly. © 2024 IEEE.

关键词： Lakes

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