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检索条件"主题词=Machine Learning Algorithms"

共 25877 条记录，以下是381-390 订阅

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Evaluating machine learning algorithms for Effective Credit Card Fraud Detection

Evaluating Machine Learning Algorithms for Effective Credit ...

引用

International Conference on Signal Processing and Communication (ICSC)

作者： Parth Saxena Independent Researcher Houston USA

ISBN: (数字)9798331511890

ISBN: (纸本)9798331511906

This study studies machine learning (ML) algorithms for tracing credit card fraud, including random forest, logistic regression, and Support Vector machines (SVMs), using a dataset of 287,807 transactions from European cardholders. To resolve data imbalance, resampling was done after applying data preparation techniques. Ten balanced subsets of data were produced by undersampling the majority (non-fraud) class, each of which concentrated on a different aspect of the majority. These subsets were used to train individual models, and predictions were produced by majority voting. For comparison, models were trained using the original unbalanced data in addition to resampling. Accuracy, recall, precision, F1 scores, ROC-AVC scores, and a practical business assessment to determine their practical relevance were among the evaluation criteria. Logistic regression, trained on unbalanced data, achieved a ROC-AUC of 99.06% with transparent decision-making, outperforming models trained on balanced subsets, which, despite high AUC-ROC scores, suffered low precision and reduced practical reliability in fraud detection.

关键词： Support vector machines Logistic regression machine learning algorithms Signal processing algorithms Signal processing Credit cards Data models Fraud Reliability Random forests

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Synergistic Integration of Blockchain Technology and machine learning algorithms for Optimization of Agriculture Supply Chain Management

Synergistic Integration of Blockchain Technology and Machine...

引用

Intelligent Data Communication Technologies and Internet of Things (IDCIoT), International Conference on

作者： Gayatri Asalkar Prema Sahane Atharv Pedde Samarth Hanse Tejas Ghondage Krishna Bhosale Department Of Computer Engineering JSPM's Rajarshi Shahu College of Engineering Pune Maharashtra India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

This research study focuses on the discussion of blockchain and machine learning optimization in the conventional agricultural supply chain. Optimization of both rising technologies has the possibility of addressing some of the complex and entrenched issues that confront the agricultural industry while receiving calls for more openness, honesty, and productivity. Blockchain, being the technological application that is based on secure and transparent transactions and enables the assurance of immutability and machine learning as a data driven power in optimization, leads to a significantly better traceability, erased inefficiencies, less substances and time to market. It is however not entirely without some few challenges that will have to be overcome for all these innovations to come into their own. Among the obstacles that need to be overcome for realization of these innovations some of the critical ones are Technology and Data issues and Stakeholder issues.

关键词： Technological innovation machine learning algorithms Supply chains Time to market machine learning Agriculture Blockchains Stakeholders Reliability Optimization

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Forecasting of Reliability Indicators of Base Stations of Cellular Communication Networks Using machine learning algorithms

Forecasting of Reliability Indicators of Base Stations of Ce...

引用

Systems of Signals Generating and Processing in the Field of on Board Communications

作者： D. S. Chirov N. D. Petrov Moscow Technical University of Communications and Informatics Moscow Russia

ISBN: (数字)9798331532635

ISBN: (纸本)9798331532642

The article discusses the issues of forecasting the reliability of base stations of cellular communication networks using machine learning algorithms. This task is relevant, as forecasting the reliability of base stations allows timely measures to be taken to prevent equipment failures. For example, replacing the corresponding base station nodes or performing maintenance. All this ensures an increase in QoS in cellular communication systems. An analysis of existing developments in this field has shown that the use of machine learning algorithms is a promising way to predict reliability indicators. The article discusses two of the most popular machine learning algorithms for solving forecasting problems: decision trees and recurrent neural networks. To assess the applicability of these algorithms, a special dataset has been developed that simulates the process of failures in the main elements of the base station: the power supply unit and the RF power amplifier. The simulation results showed high quality forecasting of base station reliability indicators using these algorithms. In particular, the LSTM network provides about 92% accuracy in correctly predicting base station failure.

关键词： Cellular networks Base stations machine learning algorithms Recurrent neural networks Prediction algorithms Reliability Decision trees Communication networks Forecasting Long short term memory

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Enhancing Intrusion Detection Systems with Deep learning and machine learning algorithms for Real-Time Threat Classification

Enhancing Intrusion Detection Systems with Deep Learning and...

引用

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

作者： Farooque Kadri Priyanka Singla ODYSSEY CLOUD Pune India BPIT Delhi India

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509835

Computer network intrusion has surged dramatically over the past decade, fueled by a lucrative underground cybercrime economy and the proliferation of sophisticated tools for executing such attacks. For over forty years, researchers from both industry and academia have been developing methods and systems to detect and prevent these security breaches. machine learning (ML) techniques have emerged as a powerful tool among the various methods used to classify normal and abnormal behavior. This study presents an advanced preprocessing strategy combined with a comprehensive evaluation of traditional machine learning models and a deep neural network (DNN) for real-time network intrusion detection. Preprocessing includes univariate analysis, feature selection, outlier handling, data normalization, and PCA-based dimensionality reduction. Further, the dataset was divided into train and test sets in the ratio 80:20. Traditional models GB, DT, and ET have been tested; however, ET performs better than all concerning accuracy, precision, recall, Fl score, and kappa. The proposed DNN model further enhanced detection capabilities, achieving the highest metrics across all evaluation parameters, including an AUC of 1.00, an accuracy of 99.50% and the lowest misclassification rate. The performance of both traditional and deep learning models was assessed using precision, recall, kappa, accuracy, Fl score, ROC curve analysis, and confusion matrices. The results indicate that the proposed approach is efficient for real-time network intrusion detection and threat analysis.

关键词： Deep learning Measurement Industries Accuracy machine learning algorithms Biological system modeling Network intrusion detection Artificial neural networks Real-time systems Security

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Fortifying Network Security: A Comparative Study of machine learning algorithms for Enhanced DDoS Attack Classification

Fortifying Network Security: A Comparative Study of Machine ...

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International Conference on Electrical, Computer and Communication Engineering (ECCE)

作者： Md Anamul Kabir Md Ekramul Kabir Jareer Bhuiyan Dhaka Power Distribution Company Limited (DPDC) Khulna University Bangladesh Encore Performance Seating (A Palliser Company) Canada Bangladesh University of Textiles Bangladesh Department of Computer Science Ontario Tech University Oshawa Canada

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

DDoS (Distributed Denial of Service) creates a threat to a network for its security, which causes disrupting services and results in financial losses. With this study, we will be exploring classifications of various algorithms of machine learning (ML) like Decision Trees (DT), Logistic Regression (LR), Random Forest (RF), Support Vector machines (SVM), K-Nearest Neighbors (KNN) and Gradient Boosting (GB) to expand the finding of DDoS occurrences expending the dataset of CICDDoS2019. Our models showed a extraordinary accuracy (99.95%) with Random Forest (RF), which is turning into the best performer.

关键词： Support vector machines Radio frequency machine learning algorithms Computational modeling Network security Nearest neighbor methods Denial-of-service attack Turning Computer crime Random forests

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Optimizing machine learning algorithms for Accurate Heart Disease Detection

Optimizing Machine Learning Algorithms for Accurate Heart Di...

引用

Device Intelligence, Computing and Communication Technologies, (DICCT), International Conference on

作者： SM Faizanut Tauhid Safdar Tanweer Md. Tabrez Nafis Mohd Abdul Ahad Syed Mohd Faisal Malik Syed Sibtain Khalid Department of Computer Science and Engineering Jamia Hamdard Delhi India

ISBN: (数字)9798331543358

ISBN: (纸本)9798331543365

Heart disease is a leading global health challenge, emphasizing the need for effective diagnostic solutions. This study evaluated the accuracy of machine learning algorithms in heart disease diagnosis with the aim of finding the most reliable method. The algorithms examined contain Random Forest, Support Vector Classifier, Gradient Boosting, AdaBoost, Optimized Logistic Regression, SVM, Naive Bayes, K-Nearest Neighbors (KNN), Decision Trees, and SGD Classifier. Among them, optimized logistic regression and naive Bayes have the highest accuracy of up to 88.52%, demonstrating their predictive capabilities. The result of this work is optimized logistic regression and naive Bayes are capable of early detection of cardiovascular disease. Offering major perspective for integration into clinical practice. Future work will explore the refinement and real-world application of these models to further improve healthcare outcomes.

关键词： Heart Support vector machines Logistic regression Accuracy machine learning algorithms Translation Nearest neighbor methods Vectors Bayes methods Random forests

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A Comprehensive Review of machine learning algorithms in Optimizing Power Consumption in Smart Buildings

A Comprehensive Review of Machine Learning Algorithms in Opt...

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Power Engineering Conference (SAUPEC), Southern African Universities

作者： Bertille Auyane Ngouessy Ep Ovono Temidayo Otunniyi Trudy Sutherland Faculty of Engineering Vaal University of Technology Gauteng South Africa

ISBN: (数字)9798331535162

ISBN: (纸本)9798331535179

The increasing global energy demand and the need for sustainable solutions have driven the evolution of smart buildings equipped with advanced technologies to optimize energy consumption. machine learning (ML) algorithms are pivotal in enabling these technologies, facilitating energy consumption forecasting, anomaly detection, and autonomous control systems. This paper offers a comprehensive review of state-of-the-art ML algorithms in the context of energy optimization within smart buildings. It categorizes and evaluates supervised, unsupervised, and reinforcement learning methods, focusing on their applications, advantages, and challenges. Furthermore, it discusses recent advancements and outlines future research directions, emphasizing the integration of innovative techniques like federated learning and adaptive energy management systems to enhance efficiency, scalability, and sustainability.

关键词： Energy consumption Smart buildings machine learning algorithms Reviews Federated learning Scalability Reinforcement learning Sustainable development Forecasting Optimization

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Intelligent Heart Disease Prediction using Ant Colony Optimization based machine learning algorithms

Intelligent Heart Disease Prediction using Ant Colony Optimi...

引用

machine learning and Autonomous Systems (ICMLAS), International Conference on

作者： Shaik Shafi Abhilash Ch Manoj A Snehil B Nandan TPK Anusha M Electronics and Communication Engineering B V Raju Institute of Technology Narsapur Medak India ECE BVRIT Narsapur Medak India

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

Cardiovascular diseases continue to be the primary cause of death worldwide, responsible for around 31% of all deaths each year, as reported by the World Health Organization (WHO). Although significant progress has been made in their diagnosis and treatment, heart disease remains highly prevalent. It has risen significantly due to unhealthy dietary habits and reduced physical activity. Predicting heart diseases using patient-related data, such as blood pressure, cholesterol levels, and other health metrics, has emerged as one of the most complex tasks for human analysts in modern times. As a result, there is a growing global demand for timely and accurate predictive models in identifying heart disease in healthcare industry, predominantly in the field of cardiology. In this paper, an intelligent heart disease prediction using ant colony optimization using machine learning algorithms is proposed. It includes two phases namely: 1) Feature Selection and 2) Classification. Ant Colony Optimization (ACO) is used to reduce the redundancy in feature selection from the large data set and helps to upgrade the performance of the classifier in the next stage. Then, we exploit the different classification algorithms such as K-Nearest Neighbors (KNN), Support Vector machine (SVM), Decision Tree (DT), Random Forest (RF) and XGBoost (XGB). These algorithms are chosen for their ability to deliver high accuracy, computational efficiency, and optimized memory usage, making them well-suited for this application. Furthermore, performance of the machine learning algorithms has been evaluated using accuracy, precision, F 1 score, recall and support parameters. The results illustrate that the amalgamation of the ACO algorithm and SVM classifier has the utmost performance for use in heart disease estimation.

关键词： Heart Support vector machines Ant colony optimization machine learning algorithms Accuracy Nearest neighbor methods Predictive models Feature extraction Classification algorithms Classification tree analysis

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Model for Endometriosis Detection Using machine learning algorithms

Model for Endometriosis Detection Using Machine Learning Alg...

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Software Engineering and Computer Science (CSECS), International Conference on

作者： Alexis Bautista Jahir Tardillo José Luis Castillo-Sequera Lenis Wong Information Systems Engineering Program Universidad Peruana de Ciencias Aplicadas Lima Peru Department of Computer Science Universidad de Alcalá Alcalá de Henares Spain

ISBN: (数字)9798331522216

ISBN: (纸本)9798331522223

Endometriosis is a chronic disease that affects a considerable percentage of women of reproductive age and is characterized by the presence of endometrial tissue outside the uterine cavity, leading to symptoms such as pelvic pain and dysmenorrhea. The aim of this study is to develop a predictive model for the classification of endometriosis using four machine learning algorithms: Random Forest, LASSO, SVM, and Naive Bayes. For this purpose, a dataset from the Global Health Data Exchange was utilized, consisting of 1,000 cases of patients with endometriosis. The methodology included data cleaning and preprocessing, as well as the evaluation of each algorithm's performance using four metrics: precision, recall, F1-Score, and accuracy. The findings revealed that the Random Forest algorithm was the most effective in identifying endometriosis, outperforming the other algorithms with a precision of 0.99 for the “endometriosis” class and an overall accuracy of 0.98.

关键词： Support vector machines Measurement Training machine learning algorithms Accuracy Software algorithms Predictive models Bayes methods Random forests Software engineering

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Prediction of Customer Satisfaction using machine learning algorithms: Perspectives of Service and Operational Factors in Airline Industry

Prediction of Customer Satisfaction using Machine Learning A...

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International Conference on Communication Systems and Network Technologies (CSNT)

作者： N. Shafrin Fathima K. Ajithkumar P. Suganthi V. Srinivasa Kumar School of Management SASTRA Deemed University Thanjavur Tamil Nadu India

ISBN: (数字)9798331531935

ISBN: (纸本)9798331531942

Customer satisfaction in the airline industry is influenced by demographic, service-related, and operational factors. As competition grows and passenger expectations evolve, understanding these determinants is crucial for improving service quality and loyalty. This study uses machine learning algorithms to analyze key drivers of customer satisfaction and derive actionable insights. It employs techniques such as Logistic Regression, Gaussian Naive Bayes, Gradient Boosting, K-Nearest Neighbors, and Random Forest classifiers. Among these, Gradient Boosting and Random Forest achieved the highest predictive accuracy, with AUC scores of 0.99 and 0.9937. A strong correlation (0.97) between departure and arrival delays highlights the importance of punctuality. Inflight services like entertainment and legroom, especially on long-haul flights, significantly enhance satisfaction. Demographic analysis shows passengers aged 40+ report higher satisfaction, emphasizing the need for tailored service strategies, while younger passengers (20-40) show lower satisfaction, indicating a need for personalized engagement. These findings provide a data-driven foundation for improving airline operations and service delivery. Future research should integrate real-time data for more adaptive service improvements.

关键词： machine learning algorithms Correlation Customer satisfaction Entertainment industry Aging Boosting Real-time systems Delays Random forests Airline industry

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