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检索条件"主题词=Machine Learning Algorithms"

共 25877 条记录，以下是841-850 订阅

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Review and Analysis of WSN with Cluster Routing Issues and Provide Solutions Through Artificial Intelligence and machine learning algorithms

Review and Analysis of WSN with Cluster Routing Issues and P...

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International Conference on Energy, Power and Environment: Towards Sustainable Growth (ICEPE)

作者： S. Archana V. Jayapradha Department of Electronics and Communication Engineering Sri Chandrasekharendra Saraswathi Viswa Mahavidyalaya Kanchipuram Tamil Nadu India

ISBN: (数字)9798350379297

ISBN: (纸本)9798350379303

Wireless Sensor Networks (WSNs) technology has emerged extensively. Network clustering is the procedure to enhance energy efficiency. The conventional approaches resolve several issues. However, they may not develop a precise result for forecasting system behavior. In this article, we investigate WSN with Cluster routing issues and provide solutions through Artificial intelligence and machine learning algorithms. This article describes WSN routing issues and functional issues. Also, it explains Artificial Intelligence and machine learning (ML) techniques to solve the data collection, aggregation, and distribution issues in WSN. The significant objective of cluster formation, Cluster Head (CH) selection, and distribution algorithms is to collect and forward the data in an energy-resourceful method, as a result improving the lifetime. The benefits of clustering in WSNs comprise better scalability, minimizing delay, and raising energy efficiency. This Artificial Intelligence and machine learning techniques applied in WSNs will be a lead for the research society in describing the typically adapted approaches for solving the issues interrelated to cluster with WSN routing.

关键词： Wireless sensor networks machine learning algorithms Reviews Scalability Clustering algorithms machine learning Routing

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Enhancing machine learning algorithms Using Recursive Feature Elimination For Student Performance Prediction

Enhancing Machine Learning Algorithms Using Recursive Featur...

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International Seminar on Application for Technology of Information and Communication (ISemantic)

作者： Wijiyanto Aris Marjuni Ahmad Zainul Fanani Ruri Suko Basuki Department of Informatics Engineering Dian Nuswantoro University Semarang Indonesia Fakulty of Computer Science Duta Bangsa University Surakarta Indonesia

ISBN: (数字)9798350366648

ISBN: (纸本)9798350366655

Prediction systems for students' performance are crucial in improving educational outcomes in higher education institutions. Big data can be sourced from various channels, such as examination centers, academic systems, registration departments, and e-learning platforms. Extracting meaningful insights from educational data is challenging, necessitating data dimensionality reduction. This study introduces an enhanced recursive feature elimination (RFE) algorithm as a wrapper feature selection technique. We adopt machine learning by combining the proposed RFE with five different classifiers, including Gradient Boosting Classifier (GB), Random Forest Classifier (RF), Support Vector Classifier (SVC), K-Neighbors Classifier (KNN), and Decision Tree Classifier (DT). The dataset was originally intended to influence factors in dropout students, and it has 329 records and 9 features. The AUC score improved by 8% compared to the model tested without the proposed feature selection method by testing the proposed methodology with the Decision Tree Classifier. Educators and academic institutions can utilize this information to develop targeted intervention strategies, tailored learning experiences, and personalized recommendations for students, ultimately promoting a more effective learning environment and improving overall educational outcomes.

关键词： machine learning algorithms Education Static VAr compensators Nearest neighbor methods Feature extraction Boosting Vectors Data mining Random forests Classification tree analysis

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Enhancing Healthcare Facility Management through Non-intrusive Occupancy Detection: A Comparative Study of machine learning algorithms

Enhancing Healthcare Facility Management through Non-intrusi...

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Communications, Information, Electronic and Energy Systems (CIEES), International Conference on

作者： Adem Korkmaz Selahattin Koşunalp Ayşe Çiçek Korkmaz Dept. of Computer Technologies Bandırma Onyedi Eylül University Balikesir Türkiye Nursing Department Bandırma Onyedi Eylül University Balikesir Türkiye

ISBN: (数字)9798350352863

ISBN: (纸本)9798350352870

Optimizing patient environments in healthcare is crucial for improving care quality and operational efficiency. This study explores the application of machine learning algorithms for non-intrusive room occupancy detection in healthcare settings, which is essential for creating responsive, patient-centered environments. Using sensors to monitor temperature, light, sound, and CO2 levels, we applied Random Forest (RF), k-nearest Neighbors (k-NN), Support Vector machines (SVM), and Decision Trees (DT) to predict room occupancy. The study focused on evaluating the accuracy and privacy-preserving capabilities of these algorithms. The results demonstrated that RF, k-NN, and DT performed exceptionally well, achieving high precision, recall, and F1 metrics scores, highlighting their potential for error-free occupancy prediction in healthcare environments. In contrast, while typically robust, the SVM algorithm exhibited slight variations in precision for certain classes, underscoring the importance of algorithm-specific considerations in different healthcare settings. Feature importance analysis revealed that light-based sensors and CO2 levels were critical in determining room occupancy, aligning with existing literature emphasizing environmental factors' significance in patient care settings. This study underscores the valuable role of machine learning in enhancing healthcare environments and advocates for the integration of such technologies into healthcare management systems. This integration promises significant improvements in patient care and resource optimization. Future research should focus on examining the impact of these technologies on patient outcomes and healthcare efficiency.

关键词： Temperature sensors Support vector machines Radio frequency machine learning algorithms Medical services Prediction algorithms Sensors Decision trees Random forests Facilities management

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Predicting Anxiety Among Young Adults Using machine learning algorithms

Predicting Anxiety Among Young Adults Using Machine Learning...

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International Conference for Convergence of Technology (I2CT)

作者： Pralhad R Yadawad Lokesh Pandey Kaushik Mallibhat Uma Mudenagudi School of Electronics and Communication Engineering KLE Technological University Hubballi India

ISBN: (数字)9798350394474

ISBN: (纸本)9798350394481

This study describes the process, procedure, and analysis used to quantify the amount of anxiety experienced by engineering students during the set of activities. The paper presents a comprehensive literature review, collection methods for anxiety detection, and emphasizes the advantages of portable EEG devices. The core methodology involves inducing specific anxiety types through structured activities, coupled with EEG data analysis followed by use of a machine learning algorithm towards classification of anxiety. The experimentation included data from nine participants aged 18-21 years. EMOTIV EPOC+ portable EEG machine was used to record the EEG from the participants during anxiety-triggering activities. The authors used statistical methods such as the Wilcoxon signed-rank test and Mann-Whitney U test to identify significant changes in EEG signals during anxiety-triggering activities compared to the relaxed state. The Self-Assessment Manikin(SAM)method is employed to categorize data into light, moderate, severe anxiety, and normal states. machine learning algorithms, including Random Forest and XGBoost, demonstrate notable efficacy, achieving an accuracy of 72.27%. This research contributes valuable insights to mental health research and treatment, emphasizing the potential for personalized interventions based on neural and behavioral markers.

关键词： machine learning algorithms Data analysis Statistical analysis Anxiety disorders Mental health Electroencephalography Reliability

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Integration of machine learning algorithms for Predictive Maintenance in IoT-Enabled Smart Safety Helmets

Integration of Machine Learning Algorithms for Predictive Ma...

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Emerging Technology (INCET), International Conference for

作者： Dankan Gowda V V Nuthan Prasad Vaishali N. Agme KDV Prasad Sheetalrani Rukmaji Kawale Shruti Mallikarjun Department of Electronics and Communication Engineering BMS Institute of Technology and Management Bangalore Karnataka India Department of Electronic and Communication MSRIT Bangalore Karnataka Department of Applied Science Bharati Vidyapeeth College of Engineering Navi Mumbai Maharashtra India Department of Research Symbiosis Institute of Business Management Hyderabad Symbiosis International (Deemed University) Pune India Department of Computer Science Karnataka State Akkamahadevi Women University Vijayapura Karnataka India

ISBN: (数字)9798350361155

ISBN: (纸本)9798350361162

This paper focuses on the combination of ML algorithms for predictive maintenance through Internet of Things (IoT)-based smart safety helmets in order to increase not only the level of safety operations and but as well equipment life in industrial settings. We study the present helmet technologies and reveal the weaknesses by tomorrow foreseeing technologies which exist in current predictive maintenance strategies. Proposing a new technique, ML algorithms suite is applied as an analyzing tool to collected data from sensors within the helmets. The system architecture is real-time data processed by an IoT framework to predict and act on maintenance needs up front. The results of the experimental validation confirm the implementation of our approach, that shows the significant increase of the predictive accuracy and timeliness as compared to the traditional methods. The research has not only enabaled to create smart safety equipment but also a scalable approach that could be maximized across other Internet of things applications.

关键词： Head machine learning algorithms Accuracy Systems architecture Prediction algorithms Real-time systems Safety

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Performance comparison of machine learning algorithms for coronary heart disease prediction

Performance comparison of machine learning algorithms for co...

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Computational Intelligence for Security, Communication and Sustainable Development (CISCSD), International Conference on

作者： Bharath RC Aneesh RT Aniruddha GJ Shridhar B. Devamane Ravindranath R.C Lakshmikantha G C Department of Computer Science Global Academy of Technology Bengaluru Karnataka India Department of AI & ML Global Academy of Technology Bengaluru Karnataka India

ISBN: (数字)9798350365405

ISBN: (纸本)9798350365412

The algorithms used in these machine learning models are quite straightforward and easy to understand, and they may produce predictions. These models have numerous business and scientific applications. machine learning model predictions are employed across many disciplines, including biology, behavioural sciences, environmental sciences, social sciences, and economics. We have discussed both the theoretical and practical elements in this paper.

关键词： Heart machine learning algorithms Biological system modeling Social sciences Predictive models Prediction algorithms Security Sustainable development Regression tree analysis Random forests

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Research and Implementation of machine learning algorithms in Tourism Recommendation

Research and Implementation of Machine Learning Algorithms i...

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Advancement in Technology (ICONAT), International Conference for

作者： Ma Qian Chongqing Three Gorges Vocational College Chongqing China

ISBN: (数字)9798350354171

ISBN: (纸本)9798350354188

The role of travel recommendation in the tourism industry is very important, but there is the problem that travel recommendation reviews are not true. Ordinary methods cannot solve the problem of travel recommendation in the tourism industry, and the recommendation is unreasonable. Therefore, this paper proposes a machine learning algorithm to conduct real travel recommendation analysis. First of all, the computer is used to collect and analyze the tourism review data, and the indicators are divided according to the requirements of tourism recommendation to reduce it Distractions in travel recommendations. Then, the computer analyzes the results of tourism recommendation in the tourism industry, forms a tourism recommendation plan, and recommends tourism The results were comprehensively analyzed. MATLAB simulation shows that under certain evaluation criteria, machine learning algorithms can make tourism recommendations accurate to the tourism industry The authenticity of travel recommendations is better than ordinary methods.

关键词： Training machine learning algorithms Accuracy Reviews Tourism industry Government Total quality management Personnel Sustainable development Protection

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Integrated Computer Network Security System: Intrusion Detection and Threat Prediction Using machine learning algorithms

Integrated Computer Network Security System: Intrusion Detec...

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Smart Information Systems and Technologies (SIST), IEEE International Conference on

作者： Mariya Utarbayeva Makpal Mukanova Nazarbayev Intellectual School Nazarbayev Intellectual School Almaty Kazakhstan Department of Computer Science Nazarbayev Intellectual School Nazarbayev Intellectual School Almaty Kazakhstan

ISBN: (数字)9798350374865

ISBN: (纸本)9798350374872

The escalating use of computer networks and associated applications has made cybersecurity a critical concern. This research presents a comprehensive approach to addressing network security issues by developing an Intrusion Detection System (IDS) based on the dataset from the Security Information and Event Management System (SIEM) using various machine learning algorithms. The study includes a detailed comparison of models such as Logistic Regression, K-Nearest Neighbors, Gusain Naive Bayes, Support Vector machine, Decision Tree, Random Forest, XGBoost, and Artificial Neural Networks. The results demonstrated using histograms and tables show the effectiveness of Random Forest and PCA Random Forest, emphasizing their accurate traffic classification. The research evaluates the model’s performance across various cyber-security datasets containing multiple categories of cyber-attacks. It evaluates efficiency by utilizing criteria such as accuracy, precision, and recall. By applying a multilevel approach aligned with the latest trends in machine learning, the study aims to facilitate swift and precise threat analysis and response, ultimately enhancing the overall effectiveness of the cybersecurity system. The study is an effective educational resource that introduces IT students to innovative machine learning and cybersecurity ideas. Through the integration of the results into IT curriculum, educators may close the knowledge gap between theory and practical application by offering students real-world experience in addressing cyber threats. This combined emphasis on innovation and education seeks to develop a new generation of IT professionals with the expertise required for enhanced cybersecurity.

关键词： Surveys Support vector machines Technological innovation Accuracy machine learning algorithms Education Intrusion detection

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Comparative Analysis of Ensemble machine learning algorithms for Heart Disease Prediction

Comparative Analysis of Ensemble Machine Learning Algorithms...

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Innovative Engineering Sciences and Technological Research (ICIESTR), International Conference on

作者： Savir Ali Mohammad Ubaidullah Bokhari Department of Computer Science Aligarh Muslim University Aligarh India

ISBN: (数字)9798350348637

ISBN: (纸本)9798350348644

The leading cause of death worldwide in recent decades has been overtaken by cardiovascular disease, which encompasses a wide spectrum of heart-related disorders. In the majority of countries, there is both a shortage of cardiovascular specialists and a large increase in the number of patients whose conditions are misdiagnosed. These problems might be resolved by developing precise and effective early-stage cardiac disease prediction using digitized patient information as analytical assistance for medical decisions. This would allow for better treatment of patients at an earlier stage of heart disease. The objective of this study is to present a comparative study to improve the prediction of heart disease through the use of various machine learning techniques, including xgboost, decision trees, support vector machine, k-nearest neighbor, random forests, and naive bayes. This was done with the intention of determining which classifiers have the highest accuracy. Comparing the overall performance and accuracy of a number of supervised machine learning algorithms was done for the goal of predicting cardiac illness. the xgboost classifier had the highest accuracy 90.8%. According to the study’s findings, ensemble techniques like stacking help weak classifiers forecast more accurately. Ensemble classification helped less reliable classifiers increase their accuracy to a maximum of 1.68%.

关键词： Heart Support vector machines Accuracy machine learning algorithms Stacking Nearest neighbor methods Classification algorithms Bayes methods Decision trees Random forests

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Comparative Analysis of Python-Based machine learning algorithms for Stock Market Prediction

Comparative Analysis of Python-Based Machine Learning Algori...

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Advances in Computing, Communication and Networking (ICAC2N), International Conference on

作者： Vinod Sharma Shrawan Kumar J. Monisha Sidharth Jain Ritu Sindhu Omveer Singh Dept of CSE SLP College Gwalior India Dept. of Humanities & S.Science Madhav University Pindwara India Dept. of Commerce Meenakshi A.H.E.R Chennai India Dept. of Business Management and Commerce Mangalayatan University Aligarh India Dept. of CSE Lingayas Vidyapeeth Faridabad India Dept. of Management J.S. University Shikohabad India

ISBN: (数字)9798350356816

ISBN: (纸本)9798350356823

It is very difficult to forecast the stock market because of the high volatility and unpredictable nature of stock prices. Because of the remarkable advancements in machine learning algorithms over the past ten years, stock market forecasting has gained more attention and developed into one of the most reliable methods for predicting stock movements. The current research project includes a thorough analysis of a few machine learning techniques based on Python for stock market forecasting. Several algorithms' performance will be compared and their ability to predict stock prices will be assessed. The results have demonstrated that the application of machine learning techniques can significantly enhance stock market prediction. Consequently, this review will be helpful to academics and others who may be interested in the use of machine learning in stock market forecasting.

关键词： Adaptation models machine learning algorithms Accuracy Reviews Market research Solids Reliability Stock markets Forecasting Random forests

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