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检索条件"主题词=Machine Learning Algorithms"

共 25782 条记录，以下是91-100 订阅

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Research on Iron Core Loss Prediction Based on Data-Driven and machine learning algorithms

Research on Iron Core Loss Prediction Based on Data-Driven a...

引用

Electronics, Energy Systems and Power Engineering (EESPE), IEEE International Conference on

作者： Hao Jin Lei Dong China University of Petroleum (East China) Qingdao China

ISBN: (数字)9798350389579

ISBN: (纸本)9798350389586

This paper focuses on the performance optimization of magnetic components, and discusses the key issues such as the construction of a data-driven core loss prediction model. A data-driven core loss prediction model is constructed, which integrates a variety of machine learning algorithms, such as random forest, gradient lift tree, K-nearest neighbor regression, decision tree regression and artificial neural network, and improves the stability and accuracy of prediction through weighted prediction model. Finally, with the core loss prediction model as the objective function, particle swarm optimization algorithm was used to determine the optimization conditions of the magnetic components, including the optimal material, temperature, frequency, waveform and magnetic flux density peak, and the overall performance optimization of the magnetic components was realized.

关键词： machine learning algorithms Magnetic cores Core loss Predictive models Prediction algorithms Stability analysis Particle swarm optimization Optimization Thermal stability Regression tree analysis

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A Comparative Study of machine learning algorithms for Early Prediction of Alzheimer's Disease

A Comparative Study of Machine Learning Algorithms for Early...

引用

International Conference on Trends in Electronics and Informatics (ICEI)

作者： A. Akilandeswari Saveetha School of Engineering Simats University

ISBN: (数字)9798331544607

ISBN: (纸本)9798331544614

Alzheimer's disease (AD) is one of the most common neurodegenerative disorders, leading to progressive impairment of cognitive function. There is an immediate need to diagnose AD early to intervene in a timely fashion that could slow up disease progression. Traditional diagnosis methods are often expensive and take a very long time, motivating the pursuit of ML as an alternative diagnosis approach. In terms of algorithms, the effectiveness of five types was compared: Random Forest, Gradient Boosting, Logistic Regression, Support Vector machine (SVM), and k-Nearest Neighbors (k-NN) in predicting Alzheimer's disease. Different metrics such as accuracy, precision, recall, and F1-score were utilized using a patient dataset. Results show that Gradient Boosting achieved the highest accuracy at 95.81%, followed by Random Forest at 92.79%. The ensemble methods were seen to perform pretty well since they had the ability to manage complex patterns and noisy data. On the contrary, the performance was relatively less precise for k-NN (72.55%) due to the very high dimensionality of the dataset itself. The findings emphasize the requirement of the suitable machine learning approach that would be used for better predictive accuracy, particularly the earlier detection of AD.

关键词： Support vector machines Logistic regression Accuracy machine learning algorithms Boosting Prediction algorithms Ensemble learning Reliability Alzheimer's disease Random forests

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Research Intelligent Methods and machine learning algorithms for Identification Anomalies in the Drilling Operations

Research Intelligent Methods and Machine Learning Algorithms...

引用

International Russian Smart Industry Conference (SmartIndustryCon)

作者： Anton Parfenov Alexander Borovsky Denis Parfenov Research Institute of Digital Intelligent Technologies Orenburg State University Orenburg Russia Aerospace Institute Orenburg State University Orenburg Russia

ISBN: (数字)9798331511241

ISBN: (纸本)9798331511258

Intelligent methods can analyze large volumes of data coming from a digital twin and identify bottlenecks in technological processes. Digital twins provide the ability to conduct scenario analysis and forecast various situations within technological processes. The use of digital twins and algorithms based on machine learning and data analysis helps improve product quality by identifying anomalies and problems at early stages, thereby allowing you to promptly make the necessary changes, which significantly reduces the time and financial costs of implementing the final product. Intelligent decision support systems can provide recommendations based on data analysis, both retrospective and received in real time, which significantly simplifies solving complex problems and improves the quality of decisions made. Thus, the integration of intelligent decision support methods and algorithms with digital twins can significantly improve the productivity, competitiveness and innovation of oil and gas enterprises. As part of the study, we analyzed the most suitable machine learning methods and algorithms for data labeling aimed at identifying anomalies in the process of stirring.

关键词： Drilling Technological innovation machine learning algorithms Data analysis Time series analysis machine learning Search problems Real-time systems Digital twins Quality assessment

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Music Classification Model based on Multi-Feature Image Fusion and machine learning algorithms

Music Classification Model based on Multi-Feature Image Fusi...

引用

Intelligent Systems and Computational Networks (ICISCN), International Conference on

作者： Chen Wang School of Children’s Welfare Changchun Humanities and Sciences College Changchun China

ISBN: (数字)9798331529246

ISBN: (纸本)9798331529253

How to effectively classify massive music data has become a key research topic in the field of music information retrieval. This study proposes a novel music classification model that combines multi-feature image fusion (MFIF) and machine learning (ML) algorithms to improve the accuracy and efficiency of music classification. The specific method is to first convert music signals into various visual representations such as spectrograms, Mel spectrograms, and chromaticity maps, and then combine these different visual features into a comprehensive feature map through image fusion technology. In addition, the study also introduced a deep learning framework, using Convolutional Neural Network (CNN) to learn these fused image features. In the absence of noise, the model exhibits high performance with an accuracy of 0.87 and an F1 score of 0.855, indicating that the model can perform music classification tasks well under ideal input conditions. This article provides technical support for related music recommendation systems and music copyright management.

关键词： Deep learning Visualization Accuracy machine learning algorithms Image recognition Computational modeling Multiple signal classification Convolutional neural networks Image fusion Spectrogram

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Optimizing Brain Tumor Classification with ResNet-50 Feature Extraction and machine learning algorithms

Optimizing Brain Tumor Classification with ResNet-50 Feature...

引用

International Conference on Ubiquitous Information Management and Communication (IMCOM)

作者： Andi Widya Mufila Gaffar Nurul Azmi Erick Irawadi Alwi Syahrul Mubarak Abdullah Rabiatul Adawiyah Dewi Widyawati Faculty of Computer Science Universitas Muslim Indonesia Makassar Indonesia

ISBN: (数字)9798331507817

ISBN: (纸本)9798331507824

MRI is a very important diagnostic tool in finding brain tumors, but recently the development of manual interpretation of MRI images has developed some challenges, such as diagnosis is often delayed, and there is a high chance of making mistakes. Recently, to hurry up the process, brain tumor detection has started applying machine learning methods. The classification of MRI images for brain tumors is done in this research paper by extracting their features with the ResNet-50 model. The classical machine learning algorithms that have been applied in the paper for classifying the tumors using the extracted features include Naive Bayes, Decision Tree, Support Vector machine (SVM), K-Nearest Neighbors (KNN), and Random Forest. In this respect, SVM and KNN have yielded the highest accuracy of 0.95 and 0.96 respectively, hence are the best methods in this task. The study's findings contribute to the development of quicker and more precise methods to help with brain tumor diagnosis in a medical context.

关键词： Support vector machines machine learning algorithms Accuracy Magnetic resonance imaging Nearest neighbor methods Feature extraction Brain modeling Bayes methods Decision trees Random forests

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Adaptive VR Systems Based on EEG and machine learning algorithms

Adaptive VR Systems Based on EEG and Machine Learning Algori...

引用

International Youth Conference on Radio Electronics, Electrical and Power Engineering (REEPE)

作者： Alexander Yu. Tychkov Robert N. Khizbullin Radik N. Khizbullin Alexander D. Ivanov Alan K. Alimuradov Denis S. Chernyshov Department of Radioengineering and Radioelectronic Systems Penza State University Penza Russia Department of Instrument Engineering and Mechatronics Kazan State Power Engineering University Kazan Russia Department of Electric Power Stations Kazan State Power Engineering University Kazan Russia

ISBN: (数字)9798331531836

ISBN: (纸本)9798331531843

This paper presents a concept for a system that can adapt virtual reality (VR) scenes in real time based on the user's electroencephalographic (EEG) signals using machine learning algorithms. The proposed approach aims to increase the level of personalization and depth of immersion in the virtual environment by dynamically adjusting scene parameters according to the individual's emotional and cognitive state. The paper considers methods for EEG signal preprocessing, feature extraction, the use of modern neural network architectures for state classification, as well as mechanisms for mapping classification results onto visual and auditory VR elements. Potential applications are discussed, such as adaptive training simulators, therapeutic VR programs, games, and multimedia environments. Issues of scalability, the need for model personalization, minimizing processing delays, and possible solutions are also addressed. This work provides a theoretical foundation for future experimental research and the practical implementation of next-generation adaptive VR systems.

关键词： Training Visualization Solid modeling machine learning algorithms Adaptive systems Sensitivity Virtual environments machine learning Feature extraction Electroencephalography

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Prediction Of Mental Health in Working People Using machine learning algorithms

Prediction Of Mental Health in Working People Using Machine ...

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Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE), International Conference

作者： Kumar P Senthil Pandi S Jain Bai B Mithun Dineshkumar Department of CSE Rajalakshmi Engineering College Chennai India UST Global Trivandrum India Khoury College of Computer Science Northeastern University Boston United States

ISBN: (数字)9798331515911

ISBN: (纸本)9798331515928

The professional and personal quality of life are both improved by early diagnosis and treatment of mental health issues. Depression is a major factor in disability all over the world. Professionals can treat internal health conditions earlier and improve their patients' quality of life by examining these issues. Depression is the leading cause of mental health problems worldwide. Professionals in the workforce must address early internal health issues to avoid more complex issues. A comprehensive overview of ML and its use in healthcare operations is provided in this paper. In addition, it provides a summary of the author's own research on the application of ML to internal health, highlighting the limitations that are currently in place and the areas that require additional observation. algorithms based on ML are now extremely useful for diagnosing issues and analyzing medical data. Joining information from many sources and AI approaches assisted us with achieving the best exactness of 94.061 with the irregular woodland classifier.

关键词： Accuracy machine learning algorithms Mental health Medical services Predictive models Depression Prediction algorithms Mobile applications Random forests Medical diagnostic imaging

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Evaluating Factors Influencing University Ranking Based on QS Ranking 2023-2024 Using machine learning algorithms

Evaluating Factors Influencing University Ranking Based on Q...

引用

Computational Intelligence Approaches and Applications (ICCIAA), International Conference on

作者： Sherin Hijazi Mahmoud Moshref Samer Mohammed Arqawi Department of Computer Science Palestine Technical University - Kadoorie Tulkarem Palestine Department of Computer Science Ministry of Education & Higher Education Tulkarem Palestine Department of Logistics Management Palestine Technical University - Kadoorie Tulkarem Palestine

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

This paper examines the use of machine learning algorithms to assess the factors influencing university rankings, with a focus on the QS World University Rankings for 2023-2024. The study employs multiple machine learning techniques, including Linear Regression, Multi-Layer Perceptron (MLP), Decision Trees, Random Forest, Gradient Boosted Trees, and H2O Deep learning, to evaluate the relative importance of various ranking attributes. Key findings reveal that Gradient Boosted Trees and MLP models outperform other algorithms, achieving an R 2 of 0.978 and a Mean Absolute Percentage Error (MAPE) of 7.2% on test data. Academic Reputation, Citations per Faculty, and Sustainability emerged as the most significant factors, while attributes like Age Band and Size had minimal impact. These results provide actionable insights for universities aiming to enhance their global rankings. The study concludes that machine learning offers a robust framework for understanding and predicting university performance, enabling institutions to refine their strategies and improve their global competitiveness

关键词： Water Deep learning machine learning algorithms Linear regression Data models Decision trees Sustainable development Random forests Computational intelligence

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Leveraging Sentinel-2 Multispectral Data and machine learning algorithms for Land Use Land Cover Mapping in Semi-Arid Regions

Leveraging Sentinel-2 Multispectral Data and Machine Learnin...

引用

Advancement in Computation & Computer Technologies (InCACCT), International Conference on

作者： Narayan Vyas Koushik Sundar Amit Sharma Pradeep Shree Adhikari Department of Computer Science and Application Vivekananda Global University Jaipur India Citibank NA Jersey City NJ USA Department of Computer Science and Engineering Vivekananda Global University Jaipur India

ISBN: (数字)9798331543891

ISBN: (纸本)9798331543907

Land use and land cover (LULC) classification is a fundamental approach for landscape pattern mapping and monitoring of natural ecosystems and anthropogenic activities and is, therefore, an essential base for resource management to support sustainability. The land cover classes were classified in this study using multispectral data. The study utilizes Sentinel-2 data captured in February 2024, and a median composite image was generated using the Google Earth Engine (GEE) platform. Three different classification algorithms, random forest (RF), classification and regression tree (CART), and naive bayes (NB), were used to produce LULC maps. The algorithms are efficient in capturing different land cover patterns with spectral diversity. The performance with essential measures such as overall accuracy (OA) and Kappa coefficient (KC) has been evaluated to assess classification outcomes. Among these classifiers, the RF classifier with the maximum OA (96.91%) and KC (96.10%) exhibited optimal results and robustness for LULC classification. CART followed this, with OA of 95.88% and KC of 94.81%. NB achieved an OA of 85.57% and KC of 81.93%. It confirms the validity of the suggested method for obtaining LULC maps through Sentinel-2 data and ML algorithms, achieving maximum accuracy in semi-arid regions.

关键词： Earth machine learning algorithms Accuracy Satellites Land surface Classification algorithms Internet Random forests Engines Classification tree analysis

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Topic-Aware Multi-Class Classification for Financial Complaints: Comparing BERTopic with Classical machine learning algorithms

Topic-Aware Multi-Class Classification for Financial Complai...

引用

Human-Computer Interaction, Optimization and Robotic Applications (HORA), International Congress on

作者： Sezer Uğuz Mert Kumbasar Gül Tokdemir Department of Computer Engineering Çankaya University Ankara Türkiye

ISBN: (数字)9798331510886

ISBN: (纸本)9798331510893

In today’s digital world, customers can utilize a variety of communication channels, such as business emails, consumer forms, feedback platforms, and dedicated complaint websites, to communicate their complaints. This study compares the performance of the supervised Bidirectional Encoder Representations from Transformers for Topic Modeling (BERTopic) with traditional machine learning algorithms, including Random Forest (RF), Support Vector machines (SVM), Logistic Regression (LR), Naive Bayes (NB), K-nearest Neighbors (KNN), and eXtreme Gradient Boosting (XGBoost), for multi-class classification of financial customer complaints. The dataset consists of 16,715 balanced training data and 3,808 test data across five different categories, with the financial complaint data. Experimental results demonstrate that traditional machine learning models, particularly XGBoost, SVM, and LR, achieved the highest classification performance with accuracy rates close to 88%. BERTopic showed a competitive performance with an accuracy of 82.48%. The results suggest that while BERTopic offers interpretability advantages through topic modeling techniques, traditional algorithms provide higher accuracy. This study highlights the promising potential for future financial text analysis and customer complaint classification using hybrid methods, which could lead to more detailed, topic-aware classification approaches.

关键词： Support vector machines machine learning algorithms Accuracy Text categorization Training data Nearest neighbor methods Transformers Natural language processing Robots Random forests

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