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检索条件"主题词=Machine Learning Algorithms"

共 25808 条记录，以下是581-590 订阅

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Run Time and Accuracy of machine learning algorithms for Speaker Identification

Run Time and Accuracy of Machine Learning Algorithms for Spe...

引用

2024 International Symposium on Computer Science and Educational Technology (ISCSET)

作者： Stephan Lede René Schmidt Wolfram Hardt Department of Computer Engineering Chemnitz University of Technology Chemnitz Germany

ISBN: (数字)9798350390803

ISBN: (纸本)9798350390810

The efficiency and accuracy of machine learning algorithms in embedded systems are paramount for real-time processing tasks such as identifying an intended speaker out of potential sound sources. This paper presents an evaluation of support vector machines (SVMs) and feed-forward neural networks (FFNNs) for the regression problem of speaker identification, with a focus on constant calculation time and the influence of the use of different features. Utilizing a dataset comprising different speaker configurations and combinations of features were examined to assess their influence on accuracy and runtime. Results indicate that SVMs with linear, polynomial, and RBF kernels, as well as FFNNs, demonstrate effectiveness in solving the identification problem by providing real-time capabilities.

关键词： Support vector machines Accuracy machine learning algorithms Embedded systems Runtime Educational technology Real-time systems Polynomials Feedforward neural networks Kernel

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Advancements in Heart Disease Detection: Integrating machine learning algorithms and Clinical Insights

Advancements in Heart Disease Detection: Integrating Machine...

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Technological Advancements in Computational Sciences (ICTACS), International Conference on

作者： Sagar Sharma Ramandeep Sandhu Manik Rakhra School of Computer Science and Engineering Lovely Professional University Phagwara Jalandhar Punjab India

ISBN: (数字)9798350387490

ISBN: (纸本)9798350387506

This study evaluates various machine learning algorithms, including deep learning, ensemble methods, and hybrid approaches, for classifying heart rhythms. It emphasizes the need for a detailed comparative analysis to understand each algorithm's strengths and weaknesses. The research highlights the importance of feature selection in enhancing predictive capabilities but notes the absence of detailed evaluation metrics such as precision, recall, F1 score, and ROC-AUC. Additionally, the study identifies gaps in understanding the significance of specific features in heart disease prediction, suggesting further investigation into feature relevance. Ethical concerns, such as data privacy, algorithmic bias, and decision-making transparency, are underscored as critical issues that need addressing to ensure the responsible and equitable deployment of predictive models in healthcare. The study advocates for a holistic approach, combining technical and ethical considerations, to advance heart disease prediction technology. Using KNN within the CDSS and a hybrid algorithm combining SVM and Random Forest, the study seeks to find the best predictive model for heart disease.

关键词： Heart Support vector machines Measurement Ethics machine learning algorithms Predictive models Prediction algorithms Feature extraction Random forests Diseases

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Interpretable machine learning algorithms for Interpretation of Clinical Data

Interpretable Machine Learning Algorithms for Interpretation...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Gautham Krishna Manoj Kumar Pathak Meenakshi Dheer Department of Civil Engineering School of Engineering and Technology JAIN (Deemed to be University) Bangalore Karnataka India Department of English ARKA JAIN University Jamshedpur Jharkhand India Department of Agriculture Vivekananda Global University Jaipur India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Interpretable machine learning (IML) algorithms use mathematical principles of machine learning to assist know-how, perception, and manipulation of scientific facts. Those algorithms aim to bridge the gap between clinicians’ knowledge of the facts and the output of traditional predictive analytics. IML algorithms have proved suitable for diverse obligations related to records analysis and interpretation of scientific facts, inclusive of predicting medical responses to remedy, exploring medical pathways, predicting future medical outcomes, and clustering affected person populations. Numerous techniques may be employed while using IML algorithms, which include linear fashions, choice tree models, help vector machines, neural networks, and Bayesian networks. Those fashions are mixed with specific parameters, including feature selection and weighting that can be used to evaluate the importance of every characteristic in predictive models. Moreover, those models can be implemented for non-linear facts by using regularization techniques. One essential advantage of IML over traditional devices in getting to know algorithms is the capacity to interpret the version outputs. Through using interpretable facts visualizations consisting of bar plots, scatter plots, and heat maps, clinicians can take advantage of insights into the relationships among features and goal variables.

关键词： Knowledge engineering machine learning algorithms Neural networks Clustering algorithms machine learning Predictive models Prediction algorithms Vectors Reliability Predictive analytics

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Plant Diseases Recognition Using machine learning algorithms

Plant Diseases Recognition Using Machine Learning Algorithms

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IEEE/ACM International Symposium on Big Data Computing (BDC)

作者： S. Alketbi T. Bonny Department of Computer Engineering University of Sharjah Sharjah UAE

ISBN: (数字)9798350367300

ISBN: (纸本)9798350367317

This report explores the critical field of plant disease recognition through the use of various machine learning algorithms. As agriculture faces increasing challenges from pathogens and diseases, the rapid and accurate identification of plant afflictions is essential for effective crop management and improved yields. We analyzed a range of machine learning techniques, from traditional classifiers to advanced deep learning models, focusing on their ability to classify and diagnose plant diseases using symptomatology and image analysis. Our study evaluates the strengths and limitations of these approaches, providing insights into their technical details and practical applicability in agriculture. Through comprehensive experimentation and performance comparisons, we highlight the efficacy and robustness of different algorithms in plant disease management. The findings offer valuable guidance on how these methodologies can contribute to agricultural sustainability, helping stakeholders refine disease management strategies, optimize resources, and enhance resilience against environmental and pathogenic challenges. In summary, this report underscores the potential of diverse machine learning algorithms in advancing plant disease recognition, aiming to support effective and sustainable agricultural practices.

关键词： Plant diseases machine learning algorithms Image analysis Plants (biology) Feature extraction Agriculture Data models Sustainable development Monitoring Resilience

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Classification of Pulsar Candidates Using Classical machine learning algorithms

Classification of Pulsar Candidates Using Classical Machine ...

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Electrical, Computer, Communications and Mechatronics Engineering (ICECCME), International Conference on

作者： Siddharth Kannan Aakash Dhananjay Shanbhag 12th Grade A-Levels Prabhavati Padamshi Soni Intl. Junior College Juhu Mumbai India

ISBN: (数字)9798350391183

ISBN: (纸本)9798350391190

Pulsars, rapidly spinning neutron stars with powerful radio emissions, are of significant interest in astrophysics. This study examines the effectiveness of various machine learning methods-Random Forest, XGBoost, Logistic Regression, Support Vector machines (SVM), and Gradient Boosting-in identifying pulsars from a given dataset. We addressed the challenge of data imbalance, where pulsars were under-represented, resulting in an accuracy increase from 82.4% to 93.7% after correction. Additionally, we removed higher-order central moments from the data and found that this had no statistically significant impact on prediction accuracy. Our analysis of feature importance revealed that attributes from the Integrated Profile are more crucial for identifying pulsars than those from the DM-SNR curve.

关键词： Support vector machines Logistic regression Accuracy Neutron stars Mechatronics machine learning algorithms machine learning Forestry Predictive models Spinning

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Leukemia Subtype Classification via Gene Expression Using machine learning algorithms

Leukemia Subtype Classification via Gene Expression Using Ma...

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Information Technology, Electronics and Intelligent Communication Systems (ICITEICS), IEEE International Conference on

作者： Roshani Raut Shreya Lokare Shraddha Gavkare Mayuri Narute Dept. of Information Technology Pimpri Chinchwad College of Eng. Pune India

ISBN: (数字)9798350382693

ISBN: (纸本)9798350382709

This project aims to improve patient care and treatment outcomes by using gene expression data to reliably diagnose acute myeloid leukemia (AML) and acute lymphoblastic leukemia (ALL). To create trustworthy classification models, six machine learning (ML) techniques were used: Gaussian Naive Bayes (NB), Random Forest, Support Vector machine (SVM), K Nearest Neighbors (KNN), Multi-Layer Perceptron(MLP), and Logistic Regression. The experiment's outcomes show that the algorithms were successful; 91% accuracy rates were achieved by both logistic regression and MLP, while the other approaches produced results that were competitive. Good findings for the ROC curve analysis area under the curve (ROCAUC) demonstrated the models' ability to distinguish between different forms of cancer. These results demonstrate the application of ML algorithms in oncology.

关键词： Support vector machines Logistic regression machine learning algorithms Accuracy Soft sensors Vectors Classification algorithms

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Investigation of Diabetes Prediction Using machine learning algorithms

Investigation of Diabetes Prediction Using Machine Learning ...

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Students Conference on Engineering and Systems (SCES)

作者： Jyotirmay Jain Geetla Sruthi P. Karuppanan Department of ECE MNNIT Allahabad Prayagraj INDIA

ISBN: (数字)9798350374711

ISBN: (纸本)9798350374728

Diabetes has emerged as a pervasive and potentially fatal condition worldwide, impacting individuals across diverse age groups and demographics. The Diabetes is a multifaceted nature encompasses lifestyle choices, genetic pre-dispositions, and stress levels. Recognizing the urgency, a comprehensive predictive analysis framework is proposed for aiming in early detection. This research work integrates outlier rejection, missing data handling, standardization, and feature selection to refine predictive accuracy. Employing K-fold cross-validation bolsters robustness, ensuring reliable performance. Moreover, employing a variety of machine learning such as K-Nearest Neighbor (KNN), Random Forest (RF), XGBoost and Support Vector machine (SVM) classifiers enhances adaptability across datasets, augmenting the tool's effectiveness. Ultimately, the objective is to develop a potent proactive healthcare solution, facilitating early identification and management of diabetes on a global scale. Through proactive intervention, strives to mitigate the adverse effects of diabetes and improve overall public health outcomes.

关键词： Support vector machines Data privacy machine learning algorithms Accuracy Predictive models Nearest neighbor methods Prediction algorithms

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Heart Disease Prediction Using machine learning algorithms: Performance Analysis

Heart Disease Prediction Using Machine Learning Algorithms: ...

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International Conference on Advancement in Electrical and Electronic Engineering (ICAEEE)

作者： Subrata Saha Md. Motinur Rahman Tahmid Tamrin Suki Md. Mahbub Alam Md. Shah Alam Mohammod Abu Sayid Haque Institute of Electronics Bangladesh Atomic Energy Commission Dhaka Department of Computer Science and Engineering Bangladesh University of Professionals Dhaka

ISBN: (数字)9798350388282

ISBN: (纸本)9798350388299

Cardiovascular disease (CVD) is one of the major causes of death worldwide. Early detection of this disease can decrease the mortality rate significantly and it can also reduce the costs of medical treatment. Researchers are trying to develop automated systems using data mining techniques that can help in this regard. Although they would be extremely difficult to build, machine learning (ML) based methods for heart disease prediction might be very useful in clinical settings. Moreover, the majority of nations lack doctors specialized in cardiovascular treatments, and a high percentage of cases are misdiagnosed. These issues could be resolved by creating an accurate and effective early-stage heart disease prediction system using ML. In this paper, we propose an ML technique to identify cvd patients depending on different features. We collected a heart disease dataset from the Center for Disease Control and Prevention (CDC) then applied different pre-processing steps, and transformed several attributes to make them readable for the ML algorithms. 5 supervised ML algorisms were implemented and the Random Forest algorithm provided the best outcomes in all the performance metrics that we studied, with maximum accuracy of 91% and, ROC-AUC (Area under the ROC Curve) of 0.91. It also surpasses the performance of other algorithms with a maximum True Positive Rate (TPR) of 0.922 and a minimum False Negative Rate (FNR) of.078.

关键词： Heart Measurement machine learning algorithms Accuracy Medical treatment Forestry Prediction algorithms

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FertiForecast: Identification of Fertilizer Based on NPK Levels using machine learning algorithms

FertiForecast: Identification of Fertilizer Based on NPK Lev...

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Computing and Data Science (ICCDS), International Conference on

作者： M. Vimaladevi Thangamani R Dhivyasubashini K Kandavel G Aravind S Department of Artificial Intelligence Kongu Engineering College Tamil Nadu India Department of AI-DS Kongu Engineering College Erode Tamilnadu India

ISBN: (数字)9798350365337

ISBN: (纸本)9798350365344

For many people in India, especially those living in rural areas, agriculture is their main source of income. Approximately 50% of the labour force in the nation is employed by it, either directly or indirectly. Fertilizers playa crucial role in modern agriculture by enhancing soil fertility and promoting optimal plant growth. They provide essential nutrients to plants, including nitrogen, phosphorus, potassium, and micronutrients. To boost crop yields, commercial fertilizers are sprayed on agricultural crops. The majority of farming took place on tiny family farms with little usage of chemicals before to the 1950s. Since then, the usage of chemical fertilizers in contemporary agricultural methods has paralleled with the move toward larger corporate farms. Farmers may lack knowledge about the various types of fertilizers available, their formulations, and the appropriate selection for specific crops and soil conditions. This can lead to the use of inappropriate or imbalanced fertilizers. machine learning algorithms can play a significant role in fertilizer prediction in agriculture, offering several advantages that contribute to more efficient and sustainable fertilizer management. ML algorithms analyze diverse datasets, including soil characteristics, weather patterns, crop types, and historical yield data. This data-driven approach enhances decision-making by providing insights into the factors influencing fertilizer needs.

关键词： machine learning algorithms Plants (biology) Rural areas Soil Prediction algorithms Phosphorus Agriculture

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Towards Skin Lesion Classification Using machine learning algorithms and Stokes Imaging Polarimetry

Towards Skin Lesion Classification Using Machine Learning Al...

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Pacific Rim Conference on Lasers and Electro-Optics (CLEO/Pacific Rim)

作者： Oscar I. Rodríguez-Cortés Geminiano Martínez-Ponce Centro de Investigaciones en Óptica A. C. Loma del bosque 115 Lomas del campestre León Guanajuato México

Melanoma, the deadliest skin cancer, demands enhanced detection methods. A handheld device integrating polarimetric imaging and machine learning for lesion classification via the ABCDE method is proposed. Preliminary ... 详细信息

ISBN: (数字)9798350372076

ISBN: (纸本)9798350372083

关键词： Stokes parameters machine learning algorithms Imaging Lighting Melanoma machine learning Medical services

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