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检索条件"主题词=Machine Learning Algorithms"

共 25808 条记录，以下是721-730 订阅

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Forecasting vertical grid load using machine learning algorithms

Forecasting vertical grid load using machine learning algori...

引用

IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT Europe)

作者： Franziska Popp Marie-Louise Kloubert Ieva Axt Tim Schuessler Amprion GmbH Dortmund Germany

ISBN: (数字)9798350390421

ISBN: (纸本)9798350390438

The vertical grid load is one of the main input variables to determine the load flow on the transmission grid in system operation centers. As it depends on a variety of input variables, a method is needed that allows a good forecast despite incomplete and uncertain information about underlying grids, loads and feed-ins. With each underlying distribution grid having its own characteristics, the authors propose to model the vertical grid load individually for each interconnection point between the transmission and distribution grid. The following machine learning algorithms are tested and compared to develop a reliable forecast model: Linear regression, Lasso and Ridge regression, generalized additive regression, Random Forests and XGBoost. The case study shows the results from a German transmission system operator for a forecast horizon of one day ahead.

关键词： Renewable energy sources machine learning algorithms Computational modeling Systems operation Weather forecasting Predictive models Data models Random forests Power generation Load modeling

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Enhanced Image Processing for Improved Detection and Analysis of COVID-19 in Medical Imaging by U sing machine learning algorithms

Enhanced Image Processing for Improved Detection and Analysi...

引用

International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT)

作者： Sinan Ghanem Mikhwir Albadwi Islamic Azad university Isfahan Branch Khorasgan Iran Artificial Intelligence Engineering Department College of Engineering Al-Ayen University Thi-Qar Iraq

ISBN: (数字)9798350354423

ISBN: (纸本)9798350354430

This work intends to increase COVID-19 identification and analysis utilizing modern image processing methods and machine learning algorithms. We deployed a dataset including 5,000 chest X-ray pictures from the COVID-19 Image Data Collection and performed numerous preprocessing methods, including noise reduction and normalization, to prepare the data. For machine learning, we deployed a convolutional neural network (CNN) with a model architecture of 3 convolutional layers and 2 dense layers. Our CNN model obtained an outstanding accuracy of 94.7% in recognizing COVID-19 patients from the X-ray pictures. This high accuracy illustrates the efficiency of merging modern image processing techniques with machine learning to increase the diagnostic precision for COVID-19, possibly assisting in more rapid and precise illness identification.

关键词： COVID-19 Technological innovation machine learning algorithms Accuracy Noise reduction machine learning Convolutional neural networks Reliability X-ray imaging Biomedical imaging

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Cyber Bullying Detection and Classification Using machine learning algorithms

Cyber Bullying Detection and Classification Using Machine Le...

引用

Cybernation and Computation (CYBERCOM), International Conference on

作者： Shipra Gupta Indu Bharti Jain Merry Saxena Pradeepta Kumar Sarangi Ashok Kumar Sahoo Alok Kumar Agrawal Department of Law Maharishi Markandeshwar (Deemed to be University) Mullana India Department of Interdisciplinary Courses in Engineering Chitkara University Institute of Engineering & Technology Chitkara University Punjab India Chitkara University School of Engineering & Technology Chitkara University Himachal Pradesh Graphic Era Hill University Graphic Era Deemed to be University Dehradun India

ISBN: (数字)9798331542344

ISBN: (纸本)9798331542351

Cybercrime has become a significant concern for both private and public entities. Growing popularity of social media platforms and the concept of freedom to speech encouraging the negative minded people to create nuisance on social media platforms. Creating real-time monitoring systems to tackle digital criminal activities presents a complex but highly effective strategy for the prompt identification of cyberattacks. machine learning (ML) provides automated solutions to meet these challenges. Various ML algorithms can be employed in cybersecurity to prevent attacks and mitigate security risks. Cyberbullying which is a form of harassment on social media platforms often invites legal complicacies. This study employs three machine learning models: Logistic Regression, Naïve Bayes, and Random Forest. The dataset utilized consists of user tweets obtained from Kaggle. The experimental findings indicate that Logistic Regression outperforms the other two models, achieving an accuracy of 97% on the training data and 91% on the test data.

关键词： Logistic regression machine learning algorithms Accuracy Computational modeling Cyberbullying Training data Data models Bayes methods Computer crime Regression tree analysis

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Predicting Future Power Generation and Diagnosing Failure Rate at a Solar Power Plant in India Using machine learning algorithms

Predicting Future Power Generation and Diagnosing Failure Ra...

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International Conference on Systems and Control (ICSC)

作者： Djemaa Rahmouni Mohamed Djamel Mouss Leïla-Hayet Mouss Mohamed Benbouzid Laboratory of Automation and Manufacturing Engineering University of Batna 2 Batna Algeria University of Brest Brest France Logistics Engineering College Shanghai Maritime University Shanghai China

ISBN: (数字)9798331531812

ISBN: (纸本)9798331531829

In light of the global energy crisis and the need to meet the huge demands for electricity, it is crucial to make short-term predictions about energy production. Solar energy is a clean and sustainable source of electricity generation, and solar systems are connected to smart grids that feed all categories of users. Therefore, it is important to consider the potential for solar energy production in the short term. This paper presents an approach based on machine learning algorithms to predict solar energy generation in a solar plant in India. Performance of the proposed models, linear regression and random forest regression, is verified using metrics such as R2, mean square error (MSE), MAE, The RFR model demonstrated an accuracy rate of 99%, while the LR model achieved 98% in forecasting the amount of energy generated.

关键词： machine learning algorithms Accuracy Electricity Linear regression Solar energy Production Predictive models Forecasting Random forests Power generation

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Predictive Modeling of Life Expectancy Using machine learning algorithms

Predictive Modeling of Life Expectancy Using Machine Learnin...

引用

Communications and Information Technologies (GCCIT), Global Conference on

作者： Tamanna Choudhury Shivam Kumar Bharti Mahendra Kumar Gourisaria Junali Jasmine Jena Dayal Kumar Behera Anjan Bandyopadhyay School of Computer Engineering KIIT Deemed to be University Bhubaneswar Odisha India

ISBN: (数字)9798350388916

ISBN: (纸本)9798350388923

Life expectancy, a pivotal public health measure, indicates the average lifespan based on current mortality rates and reflects societal well-being. Over the last century, it has significantly increased due to medical advancements, improved living standards, and public health initiatives. Factors like healthcare quality, lifestyle, social status, and environment have profound influence on this improvement in Life Expectancy. However, disparities persist due to uneven access to healthcare, education, and jobs. This study deploys various machine learning models, to predict life expectancy, achieving an adjusted R-squared value of 0.9729 by Extra Tree Regression, indicating exceptional model accuracy. By analyzing a comprehensive dataset, socio-economic, health, and environmental factors such as GDP per capita, composition of resources, education levels etc., were identified as key predictors of life expectancy by the model. The high R-squared value highlights the model's robustness in capturing dynamic relationships between these variables and life expectancy.

关键词： machine learning algorithms Education Mortality machine learning Predictive models Robustness Public healthcare Socioeconomics Regression tree analysis Standards

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Predictive Modeling for ***. Nursing Placement Using machine learning algorithms

Predictive Modeling for ***. Nursing Placement Using Machine...

引用

Advances in Computing, Communication and Applied Informatics (ACCAI), International Conference on

作者： Ranjana Chavan Dipali Dumbre Seeta Devi Symbiosis College of Nursing (SCON) Symbiosis International (Deemed University) (SIU) Pune India

ISBN: (数字)9798350389449

ISBN: (纸本)9798350389456

The demand for well-trained nursing professionals is ever-increasing, necessitating efficient placement processes post-graduation. This study explores the application of machine learning (ML) algorithms to predict ***. Nursing placement outcomes, aiming to optimize workforce management and graduate placement efficiency. Traditional placement methods often suffer from subjectivity and inefficiency. Leveraging historical placement data and relevant features, ML algorithms discern patterns to forecast individual placement likelihood accurately. Various ML techniques including logistic regression, decision trees, support vector machines, and ensemble methods are employed and evaluated for their predictive efficacy. The CN2 rule inducer model emerges as the top-performing model, demonstrating superior discriminatory ability and accuracy. These findings offer significant implications for nursing education institutions and healthcare organizations, facilitating better alignment of education with workforce demands. However, the study also emphasizes the importance of balancing model performance with interpretability. Further research and implementation efforts are warranted to refine predictive modeling approaches and seamlessly integrate them into nursing education and workforce management practices, ultimately contributing to the advancement of nursing practice and patient care delivery.

关键词： Support vector machines Logistic regression machine learning algorithms Computational modeling Education Medical services Organizations

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Enhancing Diabetes Detection Through Data Preprocessing: A Comparative Analysis of machine learning algorithms

Enhancing Diabetes Detection Through Data Preprocessing: A C...

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Computing, Internet of Things and Microwave Systems (ICCIMS), International Conference on

作者： Btissam Elaziz Younes Ommane Mohamed Eddabbah Yassin Laaziz LabTIC ENSA of Tangier Abdelmalek Essaadi University Tetuan Morocco ISSB Faculty of Medical Sciences Mohammed VI Polytechnic University Ben-Guerir Morocco Higher School of Technology Essaouira Cadi Ayyad University Marrakesh Morocco

ISBN: (数字)9798350351736

ISBN: (纸本)9798350351743

This study evaluates the performance of K-means, SVM, and KNN algorithms for diabetes detection using the PIMA Indian database. We apply data preprocessing techniques like PCA, LDA, and various scaling methods. Results indicate that data preparation critically impacts algorithm effectiveness. SVM, combined with PCA and MinMaxScaler, achieves the highest accuracy. KNN also benefits from PCA and MinMaxScaler. K-means shows the greatest improvement, with the WAF1-Score rising from 0.56 to 0.76 when paired with PCA and Robust Scaler.

关键词： Support vector machines machine learning algorithms Accuracy Data preprocessing Focusing Nearest neighbor methods Prediction algorithms Diabetes Optimization Principal component analysis

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Predictive Modeling for Heart Disease Detection using machine learning algorithms

Predictive Modeling for Heart Disease Detection using Machin...

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Software, Systems and Information Technology (SSITCON), International Conference on

作者： Arshan Ali Khan M. D Sha Fahad R M Savithramma B P Ashwini Department of CSE (AI&ML) Siddaganga Institute of Technology Visvesvaraya Technological University Tumakuru India Department of CSE Siddaganga Institute of Technology Visvesvaraya Technological University Tumakuru India

ISBN: (数字)9798350352931

ISBN: (纸本)9798350352948

machine learning is a state-of-art technology applied widely in various sectors including health care and has made significant advancements in this field by revolutionizing diagnostic and treatment approaches. Cardiovascular diseases (CVD) have remained a leading global cause of death witnessed across the world. However, early detection of this disease has proven crucial for improving patient health outcomes. In this context, the current study has investigated the use of machine learning models to predict cardiovascular disease risk by examining clinical and lifestyle factors, including age, gender, blood pressure, cholesterol and glucose levels, smoking habits, and alcohol consumption. Six different machine learning models are trained and tested over the available data from Kaggle. The built models are evaluated based on various performance metrics including accuracy score, training time, F1-Score, recall, etc. The final inference of the study is that the decision tree models, particularly, random forest and gradient boosting algorithms proved to be more accurate among all other models used in this study. The research emphasized how these machine learning techniques have improved early diagnosis and facilitated timely interventions, helping healthcare providers make more informed decisions and ultimately enhanced patient care.

关键词： Measurement Accuracy machine learning algorithms machine learning Medical services Predictive models Data models Blood pressure Cardiovascular diseases Random forests

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Application of machine learning algorithms for Fault Detection and Diagnosis in Power Systems

Application of Machine Learning Algorithms for Fault Detecti...

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Advances in Computing, Communication and Applied Informatics (ACCAI), International Conference on

作者： M P Haripriya R Durai Vasanth M. Suresh Anand Vikas Vitthal Kulkarni S Farook K.R. Senthil Kumar Department of Computer Science Kristu Jayanti College Bangalore Karnataka India Department of Computer Science and Engineering Vel Tech Rangarajan Dr Sagunthala R&D Institute of Science and Technology Avadi Chennai Tamil Nadu India Department of Computing Technologies School of Computing SRM Institute of Science & Technology Kattankulathur Tamil Nadu India Department of Electrical Engineering AISSMS College of Engineering Pune India Department of Electrical and Electronics Engineering School of Engineering Mohan Babu University Tirupathi Andhar Pradesh India Department of Mechanical Engineering R.M.K. Engineering College Kavaraipettai Tamil Nadu India

ISBN: (数字)9798350389449

ISBN: (纸本)9798350389456

The operation of machine literacy algorithms for disfigurement identification and opinion within power systems is presented in this exploration composition. In light of the added complexity of moment’s power grids, it’s of the utmost significance to develop fault discovery and opinion styles that are both effective and effective. As a result of their capacity to assay big datasets and honor patterns, machine literacy ways present several openings that hold great pledges. Several different machine learning algorithms, including as neural networks, decision trees, support vector machines, and clustering approaches, are delved in this study. The purpose of the disquisition is to estimate the utility of these algorithms in relating and diagnosing blights in power systems. Through the use of empirical evaluation and case studies, the exploration reveals that these algorithms are applicable in the process of perfecting the trustability and effectiveness of power system operations. This exploration donates to the advancement of fault identification and opinion approaches in power engineering, hence paving the way for further exploration.

关键词： Support vector machines Fault diagnosis Power engineering machine learning algorithms Fault detection Neural networks Clustering algorithms

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Hypertension Stage Detection Using Ensemble machine learning algorithms

Hypertension Stage Detection Using Ensemble Machine Learning...

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IEEE International Conference on Emerging & Sustainable Technologies for Power & ICT in a Developing Society (NIGERCON)

作者： Wasihun Kindalem Worku Abebaw Agegne Engda Gizachew Mulu Setegn Ayodeji Olalekan Salau Nigus Wereta Asnake Department of Computer Science Debark University Debark Ethiopia Department of Electrical/Electronics and Computer Engineering Afe Babalola University Ado-Ekiti Nigeria Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India Department of Information Technology Debark University Debark Ethiopia

ISBN: (数字)9798331542559

ISBN: (纸本)9798331542566

Hypertension, described by high or rising blood pressure, poses significant health risks and is classified into three stages: stage one, stage two, and hypertensive crisis. Hypertensive crisis, an abnormal form of hypertension, contributes to long-term and short-term consequences, including obesity, diabetes, cardiovascular complications during pregnancy, increased mortality and morbidity in older adults, and the development of chronic diseases later in life. To design the proposed approach, first collect the data from the University of Gondar Hospital and then apply preprocessing techniques such as data cleaning, transformation, and optimization to enhance its quality for machine learning tasks. The ensemble model evaluates 92.79% accuracy, 94.3% precision, 92.07% recall, 92.97% F1 score, and 79.07% ROC score. The experimental results demonstrated that the Bagged Decision Tree ensemble algorithm exhibited superior performance with an accuracy of 92.79%. This study highlights the effectiveness of ensemble learning techniques in the healthcare sector, specifically in identifying the stage of hypertension and reducing morbidity and mortality rates in Ethiopia.

关键词： Hypertension Measurement Training Accuracy machine learning algorithms Mortality Feature extraction Decision trees Random forests Testing

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