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检索条件"主题词=Machine Learning Algorithms"

共 25877 条记录，以下是431-440 订阅

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Comparative Analysis of machine learning algorithms and Ensemble Techniques for Diverse Image Classification Tasks

Comparative Analysis of Machine Learning Algorithms and Ense...

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International Conference on Ubiquitous Information Management and Communication (IMCOM)

作者： Yulita Salim Athar Fathana Rakasyah Herdianti Darwis Harlinda Irawati Abdul Rachman Manga Department of Computer Science Universitas Muslim Indonesia Makassar Indonesia

ISBN: (数字)9798331507817

ISBN: (纸本)9798331507824

In the era of burgeoning image data, the demand for robust image classification algorithms has never been more pressing. This research article delves into the realm of image classification, encompassing a comprehensive analysis of diverse datasets and machine learning algorithms, While simultaneously studying the efficiency of ensemble approaches in improving classification performance. The study employs five distinct datasets, spanning medical images, everyday objects, and natural scenery, ensuring a broad spectrum of classification challenges. These datasets include the Skin Cancer ISIC dataset, CIFAR-10, Flowers, Apparel Image, and Brain Tumor dataset. The image classes within these data sets vary significantly, presenting an ideal testbed for assessing algorithmic versatility. Our investigation scrutinizes five machine learning algorithms: Support Vector Classifier (SVC), Random Forest Classifier (RFC), Gradient Boosting Classifier (GBC), K-Nearest Neighbors (KNN), and Gaussian Naive Bayes (GNB). Each algorithm is evaluated individually, followed by a comprehensive ensemble approach involving a Voting Classifier. The results unveil nuanced performance variations across diverse datasets. Notably, RFC and GBC exhibit remarkable accuracy in brain tumor image classification, while KNN demonstrates strengths in classifying apparel images. Ensemble techniques, embodied by the Voting Classifier, harmonize these algorithms, yielding competitive and balanced performance across the datasets. This article contributes valuable insights into the realm of image classification, shedding light on algorithmic strengths and limitations, the efficacy of ensemble techniques, and their applicability to diverse image datasets. These findings hold significance for fields ranging from medical diagnostics to everyday object recognition, paving the way for more precise and versatile image classification solution

关键词： machine learning algorithms Accuracy Heuristic algorithms Brain tumors Nearest neighbor methods Classification algorithms Complexity theory Ensemble learning Random forests Image classification

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Partial Discharge Source Classification Using machine learning algorithms

Partial Discharge Source Classification Using Machine Learni...

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Power Engineering Conference (SAUPEC), Southern African Universities

作者： Lucas T. Thobejane Bonginkosi A. Thango Department of Electrical Engineering University of Johannesburg Johannesburg South Africa

ISBN: (数字)9798331535162

ISBN: (纸本)9798331535179

This article proposes a machine-learning algorithm for the automatic classification of single-source partial discharge (PD) in power transformers. PD testing provides valuable information of the state and deterioration of the insulation systems of transformer windings and core. A PD testing setup based on the IEC 60270 International standard is used to test PD from transformers of varying size and operational age. PD is recorded at different voltage levels applied to the transformer under test. Where the majority of PD classification literature has focused on laboratory developed artificial PD models, this work uses practical power transformers as a basis for testing and collecting the PD database. The PD database collected from this testing is utilized for the training, validation and testing of the machine learning algorithm. In this article, a comparative analysis of various trained machine learning algorithms for classifying PD is performed. The results of the classification show very pleasing performance from the tested classifier algorithms, with Bilayered Neural Network achieving a 96.97% validation accuracy of and a test accuracy of 97%.

关键词： Partial discharges machine learning algorithms Accuracy Databases Voltage machine learning Classification algorithms Standards Testing Power transformer insulation

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Comparative Performance Analysis of machine learning algorithms for Non-Performing Loan Prediction

Comparative Performance Analysis of Machine Learning Algorit...

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Computational, Communication and Information Technology (ICCCIT), International Conference on

作者： Tanmay Chaturvedi Sukanta Halder Uppalapati Sudheer kumar Nilanjan Das SK Bittu Department of Electrical Engineering Sardar Vallabhbhai National Institute of Technology Surat India Department of Electrical Engineering Indian Institute of Technology (ISM) Dhanbad India

ISBN: (数字)9798331512965

ISBN: (纸本)9798331512972

This paper investigates the application of machine learning models for Non-Performing Loan (N. P. L) prediction in the banking sector. The study evaluates the accuracy of diverse models, including traditional algorithms and advanced machine learning approaches such as Random Forest, Gradient Boost, XGBoost, LSTM, Decision Tree, Gaussian Naive Bayes and LightGBM. Training of the models are done on the dataset 'Training Data,’ available on Kaggle under the notebook named “Loan Prediction Based on Customer Behaviour”. Data processing and analysis of the given dataset was done on Kaggle's Jupyter Notebook cloud environment by using Python programming libraries. Through rigorous experimentation, it has been attested that Random Forest classifiers stands out to be best performing model when tested across all the performance benchmarks. This information thereby offers valuable guidance for institutions aiming to strengthen N. P. L prediction mechanisms and overall risk management strategies.

关键词： Training Accuracy machine learning algorithms Computational modeling Predictive models Vectors Bayes methods Decision trees Random forests Long short term memory

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Enhancing Phishing Website Detection with machine learning algorithms

Enhancing Phishing Website Detection with Machine Learning A...

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International Symposium on Digital Forensic and Security (ISDFS)

作者： Weijie Pang Ayrton Joseph DiPina Ashar Neyaz School of Computing & Data Science Wentworth Institute of Technology Boston MA USA

ISBN: (数字)9798331509934

ISBN: (纸本)9798331509941

This paper examines the impact of feature selection on phishing website detection using machine learning techniques. We utilize the UCI Phishing Website dataset, which consists of 11,055 instances and 30 features with no missing values. Our results show that the random forest outperforms logistic regression, achieving an accuracy of 97.11%. By analyzing feature importance, we optimize the model by removing detrimental features, further improving accuracy to 97.74%. This study highlights the effectiveness of the random forest in phishing detection and underscores the importance of feature selection in enhancing model performance.

关键词： Logistic regression Analytical models Accuracy machine learning algorithms Phishing Digital forensics Feature extraction Security Random forests

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Predicting Coronary Heart Disease Mortality Using machine learning algorithms

Predicting Coronary Heart Disease Mortality Using Machine Le...

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Artificial Intelligence x Multimedia (AIxMM), Conference on

作者： Soha Parto Ali Akbar Safavi Shiva Naghsh Mahsa Keikha Amir Sharafkhaneh School of Electrical and Computer Engineering Shiraz University Shiraz Iran Department of Mechatronic Systems Engineering Simon Fraser University Burnaby Canada Department of Medicine Baylor College of Medicine Houston TX USA

ISBN: (数字)9798350390971

ISBN: (纸本)9798350390988

This study aimed to predict Coronary Heart Disease (CHD) mortality over a 10 to 15-year period using machine learning (ML) techniques. We studied a rather large variety of features associated to CHD mortality from the Sleep Heart Health Study (SHHS) dataset. Using the most important features identified through mutual information, several ML algorithms, including LR, SVM, KNN, ETC, and RF, were trained. A 10-fold cross-validation was employed to ensure accuracy. As a result, the KNN algorithm (k = 18) outperformed the others, achieving an accuracy of 76.05%, an AUC of 79.41%, a sensitivity of 81.08%, a specificity of 71.28%, and a precision of 73.48%. Our proposed approach reasonably well predicts CHD mortality many years in advance, aiming to identify critical factors for CHD mortality prediction, monitor patients, and provide early warnings of the need for prevention and treatment through medical intervention.

关键词： Heart Training machine learning algorithms Accuracy Sensitivity Mortality Nearest neighbor methods Prediction algorithms Mutual information Diseases

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Comparison of Different machine learning algorithms for Stock Price Prediction

Comparison of Different Machine Learning Algorithms for Stoc...

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International Symposium on Applied machine Intelligence and Informatics (SAMI)

作者： Dušan Čatloch Eva Chovancová Sylvia Matašová Department of Computers and Informatics Faculty of Electrical Engineering and Informatics Technical University of Košice Košice Slovakia

ISBN: (数字)9798350379365

ISBN: (纸本)9798350379372

Stock price prediction is a challenging and dynamic task due to the high volatility and complexity of financial markets. In this study, we present a comparative analysis of several machine learning algorithms applied to the prediction of stock prices. The models explored include Gradient Boosting Regressor (GBR), Gated Recurrent Unit (GRU), Linear Regression (LR), Long Short-Term Memory (LSTM), Random Forest (RF), and Support Vector Regression (SVR). Using a dataset comprising historical stock prices from multiple companies, we evaluate the performance of each model based on accuracy and predictive stability. Daily predictions were generated for each model, and the results were assessed using metrics such as average deviation from real price. The analysis indicates that while deep learning models like GRU and LSTM exhibit a strong capacity to capture temporal patterns, ensemble methods such as GBR and RF also provide competitive accuracy for short-term forecasts. This research highlights the strengths and limitations of various approaches to stock price forecasting and offers insights into their potential applications for investment decision-making. Future work may involve extending this analysis to broader financial datasets and investigating the integration of additional market factors.

关键词： Radio frequency Support vector machines machine learning algorithms Accuracy Predictive models Boosting Vectors Stability analysis Long short term memory Random forests

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A Comprehensive Analysis of machine learning algorithms for Diabetes Prediction: Comparative Performance and Insights

A Comprehensive Analysis of Machine Learning Algorithms for ...

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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

Diabetes is one of the most chronic conditions seen worldwide. Its prevalence has been causing many complications, such as cardiovascular diseases, kidney failure, and neuropathy. Early prediction is crucial in helping prevent these from occurring or, at worst, to be affected minimally. In this work, we implemented various machine learning techniques to predict the onset of diabetes based on demographic and health-related data like age, body mass index (BMI), hypertension, heart disease, and blood glucose levels. The used algorithms were XGBoost, Random Forest, Stacked Ensemble, Gradient Boosting machine (GBM), and Support Vector machine (SVM). XGBoost could reach a maximum AUC of 0.979653 and a log loss of 0.0789284 with an F1-score of 0.811485. These really demonstrated strong power in the prediction of diabetes. High performance was also highlighted with the Stacked Ensemble Model, reaching an AUe of 0.979579 and an accuracy of 97.22%. These outcomes suggest that the superior ensemble methods, XGBoost and Stacked Ensembles, will provide quality prediction such that earlier diagnosis of patients with diabetes might be achieved consequently and, therefore, good recovery.

关键词： Support vector machines Analytical models machine learning algorithms Medical services Predictive models Boosting Prediction algorithms Market research Diabetes Random forests

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Optimization and Improvement of Reliability of machine learning algorithms Based on Regularization Methods

Optimization and Improvement of Reliability of Machine Learn...

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International Russian Smart Industry Conference (SmartIndustryCon)

作者： Sherzodjon Yarmatov Xudaykul Bustanov Lola Safarova Samarkand State University named after Sharof Rashidov Samarkand Uzbekistan Samarkand State University of Veterinary Medicine Samarkand Uzbekistan

ISBN: (数字)9798331511241

ISBN: (纸本)9798331511258

Nowadays, optimizing the results of machine learning algorithms is of great importance in the field of artificial intelligence. This article analyzes the effectiveness of Lasso, Ridge, and Elastic Net regression methods to increase the reliability of the results of machine learning algorithms in assessing economic indicators. These methods help to eliminate the problems of overfitting and multicollinearity, and are aimed at improving models based on machine learning algorithms. The study tests the effectiveness of these methods on a data set of economic indicators, and uses the Grid Search method to determine the optimal values for model parameters. As a result, it is shown that these approaches can improve the accuracy and generalizability of economic indicator assessments.

关键词： machine learning algorithms Accuracy Economic indicators Biological system modeling Estimation Prediction algorithms Search problems Data models Reliability Overfitting

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Comparative Analysis of machine learning algorithms on Credit Card Fraud Detection

Comparative Analysis of Machine Learning Algorithms on Credi...

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Cognitive Computing in Engineering, Communications, Sciences and Biomedical Health Informatics (IC3ECSBHI), International Conference on

作者： Dev Sharma Esha Kaur Nikunj Sharma Daksh Rawat Satya Prakash Yadav Manika Manwal Computer Science and Engineering Graphic Era Hill University Dehradun India School of Computer Science Engineering and Technology (SCSET) Bennett University Greater Noida U.P. India Computer Science and Engineering Graphic Era Hill University Dehradun Uttarakhand India R/S Graphic Era Deemed to be University Dehradun Uttarakhand India

ISBN: (数字)9798331518523

ISBN: (纸本)9798331518530

Fraud detection is a crucial area not only for credit cards but also for industries handling financial transactions where fraud detection is crucial. machine learning (ML) had come up powerfully with a variety of tools that can process large amounts of data and can find complex hidden patterns from the data. This abstract provides the usage of ML algorithms in Fraud Detection with different types of techniques used. In this paper a comparison has been made between different algorithms based on their Accuracy, Precision, Recall, and F1-score. algorithms Used are SVM, KNN, Decision Tree, Random Forest, Logistic Regression. Dataset was collected from Kaggle and it contains 284806 rows and a total of 31 columns.

关键词： Support vector machines Logistic regression machine learning algorithms Accuracy Computational modeling Biological system modeling Nearest neighbor methods Credit cards Fraud Decision trees

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Predicting Compulsory Earthquake Insurance Tendencies In Turkey Using machine learning algorithms

Predicting Compulsory Earthquake Insurance Tendencies In Tur...

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

作者： Elif Serra Bayram Ezgi Aktar Demirtas Department of IE Eskisehir Eskisehir Osmangazi University Turkiye

ISBN: (数字)9798331510886

ISBN: (纸本)9798331510893

This study aims to predict the tendency of homeowners in Turkey to purchase Compulsory Earthquake Insurance (CEI) using nine different machine learning (ML) classification algorithms. The dataset includes 11 independent variables related to demographic, structural, and geographic attributes, and one binary dependent variable. Data were collected via an online survey and preprocessed by removing incomplete responses. The Gradient Boosting (GB) algorithm achieved the highest performance with 70% accuracy. According to the SHAP (SHapley Additive exPlanations) analysis, "Monthly Income," "Square Meter Area," "Number of Floors," and "Construction Year" were the most significant features. The Odds Ratios (ORs) demonstrate that individuals with higher income and those living in newer and larger residences exhibit a significantly greater likelihood of possessing CEI. It was observed that individuals with higher education levels were less inclined to purchase insurance. Detailed analysis of the obtained results is expected to contribute to more effective policy-making in the insurance sector and among public authorities.

关键词： Surveys Meters machine learning algorithms Earthquakes Insurance Boosting Classification algorithms Robots Random forests Optimization

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