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检索条件"主题词=Machine Learning Algorithms"

共 25364 条记录，以下是81-90 订阅

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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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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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Classification of Autism Spectrum Disorder using Supervised machine learning algorithms: A Review

Classification of Autism Spectrum Disorder using Supervised ...

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International Conference on ent Computing and Control Systems (ICICCS)

作者： P Yugander M Jagannath School of Electronics Engineering Vellore Institute of Technology Chennai India Department of ECE Kakatiya Institute of Technology and Science Warangal India

ISBN: (数字)9798331512088

ISBN: (纸本)9798331512095

Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder that affects the cognitive, speech, and social communication of individuals. Globally, around 2% of the population suffers from ASD. The problems associated with ASD start in childhood and continue through adulthood. It can be identified using several methods, such as Autism Diagnostic Observation Schedule (ADOS), machine learning, deep learning, and computer-aided design (CAD) tools. Over the past two decades, CAD tools have become more prevalent in diagnosing diseases. The CAD tools identify abnormalities in autistic individuals using machine learning algorithms and magnetic resonance images. This paper aims to present various ASD classification algorithms and abnormalities of ASD controls. Articles are searched in various databases such as IEEE Explore, Springer, ACM Digital Library, PubMed, and Science Direct. A total of 22 complete articles from 2010 to 2024 are included in the review. Various supervised ML algorithms like Logistic regression (LR), Random Forest (RF), Decision tree (DT), K-Nearest Neighbor (KNN), and Support vector machine (SVM) are investigated. This study revealed numerous structural and functional abnormalities of ASD controls.

关键词： Support vector machines Autism Logistic regression machine learning algorithms Design automation Reviews Nearest neighbor methods Classification algorithms Decision trees Random forests

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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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Identification and Classification of Medicinal Plants using machine learning algorithms

Identification and Classification of Medicinal Plants using ...

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Electronics and Renewable Systems (ICEARS), International Conference on

作者： G Bhargavi Venkatesh Jayaraman Jabez J Deepa R Surendran R Department of Computing Technologies SRM Institute of Science and Technology Chennai India Department of Computer Science and Engineering Chennai Institute of Technology Chennai India Department of Computer Science and Engineering School of Computing Sathyabama Institute of Science and Technology Chennai India Department of Computing Technologies School of Computing SRM Institute of Science and Technology Chennai Tamilnadu India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

The increaing significance of plant life and botanical expertise extends beyond mere visual appreciation. With the growing interest in sustainable living and alternative remedies, there is a pressing demand for easily accessible and credible information on plant categorization, particularly focusing on medicinal plants. This innovative project synergizes machine learning capabilities, botanical proficiency, and user-friendly technology to give users the insights required to harness the benefits of medicinal plants responsibly and sustainably. The platform facilitates precise categorization and a comprehensive understanding of the therapeutic attributes of plants.

关键词： Medicinal plants Support vector machines Visualization Renewable energy sources machine learning algorithms Biological system modeling Pressing Nearest neighbor methods Convolutional neural networks Random forests

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Utilizing machine learning algorithms To Predict Chronic Kidney Disease

Utilizing Machine Learning Algorithms To Predict Chronic Kid...

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Pervasive Computational Technologies (ICPCT), International Conference on

作者： Harshpal Singh Rakesh Kumar Meenu Gupta Vasubabu Machavarapu Department of CSE Chandigarh University Mohali India Davita Inc Texas US

ISBN: (数字)9798331508685

ISBN: (纸本)9798331519476

chronic kidney disease (CKD) is a big issue because it happens over time and it doesn't show symptoms until it’s advanced. The models are produced through the use of various machine learning (ML) techniques such as Decision Tree (DT), Random Forest (RF), Ada Boost, Extra Tree Classifier and Light Gradient Boosting (LGBM). The training and evaluation dataset was provided by the UCI machine learning repository and comprised a huge variety of clinical and laboratory characteristics that are associated with the diagnosis of CKD. Performances of the constructed models are evaluated with traditional measures, as common as F1-score, accuracy, precision and recall. Hyperparameter tuning is also used to improve both model accuracy and it’s performance. The additional results are meant to illustrate how well machine learning algorithms can predict CKD, as hyper parameter tuning the extra tree classifier gave an accuracy of 99.16\%. This research may get the field to earlier CKD prediction to guide intervention and improve patient outcomes.

关键词： Training Accuracy machine learning algorithms Sensitivity Medical services Predictive models Chronic kidney disease Random forests Tuning Classification tree analysis

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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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Comparative Analysis of machine learning algorithms for Disease Prediction Using Medical Datasets

Comparative Analysis of Machine Learning Algorithms for Dise...

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Intelligent Data Communication Technologies and Internet of Things (IDCIoT), International Conference on

作者： C. Roopa R.S. Latha M. Dhavamani G. Bommisrilekkhaa P. Angel Shalini R. Dhanyajothi Department of Computer Science and Engineering Kongu Engineering College Erode Tamilnadu Department of Artificial Intelligence Kongu Engineering College Erode Tamilnadu Department of Mathematics Kongu Engineering College Erode Tamilnadu

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

Application of machine learning in the analysis of medical data can be said to be one of the current transformations happening within the healthcare fraternity. This paper highlights several ways in which various machine learning techniques can be used for gaining valuable insight into disparate types of medical data, such as patient records or genetic information, toward further clinical decision-making with a view to better prediction of diseases or strategies related to personalized medicine. The study critically evaluated various algorithms by considering their performances, suitability with regard to certain medical datasets they would be set upon to analyze, and to interpret the performance, particularly within the supervised learning frameworks. It talks about methodologies through which feature selection can take place to make sure that there is effective model development. Consequential applications in early disease detection as well as the optimization of their treatments are further emphasized, representing great potential for massive improvements in their recovery outcomes. The results highlight how far reaching data-driven approaches can affect the future of healthcare. It is envisioned that, with the use of such methods, the paper foresees a paradigm shift in disease diagnosis and treatment for personalized medical care and improved health outcomes.

关键词： machine learning algorithms Precision medicine Supervised learning machine learning Predictive models Feature extraction Prediction algorithms Medical diagnostic imaging Optimization Diseases

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Intelligent Event Detection in Unbalance Active Distribution System Using Reinforcement machine learning algorithms

Intelligent Event Detection in Unbalance Active Distribution...

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Smart and Sustainable Developments in Electrical Engineering (SSDEE), IEEE International Conference on

作者： Archana Tamrakar Saumendra Sarangi Electrcical Engineering MNNIT Allahabad Prayagraj India

ISBN: (数字)9798331542108

ISBN: (纸本)9798331542115

With integration of Distributed Energy Resources (DERs) in the distribution system (DS), the power quality has decreased causing large deviation in voltage, frequency and, especially during disturbances, the security of the system may lose due to the maloperation of the relays. However, effective event detection can reduce the outage time and initiate faster restoration of the healthy section. Furthermore, the data collected from the DS is large in volume and variety and hence, to capture the accurate information from those data during any disturbance, is difficult and demands application machine learning techniques for event identification. In this paper, a Q-learning algorithm, is proposed for accurate event detection in distribution system following a disturbance. The previous and subsequent events data is utilized to identify the event. On the basis of events, the corresponding actions include isolating faulty sections, facilitating fast restoration, and load reduction as necessary. The efficacy of these algorithms is evaluated through simulations conducted on the IEEE 33-bus system with integration of photo-voltaic, utilizing MATLAB/SIMULINK 2023 and Jupyter Notebook and necessary actions are derived following the disturbance. The results obtained are satisfactory.

关键词： Meters machine learning algorithms Accuracy Q-learning Event detection Power quality Voltage Security Sustainable development Relays

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Comparative Analysis of machine learning algorithms for EMG Signal Classification

Comparative Analysis of Machine Learning Algorithms for EMG ...

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Emerging Smart Computing and Informatics (ESCI), Conference on

作者： Anagha Shinde Virendra Shete Rageshri Bakre MIT-ADT University Pune India

ISBN: (数字)9798331515683

ISBN: (纸本)9798331515690

We present a comparative analysis of different machine learning algorithms for classifying electromyography (EMG) signals for the application of prosthetic arm development. We applied four machine learning algorithms, namely Support Vector machines (SVM), K-Nearest Neighbors (KNN), Decision Trees, and Logistic Regression. These algorithms classified the signals into three classes mentioned. Our results show that SVM achieved the highest accuracy at 83 %, followed by KNN (77 %), Logistic Regression (75 %), and Decision Tree (74 %). These findings suggest that SVM is the most promising approach for EMG signal multiclass classification for the mentioned application. While these results provide a solid foundation, we discuss potential improvements through hyperparameter tuning and study with real EMG sensor data for prosthetics movement. This study contributes to the development of more responsive and intuitive machine learning-powered prosthetic arms, potentially enhancing the quality of life for individuals with upper limb amputations.

关键词： Support vector machines Logistic regression machine learning algorithms Accuracy Pattern classification Nearest neighbor methods Electromyography Real-time systems Decision trees Prosthetics

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