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检索条件"主题词=Machine Learning Algorithms"

共 25782 条记录，以下是521-530 订阅

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machine learning algorithms applied to Genetic Promoter Sequences Recognition

Machine learning Algorithms applied to Genetic Promoter Sequ...

引用

International Conference on Mechatronics, Electronics and Automotive Engineering (ICMEAE)

作者： Omar Guzmán-Ruiz Manuel Mejía-Lavalle Alicia Martínez Yasmín Hernández Tecnológico Nacional de México / CENIDET Cuernavaca Morelos MEX

A novel approach presents to recognize promoter sequences using supervised learning algorithms. For the proposed approach was possible to recognize correctly more than 95% of the tested cases, the cases were previousl... 详细信息

ISBN: (数字)9781728199047

ISBN: (纸本)9781728199054

关键词： machine learning algorithms Mechatronics Databases Supervised learning Genomics Bioinformatics Automotive engineering

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machine learning algorithms for real time arrhythmias detection in portable cardiac devices: microcontroller implementation and comparative analysis

Machine learning algorithms for real time arrhythmias detect...

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Symposium of Image, Signal Processing, and Artificial Vision (STSIVA)

作者： Santiago Rúa Santiago A. Zuluaga Alfredo Redondo Andrés Orozco-Duque José V. Restrepo John Bustamante Grupo de Investigación en Microelectrónica Grupo de Investigación en Dinámica Cardiovascular Universidad Pontificia Bolivariana Colombia Grupo de Investigación en Microelectrónica Grupo de Investigación en Dinámica Cardiovascular Grupo de Dinámica Cardiovascular Universidad Pontificia Bolivariana Colombia Grupo de Investigación en Microelectrónica Grupo de Investigación en Dinámica Cardiovascular Facultad de Ingeniería Universidad Pontificia Bolivariana Colombia Grupo de Investigación en Microelectrónica Grupo de Investigación en Dinámica Cardiovascular Centro de Bioingeniería y el Grupo de Dinámica Cardiovascular y docente de la Facultad de Medicina Universidad Pontificia Bolivariana Colombia

This paper presents the development of two machine learning algorithms on a 32-bit ARM ® Cortex ® M4 microcontroller core from Freescale Semiconductors. A neural network (ANN) and a support vector machine (SVM) were implemented for real time detection of ventricular tachycardia (VT) and ventricular fibrillation (VF), and they were compared in terms of accuracy. In the feature extraction step a Fast Wavelet Transform (FWT) was used; which was analyzed using the time-frequency characteristics of energy in each sub-band frequency. For the training and validation algorithms, signals from MIT-BIH database with normal sinus rhythm, VF and VT in a time window of 2 seconds were used. Validation results achieve test accuracy of 99.46% by ANN and SVM in VT/VF detection.

关键词： Support vector machines Electrocardiography Artificial neural networks Wavelet transforms Mathematical model MATLAB machine learning algorithms

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machine learning algorithms for Classification Geology Data from Well Logging

Machine Learning Algorithms for Classification Geology Data ...

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International Conference on Electronics, Computer and Computation (ICECCO)

作者： Timur Merembayev Rassul Yunussov Amirgaliyev Yedilkhan IICT Almaty Kazakhstan Satbayev University Almaty Kazakhstan Suleyman Demirel University Kaskelen Kazakhstan

machine learning today becomes more and more effective instrument to solve many particular problems, where there are difficulties to apply well known and described math model. In other words - it is a great tool to describe nonlinear phenomena. We tried to use this technique to improve existing process of stratigraphy and lithology interpretation and reduce costs on site by applying computer leaded predictions on the basis of existing on-field collected data. Article describes usage of machine learning algorithms for several geology data stratigraphy and lithology boundaries classification based on geophysics logging data for deposits in Kazakhstan. Correct marking of stratigraphy and lithology from geophysics logging data is complex non-linear task. To solve this task we applied several algorithms of machine learning: random forest, logistic regression, gradient boosting (scikit-learn library), k - nearest neighbour (KNN) and XGBoost.

关键词： machine learning algorithms Rocks Geophysics Boosting Training Logistics

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Comparative Analysis of machine learning algorithms for Arabic Sentiment Analysis on Imbalanced Social Media Data

Comparative Analysis of Machine Learning Algorithms for Arab...

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2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems, ICETSIS 2024

作者： Almuhaya, Basheer Saha, Bishal Kaur, Manbir Bazel, Mahmood A. Mohammed, Rehab School of Cse Lovely Professional University Punjab Phagwara144411 India Taiz University Faculty of Engineering and Information Technology Taiz Yemen Taiz University Software Engineering Dept Taiz Yemen

ISBN: (纸本)9798350372229

Sentiment analysis is crucial in understanding public opinions and attitudes on social media platforms. However, dealing with imbalanced datasets, especially in the context of Arabic sentiment analysis, poses significant challenges. In this study, we utilize five well-known machine learning algorithms: support vector machine, logistic regression, random forest, multinomial naive Bayes, and Knearest neighbors, in combination with appropriate preprocessing techniques and pipelines, such as text cleaning, normalization, tokenization, and feature extraction using TFIDF vectorizer. Additionally, we addressed the imbalanced data issue through synthetic minority over-sampling technique and random under-sampling. The evaluation was conducted on the SS2030 Twitter dataset, consisting about 2,436 and 1,816 positive and negative tweets in Arabic. This study provides insights into the performance of various algorithms and the effectiveness of sampling techniques in mitigating the challenges posed by imbalanced data in Arabic sentiment analysis. © 2024 IEEE.

关键词： Arabic Sentiment Analysis Imbalanced Datasets machine learning algorithms Sampling Techniques Sentiment Analysis Text Preprocessing

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machine learning algorithms in 5g Cellular Network for Efficient Cognition

Machine Learning Algorithms in 5g Cellular Network for Effic...

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International Conference on Computer and Communications Technologies (ICCCT)

作者： Chandra Sekhar Pasumarthi Akash Jain Narender Ravula Shanthi Palaniappan Sankararao Majji S K Mydhili Microsoft India Pvt Ltd Hyderabad India Electronics & Telecommunication SSIPMT Raipur Chhattisgarh India GITAM (Deemed to be University) Hyderabad India Sri Krishna College of Engineering and Technology Coimbatore India GRIET Hyderabad India KGiSL institute of technology Coimbatore India

ISBN: (纸本)9781665414487

In today's communications and information technology business, cellular mobile networks are one of the technologies that has had the most significant impact on the industry. As part of the steps made to improve the overall quality of life, many aspects of everyday living, as well as technical breakthroughs, are becoming increasingly reliant on smart gadgets, which are becoming increasingly common. It is projected that, in the near future, every electric gadget will be a smart device that can be connected to the internet on a regular basis. A new network paradigm known as the vast cellular Internet of Things is created as a result, in which a large number of simple battery-powered heterogeneous devices work together for the improvement of humanity in all aspects. This system was developed in accordance with the standard simulation specifications for such systems, and the realistic data that will be extracted from it will aid in demonstrating the effectiveness of the proposed algorithms in order for them to be included in the 5G cellular communications technology.

关键词： Industries machine learning algorithms 5G mobile communication Neural networks Routing Data models Information and communication technology

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Based on the Application of machine learning algorithms in Business Data Analysis 3

Based on the Application of Machine Learning Algorithms in B...

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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Zhang, Yiwen The University of Auckland Auckland Cbd Auckland1010 New Zealand

ISBN: (纸本)9798350319125

With the increasing complexity of business environment, the importance of data analysis in business decision-making has become increasingly prominent. As a powerful data analysis tool, machine learning algorithm has been widely used in the field of business data analysis. This paper will introduce the application of machine learning algorithm in business data analysis, including classification, clustering, prediction and association rule mining. © 2023 IEEE.

关键词： artificial intelligence Business data analysis commercial market machine learning algorithms

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machine learning algorithms to Model Crash Severity and Collision(s)

Machine Learning Algorithms to Model Crash Severity and Coll...

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North Karnataka Subsection Flagship International Conference (NKCon), IEEE

作者： V Prasad Parimala Vellivel R Chandru Department of CSE GMR Institute of Technology Rajam Andhrapradesh India Department of EEE KPR Institute of Engineering and Technology Coimbatore India Department of ECE Sri Sri Ramakrishna Engineering College Coimbatore India

The fact that traffic accidents annually result in many lives, serious injuries, and economic losses makes them one of the most pressing problems the world is now dealing with. The issue of developing precise models to forecast the severity of traffic accidents is crucial for transportation systems. This research project develops models to choose a group of significant characteristics and to construct a system for classifying injury severity. Different machine learning techniques are used to generate these models. supervised machine learning methods. Currently, Random Forest, Support Vector machine, Decision Tree, and K-Nearest Neighbor are the best methods for predicting the severity of injuries in traffic crashes. There is still a lot of opportunity to investigate other methods that can best serve this goal as not only the model's performance but also causality difficulties, unobserved heterogeneity, and temporal instability should be taken into account. Researchers may learn about the most recent strategies used in the study of injury severity modelling in this publication, as well as the ones that produced the greatest performance outcomes. Challenges and potential areas for future research are offered based on the examined papers.

关键词： machine learning algorithms Biological system modeling Roads Predictive models Classification algorithms Sustainable development Injuries

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machine learning algorithms In User Authentication Schemes

Machine Learning Algorithms In User Authentication Schemes

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Electrical, Computer and Energy Technologies (ICECET), International Conference on

作者： Laura Pryor Rushit Dave Jim Seliya Evelyn Sowells Boone University of Wisconsin - Eau Claire Eau Claire WI USA North Carolina A&T State University Greensboro NC USA

ISBN: (纸本)9781665442329

In the past two decades, the number of mobile products being created by companies has grown exponentially. However, although these devices are constantly being upgraded with the newest features, the security measures used to protect these devices has stayed relatively the same over the past two decades. The vast difference in growth patterns between devices and their security is opening up the risk for more and more devices to easily become infiltrated by nefarious users. Working off of previous work in the field, this study looks at the different machine learning algorithms used in user authentication schemes involving touch dynamics and device movement. This study aims to give a comprehensive overview of the current uses of different machine learning algorithms that are frequently used in user authentication schemas involving touch dynamics and device movement. The benefits, limitations, and suggestions for future work will be thoroughly discussed throughout this paper.

关键词： Measurement machine learning algorithms Biometrics (access control) Heuristic algorithms Computational modeling Biological system modeling Authentication

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Prediction of Health Insurance Price using machine learning algorithms 18

Prediction of Health Insurance Price using Machine Learning ...

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18th INDIAcom;11th International Conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Goel, Shivam Chaudhary, Alka Amity University Noida Amity Institute of Information Technology Uttar Pradesh India

ISBN: (纸本)9789380544519

In the realm of health insurance pricing prediction, this research leverages advanced machine learning techniques, including linear regression and neural networks, to uncover pivotal insights. Smoking habits emerged as a major driver of elevated insurance charges, emphasizing the financial impact of this behavior on policyholders. Age also played a critical role, highlighting the progressive nature of healthcare costs as individual's age. These findings offer invaluable guidance for insurers and individuals. Additionally, by providing transparent linear regression models and the predictive power of neural networks this research equips the industry with practical tools to enhance pricing accuracy in a dynamic healthcare landscape. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Correlation Matrix Dataset machine learning algorithms

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Prediction of Hypertensive Disorders in Pregnancy using machine learning algorithms

Prediction of Hypertensive Disorders in Pregnancy using Mach...

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2023 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2023

作者： Devi, Seeta Wagle, Shivali Amit Pune412115 India Pune412115 India

ISBN: (纸本)9798350319200

Objective: The primary objective of this study was to evaluate the efficacy of the HDP gestosis score by adopting machine learning algorithms (MLA) to predict preeclampsia. Methods: The information was gathered retrospectively by reviewing the patient's records. The HDP Gestosis Score predictors were evaluated for their ability to predict preeclampsia. The following machine learning models were utilized in this study: XG Boost classifier, Naive Bayes classifier, Support Vector machine (SVM), RF classifier, and Decision Tree to evaluate the predictive efficiency of the HDP factors in predicting hypertensive diseases during pregnancy. Results: Approximately 55% of women were assessed as being at high risk for preeclampsia, 17% at low risk, and the rest at moderate risk. Compared to other models, Naive Bayes (NB) and Random forest classifier performed better for prediction of women at risk for preeclampsia. The precision and accuracy of NB were 0.97 and 0.96, respectively, with an AUC of 0.96. The precision and specificity of RF were 0.88 and 1.0;correspondingly, with an AUC of 0.82, both models demonstrated greater under the ROC curve values. Conclusion: Gestosis score and the MLA are effective models for predicting preeclampsia. © 2023 IEEE.

关键词： gestosis score Hypertensive Disorders in Pregnancy machine learning algorithms Preeclampsia

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