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检索条件"主题词=Machine Learning Algorithms"

共 25877 条记录，以下是471-480 订阅

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SONAR Based Under Water Mine Detection Using machine learning algorithms

SONAR Based Under Water Mine Detection Using Machine Learnin...

引用

Innovative Practices in Technology and Management (ICIPTM), International Conference on

作者： B. Nikhil Krishna Reddy Rashmi M. R Chockalingam Aravind Vaithilingam S. Kamalakkannan Department of Electrical and Electronics Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru India School of Engineering Faculty of Innovation & Technology Taylor's University Malaysia Department of Electrical and Electronics Engineering S. A. Engineering College Chennai India

ISBN: (数字)9798350307757

ISBN: (纸本)9798350307764

In modern naval warfare, submarines play a vital role in strategic operations. However, submarines face a significant risk of encountering ocean mines, which are lethal and can cause severe damage. To mitigate this risk, submarines are equipped with Sound Navigation and Ranging systems (SONAR) which uses active/passive SONAR to detect objects in the surrounding water. However, distinguishing between the mines and other underwater objects, such as rocks, is a critical challenge. machine learning based object detection is proposed in this paper to distinguish between under water rock versus mines. techniques. By leveraging the capabilities of machine learning algorithms, submarines can make informed decisions in real-time, helping to ensure the safety of naval forces and successful mission outcomes. Real time dataset of SONAR recordings of mines and rocks is collected and considered for classification. Six machine learning algorithms are considered in this work. Support Vector machines (SVM), Decision Tree Classifier, Random Forest Classifier, KNN Classifier, XGboost and Ensemble methods are used to grade whether the detected object is rock or mine. Comparative results of all these algorithms are presented in this paper.

关键词： Support vector machines machine learning algorithms Sonar Rocks Sonar navigation Real-time systems Vectors

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Abusive Words Detection on Reddit Comments Using machine learning algorithms

Abusive Words Detection on Reddit Comments Using Machine Lea...

引用

Device Intelligence, Computing and Communication Technologies, (DICCT), International Conference on

作者： Madhurima S K Abhijith Ajith Swathy VS Boppuru Rudra Prathap Computer Science and Engineering Christ (Deemed to be University) Bangalore Karnataka India

ISBN: (数字)9798350372847

ISBN: (纸本)9798350372854

Utilization of artificial intelligence contributes to the efficient examination of emotions, resulting in valuable insights into the psychological condition of users on a large scale. In this research endeavor, sentiment analysis is conducted on a dataset from Reddit, which was obtained through Kaggle. The feedback in this collection of data was divided into downbeat, neutral, and upbeat sentiments. Various machine learning techniques, like Random Forest, Extreme Gradient Boosting Classifier (XGB), Gradient Boosting machine (GBM), Support Vector machine (SVM), and Convolutional Neural Network (CNN), were detected and examined to assess their effectiveness in sentiment classification. The review of these techniques comprised performance criteria such as F1 Score, accuracy, precision, and recall. Additionally, confusion matrices were utilized to assess the algorithms' proficiency in identifying abusive language. The investigation's conclusions indicate that, when it comes to sentiment analysis, the random forest method performs better than any other strategy, with a maximum accuracy of 0.99 that is on par with the CNN model's accuracy of 0.98. Moreover, random forest proves to be the most effective algorithm in recognizing negative comments and abusive language. This study underscores the significance of employing machine learning algorithms in sentiment analysis, content moderation, social media monitoring, and customer feedback analysis, emphasizing their role in enhancing automated systems that aim to comprehend user sentiments in online discussions.

关键词： Support vector machines Sentiment analysis machine learning algorithms Toxicology Social networking (online) Reviews Boosting

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Comparative Study of Feature Selection Techniques in machine learning algorithms for Predicting Female School Attrition in Bangladesh

Comparative Study of Feature Selection Techniques in Machine...

引用

IEEE International Conference on Biomedical Engineering, Computer and Information Technology for Health

作者： Muminur Rahman Mohammad Nayeem Hasan Faizul Islam Sabrin Sultana Shomi Khan Department of Computer Hazrat Shahjalal (Rh.) Degree College Sylhet Bangladesh Department of Statistics Shahjalal University of Science and Technology Sylhet Bangladesh Department of Banking and Insurance University of Chittagong Chittagong Bangladesh Department of Electrical and Electronic Engineering Shahjalal University of Science and Technology Sylhet Bangladesh

ISBN: (数字)9798331534356

ISBN: (纸本)9798331534363

In real-world scenarios, such as school attrition, it is important to consider relevant features when applying machine learning algorithms. However, not all features are essential, and adding irrelevant ones can diminish the effectiveness of the algorithm. Feature selection (FS) is a vital process in machine learning to minimize data dimensionality and increase algorithm efficiency. Evolutionary algorithms (EA) have attracted plenty of attention as a technique of solving FS challenges. Surprisingly, no previous research has applied these methods specifically to predict factors related to female school attrition. Thus, the purpose of this study is to use multiple feature selection strategies. Genetic Algorithm (GA), Chi-squared, Mutual Information (MI), Exhaustive Feature Selection (EFS) and evaluate their effectiveness by comparing results with and without the application of feature selection techniques. Data for this study is collected from the Multiple Indicator Cluster Surveys, 2019. Among the methods tested, artificial neural networks (ANN) demonstrated the highest accuracy in predicting school attrition. Using GA, Chi-squared, MI, and EFS techniques, the ANN model’s accuracy was 83.76%, 84.11%, 84.23%, and 84.88%, respectively. Using the same feature selection methods, Support Vector machine (SVM) produced results of 82.49%, 83.18%, 81.53%, and 82.65% whereas Logistic Regression (LR) produced accuracy numbers of 81.72%, 81.38%, 82.29%, and 82.31%. Without applying any feature selection techniques, ANN provided 82.38%, SVM provided 81.17% and LR provided 81.14% of accuracy. Therefore, the findings indicate that all models performed well in classifying students as in-school or out-of-school and we achieved higher accuracies after applying FS approaches than without FS. Thus, tailoring filter measures based on specific FS technique characteristics can significantly enhance efficiency and effectiveness.

关键词： Support vector machines Accuracy machine learning algorithms Artificial neural networks machine learning Evolutionary computation Feature extraction Prediction algorithms Mutual information Genetic algorithms

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Incorporating MFCC Feature Extraction to the Classification of Respiratory Sounds by machine learning algorithms

Incorporating MFCC Feature Extraction to the Classification ...

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Computer Sciences, Engineering, and Technology Innovation (ICoCSETI), International Conference on

作者： Muhammad Dimas Erlangga Mohammad Reza Faisal Muliadi Muliadi Fatma Indriani Dwi Kartini Kenji Satou Department of Computer Science Lambung Mangkurat University Banjarbaru Indonesia Faculty of Transdisciplinary Sciences for Innovation Kanazawa University Kanazawa Japan

ISBN: (数字)9798331508616

ISBN: (纸本)9798331508623

Respiratory diseases such as asthma, chronic obstructive pulmonary disease (COPD), and lower respiratory infections are among the leading causes of death globally, particularly in remote areas. Disease diagnosis is often performed using traditional methods such as auscultation, which relies on the subjective expertise of medical professionals. As machine learning algorithms gain prominence in medical data analysis, they provide a more accurate alternative for analyzing respiratory sounds. The purpose of the research is to compare the performance of several machine learning models in classifying respiratory sounds. The method used is a machine learning-based approach utilizing Mel-Frequency Cepstral Coefficients (MFCC) for feature extraction, followed by classification algorithms such as Support Vector machine (SVM), Random Forest, K-Nearest Neighbors (KNN), Naïve Bayes and Deep Neural Networks (DNN). The result of this study shows that the Random Forest classifier exhibited a superior accuracy of 79%. In contrast, SVM, DNN, and KNN classifiers demonstrated stable accuracies ranging from 75% to 77%, and Naïve Bayes recorded the lowest of 52%. Across all accuracy metrics, the Random Forest outperformed the rest of the models as the most effective choice for classifying the respiratory sound. This research provides a foundation for developing more accurate diagnostic tools for respiratory abnormality.

关键词： Support vector machines Accuracy machine learning algorithms Nearest neighbor methods Feature extraction Distance measurement Bayes methods Mel frequency cepstral coefficient Random forests Medical diagnostic imaging

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Modern Approaches to Stock Price Forecasting: Analysis and Application of machine learning algorithms

Modern Approaches to Stock Price Forecasting: Analysis and A...

引用

International Conference on Emerging elearning Technologies and Applications (ICETA)

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

ISBN: (数字)9798331527716

ISBN: (纸本)9798331527723

This paper discusses the development and application of linear regression (LR) to predict stock prices in the stock market. The main objective is to analyse the efficiency and accuracy of linear regression in relation to financial markets, and specifically to predict stock prices one day ahead. The paper begins with a detailed review of the related theoretical foundations and existing methods in the field of stock market forecasting. Next, it discusses the formulation of the stock price forecasting problem and the choice of linear regression as a tool to solve it. Particular emphasis is placed on understanding the properties of linear regression and its potential contribution to predictive models. The second part of the paper describes the implementation and testing of the LR algorithm, including the design and execution of experiments to evaluate its performance. The results are analysed using graphs and individual numerical predictions that were collected on a sample of multiple firm stocks. These results are described and discussed in detail, assessing their sensitivity to short-term volatility and deviation from fair value. The paper provides a new perspective on the use of linear regression in forecasting stock markets and contributes to a better understanding of its effectiveness and limitations.

关键词： Hands Accuracy machine learning algorithms Linear regression machine learning Companies Predictive models Prediction algorithms Stock markets Forecasting

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Exploring machine learning algorithms for Infection Detection Using GC-IMS Data: A Preliminary Study

Exploring Machine Learning Algorithms for Infection Detectio...

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Electronic Engineering, Information Technology & Education (EEITE), International Conference in

作者： Christos Sardianos Chrysostomos Symvoulidis Matthias Schlogl Iraklis Varlamis Georgios Th. Papadopoulos Dept. of Informatics and Telematics Harokopio University of Athens Athens Greece Research & Development Dept. Telesto Technologies Athens Greece Solgenium GmbH Linz Austria

ISBN: (数字)9798350372878

ISBN: (纸本)9798350372885

The developing field of enhanced diagnostic techniques in the diagnosis of infectious diseases, constitutes a crucial domain in modern healthcare. By utilizing Gas Chromatography-Ion Mobility Spectrometry (GC-IMS) data and incorporating machine learning algorithms into one platform, our research aims to tackle the ongoing issue of precise infection identification. Inspired by these difficulties, our goals consist of creating a strong data analytics process, enhancing machine learning (ML) models, and performing thorough validation for clinical applications. Our research contributes to the emerging field of advanced diagnostic technologies by integrating Gas Chromatography-Ion Mobility Spectrometry (GC-IMS) data and machine learning algorithms within a unified Laboratory Information Management System (LIMS) platform. Preliminary trials demonstrate encouraging levels of accuracy when employing various ML algorithms to differentiate between infected and non-infected samples. Con-tinuing endeavors are currently concentrated on enhancing the effectiveness of the model, investigating techniques to clarify its functioning, and incorporating many types of data to further support the early detection of diseases.

关键词： Spectroscopy Analytical models machine learning algorithms Data analysis Infectious diseases Education Medical services

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URL Phishing attack Detection using machine learning algorithms

URL Phishing attack Detection using Machine Learning Algorit...

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Emerging Trends in Industry 4.0 (ETI 4.0)

作者： M Karthick Kumar N Sivakumar Department of computer applications Hindustan institute of technology and science Chennai India

ISBN: (数字)9798350373783

ISBN: (纸本)9798350373790

The pervasive integration of the Internet into our daily lives has made it an essential aspect; however, it has also created opportunities for malicious activities, such as Phishing. Perpetrators employ deceptive strategies, utilizing social engineering and mockup URLs, to surreptitiously obtain sensitive information such as account IDs, usernames, and passwords from individuals and organizations. Despite numerous proposed methods for detecting phishing websites, perpetrators adapt continuously, evading conventional detection techniques. machine learning emerges as a robust approach in identifying these malicious activities due to the shared characteristics inherent in most Phishing attacks. This paper conducts a comparative analysis of various machine learning methods utilized in predicting phishing websites, emphasizing the efficacy of these techniques in addressing cybercrime.

关键词： Support vector machines Uniform resource locators Technological innovation machine learning algorithms Phishing Heuristic algorithms Passwords Organizations Multilayer perceptrons Random forests

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Comparative Analysis of Transformer Fault Classification Based on DGA Data Using machine learning algorithms

Comparative Analysis of Transformer Fault Classification Bas...

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Global Power, Energy and Communication Conference (GPECOM)

作者： Melih Coban Murat Fidan Oktay Aytar Electrical - Electronic Engineering Department Bolu Abant Izzet Baysal University Bolu Türkiye

ISBN: (数字)9798350351088

ISBN: (纸本)9798350351095

Dissolved gas analysis (DGA) is considered a leading technique for fault classification in power transformers. However, accurate analysis results can only be achieved if the measured gases are interpreted, appropriately. In DGA interpretation, traditional techniques, artificial intelligence techniques such as machine learning algorithms, and hybrid techniques are generally used. In this study, four well-known machine learning algorithms have been compared in terms of DGA fault classification: Support Vector machine (SVM), K-Nearest Neighbor (KNN), Naive Bayes (NB) and Decision Tree (DT). The lowest accuracy rate was obtained as 63.63% using the NB algorithm and raw data. In addition to raw data, data converted to logarithmic form has been also used to develop classification models. The highest accuracy rate was determined as 94.54% using the DT algorithm and logarithmic data. The obtained results have been demonstrated the efficiency and stability of the DT algorithm for transformer fault classification, especially when the data was appropriately preprocessed.

关键词： Support vector machines Accuracy machine learning algorithms Sensitivity Oil insulation Data models Stability analysis

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Research on Multidimensional Data Analysis and Decision Making Based on Multiple machine learning algorithms

Research on Multidimensional Data Analysis and Decision Maki...

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Electrical, Automation and Computer Engineering (ICEACE), International Conference on

作者： Xinyuan Wang Chenxuan Ma Peiyan Chen Hebei University of Technology Tianjin China

ISBN: (数字)9798350368208

ISBN: (纸本)9798350368215

Focusing on multidimensional data analysis, this study applies a variety of machine learning algorithms for an in-depth exploration of decision preferences and influencing factors. A comprehensive empirical analysis is conducted through the entropy-weighted TOPSIS comprehensive evaluation method, K-Means clustering model, decision tree and random forest, demonstrating the superiority of the algorithms in complex data processing. First, the entropy weight-based TOPSIS evaluation method identifies and quantifies the key influencing factors and analyzes their weights. On this basis, the variance decomposition methods of Dagum's Gini coefficient decomposition model and VAR model are used to reveal the structural differences in the dataset. Next, the decision preference patterns in the data were analyzed using K-Means clustering and decision tree models. Finally, the random forest model further reveals the important influences behind the data decisions. This research provides a strong theoretical basis and practical tools for complex data analysis and decision support through the application of multiple machine learning algorithms.

关键词： Analytical models machine learning algorithms Data analysis Computational modeling Decision making Clustering algorithms Data models Entropy Decision trees Random forests

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Comparative Analysis of machine learning algorithms for Cardiovascular Disease Risk Prediction

Comparative Analysis of Machine Learning Algorithms for Card...

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Trends in Engineering Systems and Technologies (ICTEST), International Conference on

作者： Anushka Doiphode Karan Bora P. R. Navghare Department of Computer Engineering Pune Institute of Computer Technology Pune India

ISBN: (数字)9798350307795

ISBN: (纸本)9798350307801

Cardiovascular conditions (CVDs) remain a major global health concern, challenging early threat assessment and forestalment. In this study, we employ four distinct machine learning algorithms — K Nearest Neighbors (KNN), Random Forest, Decision Tree and Support Vector machine — to prognosticate the threat of cardiovascular conditions. The dataset used in this exploration consists of 14 essential features and has been sourced from Kaggle, a prominent platform for data wisdom competitions. Our primary idea is to estimate the prophetic performance of these algorithms and determine which algorithm gives us maximum accuracy. Among the algorithms examined, the KNN classifier emerges as the top pantomime, achieving an accuracy of 84.48. This result showcases the efficacy of the KNN algorithm in handling the complexity of CVD threat vaticination. These findings provide critical guidance for healthcare professionals and experimenters in enforcing effective prophetic models for early cardiovascular complaint threat identification and intervention.

关键词： Support vector machines machine learning algorithms Accuracy Forestry Prediction algorithms Threat assessment Classification algorithms

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