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检索条件"主题词=Machine Learning Algorithms"

共 25877 条记录，以下是821-830 订阅

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Weighted Ensemble Based Noninvasive HbA1c Estimation from PPG signals Using machine learning algorithms

Weighted Ensemble Based Noninvasive HbA1c Estimation from PP...

引用

IEEE International Conference on Consumer Electronics-Asia (ICCE-Asia)

作者： Shama Satter Tae-Ho Kwon Ki-Doo Kim Department of Electronics Engineering Kookmin University Seoul Korea

ISBN: (数字)9798331530839

ISBN: (纸本)9798331530846

Noninvasive assessment of glycated hemoglobin (HbA1c) levels is an ongoing area of research in diabetes management, providing a patient-friendly alternative to traditional blood-based measurements. In this study, we introduce an approach to estimate HbA1c from photoplethysmography (PPG) signals by applying a weighted ensemble of machine learning models, increasing accuracy and improving prediction reliability by prioritizing models with lower mean squared error (MSE). machine learning algorithms such as random forest and various boosting algorithms such as XGBoost, LightGBM, CatBoost, gradient boosting and AdaBoost were used to estimate HbA1c levels from wrist PPG signals.

关键词： Wrist machine learning algorithms Accuracy Estimation Predictive models Prediction algorithms Boosting Data models Reliability Overfitting

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Lung Cancer Prediction and Classification Using machine learning algorithms

Lung Cancer Prediction and Classification Using Machine Lear...

引用

Expert Clouds and Applications (ICOECA), International Conference on

作者： Satyanarayana Murthy Nimmagadda Kalli Likhitha Gurindapalli Srilatha Sajja Monika Sree Electronics and Communication VR siddhartha engineering college(Jntuk) Vijayawada India

ISBN: (数字)9798350385793

ISBN: (纸本)9798350385809

This research addresses the challenges in the early identification of lung cancer by proposing an automated system using various machine learning (ML) and image processing techniques. This study has utilized a collection of images including different lung cancer types and healthy lung tissue. By analyzing this data, deep understanding has gained on its properties. Then, this study has tested various machine learning models and selected the most accurate one for real-time implementation. The proposed system's effectiveness in lung cancer detection is finally analyzed by using performance evaluation. Finally, the system is deployed with a user-friendly interface to aid healthcare professionals in diagnosing lung cancer.

关键词： Radio frequency Performance evaluation machine learning algorithms Accuracy Lung cancer Lung Prediction algorithms

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Galaxy Clustering and Classification using machine learning algorithms and XAI

Galaxy Clustering and Classification using Machine Learning ...

引用

International Conference on Information Technology Research (ICITR)

作者： Amasha Elvitigala U dani Navaratne Samadhi Rathnayake Kapila Dissanayaka Dept. of Information Technology Sri Lanka Institute of Information Technology Malabe Sri Lanka Dept. of Information Technology Sri Lanka Institute of Information Technology Kandy Sri Lanka Dept. of Information Technology Sri Lanka Institute of Information Technology Colombo Sri Lanka

ISBN: (数字)9798331511425

ISBN: (纸本)9798331511432

This paper presents a novel approach to galaxy clustering and analysis using machine learning (ML), coupled with Explainable Artificial Intelligence (XAI) methods. Traditional astronomical research often relies on manual analysis of galaxy clusters, which can be time-consuming and potentially biased. Our study aims to automate this process using advanced computational methods, focusing on clustering algorithms such as Gaussian Mixture Models (GMM) and Density-Based Spatial Clustering of Applications with Noise (DBSCAN). We also employ classification algorithms including Random Forests, Decision Trees, and Support Vector machines (SVM). The application of XAI techniques enhances the interpretability and trustworthiness of our models' outputs. By leveraging data from the Sloan Digital Sky Survey (SDSS), we demonstrate the efficacy of our approach in identifying and analyzing galaxy clusters, potentially revolutionizing the field of astronomical research.

关键词： Surveys Support vector machines Accuracy machine learning algorithms Explainable AI Noise Clustering algorithms Classification algorithms Random forests Gaussian mixture model

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ASMPM- Automation to Stock Market Predictions using machine learning algorithms

ASMPM- Automation to Stock Market Predictions using Machine ...

引用

Cybernation and Computation (CYBERCOM), International Conference on

作者： Kamalpreet Kaur Nitish Kumar Arwinder Kaur Chirag Sharma Computer Science and Engineering Department Lovely Professional University Phagwara Computer Science and Engineering Department Lovely Professional University Punjab Computer Science and Engineering Department Lovely Professional University Punjab India

ISBN: (数字)9798331542344

ISBN: (纸本)9798331542351

General stock market consists of a few listed companies that people can trade on depending on the state of the market. The prices of individual stocks can fluctuate between 52-week highs and lows, which can have a significant impact on the retail industry and make retailers nervous when deciding whether to hold or sell. To be more adaptable during these pivotal moments, we have created a stock market prediction algorithm. Exploring the complex field of stock price prediction, this work welcomes the combination of machine learning and finance. Equipped with decision trees, random forests, logistic regression, and k-nearest-neighbors as our compasses, we set out on results that oceans of market fluctuations. Loaded with information from the National Stock Exchange (BSE), our ship carefully extracts important features as it navigates through years' worth of stock prices, financial measures, and news mood. utilizing a brilliant bit of experimental *** NSE (or BSE) data, we employed four algorithms to predict our stock market analysis: logistic regression, decision trees, random forests, and K-nearest. The performance measures we provided were 51% for logistic regression, 100% for decision trees, 98% for random forests, and 63% for K-nearest-neighbor algorithms.

关键词： Logistic regression machine learning algorithms Sea measurements Predictive models Nearest neighbor methods Prediction algorithms Feature extraction Decision trees Stock markets Random forests

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Multi Spectral Imaging Sensor Integration and Optical Path Fusion Based on machine learning algorithms

Multi Spectral Imaging Sensor Integration and Optical Path F...

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Interactive Intelligent Systems and Techniques (IIST), International Conference on

作者： Jie Yang Yunfeng Xia Qishuai Liang Sheng Ye Yeming Lai Transmission Operation & Maintenance Branch of Hainan Power Grid Corporation China Southern Power Grid Co. Ltd Haikou China

ISBN: (数字)9798350374421

ISBN: (纸本)9798350374438

This paper mentions the wide application of multispectral imaging technology in remote sensing and its importance in different fields. This paper introduces the key functions of machine learning algorithm, and points out that machine learning (ML) algorithm can effectively process multi- spectral data and improve the performance of tasks such as classification, Convolutional Neural Network (CNN) image reconstruction and feature extraction. Finally, the research results indicate that we used different thresholds for anomaly detection and calculated the values of accuracy and precision. When the threshold is 0.00, the accuracy is 0.72 and the precision is 0.68. When the threshold is 0.05, the accuracy is 0.78 and the precision is 0.74. When the threshold is 0.10, the accuracy is 0.82 and the precision is 0.79. From these data, we can observe that as the threshold increases, the accuracy gradually improves, indicating that the model is more accurate in detecting normal land surfaces.

关键词： Integrated optics Accuracy machine learning algorithms Multispectral imaging Land surface Optical imaging Classification algorithms Optical sensors Remote sensing Image reconstruction

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Performance Comparison of machine learning algorithms for Keratoconus Detection

Performance Comparison of Machine Learning Algorithms for Ke...

引用

International Conference on Telecommunications (ICT)

作者： Zahra J. Muhsin Rami Qahwaji Ibrahim Ghafir Muawyah Al Bdour Saif AlRyalat Mo'ath AlShawabkeh Majid Al-Taee University of Bradford Bradford United Kingdom School of Medicine The University of Jordan Amman Jordan AI-Taif Eye Centre Amman Jordan Independent ICT Consultant Liverpool United Kingdom

ISBN: (数字)9798350356694

ISBN: (纸本)9798350356700

This paper provides an in-depth examination of eight machine learning (ML) algorithms commonly employed in diagnosing keratoconus (KC) over the past decade. The analysis is conducted using a clinical dataset accumulated by the authors from two prominent eye-care centers in Jordan over the same period. By scrutinizing the data collected from these centers, the proposed study aims to shed light on the efficacy and comparative performance of various ML algorithms in KC diagnosis. Through rigorous evaluation and comparison, we seek to identify the algorithms that exhibit superior predictive accuracy and reliability in differentiating between normal, subclinical (or suspect) KC, and clinical KC corneas. The obtained results demonstrated that the tree-based models achieved superior performance compared to other models. Specifically, the Random Forest attained the highest classification accuracy at 99.6%, closely followed by Gradient Boosting at 99.2%, and Decision Tree at 98.4%. These promising findings underscore the effectiveness of these classifying algorithms in KC detection and their promise for practical implementation in clinical settings.

关键词： Radio frequency Accuracy machine learning algorithms Sensitivity Prediction algorithms Classification algorithms Telecommunications

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Application of machine learning algorithms for Analyzing Sentiments Using Twitter Dataset

Application of Machine Learning Algorithms for Analyzing Sen...

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Sustainable Computing and Smart Systems (ICSCSS), International Conference on

作者： Bhairavi Rewatkar Aditya Barhate Prateek Verma Department of Artificial Intelligence and Data Science Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Wardha Maharashtra India Department of Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Wardha Maharashtra India

ISBN: (数字)9798350379990

ISBN: (纸本)9798350391558

Sentiment analysis is a Natural Language Processing (NLP) approach used to determine whether a piece of text is negative, positive, or neutral based on the emotions it expresses. Despite advancements, several challenges persist in this field, including handling sarcasm, context ambiguity, and the presence of noisy data. Recent techniques, such as deep learning models and transformer-based approaches, have improved accuracy but still face limitations in computational efficiency and real-time analysis. This article employs machine learning (ML) techniques to analyze text and determine the sentiments of reviews shared on Twitter, aiming to address these challenges. The proposed system extracts attributes from the text, such as the sentiment polarity (whether the sentiment is negative or positive) and the topic or theme of the discussion. A comparison with prior work on sentiment analysis in tweets is presented, along with a comprehensive study of feature extraction and representation methods. The study evaluates the performance of six classifiers: Naïve Bayes, Decision Tree, Logistic Regression, Support Vector machine (SVM), Gradient Boosting, and Random Forest, based on different feature extraction techniques. Using the Bag of Words technique, SVM achieved the highest accuracy of 94.58%, while using term frequency, Random Forest showed the highest accuracy of 94.99%. The developed algorithms for sentiment analysis in tweets are useful for analyzing social feedback, product surveys, and social media comments. Combining neural networks and deep learning improves the accuracy of sentiment analysis and provides opportunities to analyze various types of emotions, enabling proactive responses to public sentiment.

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

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Assessing machine learning algorithms for Stress Prediction: A Healthcare Perspective

Assessing Machine Learning Algorithms for Stress Prediction:...

引用

Artificial Intelligence and Emerging Technology (Global AI Summit), International Conference on

作者： C Arulmozhi M Diviya Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India

ISBN: (数字)9798350379716

ISBN: (纸本)9798350379723

As stress related illnesses become more frequent, it is crucial that new techniques for reliable and quick forecasting are developed. This paper aims to analyse machine learning techniques with health data to measure stress level. The data collected from the Chengalpattu Medical Hospital has information of approximately 1000 entries with various parameters such as gender, seasonal cortisol level, sugar level, insulin level and tested HOMA-IR algorithm including Random forest(RF), Light gradient, Support Vector Classification (SVC), Logistic regression, K-nearest neighbors (KNN), Decision tree etc. Through the extensive comparison of these models, the LightGBM algorithm seems to have better accuracy. This study revealed that there is a possibility for high level machine learning to enhance the prediction of stress related-conditions thus enhancing the provision of health care and results will be advanced.

关键词： Logistic regression Accuracy machine learning algorithms Medical services Human factors machine learning Nearest neighbor methods Predictive models Prediction algorithms Stress

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Evaluating machine learning algorithms for Landuse Dynamics

Evaluating Machine Learning Algorithms for Landuse Dynamics

引用

Self Sustainable Artificial Intelligence Systems (ICSSAS), International Conference on

作者： Suneetha Manne Satya Sahithi Veeramallu Radhesyam Vaddi Hemanth Kumar Cheepulla Girish Pujar M S R Murthy Academic Advisor Department of Information Technology Velagapudi Ramakrishna Siddhartha Engineering College Vijayawada India Land Use Division National Remote Sensing Centre India Space Research Organisation Hyderabad India Velagapudi Ramakrishna Siddhartha Engineering College Vijayawada India

ISBN: (数字)9798350368413

ISBN: (纸本)9798350368420

The study analyses various machine learning algorithms for land use and land cover classification and change detection of prakasam district, Andhra Pradesh. The study makes use of multi- temporal Remote Sensing imagery for the years 2017 and 2023 from Landsat - 8 sensor of Prakasam district. Three different machine learning algorithms were used to detect changes in land cover features over time based on classes like vegetation cover, water bodies and human settlements. Later, the work aims to present a complete understanding of change detection methodology used within this research, from preprocessing high-quality Landsat 8 satellite images for better interpretation and identification of spectral values, up to machine learning algorithms in terms land use/land cover classification. The findings reveal the positive transitions in areas of land use/cover between 2017 and 2023, especially cultivated lands as well as water bodies to less extent with a few urban parts. This research will be useful for the remote sensing community as an example of how to detect changes in land cover using ML methods with a high degree of accuracy, overtaking the traditional methods.

关键词： machine learning algorithms Accuracy Surveillance Urban areas Land surface Vegetation mapping Landsat Satellite images Remote sensing Water resources

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Exploring the Relationship Between eWOM and Purchase Intention Through machine learning algorithms

Exploring the Relationship Between eWOM and Purchase Intenti...

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International Conference on Artificial Intelligence and Data Sciences (AiDAS)

作者： Bhawana Kothari Ambica Prakash Mani V. M. Tripathi Department of Commerce Graphic Era Deemed to be University Dehradun India Department of Commerce Graphic Era Hill University Dehradun India

ISBN: (数字)9798331528553

ISBN: (纸本)9798331528560

The integration of artificial intelligence (AI), machine learning, and other smart technologies has changed market dynamics in the current digital era, which is marked by widespread technical developments. Companies increasingly use these techniques to get a thorough understanding of the behavior and preferences of their customers, with the goal of providing personalized services that meet their requirements rather than merely promoting their products. Additionally, eWOM (Electronics word of mouth) is used to find out exactly what consumers think of a product and influence their intentions to purchase it. This procedure is further improved by real-time analytics, which makes it possible to quickly adjust to shifting market emotions. In order to assess customer purchase intention in real-time circumstances, our research suggests a reliable machine learning approach. Through the integration of theoretical models, empirical investigations, and sophisticated algorithms, our objective is to reveal the fundamental trends influencing consumer choices in various market niches and product categories. Our methodology offers reliable forecasting, overcoming the constraints of traditional research and providing firms with useful information for focused marketing strategies.

关键词： machine learning algorithms Oral communication machine learning Companies Reliability theory Market research Real-time systems Forecasting Consumer electronics

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