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检索条件"主题词=Machine Learning Algorithms"

共 25188 条记录，以下是61-70 订阅

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Research Intelligent Methods and machine learning algorithms for Identification Anomalies in the Drilling Operations

Research Intelligent Methods and Machine Learning Algorithms...

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International Russian Smart Industry Conference (SmartIndustryCon)

作者： Anton Parfenov Alexander Borovsky Denis Parfenov Research Institute of Digital Intelligent Technologies Orenburg State University Orenburg Russia Aerospace Institute Orenburg State University Orenburg Russia

ISBN: (数字)9798331511241

ISBN: (纸本)9798331511258

Intelligent methods can analyze large volumes of data coming from a digital twin and identify bottlenecks in technological processes. Digital twins provide the ability to conduct scenario analysis and forecast various situations within technological processes. The use of digital twins and algorithms based on machine learning and data analysis helps improve product quality by identifying anomalies and problems at early stages, thereby allowing you to promptly make the necessary changes, which significantly reduces the time and financial costs of implementing the final product. Intelligent decision support systems can provide recommendations based on data analysis, both retrospective and received in real time, which significantly simplifies solving complex problems and improves the quality of decisions made. Thus, the integration of intelligent decision support methods and algorithms with digital twins can significantly improve the productivity, competitiveness and innovation of oil and gas enterprises. As part of the study, we analyzed the most suitable machine learning methods and algorithms for data labeling aimed at identifying anomalies in the process of stirring.

关键词： Drilling Technological innovation machine learning algorithms Data analysis Time series analysis machine learning Search problems Real-time systems Digital twins Quality assessment

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Comparative Analysis of machine learning algorithms for Binary Classification of Tea Leaf Diseases

Comparative Analysis of Machine Learning Algorithms for Bina...

引用

Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT), International Conference on

作者： Nidhi Shekhar J.S Megha J.S Varsha G.P. Revathi Dept. of Computer Science and Engineering PESU - ISFCR Bengaluru India

ISBN: (数字)9798350356236

ISBN: (纸本)9798350356243

India stands as one of the world's largest tea exporters. This requires for an implementation of an efficient and robust system for disease detection and prevention. The most important challenge faced by the tea industry is tea leaf diseases due to continuous pathogen infestation. These diseases can cause full-size crop losses, adversely affecting the economic system and the livelihood of tea farmers. The ability to detect these diseases early is essential. Early detection can cause timely intervention that can notably reduce harm to tea plants and keep them at their best possible quality. Unfortunately, traditional diagnostic techniques often depend on visible inspection and are hence inefficient as those methods can be gradual, labour in-depth and vulnerable to human errors, reducing the effectiveness in larger fields. This research paper aims to classify whether a leaf is diseased or healthy through a two-step process involving feature extraction using a pretrained Convolutional Neural Network (CNN) and classification using four machine learning models - LightGBM, CatBoost, Autoencoders and Self Organized Maps (SOMs). This paper utilizes a pre-trained VGG16 model, which is trained on the ImageNet dataset. This model is capable of extracting high-level features from images.

关键词： Pathogens machine learning algorithms Biological system modeling Computational modeling Prevention and mitigation Autoencoders machine learning Feature extraction Convolutional neural networks Diseases

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A Music Recommender System to Identify the Emotional State of a Person Using machine learning algorithms

A Music Recommender System to Identify the Emotional State o...

引用

Sentiment Analysis and Deep learning (ICSADL), International Conference on

作者： Muruganantham A C. Malarvizhi Divya Vahini. S D. Saranya A. Uthiramoorthy Department of Computer Science (PG) Kristu Jayanti College Bengaluru Karnataka India Department of Computer Science PSG Institute of Advanced Studies Coimbatore India Department of Computer Applications Dr.Mahalingam College of Engineering and Technology Pollachi Tamil Nadu India Department of Information Technology Sri Eshwar College of Engineering Coimbatore Department of Computer Science Rathinam College of Arts and Science

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

In today's digital world, customized recommendation algorithms can improve user experiences in many different areas. Within the sphere of intelligent human-technology interaction, this paper specifically examines a music recommender system driven by machine learning algorithms. It figures out what mood users are in and can provide an appropriate response in real time. The system being propounded in this essay uses advanced technology in facial expression recognition. With the help of a webcam, it aims to accurately detect user emotions, including joy, sadness, and anger. Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN) and Support Vector machines (SVM) have been employed to effectively classify people's mood and recommend an appropriate song. Our extensive tests and examinations show that CNN, RNN, and SVM models are all very good at guessing users' moods--with high precision, recall, F1-score, and accuracy percentages. Furthermore, in-depth analyses like confusion matrixes illuminate how well each model serves as an emotion recognizer. This study's findings are representative of future prospects in affective computing around the globe with major implications for intelligent recommendation systems to which it's important that the models are sensitive. In conclusion, the suggested Music Recommender System is something new for taking a more personal view: We can be as deeply entertained as the web allows us to be--make tailored entertainment paths in the digital era.

关键词： Support vector machines Emotion recognition Recurrent neural networks machine learning algorithms Accuracy Mood Face recognition Computational modeling Convolutional neural networks Recommender systems

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Soil Analysis for Crop Recommendation using machine learning algorithms

Soil Analysis for Crop Recommendation using Machine Learning...

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

作者： Sathya J V. Asha A. Kalaivani Sanjeev Sangappa Hanchinal Sarana Fathima Sachin K S Department of MCA New Horizon College of Engineering Bengaluru India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

In the area of farming, parts of the soil are of special note as it has to do with growing crops and provision of food for consumption. Soil analysis is also important in crop rating since it reflects some important Indices of the soils such as nutrients, pH, and texture which all affect plants' growing condition. Farmers are able to utilize this strategy in order to plant the right sorts of crops on their area of land, hence gaining scope in the yield and resources consumed. This paper showcases the application of advanced machine learning algorithms to optimize crop recommendations based on essential climatic, geographic, and eco-spatial parameters. In such techniques, depending on humidity, temperature and rainfall, certain patterns and relations are established and the best crops are recommended. For the purpose of generating predictive models that recommend the growth of specific crops in specific regions with the aim of increasing agricultural productivity, the system deploys the algorithms of Gradient Boosting, XGBoost, and LightGBM.

关键词： Productivity machine learning algorithms Rain Crops Soil Boosting Prediction algorithms Real-time systems Nitrogen Farming

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Classification of Diabetes Using Ensemble and Individual machine learning algorithms

Classification of Diabetes Using Ensemble and Individual Mac...

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Advanced Innovations in Smart Cities (ICAISC), International Conference on

作者： Helmi Imaduddin Widi Widayat Fiddin Yusfida A'Ia Departement of Informatics Engineering Universitas Muhammadiyah Surakarta Surakarta Indonesia Departement of Informatics Engineering Sebelas Maret University Surakarta Indonesia

ISBN: (数字)9798331506995

ISBN: (纸本)9798331507008

Diabetes is a chronic metabolic condition characterized by elevated blood glucose levels, posing significant health and economic challenges globally. This study aimed to evaluate the performance of Random Forest and Decision Tree algorithms in classifying diabetes, addressing the increasing prevalence of the disease and the necessity for accurate diagnostic tools. Both algorithms were applied to a dataset containing 100,000 entries sourced from Kaggle. The study employed systematic preprocessing steps, including normalization and feature selection, to optimize data quality. The dataset was split into training (80 % ) and testing (20 % ) subsets, ensuring robust evaluation. Random Forest and Decision Tree algorithms were implemented with carefully tuned parameters to maximize performance, and their outputs were assessed using metrics such as accuracy, precision, sensitivity, and specificity. Results revealed that Random Forest achieved a higher accuracy of 96%, outperforming Decision Tree's 95%, with superior sensitivity and specificity. These findings highlight Random Forest's effectiveness in capturing complex patterns and reducing overfitting, which is consistent with prior studies on medical data classification. This research underscores the importance of algorithm selection and data preprocessing in improving diabetes classification. Future studies should explore ensemble methods and diverse datasets to enhance generalizability and predictive accuracy, offering valuable insights for healthcare applications.

关键词： Training Accuracy machine learning algorithms Medical services Sensitivity and specificity Diabetes Classification algorithms Decision trees Random forests Testing

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Intelligent Heart Disease Prediction using Ant Colony Optimization based machine learning algorithms

Intelligent Heart Disease Prediction using Ant Colony Optimi...

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machine learning and Autonomous Systems (ICMLAS), International Conference on

作者： Shaik Shafi Abhilash Ch Manoj A Snehil B Nandan TPK Anusha M Electronics and Communication Engineering B V Raju Institute of Technology Narsapur Medak India ECE BVRIT Narsapur Medak India

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

Cardiovascular diseases continue to be the primary cause of death worldwide, responsible for around 31% of all deaths each year, as reported by the World Health Organization (WHO). Although significant progress has been made in their diagnosis and treatment, heart disease remains highly prevalent. It has risen significantly due to unhealthy dietary habits and reduced physical activity. Predicting heart diseases using patient-related data, such as blood pressure, cholesterol levels, and other health metrics, has emerged as one of the most complex tasks for human analysts in modern times. As a result, there is a growing global demand for timely and accurate predictive models in identifying heart disease in healthcare industry, predominantly in the field of cardiology. In this paper, an intelligent heart disease prediction using ant colony optimization using machine learning algorithms is proposed. It includes two phases namely: 1) Feature Selection and 2) Classification. Ant Colony Optimization (ACO) is used to reduce the redundancy in feature selection from the large data set and helps to upgrade the performance of the classifier in the next stage. Then, we exploit the different classification algorithms such as K-Nearest Neighbors (KNN), Support Vector machine (SVM), Decision Tree (DT), Random Forest (RF) and XGBoost (XGB). These algorithms are chosen for their ability to deliver high accuracy, computational efficiency, and optimized memory usage, making them well-suited for this application. Furthermore, performance of the machine learning algorithms has been evaluated using accuracy, precision, F 1 score, recall and support parameters. The results illustrate that the amalgamation of the ACO algorithm and SVM classifier has the utmost performance for use in heart disease estimation.

关键词： Heart Support vector machines Ant colony optimization machine learning algorithms Accuracy Nearest neighbor methods Predictive models Feature extraction Classification algorithms Classification tree analysis

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Prediction of Customer Satisfaction using machine learning algorithms: Perspectives of Service and Operational Factors in Airline Industry

Prediction of Customer Satisfaction using Machine Learning A...

引用

International Conference on Communication Systems and Network Technologies (CSNT)

作者： N. Shafrin Fathima K. Ajithkumar P. Suganthi V. Srinivasa Kumar School of Management SASTRA Deemed University Thanjavur Tamil Nadu India

ISBN: (数字)9798331531935

ISBN: (纸本)9798331531942

Customer satisfaction in the airline industry is influenced by demographic, service-related, and operational factors. As competition grows and passenger expectations evolve, understanding these determinants is crucial for improving service quality and loyalty. This study uses machine learning algorithms to analyze key drivers of customer satisfaction and derive actionable insights. It employs techniques such as Logistic Regression, Gaussian Naive Bayes, Gradient Boosting, K-Nearest Neighbors, and Random Forest classifiers. Among these, Gradient Boosting and Random Forest achieved the highest predictive accuracy, with AUC scores of 0.99 and 0.9937. A strong correlation (0.97) between departure and arrival delays highlights the importance of punctuality. Inflight services like entertainment and legroom, especially on long-haul flights, significantly enhance satisfaction. Demographic analysis shows passengers aged 40+ report higher satisfaction, emphasizing the need for tailored service strategies, while younger passengers (20-40) show lower satisfaction, indicating a need for personalized engagement. These findings provide a data-driven foundation for improving airline operations and service delivery. Future research should integrate real-time data for more adaptive service improvements.

关键词： machine learning algorithms Correlation Customer satisfaction Entertainment industry Aging Boosting Real-time systems Delays Random forests Airline industry

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Predictive Analysis of Diabetes Data in Healthcare System Using machine learning algorithms

Predictive Analysis of Diabetes Data in Healthcare System Us...

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International Conference on Robotics,Electrical and Signal Processing Techniques (ICREST)

作者： Kazi Abdullah Al Arafat Md Al Amin Rahman Sumaya Siddique Mahmudur Rahman Roni Imtiaz Parvez Department of Computer Science and Engineering Atish Dipankar University of Science and Technology Dhaka Bangladesh Department of Computer Science and Engineering American International University Dhaka Bangladesh Department of Computer Science Utah Valley University Orem UT

ISBN: (数字)9798331530952

ISBN: (纸本)9798331530969

Diabetes, influenced by factors like high blood pressure, aging, obesity, and poor lifestyle choices, has become a significant health issue, increasing the risk of heart disease, kidney disease, stroke, and other serious conditions. Traditional diagnostic methods in hospitals can sometimes be misleading, underscoring the need for big data analysis in healthcare. This approach can uncover hidden patterns in diabetes-related data, enabling more accurate predictions of outcomes. This paper aims to classify large diabetes datasets to predict the disease's behavior using various attributes. A machine learning model with sixteen classifiers, fine-tuned through k-fold cross-validation, was developed to ensure optimal results, with accuracy measured by F1 score, precision, and recall.

关键词： Deep learning Obesity Accuracy machine learning algorithms Predictive models Signal processing Diabetes Predictive analytics Robots Diseases

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TubiLearn: Predictive Analysis of Tuberculosis Using machine learning algorithms

TubiLearn: Predictive Analysis of Tuberculosis Using Machine...

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International Conference on Computer, Electrical & Communication Engineering (ICCECE)

作者： Aman Das Swaraj Bhattacharya Lisa Roy Liza Roy Subhram Das Papri Ghosh Md. Ashifuddin Mondal Computer Science and Engineering Narula Institute of Technology Kolkata India

ISBN: (数字)9798331513894

ISBN: (纸本)9798331513900

Tuberculosis(TB) remains a critical global health challenge, particularly in resource-limited regions where access to timely and accurate diagnostic tools is constrained. This study introduces TubiLearn, a machine learning-based diagnostic framework designed to predict TB presence using synthetic clinical data. By leveraging advanced algorithms such as Random Forest, K-Nearest Neighbours (KNN), and Extreme Gradient Boosting (XGBoost), TubiLearn aims to enhance diagnostic accuracy and support clinical decision-making. A synthetic dataset of 10,000 samples was generated, incorporating key clinical features including age, gender, X-ray intensity scores, and symptoms such as cough duration, fever, and weight loss. To address class imbalance, preprocessing techniques like standardization, SMOTE, and downsampling were applied. The performance of the classifiers was evaluated using accuracy, precision, recall, F1 score, and the area under the receiver operating characteristic curve (AUC). Feature importance analysis identified the most influential predictors of TB. XGBoost demonstrated superior performance, achieving an accuracy of 89.6TubiLearn presents a scalable and cost-effective framework with significant potential for deployment in low-resource settings. By integrating synthetic data and state-of-the-art machine learning techniques, this framework provides a supplementary diagnostic tool that can assist clinicians in early detection and management of TB. Future work will focus on validating the framework with real-world datasets and exploring its integration into web-based diagnostic systems to further enhance its clinical utility.

关键词： Technological innovation Humanities Accuracy machine learning algorithms Decision making Medical services Nearest neighbor methods Prediction algorithms Random forests Synthetic data

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Classifying the DNA sequence using Optimized Naive Bayes Approach: A Comparative Analysis of machine learning algorithms

Classifying the DNA sequence using Optimized Naive Bayes App...

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Computational Intelligence, Communication Technology and Networking (CICTN), International Conference on

作者： Aditya Pai H Chandrappa S Guru Prasad M S Ganesh Pathak Atul Thakare Pratik Kumar MIT School of Computing MIT Art Design and Technology University Pune India Dept. of Computer Science and Engineering JAIN (Deemed-to-be University) Bengaluru India Dept. of Computer Science and Engineering Graphic Era (Deemed to be University) Dehradun India Department of Computer Application Graphic Era (Deemed-to-be University) Dehradun India

ISBN: (数字)9798331530389

ISBN: (纸本)9798331530396

In bioinformatics, the exact classification of DNA sequences is essential to increasing comprehension of genetic structures and functionalities. This research introduces an enhanced Naive Bayes approach (ENB) specifically designed to handle more complexities related to DNA sequence data. This research integrates advanced feature weighting and optimized probability estimation techniques to enhance classification accuracy substantially. The proposed work demonstrated superior efficacy to traditional ML algorithms like Random Forest, SVM, Decision-Trees, K-Nearest Neighbors, Gradient Boosting etc. The proposed method attained a level of accuracy of 98%, and proposed approach performs better than the other models, with 94% accuracy in Logistic Regression coming in second.

关键词： Support vector machines Logistic regression Accuracy machine learning algorithms Nearest neighbor methods Boosting Bayes methods Complexity theory Medical diagnosis Random forests

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