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检索条件"主题词=Machine Learning Algorithms"

共 26022 条记录，以下是4721-4730 订阅

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Enhancing Early Diabetes Detection using Ensemble machine learning Models for Improved Prediction and Patient Care

Enhancing Early Diabetes Detection using Ensemble Machine Le...

引用

Sustainable Communication Networks and Application (ICSCNA), International Conference on

作者： Minu Inba Shanthini Watson Benjamin Arshad Syed Tamma Raja Vamsi P.S.D.Sai Kumar Department of Computer Science & Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai India

ISBN: (数字)9798331530013

ISBN: (纸本)9798331530020

Millions of individuals worldwide suffer from diabetes, a chronic condition that must be detected early to avoid major health issues like kidney failure, nerve damage, and heart disease. By using ensemble technique which combines machine learning (ML) approaches and evaluate the outputs and generates final prediction, then diabetes can be predicted more precisely. Random Forest, Support Vector machine (SVM), and Logistic Regression, were the machine learning techniques and Voting Classifier is the ensemble classification model which were examined in this study. The study evaluated these model's ability to predict diabetes using datasets such as the Pima Indian Diabetes Dataset. Through the integration of Support Vector machine (SVM), Random Forest, and Logistic Regression algorithms using a Voting Classifier, proposed model achieves an accuracy of 79%. This ensemble approach demonstrates superior performance compared to individual algorithms, with improved precision (0.81) and recall (0.78). It is identified that Predictive accuracy is found to be highly dependent on key parameters including glucose, BMI, and age with correlation coefficients of 0.47, 0.29, and 0.24 respectively. This method showed that combining multiple ML algorithms can significantly enhance diabetes diagnosis and care in addition to improving prediction capabilities. In the end, the study emphasizes how machine learning can completely change the way diabetes is treated and stresses how crucial it is to keep developing and using these technologies in order to improve patient outcomes.

关键词： Support vector machines Logistic regression Accuracy machine learning algorithms Predictive models Prediction algorithms Diabetes Classification algorithms Glucose Random forests

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Leveraging Smart Video-Based learning to Enhance Student Engagement and Collaboration

Leveraging Smart Video-Based Learning to Enhance Student Eng...

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Advancements in Computing (ICAC), International Conference on

作者： Chanukya Serasinghe Nashali Perera Devindu Samarasinghe Madusha Muthusinghe Malitha Wijesundara Dept. of Computer Science and Software Engineering Sri Lanka Institute of Information Technology Malabe Sri Lanka Faculty of Humanities and Sciences Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (数字)9798331517878

ISBN: (纸本)9798331517885

The COVID-19 pandemic has expedited the shift in education to online learning, which has exposed shortcomings in virtual learning environments' personalization and engagement. This research addresses these issues by developing a web application designed to enhance student engagement and learning efficiency. The system has gamified collaboration features, video chapter segmentation, and personalized video recommendation system. The methodology comprises models for generating chapter titles and for precise video transcription, and machine learning algorithms for tailored suggestions depending on user input. Interactive Questions and Answers modules and real-time synchronized video playback are included. The results demonstrate how the system's personalized content, interactive tests, and teamwork tools increase user engagement and comprehension. The efficacy of personalized material and real-time synchronization in enhancing learning results is discussed. This innovative approach offers a comprehensive solution to the challenges of online education, creating a more immersive and interactive learning experience.

关键词： machine learning algorithms Electronic learning Pandemics Education Natural languages Streaming media Real-time systems Teamwork Synchronization Recommender systems

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Accurate Detection and Classification of Alopecia using machine learning Models and Interpretation using LIME and SHAP Explainers

Accurate Detection and Classification of Alopecia using Mach...

引用

North Karnataka Subsection Flagship International Conference (NKCon), IEEE

作者： Deeksha R Shankar Prajwal Parappanavar Ashwini Kodipalli Dept. of AI and Data Science Global Academy of Technology Bangalore India

ISBN: (数字)9798350364569

ISBN: (纸本)9798350364576

Alopecia, a condition marked by partial or complete hair loss, presents diagnostic and treatment difficulties because of its variable manifestations and unclear cause. To understand the complexity of alopecia, this research makes use of the transformational potential of machine learning (ML) and artificial intelligence (AI). This research investigates alopecia incidence prediction models using a variety of machine learning methods, including AdaBoost, Cat Boost, Gradient Boosting, Random Forest, K-Nearest Neighbors, Support Vector machines, Decision Trees, and XG Boost. The study highlights the significance of accurate prediction models for customized treatment options by incorporating insights from related publications.

关键词： Hair Support vector machines machine learning algorithms Accuracy Predictive models Nearest neighbor methods Boosting Prediction algorithms Forecasting Random forests

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Enhanced Liver Disease Prediction Using Ensemble learning

Enhanced Liver Disease Prediction Using Ensemble Learning

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Cybernetics, Cognition and machine learning Applications (ICCCMLA), IEEE International Conference on

作者： V. Srihitha M. Aravindh Goud S. Cherishma Mannava Yesubabu G. Suryanarayana Dept of Information Technology Vardhaman College Of Engineering Hyderabad Telangana India

ISBN: (数字)9798331505790

ISBN: (纸本)9798331505806

In the healthcare field, accurate prediction of liver disease plays a critical role in diagnosis and treatment planning. This study aims to improve liver disease prediction using machine learning techniques. Through analyzing individual patient data, the objective is to enhance diagnostic methods. The models consider various factors like patient demographics and clinical features to ensure precise predictions of liver disease. algorithms such as LightGBM, AdaBoost, CatBoost and XGboost are employed. To simplify the model for users, it is integrated into a web application using the Flask framework. This enables users to input clinical features and receive predictions about their liver disease status. Challenges remain in data integration, model interpretability and clinical validation, despite these advancements. Future research will concentrate on developing strong machine learning models validated across diverse patient populations, facilitating personalized therapy for liver disease through real-time data integration. Overall, machine learning-based methods coupled with a user-friendly web interface have the potential to significantly advance liver disease diagnosis and enhance patient outcomes in clinical settings.

关键词： machine learning algorithms Liver diseases Data integration Medical treatment Predictive models Prediction algorithms Data models Real-time systems Planning Reliability

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Comparative Evaluation of machine learning and Bayesian Deep learning Methods for Estimating Ultimate Recovery in Shale Well Reservoirs

Comparative Evaluation of Machine Learning and Bayesian Deep...

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International Conference on Computational Cybernetics (ICCC)

作者： Saeed Beheshtian Sara Kishan Roodbari Hamzeh Ghorbani Mohamadreza Azodinia Mohamed Mudabbir Annamaria R. Varkonyi-Koczy Department of Petroleum Engineering Omidiyeh Branch Islamic Azad University Omidiyeh Iran Faculty of Chemistry University of Traditional Medicine of Armenia (UTMA) Yerevan Armenia Gas Engineering Department Petroleum University of Technology Ahvaz Iran Islamic Azad University Ahvaz Iran Faculty of General Medicine University of Traditional Medicine of Armenia (UTMA) Yerevan Armenia John von Neumann Faculty of Informatics Obuda University Budapest Hungary

ISBN: (数字)9798350350159

ISBN: (纸本)9798350350166

The Estimated Ultimate Recovery (EUR) is an important parameter in oil and gas production resources, particularly in hydrocarbon-rich shale wells. This study shows EUR prediction using three algorithms: Group Method of Data Handling (GMDH), Lasso, and Bayesian Deep learning (BDL). The dataset includes 2200 data records from 15 shale gas wells, divided into 70% for training, 15% for testing, and 15% for validation. After the evaluation of three algorithms, the BDL algorithm has higher accuracy than the other algorithms. This algorithm has advantages such as providing quantitative prediction uncertainty assessment, resilience to outliers, adaptability to complex relationships, intrinsic regularization, transfer learning, ensemble formation, handling of heteroscedasticity, integration into active learning, and improved model interpretability. The results obtained using the test data for the BDL algorithm include a Root Mean Square Error (RMSE) of 4.2691 m3 and an R-squared (R2) value of 0.9879. Based on the findings of this study, it is suggested that researchers utilize this algorithm to predict key parameters.

关键词： Deep learning Training machine learning algorithms Uncertainty Transfer learning Prediction algorithms Bayes methods

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Harnessing machine learning for Enhanced Agricultural Efficiency through Advanced Data Analysis and IoT Integration

Harnessing Machine Learning for Enhanced Agricultural Effici...

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： Khushboo Devi Naween Kumar Sachikant Dash Yajnaseni Dash Sasmita Padhy SCSET Bennett University Greater Noida Uttar Pradesh India Computer Science and Engineering GIET University Gunupur Odisha India School of Artificial Intelligence Bennett University Greater Noida Uttar Pradesh India School of Computing Science and Engineering VIT Bhopal University Bhopal Madhya Pradesh India

ISBN: (数字)9798350350067

ISBN: (纸本)9798350350074

The agricultural sector is poised to benefit greatly from the improved data processing and decision-making capabilities enabled by machine learning, which is significantly impacting economies worldwide. To alleviate the urgent worldwide food shortage, this study investigates the potential of incorporating machine learning algorithms to maximize harvest yields while decreasing food waste. The paper analyses the present state of these algorithms in farming, finds important problems and possibilities, and finds out how changing data labels affects the accuracy of the algorithms. The results show that farmers may increase crop yields and decrease waste by making more educated decisions about planting, watering, and harvesting using big data from farms and data collected in real time from the Internet of Things sensors. The comparative analysis of five classification algorithms for agricultural data highlighted the Naive Bayes Classifier with the highest accuracy ($\mathbf{9 9. 5 3 \%}$), while Random Forest showed balanced performance across all categories ($\mathbf{9 8. 8 1 \%}$). Each algorithm demonstrated strengths depending on the specific prediction task. These findings indicate that there is potential for improvement in terms of production rates, cost savings, and farm resilience. Future farmers may also be able to use this study’s findings to improve crop production efficiency, spot diseases earlier, and reduce food shortages.

关键词： machine learning algorithms Accuracy Decision making Production Prediction algorithms Real-time systems Classification algorithms Sensors Internet of Things Random forests

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Identifying Optimal EV Charging Station Locations: A Smart Grid and machine learning Approach

Identifying Optimal EV Charging Station Locations: A Smart G...

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International Conference on Electrical and Computer Engineering (ICECE)

作者： Mahim Ahmed Azam Jaman Md. Nahid Islam Md. Shadman Shakib Ifte Khairul Amin Electrical & Electronic Engineering Shahjalal University of Science & Technology Sylhet Bangladesh Electrical & Electronic Engineering Noakhali Science and Technology University Noakhali Bangladesh

ISBN: (数字)9798331531782

ISBN: (纸本)9798331507923

As the detrimental effects of fossil fuel consumption become increasingly evident, the quest for sustainable energy solutions has intensified. The transportation sector, a major contributor to global CO 2 emissions, stands at the forefront of this transition. Electric Vehicles (EVs) have emerged as a key player in this shift, offering a substantial reduction in harmful emissions and a path away from fossil fuel dependence. However, widespread EV adoption faces significant challenges, including power grid stress as well as the insufficient and uneven distribution of charging stations. To address these issues, researchers have developed various models to improve the viability of EVs, focusing on extending driving range, enhancing battery life, and optimizing charging infrastructure. Innovative approaches have been introduced to strategically place charging stations and develop predictive models that recommend the best charging locations based on multiple factors. This paper introduces an innovative smart grid system for EVs that employs network clouding to facilitate seamless communication between vehicles and optimal charging station location. The system uses machine learning algorithms including linear, polynomial, random forest, and decision tree regressions to identify the most energy-efficient charging stations, considering parameters such as location, route, road conditions, weather, and traffic. Utilizing real-world datasets of the Volkswagen e-Golf, the proposed model aims to enhance efficiency and locate the optimized EV charging system.

关键词： machine learning algorithms Biological system modeling Roads Transportation Charging stations Polynomials Electric vehicle charging Smart grids Regression tree analysis Random forests

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Active learning Framework for Anomaly Detection in Astronomical Data

Active Learning Framework for Anomaly Detection in Astronomi...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Ghadiyaram Hanumath Srinivasa Dixit Jitendra Parmar Atul Kumar Verma Raghavendra Mishra Gopal Behera School of Computing Science & AI VIT Bhopal University Kothri Kalan Madhya Pradesh India Dept. of Computer Sc. & Engineering Manipal University Jaipur Jaipur Rajasthan India Dept. of Computer Sc. & Engineering Government College of Engineering Kalahandi Kalahandi Odisha India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

When properly analyzed, the large astronomical datasets likely yield anomalies, which is meant to be a new phenomenon in the dataset. However, the challenge lies in the scale of the datasets, making it complex for humans to find anomalies by themselves using traditional techniques. The most reliable method for detecting noteworthy irregularities is through anomaly detection algorithms. Even though machine learning proves to be a valuable tool in this context, it faces difficulty in finding out the exact anomalies which are needed to confidently conclude the nature of a point in the data; as such, many broad points in output may not be extremely necessary for getting into a direct conclusion. This is where active learning comes into play; active learning combines human techniques with machine learning’s algorithm, strategically selecting specific data for expert labelling to minimize the workload for maximum efficiency. Here, we present a novel anomaly detection framework called Anomaly detection. In astronomical data, which employs an active learning approach to give an output of probability of finding an anomaly. Furthermore, anomaly detection in astronomical data adaptability allows for integrating new feature extraction methods, anomaly detection algorithms, and alternative active learning techniques.

关键词： machine learning algorithms Accuracy Uncertainty Time series analysis machine learning Feature extraction Labeling Reliability Anomaly detection Standards

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Towards Federated learning by Kernels

Towards Federated Learning by Kernels

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Mechatronics and Robotics Engineering (ICMRE), International Conference on

作者： Kilho Shin Takenobu Seito Chris Liu Computer Centre Gakushuin University Tokyo Japan Deloitte Tohmatsu Risk Advisory LLC. Tokyo Japan Deloitte Tohmatsu Cyber LLC. Tokyo Japan

ISBN: (数字)9798350394276

ISBN: (纸本)9798350394283

Kernelization enhances machine learning algorithms, allowing them to solve nonlinear problems by embedding data into high-dimensional reproducing kernel Hilbert spaces (RKHS). It notably expanded the utility of Support Vector machines (SVM) from linear to nonlinear classification, making SVM a powerful tool in modern machine learning. This technique, while yielding linear solutions in RKHS, translates them back to nonlinear solutions in the original data space, leveraging the representer theorem for feasible computation. The paper discusses applying kernelization to federated learning–-a collaborative model prioritizing data privacy–-where multiple parties work together without exposing individual data. The approach relies on sharing Gram matrices, representing kernel values, to preserve the confidentiality of original data while enabling collective learning. Compared to traditional multi-party computation (MPC) and homomorphic encryption (HE), our kernelization-based method requires significantly less computational resources, offering a more practical solution for federated learning. However, it’s crucial to mathematically evaluate the privacy implications of disclosing Gram matrices to ensure data security. This framework paves the way for efficient, privacy-preserving federated learning.

关键词： Support vector machines machine learning algorithms Federated learning Training data Eigenvalues and eigenfunctions Multi-party computation Outsourcing

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Developments in the Application of machine learning Techniques for Mobile Ad-Hoc Network Security

Developments in the Application of Machine Learning Techniqu...

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Innovations in High Speed Communication and Signal Processing (IHCSP), International Conference on

作者： Pratiksha Nigam Ajay Tiwari Devi Ahilya Vishwavidyalaya School of Computer Science and IT Indore

ISBN: (数字)9798350368949

ISBN: (纸本)9798350368956

The self-configuring and decentralised network system basically known as Mobile Ad-hoc Networks or in short MANETs are essential for a number of applications, including smart cities, battlefield operations, disaster mitigation and many more. However, they are vulnerable to several security concerns, including routing-based attacks such as black hole and grey hole acts of terrorism, due to their dynamic topology, inherent lack of centralised management, and a lack of resources. The present review article explores the possible risks that multiple methods of machine learning may offer to MANETs and how they can be mitigated. This research looks at several attack strategies, classifies them as either external or internal, active or passive and evaluates the shortcomings of protection methods that are currently on the market. Going through various runs of evaluation and assessment of recent improvements in domains to study the advantages in addition to the drawbacks that applies algorithmic and deep machine learning (ML) strategies to enhance MANET security. Furthermore, the studies delves into innovative detection methods, such as using a mixture of algorithms and deep neural networks that assesses their impact through different scenarios at the same time as imparting an extensive review of gift improvements and potential future directions in MANET security.

关键词： Technological innovation machine learning algorithms Reviews Network topology Terrorism Signal processing algorithms Ad hoc networks Topology Security Mobile computing

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