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检索条件"主题词=Machine Learning Algorithms"

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Design of Data Application Risk Prediction and Management System Based on machine learning algorithms

Design of Data Application Risk Prediction and Management Sy...

引用

Telecommunications and Power Electronics (TELEPE), International Conference on

作者： Shu Wang Xiaolin Yang Yun Lu State Grid Changzhou Electric Power Supply Company Changzhou Jiangsu China

ISBN: (数字)9798350369212

ISBN: (纸本)9798350380002

In recent years, data security has become the most urgent and fundamental security issue in the digital economy era. How to effectively build and improve data security protection capabilities, and avoid the occurrence of data leakage events, is currently a very important issue of concern. This article takes engineering as an example, predicts data application risks based on ML (machine learning) algorithm, and designs a management system. The main functions of the system designed in this article include risk analysis and calculation throughout the entire engineering process; Establish a risk information sharing platform for dynamic risk management and control of engineering, including risk identification, risk estimation, risk evaluation and decision-making, and risk tracking. The experimental results indicate that the prediction results of GNN (Grey neural network) PSO (Particle Swarm Optimization) are unreliable and fail to learn the inherent patterns of the data. Finally, the predicted values of SVM (Support Vector machine) in this article almost match the actual values, indicating that SVM in this article has the best performance in engineering risk prediction. Utilizing monitoring data, inspection information, and relevant risk management records, dynamic tracking and real-time control of environmental risks or safety status during the construction process, timely warning consultation, information feedback, and response processing, is an extremely important part of comprehensive environmental risk management.

关键词： Support vector machines machine learning algorithms Data security Decision making Process control Big Data applications Prediction algorithms Software Risk management Protection

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Comparative Analysis of machine learning algorithms for Alzheimer's Disease Identification: A Comprehensive Evaluation

Comparative Analysis of Machine Learning Algorithms for Alzh...

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Pioneering Developments in Computer Science & Digital Technologies (IC2SDT), International Conference on

作者： Vansh Arora Priya Sharma Avish Mehta Ankur Maurya SCSET Bennett University Greater Noida India

ISBN: (数字)9798350365016

ISBN: (纸本)9798350365023

A brain disorder, Alzheimer's disease (AD), impacts the lifestyle of around 6.5 million people around the globe, generally people over 65, which causes advanced cognitive dete-rioration. Despite the complex and comprehensive research, no such cure exists till now, accentuating the strong necessity for early diagnosis and interventions. We examine how the role of biomarkers, like MRI and genetics, can play a massive role in early detection and how Data Science and machine learning can be used to enhance diagnostic accuracy. Advanced techniques like convolutional neural networks (CNN) and hybrid deep learning algorithms are used for identifying and detecting AD.

关键词： machine learning algorithms Accuracy Magnetic resonance imaging Biological system modeling Biomarkers Genetics Data models Convolutional neural networks Alzheimer's disease Random forests

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Parkinson Prediction Using Voice Data and machine learning algorithms

Parkinson Prediction Using Voice Data and Machine Learning A...

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Advancements and Key Challenges in Green Energy and Computing (AKGEC), International Conference on

作者： B. Meena Preethi Y.S Krishha C.M Nazrin N Jayaswethaashree Department of Software Systems and AIML Sri Krishna Arts and Science College Coimbatore India

ISBN: (数字)9798350390179

ISBN: (纸本)9798350390186

Parkinson’s disease is a prevalent neurological disorder affecting a significant portion of the global population. Early diagnosis is crucial for effective intervention and management, leading to improved patient outcomes. This study evaluates Parkinson’s disease by analyzing speech data, which typically shows distinct variations in affected individuals. Apply leverage advanced machine learning techniques and artificial intelligence to develop predictive models that can efficiently differentiate between speech data from Parkinson’s patients and healthy individuals. The model is trained on an extensive dataset of audio recordings, capturing all necessary attributes for accurate diagnosis. This work employs three machine learning algorithms K-Nearest Neighbor, Random Forest Classifier, and Decision Tree Classifier to identify the most effective model. The final approach demonstrates high efficacy in detecting Parkinson’s disease, enabling early diagnosis and personalized management strategies. This study underscores the transformative potential of machine learning and AI in Parkinson’s disease detection, presenting innovative approaches to patient care and management.

关键词： Neurological diseases Green energy machine learning algorithms Accuracy Medical services Predictive models Nearest neighbor methods Prediction algorithms Random forests Diseases

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Comparing machine learning algorithms with Ensemble Model Using Random Oversampling for Predicting Student's Dropouts

Comparing Machine Learning Algorithms with Ensemble Model Us...

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International Conference on Cybernetics and Intelligent System (ICORIS)

作者： Ivan Setiawan Yithro Paulus Tjendra Alfi Yusrotis Zakiyyah Meiliana Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Mathematics Department School of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331539603

ISBN: (纸本)9798331539610

Studying at the university can be very difficult for some students, and many struggle to keep up with their academics, resorting to academics dissatisfaction and even dropping out. This study examines the impact of various machine learning models in classifying student dropout likelihood, with a focus on mitigating class imbalance using Random Oversampling (ROS). We compare machine learning algorithms such as Logistic Regression (LR), K-Nearest Neighbor (KNN), Naïve Bayes (NB), Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Soft Voting. Our results show that the Soft Voting model achieves the best performance, with an F1 score of 0.93, outperforming other models in predicting dropouts. ROS is found to improve model performance, yet it contributes to slight variability, indicating potential overfitting. These findings support educational institutions in designing targeted interventions for at-risk students, helping to prevent dropout through optimized prediction methods.

关键词： Radio frequency Logistic regression machine learning algorithms Predictive models Nearest neighbor methods Prediction algorithms Socioeconomics Intelligent systems Random forests Overfitting

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Exploring the Use of machine learning algorithms in Text Classification

Exploring the Use of Machine Learning Algorithms in Text Cla...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Preeti Naval Krishnan Batri D. Nirmala Pawan S. Wawage Narendra Kumar Jain Jatin Khurana Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India School of Computer Science and Engineering Jain (Deemed to be University) Bangalore Karnataka India Department of Electronics and Communication Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Information Technology Vishwakarma Institute of Technology Pune INDIA Department of Civil Engineering Vivekananda Global University Jaipur India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and technology Chitkara University Punjab India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

The use of device mastering algorithms has become increasingly popular in text type. The essential benefits of using these algorithms are their ability to perceive trends in text statistics and examine the information they may be uncovered. Textual content classification entails sorting textual content into predefined categories or lessons using diverse algorithms. Specific device studying strategies, including assist vector machines (SVMs) and artificial neural networks (ANNs), have been carried out for textual content class duties. Those algorithms help identify the applicable topics in extensive text collections and allow the categorization of files into nicely-defined topic clusters. This paper explores the software of machine studying algorithms in text category obligations. Further, a number of the popular algorithms and their applications on this subject and their blessings and limitations are discussed. Sooner or later, conclusions and destiny paintings are discussed.

关键词： machine learning algorithms Software algorithms Text categorization Clustering algorithms Vectors Software Classification algorithms Sorting Postal services Painting

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Predictive Analysis of CPI Based on machine learning algorithms

Predictive Analysis of CPI Based on Machine Learning Algorit...

引用

Power, Electrical Engineering, Electronics and Control (PEEEC), International Conference on

作者： Xiawei Wang School of Digital Economics Jinjiang College Sichuan University Pengshan Sichuan China

ISBN: (数字)9798350378917

ISBN: (纸本)9798350378924

In recent decades, with the vigorous development of the national economy, the living standards of residents have also risen, and various types of consumption have emerged. In order to study the consumption situation of residents, the government has proposed the Consumer Price Index (CPI) as an indicator to reflect changes in their consumption and service levels. In order to better study the Consumer Price Index, this paper proposes a predictive analysis method for CPI based on machine learning algorithms. The first stage of this research method involves data visualization, which includes two small steps: data preparation explanation and chart visualization. This stage mainly provides initial basis for subsequent modeling; The second stage is the highlight of machine learning (random forest) algorithm modeling, which uses the monthly indices of national, city, and rural levels to train the model separately, with scores of $0.9927,0.9936$, and 0.9919 respectively, and draws the best fitting line; The third stage is for the model to predict the new data for the next month. Through the verification of the entire process mentioned above, the model in this article has good predictive performance and can effectively help solve the problem of CPI prediction.

关键词： Training Analytical models machine learning algorithms Economic indicators Urban areas Data visualization Predictive models Data models Predictive analytics Standards

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Application of machine learning algorithms for Predicting Employee Attrition

Application of Machine Learning Algorithms for Predicting Em...

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Future machine learning and Data Science (FMLDS), IEEE International Conference on

作者： Mohammad Dabbagh Kashif Saleem Adel Al-Jumaily Mohammad Tahir Angela Amphawan School of IT & Engineering Melbourne Institute of Technology Sydney Australia Department of Computing University of Turku Turku Finland School of Engineering and Technology Sunway University Selangor Malaysia

ISBN: (数字)9798350391213

ISBN: (纸本)9798350391220

Employers today are becoming more concerned about keeping their workforces, yet they mostly find it difficult to discover the actual causes of employee loss. Employee attrition can be caused by a variety of factors, including cultural and financial ones, but satisfaction levels are frequently under-looked. The goal of this research is to find patterns and trends by analysing the various factors that influence employee attrition, such as total working years, job satisfaction, age group and distance from home. The significant rise in the application of machine learning algorithms in recent years may provide new opportunities to improve the accuracy of employee attrition prediction. In this paper, we have applied two machine learning algorithms, Random Forest and Decision Tree Classifiers on a real dataset of an Australian company to predict employee attrition. Our findings highlight the superior performance of Random Forest over Decision Tree algorithm by achieving an accuracy value of 89.46%.

关键词： Training machine learning algorithms Accuracy Companies Predictive models Prediction algorithms Planning Classification algorithms Decision trees Random forests

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Cardiovascular Disease Prediction Through machine learning algorithms

Cardiovascular Disease Prediction Through Machine Learning A...

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Applied Artificial Intelligence and Computing (ICAAIC), International Conference on

作者： K Niharika Umar Bashir Sofi Bisma Farooq Ahmed M Arun Jampani Ravi V. Gokula Krishnan Department of EIE V. R. Siddhartha Engineering College Vijayawada A.P India Department of CSE Ministry of Education Abudhabi United Arab Emirates Department of Biomedical Engineering Sathyabama Institute of Science and Technology Chennai Tamil Nadu India Department of ECE Panimalar Engineering College Chennai Tamil Nadu India Department of ECE S.R.K.R. Engineering College Bhimavaram A.P India Department of CSE Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Chennai Tamil Nadu India

ISBN: (数字)9798350375190

ISBN: (纸本)9798350375206

Cardio vascular diseases (CVD) remain a global health concern, necessitating advanced predictive tools for early identification and intervention. CVD is a broad term that encompasses a class of diseases related to the heart and blood vessels. These conditions can range from diseases affecting the heart muscle to those impacting the blood vessels, and they are a leading cause of unhealthy and mortality worldwide. This article presents a comprehensive study on predicting cardiovascular diseases (CVD) using the Random Forest (RF) algorithm. RF’s effectiveness lies in its randomness, which reduces overfitting and increases robustness by selecting random subsets of features at each node split and aggregating predictions from multiple decision trees. Through hyperparameter tuning, the study optimizes RF parameters and analyzes the impact of dataset size on predictive accuracy. Results demonstrate RF’s superior performance with an 88.04% accuracy rate, surpassing other classifiers such as XG Boost, SVM, and KNN. Moreover, the study examines the Receiver Operating Characteristics (ROC) curve and Area Under the Curve (AUC) score, affirming RF’s promising performance compared to other algorithms. Further evaluation against existing algorithms, GN-ANN and EHV, across varying dataset sizes reinforces RF’s superiority, indicating its potential for accurate CVD prediction.

关键词： Radio frequency Support vector machines Accuracy machine learning algorithms Receivers Prediction algorithms Robustness

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Prediction of Urinary Tract Infection Using machine learning algorithms

Prediction of Urinary Tract Infection Using Machine Learning...

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Science, Engineering and Business for Sustainable Development Goals (SEB-SDG), International Conference on

作者： Marion O. Adebiyi Oladayo G. Atanda Praise Blessing Ayodele A. Adebiyi Kayode Akindeji Francis B. Osang Department of Computer Science Landmark University Omu-Aran Nigeria Department of Electrical Power Engineering Smart Grid Research Centre Durban University of Technology Durban South Africa Department of Computer Science National Open University of Nigeria Abuja Nigeria

ISBN: (数字)9798350358155

ISBN: (纸本)9798350358162

The prevalence of urinary tract infections (UTIs) is a significant concern, particularly among women. This study aims to adopt machine learning models for predicting UTIs using gradient boosting, support vector machine (SVM), and random forest algorithms. The dataset used was obtained from the Kaggle repository, consisting of UTI articles. The performance of the models was evaluated using metrics such as accuracy, precision, recall, and F1-score. The UTI dataset was preprocessed by removing irrelevant columns and handling missing values. The dataset was then divided into training and testing sets. The three models were trained using the training data. The results showed that both random forest and gradient boosting classification achieved an accuracy of 0.8750, while SVM achieved an accuracy of 0.8333. Precision values were 0.8000 for random forest, 1.0000 for SVM, and 0.9000 for gradient boosting. F1-scores were 0.8421 for both random forest and SVM, and 0.8571 for gradient boosting. Recall values were 0.8889 for both random forest and SVM, and 0.8182 for gradient boosting. Comparing the results with previous studies, the models showed competitive performance. The ROC curves were plotted to visualize the models' performance across different classification thresholds. In conclusion, the study demonstrates the effectiveness of gradient boosting, SVM, and random forest algorithms in predicting UTIs. The adopted models provide valuable insights for early detection and management of UTIs, contributing to improved healthcare outcomes.

关键词： Support vector machines Training Accuracy machine learning algorithms Medical services Boosting Prediction algorithms

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Analysis of the Impact of Economic and Demographic Factors on Women’s FMCG Perspective and Behavior Using machine learning algorithms

Analysis of the Impact of Economic and Demographic Factors o...

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International Conference on Computing Communication Control and Automation (ICCUBEA)

作者： Sushil Dashrath Shende Prakash Ukhalkar MBA Department Indira School of Business Studies Pune India MCA Department Pimpri Chinchwad College of Engineering Pune India

ISBN: (数字)9798350391770

ISBN: (纸本)9798350391787

Leveraging machine learning (ML) techniques, the analytical study aims to unravel the underlying drivers influencing women’s perspectives & buying behavior. The ML investigation is to meticulously analyze the impact of economic & demographic variables on women’s buying behavior with FMCG products. A comprehensive primary survey dataset was collected. It was preprocessed before analysis to address any outliers or inconsistencies. Employed feature selection techniques such as Principal Component Analysis (PCA), Explained Variance and Cumulative Variance are used to identify the most relevant economic and demographic variables. ML algorithms such as regression analysis, decision tree, and clustering methods were employed for analysis of the dataset to uncover patterns, correlations and predictive insights. The performance of ML models was evaluated by using Accuracy, Precision, Recall, F1 score, Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) & confusion matrix. Through in-depth analysis & interpretation, we elucidate the key economic & demographic factors influencing Women’s behavior of FMCG products. The study reveals economic influences, like income level & spending habits; demographic dynamics, like education level, age, & lifestyle preferences; regional variances; & emerging trends, like product preferences, geographic location, and product access. This ML investigation helps shed light on complex dynamics shaping women’s perspectives & behaviors to empower stakeholders to make informed decisions that drive positive outcomes.

关键词： Economics Technological innovation machine learning algorithms Consumer behavior Biological system modeling Education machine learning Predictive models Market research Principal component analysis

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