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检索条件"主题词=Machine Learning Algorithms"

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An Adaptive Educational Content Recommendation System for UPSC as prints Based on their Past Performance Using machine learning algorithms

An Adaptive Educational Content Recommendation System for UP...

引用

2023 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2023

作者： Kundra, Danish Chitkara University Institute of Engineering and Technology Centre for Interdisciplinary Research in Business and Technology Chitkara University Punjab India

ISBN: (纸本)9798350319200

Within the space of competitive examinations, such as the Union Open Benefit Commission (UPSC) exams, the requirement for personalized and versatile instructive substance proposal frameworks has gotten to be progressively imperative. This inquiry about presents an inventive approach to building a Versatile Instructive Substance Proposal Framework (AECRS) custom-made particularly for UPSC hopefuls, leveraging their past execution information. This framework points to optimizing the learning involvement and upgrading the general victory rate of hopefuls by giving them with personalized ponder materials and assets. The proposed framework utilizes machine learning calculations, particularly the Network Factorization strategy, to extricate important bits of knowledge from the verifiable execution information of UPSC competitors. By analyzing the perplexing connections between the aspirants' execution measurements and the differing instructive substance accessible, the framework can precisely foresee the inclinations and learning designs of personal clients. Through the successful utilization of this prescient examination, the framework encourages the creation of personalized learning ways, empowering hopefuls to center on regions of shortcomings and strengthen ranges of quality. Besides, the AECRS joins a versatile learning system that ceaselessly upgrades and refines its proposals based on real-time input and client intuition. This versatile instrument guarantees that the framework remains responsive to the advancing needs and learning advance of each hopeful. By powerfully altering the prescribed substance based on the aspirants' continuous execution and inclinations, the framework maximizes engagement and maintenance, subsequently cultivating a more productive and viable learning preparation. The exploratory comes about to illustrate the viability of the proposed framework in improving the learning results of UPSC hopefuls. By giving custom-made instructive substance

关键词： Adaptive Educational Content Recommendation System etc machine learning algorithms Matrix Factorization Method Personalized learning UPSC Aspirants

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machine learning algorithms for Predicting Chronic Diseases

Machine Learning Algorithms for Predicting Chronic Diseases

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Applied Intelligence and Computing (AIC), IEEE World Conference on

作者： Furkan Buluş Bilal Saoud Ibraheem Shayea Zuleikha Syzdykova Dept. Electronics & Communications Engineering Faculty of Electrical and Electronics Engineering Istanbul Technical University (ITU) Istanbul Turkey Dept. Electrical Engineering Faculty of Sciences and Applied Sciences LISEA Laboratory University of Bouira Bouira Algeria Dept. of Computer Engineering Astana IT University Astana Kazakhstan

ISBN: (数字)9798350384598

ISBN: (纸本)9798350384604

Chronic diseases are the leading cause of death and disability worldwide, necessitating early detection and management to mitigate their adverse effects on health and improve quality of life. Leveraging machine learning algorithms has become a prominent approach in predicting the risk of various chronic diseases. These algorithms excel in analyzing complex datasets to identify patterns and risk factors associated with chronic conditions. This study explores the application of two different machine learning algorithms on an open-source dataset to predict the risk of chronic diseases. The outcomes of these implementations are analyzed and discussed in the final section, providing insights into their effectiveness and potential for enhancing chronic disease management.

关键词： machine learning algorithms Data analysis Prevention and mitigation Medical services Predictive models Prediction algorithms Robustness Bayes methods Decision trees Diseases

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Twitter Sentiment Analysis using machine learning algorithms for COVID-19 Outbreak in New Zealand

Twitter Sentiment Analysis using Machine Learning Algorithms...

引用

11th IEEE International Conference on System Engineering and Technology, ICSET 2021

作者： Baker, Oras Liu, Jay Gosai, Mayur Sitoula, Suyog Southern Institute of Technology School of Computing Invercargill New Zealand

ISBN: (纸本)9781665437660

The use of data obtained from social media for data mining has benefited the process of analysing public opinions. Sentiment analysis, also referred to as opinion mining, helps public health officials and other governmental agencies to understand the public's concerns, panics, emotions and interactions to provide effective services and information. This research focuses on the sentiment analysis of the COVID-19 pandemic outbreak in New Zealand using Twitter data. The analyses derived from several machine learning classification techniques, in particular Naive Bayes, K-Nearest Neighbour (KNN), Convolutional Neural Network (CNN), and Support Vector machine (SVM). In addition, the researchers implemented these algorithms in two different data mining platforms, namely Python and RapidMiner, then compared the metrics obtained from these techniques and platforms to identify the best algorithm suited for sentiment analysis. Finally, the researchers illustrate the experimental results that show the performance of Naïve Bayes and SVM, which indicates a longer computational time and led to an improved Twitter sentiment analysis result that outperforms the other models. After that, validate these models' effectiveness by comparing the obtained results for the same models in Python and RapidMiner. © 2021 IEEE.

关键词： Support vector machines COVID-19 Sentiment analysis machine learning algorithms Social networking (online) Pandemics Computational modeling

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Predicting countermovement jump heights by time domain, frequency domain, and machine learning algorithms

Predicting countermovement jump heights by time domain, freq...

引用

作者： Zhou, Zhaoxian Shakya, Sarbagya Sha, Zhanxin School of Computing University of Southern Mississippi HattiesburgMS United States School of Kinesiology University of Southern Mississippi HattiesburgMS United States

ISBN: (纸本)9781538636749

In this paper, we introduce an experiment evaluating performance of football players in countermovement jumps (CMJs). Three methods including time domain, frequency domain, and machine learning algorithms are proposed for performance evaluation. Correlation coefficients and p-values are given for time domain and frequency domain methods, and prediction errors are given for different machine learning algorithms. © 2017 IEEE.

关键词： Countermovement jump Frequency domain method machine learning algorithms Sports performance evaluation Time domain method Vertical ground reaction force

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Analyzing and Contrasting machine learning algorithms for Predicting the Risk of Cardiovascular Disease

Analyzing and Contrasting Machine Learning Algorithms for Pr...

引用

2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems, ICETSIS 2024

作者： Purohit, Harshita Purohit, Sarvesh Mirajkar, Riddhi Vishwakarma Institute of Information Technology Department of Information Technology Pune India

ISBN: (纸本)9798350372229

Cardiovascular conditions (CVDs) remain a major global health concern, challenging early threat assessment and forestallment. In this study, we employ three distinct machine learning algorithms - K Nearest Neighbors(KNN), Random Forest, and Decision Tree - 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, KNN classifier emerges as the top pantomime, achieving an accuracy of 84.48. This result showcases the efficacity of the KNN algorithm in handling the complexity of CVD threat vaticination. These findings give critical guidance for healthcare professionals and experimenters in enforcing effective prophetic models for early cardiovascular complaint threat identification and intervention. © 2024 IEEE.

关键词： Cardiovascular conditions (CVDs) Dataset Decision Tree K Nearest Neighbors (KNN) Kaggle machine learning algorithms Random Forest

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Application of machine learning algorithms in Network Intrusion Detection 7

Application of Machine Learning Algorithms in Network Intrus...

引用

7th International Conference on Cloud Computing and Big Data Analytics, ICCCBDA 2022

作者： Zhang, Shuchang Li, Yanling Shi, Yu Hua, Man School of Computer Science Civil Aviation Flight University of China Guanghan China School of Science Civil Aviation Flight University of China Guanghan China

ISBN: (纸本)9781665487115

With the rapid development of computer network technology, cyberspace security has received more and more attention. As an important means of ensuring cyberspace security, intrusion detection technology has also developed rapidly in recent years. machine learning is improving the efficiency of the intrusion detection and reducing the false positive. This paper is mainly divided into three parts. The first part briefly introduces the application process and common data sets of machine learning methods in the field of intrusion detection, and conducts an in-depth analysis of the application of traditional machine learning algorithms in intrusion detection. The second part expounds the specific application of deep learning as a subfield of machine learning in intrusion detection. In this part, according to the classification of generative model, recognition models and hybrid models, it is found that the intrusion detection system based on deep learning generally has a higher detection rate, which can make up for the defects of machine learning algorithm. The third part summarizes the full text and makes an outlook on the future development direction of intrusion detection technology. © 2022 IEEE.

关键词： Deep learning Cloud computing machine learning algorithms Intrusion detection Cyberspace Network intrusion detection Big Data

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Predicting Employee Voluntary Departure: Leveraging machine learning algorithms for Improved Retention Strategies 2

Predicting Employee Voluntary Departure: Leveraging Machine ...

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2nd International Conference on Smart Technologies for Smart Nation, SmartTechCon 2023

作者： Sarmento, Gonçalo Mota, Mickael Abbasi, Maryam Martins, Pedro Department of Informatics Polytechnic of Viseu Viseu Portugal Polytechnic Institute of Coimbra Applied Research Institute Coimbra Portugal Research Centre in Digital Services Polytechnic of Viseu Viseu Portugal

ISBN: (纸本)9798350305418

Employee voluntary departure presents significant challenges for organizations, affecting productivity and incurring substantial costs related to recruitment and training. This study aims to tackle this crucial issue by utilizing a comprehensive human resources dataset, which includes 14,999 records across ten columns. Its objective is to predict whether an employee will remain with the company or leave. To achieve this, seven distinct machine learning algorithms were employed: Naive Bayes, Decision Tree, Random Forest, K-Nearest Neighbors, Support Vector machines, Logistic Regression, and Multi-Layer Perceptron. The performance of each algorithm was assessed through a series of experiments involving different percentages of test data and variations in dataset processing. Notably, the Random Forest classifier emerged as the most accurate model, achieving an impressive accuracy rate of 98.47%. © 2023 IEEE.

关键词： Algorithm selection Comparative analysis Human resources machine learning algorithms Performance evaluation Predictive modeling

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Identification of Nitrogen in Soil from Remote Sensing Data Using machine learning algorithms 1

Identification of Nitrogen in Soil from Remote Sensing Data ...

引用

1st IEEE International Conference on Advances in Computing, Communication and Networking, ICAC2N 2024

作者： Anuradha, G. Yendluri, Pravalika Vasana, Medhini Bala Department of CSE Vijayawada India

ISBN: (纸本)9798350356816

Nitrogen is essential for the health of the soil and the development of crops for various reasons including photosynthesis, leaf and stem development, and also synthesis of proteins in crops. In order to increase agriculture by decreasing environmental effects and for maintaining efficient supervision of nitrogen levels present in soil. Great amount of Nitrogen results in plant stress and lower fruit quality where as insufficient nitrogen amount in soil causes stunted growth, chlorosis, and delayed maturity. Typically nitrogen comprises a percentage of to 0.5 of the soil's total weight. This project utilizes Remote Sensing Technology and machine learning algorithms with a particular focus on Landsat-8 Imagery. Traditional soil testing techniques include time-consuming processes and a restricted geographic reach. The Normalized Vegetation Index ( NDVI) is derived from Landsat-8 Imagery and correlated with ground data values of soil to determine the nitrogen level in the soil. NDVI indirectly reflects the nitrogen level with the help of many other factors. The project workflow includes gathering ground data using conventional soil testing techniques, pre-processing to extract NDVI values, and acquisition of Landsat-8 data. These datasets are used to develop and test an effective machine-learning model that can handle intricate correlations between nitrogen levels and NDVI. One of the solutions is to create a user interface that incorporates the machine learning model. The goal is to reflect the levels of nitrogen in soil in a particular field. © 2024 IEEE.

关键词： Ground-Truth Data Landsat-8 machine learning algorithms Normalized Difference Vegetation Index(NDVI) Remote Sensing User Interface

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Benchmarking machine learning algorithms for Predicting Building Energy Performance 59

Benchmarking Machine Learning Algorithms for Predicting Buil...

引用

59th International Universities Power Engineering Conference, UPEC 2024

作者： Bampoulas, Adamantios Meagher, Naill Finn, Donal Mangina, Eleni School of Computer Science University College Dublin Dublin Ireland School of Mech. and Mat. Engineering University College Dublin Dublin Ireland

ISBN: (纸本)9798350379730

Current data-driven load-forecasting models for residential buildings often overlook the impact of evolving occupancy patterns, relying on default hyperparameters. This oversight may result in reduced predictive accuracy with behavioural changes. This paper introduces a benchmarking approach evaluating common data-driven models-random forests, multilayer perceptron neural networks, extreme gradient boosting, and support vector machines-under concept drift. The day-ahead heating load prediction models are trained based on a series of input variables, including, inter alia, weather conditions, calendar information, and historical data. Optimal training periods and hyperparameter tuning are determined using a development set, while predictive accuracy is assessed through cross-validation on an evaluation set. Simulation results demonstrate that machine learning models trained on datasets with varying occupancy profiles perform comparably to those trained and evaluated with consistent profiles. Although optimal hyperparameters may vary, the models maintain consistent performance across datasets with diverse occupancy patterns, indicating resilience to changes in occupant behaviour. © 2024 IEEE.

关键词： benchmarking methodology concept drift machine learning algorithms occupancy behaviour short-term load forecasting

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Disease Prediction Models Using machine learning algorithms 3

Disease Prediction Models Using Machine Learning Algorithms

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3rd International Conference on Technological Advancements in Computational Sciences, ICTACS 2023

作者： Srinivas, Inumarthi V. Alzubaidi, Laith H. Singh, Rahul Sundaram, N. Kalyana Nirmala, D. Kj Somaiya Institute of Management Data Science and Technology Mumbai India College of Technical Engineering The Islamic University Najaf Iraq Ies College of Technology Department of Electrical & Electronics Engineering Bhopal India New Prince Shri Bhavani College of Engineering and Technology Department of Electrical & Electronics Engineering India Prince Shri Venkateshwara Padmavathy Engineering College Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798350342338

Disease Prediction Models hold paramount importance in healthcare for enabling early intervention and improving patient outcomes. machine learning algorithms, with their predictive capabilities, offer promising avenues for enhancing disease prediction accuracy. In this research, we explore the application of various machine learning techniques for disease prediction. We begin by introducing the context of disease prediction and the significance of machine learning algorithms. Our study involves preprocessing data, selecting relevant features, and implementing diverse algorithms. Through rigorous experimentation and evaluation, we present comparative insights into algorithm performance. The outcomes underscore the potential of machine learning in revolutionizing disease diagnosis and prognosis. Ethical considerations and potential biases are discussed, highlighting the responsible integration of predictive models in healthcare practices. This research contributes to the growing body of knowledge, paving the way for future advancements in disease prediction and personalized medical interventions. © 2023 IEEE.

关键词： Biomedical informatics Disease prediction Early intervention Ethical considerations Feature selection machine learning algorithms Model development Predictive modeling

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