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检索条件"主题词=Machine Learning Algorithms"

共 26022 条记录，以下是4781-4790 订阅

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Life Expectancy Prediction And Diet Recommendation System for Cardiovascular and Diabetes Disease Using machine learning

Life Expectancy Prediction And Diet Recommendation System fo...

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Knowledge Engineering and Communication Systems (ICKECS), International Conference on

作者： K Lakshmi K Deeba Vani Harave Sneha Bharti School of CSA REVA University Bangalore India Department of MCA Sir MVIT Bangalore India

ISBN: (数字)9798350359688

ISBN: (纸本)9798350359695

In the current era, people face many health issues and diseases due to inadequate and inappropriate food intake. People rely on medicine rather than having a proper dietary plan due to a lack of concise information on a proper diet. The diverse options in food components and people’s preferences with underlying health conditions make it difficult to perform real-time nutrition selection that fulfills a proper dietary plan. This problem is addressed through the implementation of a machine learning algorithm that effectively detects diseases and calculates life expectancy, enabling the formulation of suitable diet plans to mitigate their impact. The proposed work focuses on two common diseases: Diabetes and Cardiovascular Disease (CVD).A supervised classification algorithm has been used for predicting diseases and an unsupervised clustering algorithm has been used for diet recommendation. The objective is to facilitate convenient disease prediction at home and offer personalized, healthy diet recommendations. By motivating users to adopt a healthy lifestyle, the proposed work aims to prevent or reduce the influence of diseases.

关键词： Surveys machine learning algorithms Accuracy Refining Predictive models Prediction algorithms Classification algorithms

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Forecasting Customer Attrition using machine learning

Forecasting Customer Attrition using Machine Learning

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International Conference on Communications and Signal Processing

作者： T.E Kumaran B Lokesh P Arunkumar Mariammal Thirumeni Department of Electronics And Communication Engineering Rajalakshmi Engineering College (Anna University) Chennai India

ISBN: (数字)9798350353068

ISBN: (纸本)9798350353075

Customer churning has various problems in a number of different industries. Due to the loss of customers, the companies not only have a lower revenue but there is a vast effect on the reputation and client number. Therefore, it has become crucial for businesses to anticipate customer churn. In this work, a model is created for predicting customer churn using artificially generated data. The work involves creating artificial data that closely resembles the traits of actual customer data. To create a dataset that accurately reflects the customer data, a framework for the generation of synthetic data is developed. To make sure the synthetic data is appropriate for machine learning algorithms, it is pre-processed and given engineered features. Using the simulated data, the assessment on how well different machine learning algorithms predicts the customer churn is estimated. The model’s performance will be contrasted with that of conventional models developed using real world data. Later the maximum accurate machine learning model is deployed into a web application using flask for companies to input data and predict customer churn.

关键词： Analytical models machine learning algorithms Data analysis Stacking Signal processing algorithms Companies Predictive models

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Performance Analysis of Various machine learning Techniques for Classifying Topic Categories from Text News Articles

Performance Analysis of Various Machine Learning Techniques ...

引用

International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)

作者： Ratnam Dodda Ahmed Shahebaaz Mohd Miskeen Ali Zeeshan Ahmed Mohammed Department of CSE (AI&ML) CVR College of Engineering Hyderabad India Department of CSE (Data Science) CVR College of Engineering Hyderabad India Sphoorthy Engineering College Hyderabad India University of the Cumberlands Burlington USA

ISBN: (数字)9798350376425

ISBN: (纸本)9798350376432

Text classification has gained significant importance in recent years, especially for document classification based on content. Documents typically contain unstructured data, which can be transformed into a structured format using specific algorithms. These documents are then assigned to one or more categories either manually or automatically through classification algorithms. Various classification algorithms are available, differing in speed and efficiency. E-news reports or articles are crucial sources of digital media, influencing various sectors. In our study, we focused on classifying news articles using several machine learning techniques: Support Vector machine (SVM), Logistic Regression, Decision Tree, Random Forest, and Naïve Bayes classifier. This paper analyzes these techniques and identifies the best-performing algorithm for classifying news articles.

关键词： Support vector machines Logistic regression machine learning algorithms Text categorization User interfaces Media Classification algorithms Performance analysis Naive Bayes methods Random forests

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machine learning Based Recommendation Queries for Constraint Acquisition

Machine Learning Based Recommendation Queries for Constraint...

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International Conference on Tools for Artificial Intelligence (ICTAI)

作者： Hamza Islah Younes Mechqrane Ai movement International Artifcial Intelligence Center of Morocco University Mohammed VI Polytechnic Rabat Morocco

ISBN: (数字)9798331527235

ISBN: (纸本)9798331527242

Active constraint acquisition aids users in specifying problem constraints by enabling queries that facilitate convergence on a unique target constraint network. Although various algorithms have been proposed to reduce the number of queries needed for convergence, few utilize the problem's structure. In this paper, we propose leveraging the problem's structure by transforming the partially learned constraint network into features vectors used as inputs for machine learning models. These models are trained to predict missing constraints, which are then proposed to the user via recommendation queries. Experimental results demonstrate significant performance improvements over existing approaches.

关键词： Training machine learning algorithms machine learning Predictive models Programming Prediction algorithms Vectors Data models Convergence

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Optimizing Antenna Design for 6G Applications Using machine learning Techniques

Optimizing Antenna Design for 6G Applications Using Machine ...

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

作者： Praveen Kumar Sharma Vivek Prakash Yadav Aashiwal Nirmal Mukesh Sourabh Jawale Ankit Kumar Research Center for Electrical and Information Technology Seoul National University of Science and Technology Seoul Republic of Korea Department of Electronics and Communication Engineering Independent Researcher: FEAL: Flexible Electronics and Antenna Lab Pilani India Department of Electronics and Communication Engineering Independent Researcher: FEAL: Flexible Electronics and Antenna Lab Mumbai India Department of Computer Science and Engineering Medicaps University Indore India Department of Computer Science and Engineering Seoul National University of Science and Technology Seoul Republic of Korea

ISBN: (数字)9798350379716

ISBN: (纸本)9798350379723

The emergence of 6G networks requires innovative antenna designs that operate at extremely high frequencies. The proposed paper explores the application of advanced machine learning techniques to optimize the design of antennas for 6G applications at 15 GHz. Traditional design methods are often time-consuming and depend on expert knowledge, whereas machine learning can automate and expedite this process. We developed a dataset incorporating critical antenna design parameters such as geometric dimensions and applied algorithms to predict key performance metrics like return loss, gain, and bandwidth. Our results demonstrate that neural networks, in particular, offer superior predictive accuracy, significantly reducing design iteration times. This integration of machine learning into antenna design not only improves performance but also facilitates the development of adaptive antenna systems, meeting the stringent demands of 6G networks. This research highlights the potential of machine learning to revolutionize antenna design, enabling rapid and efficient development of next-generation communication systems.

关键词： 6G mobile communication Technological innovation machine learning algorithms Communication systems Design methodology Neural networks machine learning Prediction algorithms Antennas Next generation networking

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A Supervised machine learning Approach for Short-Term Forecasting and Demand Response Optimization for Enhancing Energy Flexibility in Smart home

A Supervised Machine Learning Approach for Short-Term Foreca...

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

作者： P Glaret Subin V Gomathy Krishnakumar Ramasamy R Dhivya U Arun Kumar S Shargunam Department of Electronics and Communication Engineering Faculty of Engineering and Technology SRM Institute of Science and Technology Vadapalani Campus Chennai Tamilnadu India Department of EEE Kathir College of Engineering Coimbatore Tamilnadu India Department of Electrical and Electronics Engineering Sri Ramakrishna Engineering College Coimbatore Tamil Nadu India Information Technology M.Kumarasamy College of Engineering Karur Tamilnadu India Department of Electrical and Electronics Engineering Karpagam Academy of Higher Education Coimbatore Tamil Nadu India Department of Computer Science and Engineering Kalasalingam Academy of Research and Education Virudhunagar Srivilliputhur Tamilnadu India

ISBN: (数字)9798350375190

ISBN: (纸本)9798350375206

This research investigates supervised machine learning algorithms for short-term energy forecast in smart homes. A dataset involving energy consumption in the house was used and it was analyzed across 10-times using the Support Vector machine, k-Nearest Neighbors, Gradient Boosting machines, and Ensemble Methods. Results show that the performance of the Support Vector machine was significantly higher with a mean, standard deviation, and maximum accuracy of 0.84, 0.02, and 0.88. Notably, the SVM had the minimum mean absolute error of 0.035 and root mean square error at 0.045. The coefficient of determination was also high, between 0.72 and 0.76, across 10-times of iteration. Thus, the SVM model is more accurate and reliable for forecasting energy consumption in the house. Although k-NN, GBM, and Ensemble Methods also showed considerable success, the abovementioned information proves that SVM is the most beneficial algorithm for smart home energy forecasting for the short-term. These outcomes can help design efficient energy management systems as they inform practitioners about the effectiveness of this method for energy consumption optimization to decrease costs and increase user comfort. Knowing which algorithms are capable of producing the high-quality results helps to design highly efficient short-term and long-term energy management solutions that satisfy the needs and preferences of smart home users, making the energy as sustainable and efficient as possible.

关键词： Support vector machines Energy consumption machine learning algorithms Accuracy Smart homes Predictive models Ensemble learning

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Emotion Recognition from Physiological Signals Using Ensembled machine learning Strategy

Emotion Recognition from Physiological Signals Using Ensembl...

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Electronics, Computing, Communication and Control Technology (ICECCC), International Conference on

作者： S. Fayaz Begum Fahimuddin Shaik Shaik Karimullah D. Mahalakshmi T. Lavanya M. Naveenkumarraju B. Mahendra Dept. of ECE AITS Rajampet A.P India

ISBN: (数字)9798350371802

ISBN: (纸本)9798350371819

The field of emotion recognition by physiological signal analysis is the subject of the proposed study. Its primary objective is to evaluate how effectively various classifiers predict two important emotional variables: valence and arousal. Three distinct classifiers are used in the study: kELM (Kernel Extreme learning machine), nELM (Neural Extreme learning machine), and sSOM (Simplified Self-Organising Map) in order to give a comprehensive comparison analysis. The script contributes to the advancement of emotion perception by carefully contrasting the capabilities of multiple classifiers. By using the dataset, the research hopes to clarify the benefits and drawbacks of each classifier and contribute to the development of more dependable and precise emotion identification algorithms. The suggested study focuses on the area of physiological signal processing for emotion identification. Its main goal is to assess how well different classifiers perform in predicting valence and arousal, two crucial emotional variables.

关键词： Self-organizing feature maps Emotion recognition machine learning algorithms Extreme learning machines Signal processing algorithms machine learning Prediction algorithms

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An Intelligent Supervised machine learning Framework for Diabetes Prediction

An Intelligent Supervised Machine Learning Framework for Dia...

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International Conference on ICT in Business Industry & Government (ICTBIG)

作者： Aone Maenge Tshiamo Sigwele Venumadhav Kuthadi Rajalakshmi Selvaraj Department of Computing and Informatics Botswana International University of Science and Technology Palapye Botswana

ISBN: (数字)9798331518981

ISBN: (纸本)9798331518998

Estimates suggest that 578 million people could be affected by diabetes mellitus in 2030, with an estimated 700 million affected by 2045 worldwide. Diabetes mellitus (DM) has impacted many people worldwide, with over 422 million affected and about 1.5 million deaths annually. Given the significant global burden of diabetes mellitus and alarming mortality rates, early diagnosis is crucial. machine learning (ML) techniques offer promising ways for reliably predicting diabetes. This research proposes a machine-learning framework utilizing the random forest algorithm for accurate diabetes prediction. The proposed framework was built using the Hospital Frankfurt, Germany dataset, which included eight independent variables and one target variable. The proposed framework was evaluated and compared to other existing frameworks based on accuracy and AUC metrics for Area Under Curve. The findings reveal that the proposed framework outperformed the reviewed frameworks in this study, with a 99.39% score of accuracy.

关键词： Radio frequency Measurement Accuracy machine learning algorithms Hospitals Prediction algorithms Real-time systems Diabetes Reliability Random forests

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Advances in machine learning for Stroke Prediction: A Comprehensive Review

Advances in Machine Learning for Stroke Prediction: A Compre...

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International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)

作者： F. Mohammed Arshad K. Sharmila Department of Computer Science School of Computing Sciences Vels Institute of Science Technology and Advanced Studies (VISTAS) Pallavaram Chennai Tamil Nadu India

ISBN: (数字)9798350376425

ISBN: (纸本)9798350376432

The comparative study in this review paper covered several phases of predictive modelling and examining the efficacy of different models for stroke prediction. Thorough exploratory data analysis (EDA) and strict data pretreatment were part of the initial look into, which made sure the model building process had a strong foundation. A thorough examination of the prediction performance of a various machine learning algorithms was made possible by the analysis, which included everything from traditional classifiers to state-of-the-art ensemble techniques. Lazy Predictor was used for benchmarking and initial model selection, which made it simple to identify the best-performing algorithms. These models' predictive accuracy was further improved by subsequent fine-tuning, which improved their appropriateness for stroke prediction tasks. In addition, the investigation of ensemble learning strategies cleared the way for the creation of hybrid models, which combine the advantages of distinct classifiers to produce better prediction results. The comparison analysis yielded useful insights that influence the selection of classification models for stroke prediction by healthcare researchers and practitioners. Stakeholders can embrace and apply ML approaches for stroke prediction with more knowledge if the study's findings are summarized. This will lead to improved medical outcomes and therapy.

关键词： Analytical models Accuracy machine learning algorithms Reviews Soft sensors Predictive models Prediction algorithms Data models Reliability Stakeholders

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Enhancing Intrusion Detection in Education Sector Through Advanced machine learning Techniques: A Comparative Study

Enhancing Intrusion Detection in Education Sector Through Ad...

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Women in Computing (InCoWoCo), International Conference for

作者： Glorybe E. Alegre Patrick D. Cerna College of Computer Studies Central Philippines State University Kabankalan City Philippines College of Information and Communication Technology and Engineering State University of Northern Negros Sagay City Philippines

ISBN: (数字)9798331518943

ISBN: (纸本)9798331518950

Intrusion Detection Systems (IDS) are important for identifying potential security threats, recording relevant data, reporting suspicious activity, and facilitating continuous improvement of security protocols. This study presents an IDS model developed using a range of machine learning (ML) techniques, including Naïve Bayes, Decision Tree, Support Vector machines (SVM), and Random Forest. Each model's performance was assessed using standard metrics like F1-score, recall, accuracy, and precision. The KDD Cup 99 dataset, which contains a comprehensive set of audit data simulating various types of network intrusions in a military environment, was employed for training and testing the models. The primary objective was to enhance the effectiveness of intrusion detection and safeguard computer networks in the education sector. Deploying an Intrusion Detection System (IDS) in schools and universities improves security by preventing unauthorized access, detecting potential cyber threats, and protecting sensitive information of students and faculty. Results indicate that the Random Forest algorithm, followed closely by SVM and Decision Tree, demonstrated the highest reliability and efficiency in detecting intrusions. The results emphasize the vital significance of carefully choosing machine learning models to enhance cybersecurity resilience in educational infrastructures.

关键词： Support vector machines Measurement Accuracy machine learning algorithms Intrusion detection Vectors Real-time systems Decision trees Random forests Resilience

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