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检索条件"主题词=prediction algorithms"

共 31027 条记录，以下是421-430 订阅

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Towards Precision Medicine: Nature-Inspired Optimization algorithms for Personalized Cholesterol Disease prediction

Towards Precision Medicine: Nature-Inspired Optimization Alg...

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Intelligent Systems and Advanced Applications (ICISAA), International Conference on

作者： Parul Datta Dr Balajee Maram Meesala Sravani Smritilekha Das Computer Science and Engineering Department University Institute of Engineering Chandigarh University Punjab School of Computer Science and Artificial Intelligence SR University Warangal Telangana CSE Department GMR Institute of Technology Rajam India Department of CSE Koneru Lakshmaiah Education Foundation Vaddeswaram AP India

ISBN: (数字)9798331539948

ISBN: (纸本)9798331539955

This survey article presents a number of optimization algorithms based on the natural selection with the help of collecting data on the precision medicine and cholesterol disease prediction. Of course, it refers to the fact that traditional measures go only down to individual differences. This looks into contemporary methods for enhancing the predictive capacity; these include ant colony optimization algorithm, simulated annealing, particle swarm optimization and genetic optimization. This paper examines current strategies for cholesterol levels prediction with emphasis having been placed on creating individualized treatments based on patient parameters. The comparison helps to identify that algorithms derived from nature are more effective compared to the other approaches and methodologies. The study is useful for the researchers and healthcare practitioners who are looking for the use of computational approaches in designing individual cholesterol treatment. Therefore it carries out the possible ethical problems as well as the limitation particularly the data security concern and the future recommendation for the research.

关键词： Surveys Ethics Precision medicine Simulated annealing prediction algorithms Problem-solving Particle swarm optimization Optimization Stress Diseases

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Application of Artificial Intelligence algorithms in Monitoring and prediction of Four Rate Integration in Power Grid

Application of Artificial Intelligence Algorithms in Monitor...

引用

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

作者： Chao Xun Shida Lai Dengfeng Xie State Grid Fujian Electric Power Co. Ltd Fuzhou Fujian China Fujian Yili Information Technology Co. Ltd Xiamen Fujian China

ISBN: (数字)9798350378917

ISBN: (纸本)9798350378924

In the current landscape of rapidly advancing smart grid technology, safeguarding the grid’s safety, efficacy, and reliability stands at the forefront of the power industry’s priorities. Central to this endeavor is the integration of four key grid metrics: line loss, trip frequency, complaint rate, and electricity fee recovery. This composite index serves as a pivotal yardstick for assessing the grid’s operational status. Monitoring and forecasting these metrics are crucial for enhancing grid efficiency, optimizing resource allocation, and minimizing operational expenses. This article explores the utilization of artificial intelligence (AI) algorithms in overseeing and anticipating the integration of these four grid metrics. Specifically, it introduces a prediction algorithm rooted in deep learning (DL). This approach excels at uncovering the inherent patterns and trends within grid operational data, enabling real-time monitoring of grid status and forecasting the future trajectory of the integrated four-rate index. The empirical findings demonstrate that the proposed algorithm not only elevates the intelligence quotient of grid management but also provides prompt alerts upon detecting abnormal grid conditions. This timely intervention significantly diminishes the incidence of failures, thereby bolstering the grid’s stability and reliability.

关键词： Measurement Stability criteria Power system stability prediction algorithms Market research Power grids Reliability Indexes Forecasting Monitoring

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Effective prediction of Global Solar Radiation Using Machine Learning algorithms and Satellite Images

Effective Prediction of Global Solar Radiation Using Machine...

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Geoscience and Remote Sensing Symposium (M2GARSS), Mediterranean and Middle-East

作者： M. Zaiani A. Irbah J. Delanoe M. Guermoui S.B. Boualtit K. Gairaa LATMOS/IPSL UVSQ Université Paris-Saclay Sorbonne Université Guyancourt France Unité de Recherche Appliquée en Energies Renouvelables URAER Centre de Devellopement des Energies Renouvelables CDER Ghardaïa Algeria

ISBN: (数字)9798350358582

ISBN: (纸本)9798350358599

Accurate prediction of Daily Global Solar Radiation (DGSR) is of prime importance for various applications in renewable energy, agriculture or climate studies. This paper presents the effectiveness of using Machine Learning (ML) algorithms and satellite imagery to improve DGSR prediction. The most commonly used ML methods consider as input the use of several meteorological parameters (temperature, wind speed, atmospheric pressure, sunshine duration, etc.) and/or radiometric parameters (aerosol optical thickness, water vapor, etc). In this work, the use of normalized reflectance obtained from satellite images in different channels is studied with the aim of improving the accuracy of solar radiation forecasts. Both ML approaches, ANN (Artificial Neural Network) and SVM (Support Vector Machine), were used as regression models to predict DGSR. The obtained results revealed that the number of parameters used as ML inputs significantly influences the accuracy of daily forecasts of global solar radiation. Furthermore, the results showed superior performance of ANN compared to SVM. Indeed, the errors obtained with the ANN model are an RMSE of 212.21 Wh/m 2 , an NRMSE of 3.46%, a MAPE of 2.85%, an MBE of -7.26Wh/m 2 and R of 0.99 while the SVM model gave respectively 441.95Wh/m 2 , 6.6%, 5.62%, 69.46W h/m 2 and R of 0.98.

关键词： Support vector machines Reflectivity Measurement uncertainty Artificial neural networks Machine learning Predictive models prediction algorithms

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Enhancement of Diabetes prediction Accuracy using Various Machine Learning algorithms

Enhancement of Diabetes Prediction Accuracy using Various Ma...

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Sustainable Expert Systems (ICSES), International Conference on

作者： Yaddanapudi Sarada Devi Chirumala Yasoda Gangireddy Tulasi Vemavarapu Suryateja Department of Electronics and Communication Engineering Velagapudi Ramakrishna Siddhartha Engineering College Andhra Pradesh India

ISBN: (数字)9798331540364

ISBN: (纸本)9798331540371

Diabetes Mellitus is an illness caused by a ineffective insulin management in the human body. Untreated diabetes, characterized by elevated glucose levels, poses serious health risks, including heart complications, kidney dysfunction, hypertension, vision impairment and multi-organ damage. Early prediction is vital for effective intervention. This project aims to enhance diabetes prediction accuracy by employing diverse machine learning techniques such as Logistic Regression(LR), SVM, GBDT and Random Forest (RF) on patient datasets. Risk factors such as age, obesity, sedentary lifestyle, genetic predisposition, poor dietary habits, and hypertension contribute to diabetes mellitus. Recent techniques, like ensemble learning and advanced hyper-parameter tuning, have been explored to improve predictive accuracy. However, challenges such as handling imbalanced datasets, feature selection, and model over-fitting persist. Hospitals employ comprehensive data analysis for precise diagnosis and tailored treatment plans. The primary objective of this project is to build a reliable, accurate model by overcoming these challenges through optimized algorithm selection, feature engineering, and rigorous cross-validation.

关键词： Support vector machines Radio frequency Analytical models Accuracy Machine learning algorithms Predictive models prediction algorithms Diabetes Tuning Random forests

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Study of Electric Vehicle Charging prediction using Different Machine Learning algorithms

Study of Electric Vehicle Charging Prediction using Differen...

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International Conference on Advanced Computing and Communication Systems (ICACCS)

作者： Satyam Sharma Deepa K S. V. Tresa Sangeetha Department of Electrical and Electronics Engineering Amrita School of Engineering Bengaluru Amrita Vishwa Vidyapeetham India Department of Engineering University of Technology and Applied Sciences-AIMusanna Sultanate of Oman

ISBN: (数字)9798350384369

ISBN: (纸本)9798350384376

Electric Vehicles (EV s) have become a necessary companion of humans in the last 5–6 years since these solely work on electricity. People are preferring EVs over regular vehicles since they are more energy efficient. However, there is a major problem of constant charging requirements and a limitation of charging stations. The basic purpose of the proposed work is to predict the average charging time through different machine learning algorithms, which would provide a precise idea about the best algorithm that could be used for the prediction. The next decades will depend on EVs, so having such a model would be a boon for the EV owners and the charging stations, too, as it would help set up new charging stations and see the average charging time of each vehicle.

关键词： Machine learning algorithms Communication systems Electricity Computational modeling Charging stations prediction algorithms Energy efficiency Electric vehicle charging

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Machine Learning Insights into Retail Sales prediction: A Comparative Analysis of algorithms

Machine Learning Insights into Retail Sales Prediction: A Co...

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Information Technology and Engineering (HITE), Horizons of

作者： Muhammad Zubair Aashir Waleed Ans Rehman Farhan Ahmad Mohammad Islam Saqib Javed Electrical Engineering Department University of Engineering and Technology Lahore Faisalabad Pakistan

ISBN: (数字)9798331516055

ISBN: (纸本)9798331516062

The knowledge of machine learning is expanding across all parts of existence, and it is an influential force of implemented functions. They have been implemented in education system, healthcare sector, engineering, retail sales and many other fields and its application is increasing. As the field of retail continuously grows and more companies enter this field, data processing and machine learning play crucial roles in determining or estimating the sales. The conventional approaches of sales that do not rely on comprehensive information are not efficient in the current market. Due to the growth of machine learning, various factors including but not limited to purchase behaviour, target market, and future sales have been determined making planning and growth easy. This paper aims at providing a detailed description of the application of ML methods to make predictions of retail sales with respect to linear regression, random forest and XGBoost models. The purpose is to determine which of them can be used by the retailers for decision making and which contributes to higher predictive value. All the models employed were trained and tested using the Big Mart sales recorded data that is publicly available. When using various regression models, the highest R-squared values of 0.545 were estimated by Random Forest Regression. Thus, this research aims to advance the usefulness of sales forecasting by applying and comparing the outputs of these models to real life retail data.

关键词： Adaptation models Accuracy Linear regression Predictive models prediction algorithms Data models Planning Forecasting Random forests Resilience

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Titanic Survivors prediction Analysis Using Machine Learning algorithms

Titanic Survivors Prediction Analysis Using Machine Learning...

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Computational Intelligence for Security, Communication and Sustainable Development (CISCSD), International Conference on

作者： Shridhar B. Devamane Abhijith B H Bhushan Purushotham Ravindranath R. C Lakshmikantha G. C Udayaprasad P K Department of Computer Science and Engineering Global Academy of Technology Bangalore India Department of Artificial Intelligence and Machine Learning Global Academy of Technology Bangalore India Department of AI & ML Global Academy of Technology Bangalore India

ISBN: (数字)9798350365405

ISBN: (纸本)9798350365412

Machine learning algorithms are computational models that allow computers to acquire knowledge and improve performance on a task by automatically learning patterns and rules from input data provided by the user. In this work we are predicting the Titanic disaster survivors based on a particular dataset. Based on characteristics such as age, sex, ticket class, etc., we applied the Support Vector Machine, K-Nearest Neighbors, Decision Tree, and Random Forest algorithm to identify which passenger would survive.

关键词： Support vector machines Machine learning algorithms Accuracy Disasters Nearest neighbor methods prediction algorithms Data models Vectors Sustainable development Random forests

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Enhancing Cryptocurrency Value prediction: A Comparative Study of Novel Random Forest and K-Nearest Neighbor algorithms for Improved Accuracy

Enhancing Cryptocurrency Value Prediction: A Comparative Stu...

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Electronics and Sustainable Communication Systems (ICESC), 2020 International Conference on

作者： S. Kiruthiga R. Balamanigandan R. Mahaveerakannan A. Mary Jenifer Department of Computer Science Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India Department of Spatial Informatics Saveetha School of Engineering Saveetha Institute of Medical and Technical Science Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798350379945

ISBN: (纸本)9798350379952

The effectiveness of the Novel Random Forest (RF) Algorithm for predicting cryptocurrency prices was evaluated and compared to the K-Nearest Neighbor (KNN) Algorithm. Machine learning methods were used to develop the two algorithms, and a pretest power analysis was conducted using two groups with the iteration of 10 at 85% of G-power and the setup parameters are alpha = 0.05 and beta = 0.85. Hence the P value is less than 0.005 (P<0.05) there is a statistical significance (p=0.007) between these two algorithms. The Novel RF Algorithm achieved an accuracy of 87.7330%, while the KNN Algorithm achieved an accuracy of 72.1250%. When the two algorithms were compared using an independent sample t-test, the difference in accuracy was found to be statistically significant at 0.760.

关键词： Radio frequency Accuracy Machine learning algorithms Software algorithms Estimation Nearest neighbor methods prediction algorithms Software Cryptocurrency Random forests

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Early prediction Model for Anemia in Infants Using Clinical Data from Perú Applying Supervised Machine Learning algorithms

Early Prediction Model for Anemia in Infants Using Clinical ...

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International Conference on Optimization and Applications (ICOA)

作者： Jonathan Rivera Diego Cardenas José Luis Castillo-Sequera Lenis Wong Universidad Peruana de Ciencias Aplicadas Lima Perú Universidad de Alcalá Alcalá de Henares Spain

ISBN: (数字)9798350387353

ISBN: (纸本)9798350387360

Anemia is a disease that is always present from early stages; children are the most prone to contracting it from birth if it's not diagnosed, which can lead to more severe illnesses. Therefore, a predictive model is proposed for the detection of anemia in infants using their clinical data. The Cross Industry Standard Process for Data Mining (CRISP-DM) methodology is employed, along with Analysis Services for the Extract, Transform, Load (ETL) process and four Machine Learning (ML) algorithms: Logistic Regression (LR), Decision Tree (DT), Support Vector Machine (SVM), and Random Forest (RF). All four phases of CRISP-DM were utilized: Business Understanding, Data Understanding, Data Preparation, Modeling, and Evaluation. The dataset used consists of 400,000 records related to the medical history of children from a healthcare institution in Peru, from which 27 variables were selected. The results showed that the DT algorithm performed better, achieving an AUC-ROC Curve greater than 93%, and the “Scikit Learn” library for ML in Python facilitated obtaining the results.

关键词： Support vector machines Pediatrics Machine learning algorithms Transforms Predictive models prediction algorithms Data models Data mining Random forests Standards

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Personal Finance Management and prediction using ML algorithms

Personal Finance Management and Prediction using ML Algorith...

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International Conference on Computation System and Information Technology for Sustainable Solutions (CSITSS)

作者： Sujay G Kaushik Sumukha S Kashyap Surabhi B V Chaitra M Santhosh Reddy Anitha N Dept. of Computer Science and Engineering BNM Institute of Technology Affiliated to VTU Bangalore India

ISBN: (数字)9798331505462

ISBN: (纸本)9798331505479

Effective personal finance management is essential for achieving financial stability and long-term financial health. This paper presents a comprehensive approach to summarizing, analyzing, and show monthly expenses using machine learning models. By collecting user inputs such as age, place of stay, number of dependents, monthly expenses on transport, education, subscriptions, savings, medical expenses, and more, the system uses linear regression, decision trees and random forest algorithms to predict financial values. The system provides detailed information of expenses, savings, and overall financial health, along with personal health analysis based on dietary habits. Users have access to a wide range of financial and lifestyle information, allowing the system to provide personalized recommendations to increase financial security and achieve financial goals. The model also evaluates the impact of recurring expenses and notable investments, helping users understand their spending patterns and make informed financial decisions. The results demonstrate the system's capability to deliver accurate predictions and actionable advice, enable users to better manage their finances. This approach has led to significant advances in personal financial management by integrating machine learning techniques to provide insight into people's financial health.

关键词： Machine learning algorithms Accuracy Finance Predictive models prediction algorithms Stability analysis Real-time systems Security Reliability Regression tree analysis

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