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检索条件"主题词=Machine Learning Algorithms"

共 26022 条记录，以下是4621-4630 订阅

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Research on Data Processing System Based on Computer Intelligent machine learning Algorithm

Research on Data Processing System Based on Computer Intelli...

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Communications Technology and Computer Science (ACCTCS), Asia-Pacific Conference on

作者： Xin Yu Yuan Li Modern Finance Industry School Shandong Institute of Commerce and Technology Jinan Shandong China

ISBN: (数字)9798350359985

ISBN: (纸本)9798350359992

The traditional 3D visual display technology only allows users to input less control data in the display process, and cannot be effectively integrated with massive data. Therefore, a visualization simulation method for 3D character animation is proposed in this paper. In terms of hardware, the 3D scanner is optimized, and the other hardware adopts the traditional system. The three-dimensional position, proportion and rotation Angle of human body are analyzed in detail. A deep learning method is proposed to realize the automatic generation of visual experience. Through the optimal processing of the completed visual comparison, the software design is completed. A visualization simulation method of 3D human body animation based on virtual reality technology is proposed. Experiments show that this method can process a large number of inputs, while the traditional processing method can only process some running data. The results show that this method can overcome the problems in the traditional method.

关键词： Visualization Solid modeling Three-dimensional displays Software design machine learning algorithms Data visualization Process control

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Development of machine learning Based Diabetes Mellitus Survival Prognostic Model

Development of Machine Learning Based Diabetes Mellitus Surv...

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

作者： Samuel Gbenga Faluyi Temitayo Balogun Matthew Adebayo Ogunsan Ebenezer Akinyemi Ajayi Victor Oluwaseun Aiyewumi Akinwale Trust Adewale Department of Computer Science Ekiti State Polytechnic Isan-Ekiti Isan-Ekiti Nigeria Department of Information Systems Federal University of Technology Akure Akure Nigeria Department of Computer Science Tai-Solarin University of Education Ijebu-Ode Ijebu-Ode Nigeria Department of Computer Science Ekiti State Polytechnic Isan-Ekiti Isan Ekiti Nigeria

ISBN: (数字)9798350358155

ISBN: (纸本)9798350358162

Diabetes places a heavy burden on those who have it, the healthcare system, and society at large. It is one of the major and increasingly health challenges causing serious illness to individuals with it, and which has been growing to become global crisis. This study proposed prognostic model for the survival outcome of individual with diabetes mellitus. Data with suitable attributes was collected and preprocessed using various approaches such as dealing with missing value, exploratory data analysis, 5-fold cross validation, hyperparameter tuning. The algorithms adopted includes Logistic Regression, Decision Tree, Random Forest, CatBoost, and XGBoost. Moreso, the model performance was evaluated using classification report, confusion matrix, area under curve, AUC score and receiver operating characteristics, ROC curve. Based on the results and performance comparison of the models, it was concluded that Logistic Regression with accuracy of 80% and AUC score of 84% performed better than other four algorithms used. Thus, suitable for making predictions on survival outcomes of individuals with diabetes mellitus and can be used for support system for medical practitioner.

关键词： Logistic regression machine learning algorithms Accuracy Medical services Prediction algorithms Diabetes Classification algorithms

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Maize Growth Simulation Analysis and Yield Prediction Based on Integrated learning Algorithm and WOFOST Model

Maize Growth Simulation Analysis and Yield Prediction Based ...

引用

Electronic Information Engineering and Computer Communication (EIECC), International Conference on

作者： Haibin Yang Yangfeiyi Chen Tianzi Zhang Fengzhe Zhu Electrical and Information Engineering College JiLin Agricultural Science and Technology University JiLin China

ISBN: (数字)9798331534622

ISBN: (纸本)9798331534639

Maize growth status and yield prediction are the key issues in maize planting management. Traditional prediction methods rely on single crop growth models or machine learning algorithms, which have the problems of insufficient prediction accuracy and limited adaptability. The combination of ensemble learning and crop model provides a new idea for improving the prediction accuracy. In this study, an improved maize growth prediction model was proposed based on the WOFOST model, combined with the irrigation model and the ensemble learning algorithm. By introducing K-means clustering and data assimilation technology, the model parameters were optimized to improve the prediction performance. The experimental data are derived from NASA's meteorological data from 1990 to 2020, and the results show that the improved model performs well in the prediction of key indicators such as leaf area index, soil water content and total biomass, and its MAE and MSE are reduced to 2.836 and 7.581, respectively, with a coefficient of determination R—2 It is improved to 0.942, which is significantly better than the traditional WOFOST model and other machine learning algorithms, demonstrating the efficiency and reliability of the improved model in maize growth prediction.

关键词： Adaptation models machine learning algorithms Computational modeling Biological system modeling Crops Clustering algorithms Predictive models Prediction algorithms Data models Ensemble learning

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Comparison of Supervised and Reinforcement learning Based Current Control in Power Electronic Circuits

Comparison of Supervised and Reinforcement Learning Based Cu...

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Design Methodologies Conference (DMC), IEEE

作者： Jennifer Piela Felix Ecker Nils Szabó Benjamin H. Zacher Christian Schumann P3E - Power Electronics Electronics and EMC Kaiserslautern University of Applied Sciences Kaiserslautern Germany

ISBN: (数字)9798350355864

ISBN: (纸本)9798350355871

The well-known classical control strategies are often limited to the exact design, based on the system and load model and do not consider application-based performance indicators. AI control algorithms are the proposed alternative and have the potential to replace extensive adaptive control strategies. AI algorithms use machine learning to independently adapt to changes in the electronics environment and in the load characteristics. In this paper, the two most promising neural network-based AI controller designs are compared based on applicability. The basic methods, principals, and differences between supervised learning and reinforcement learning are compared by a simulation and an experimental setup using a full-bridge power electronics circuit. The neural network models are trained within the same simulation environment and are then migrated on a hardware. Both AI controllers show similar results in the simulations and in the experiment measurements.

关键词： machine learning algorithms Microcontrollers Supervised learning Reinforcement learning Hardware Power electronics Software Artificial intelligence Optimization Load modeling

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Biomechanical Analysis of Foot Landing: A machine learning Approach Using Wearable Sensor System

Biomechanical Analysis of Foot Landing: A Machine Learning A...

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IEEE Region 10 International Conference TENCON

作者： Dhyey Shah Ronak Vyas Vineet Vashista Mechanical Engineering IIT Gandhinagar Palaj Gandhinagar India

ISBN: (数字)9798350350821

ISBN: (纸本)9798350350838

Foot landing is crucial in daily activities and sports, providing balance, stability, and energy efficiency while reducing injury risk. Proper landing distributes impact forces evenly, preventing overuse injuries. In racquet-based sports like badminton, proper foot landing and racquet handling are believed to be key factors that affect players' performance. During athletic activities or training, players can make mistakes or movements that are potentially harmful if not addressed. Therefore, performance analysis is crucial in these situations. While numerous studies have examined the biomechanical characteristics of the upper limb, including various stroke and posture analyses at different levels of gameplay, research on the lower limb has been relatively scarce in racquet-based sports. The main objective of this study was to develop a wearable motion sensor system to correctly classify foot landing, specifically distinguishing between heel and toe landing during a game of badminton, to discern the high impact on the lower limb, which is susceptible to injury. For the classification of foot landing, we developed a machine-learning algorithm to evaluate its performance. Eight healthy participants (age: $\mathbf{21.4}\pm \mathbf{1.5}$ years) were analyzed. Experimental results indicate that our trained algorithm achieves an accuracy of 97.53% for foot landing classification.

关键词： Biomechanics Training Limbs machine learning algorithms machine learning Classification algorithms Injuries Wearable sensors Foot Sports

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Energy Performance Forecasting in Buildings Using machine learning

Energy Performance Forecasting in Buildings Using Machine Le...

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International Conference on Communication, Computing and Internet of Things (IC3IoT)

作者： C. Jamunadevi N. Rajasekaran S Pandikumar R. Raja K.P. Narendra Prasanth Department of Computer Technology PG Kongu Engineering College Perundurai Erode Tamilnadu Department of Computer Applications Kongu Arts and Science College(Autonomous) Erode Tamilnadu School of CS & ITJain (Deemed to-be university) Bengaluru India Department of Information Technology Karuppannan Mariappan College Muthur Tamilnadu

ISBN: (数字)9798350352689

ISBN: (纸本)9798350352696

Energy Performance Forecasting in Buildings Using machine learning algorithms, addresses the critical issue of energy consumption and efficiency in buildings. The primary problem it seeks to overcome is the limitation of existing approaches that provide predictions for the entire building’s energy consumption without offering insights into room-wise energy patterns. So, it seeks to overcome this drawback by employing various regression techniques to predict energy levels in each room, enabling a more detailed and understanding of energy usage within the building. This study proposes a comprehensive system for predicting energy performance in each room of a building using various machine learning algorithms, including Linear Regression, Ridge Regression, K-Nearest Neighbors (KNN) Regression, Decision Tree Regression, Random Forest Regression, and Gradient Boosting Regression. The system is designed to provide accurate energy consumption forecasts for each room, ultimately contributing to more efficient energy management within the building. Here using of regression is for energy performance forecasting, regression methods are chosen over classification due to the continuous and quantitative nature of energy consumption variables. Regression models, such as Linear Regression and Decision Tree Regression, provide precise predictions of energy usage levels in buildings, allowing for a understanding of consumption patterns. Additionally, this system goes beyond total energy consumption and further refines the predictions by separately forecasting energy usage for appliances and lighting in each room. By breaking down energy consumption into these components, it offers a more detailed perspective on energy efficiency, allowing for precise optimization of individual room operations. The results show that the successful forecasting R-square of the proposed system is around RFR 0.95%.

关键词： Energy consumption machine learning algorithms Buildings Linear regression Predictive models Telecommunication computing Forecasting

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machine learning Enabled System Architecture for Automatic Grading and Sorting of Walnut Fruits: A Review

Machine Learning Enabled System Architecture for Automatic G...

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Electrical Electronics and Computing Technologies (ICEECT), International Conference on

作者： Khalil Ahmed Kajal Mithilesh Kumar Dubey Devendra Kumar Pandey School of Computer Application Lovely Professional University Phagwara Punjab India School of Computer Application Lovely Professional University Phagwaa Punjab India School of Bioengineering and Biosciences Lovely Professional University Phagwara Punjab India

ISBN: (数字)9798350378092

ISBN: (纸本)9798350378108

India is the eighth largest walnut producer worldwide and exports walnuts to 70 countries worth 10 million USD (778 million INR) annually. The grading of walnut ripeness is the main concern of worry for customers and the fruit business. The efficacy and fast classification of a walnut’s ripeness stage are crucial in judging the quality. Walnut maturity classification mainly influences walnut fruit quality and later marketing. Our approach to the automatic sorting and grading of walnut ripeness focuses on the identification of marks that appear on the outer surface of walnut fruits. The image dataset for the identification of marks or patches is divided into four different classes unripe, mid-ripe, ripe, and overripe acquired through high-quality cameras used in the proposed architecture, performing feature engineering and classification using ANN, SVM, and CNN techniques. The Raspberry Pi function counts fruits in a fruit box, using a conveyor belt to detect marks and patches for grading and sorting of walnut fruit based on texture, size, and color features. A motor powers the belt, moving walnuts from the fruit placer to the mechanical part. This architecture automates the fruit grading process, significantly reducing labor costs and time compared to manual sorting. This improves productivity and allows for faster processing of large quantities of fruits and is capable of quality control, increased efficiency, reduced errors, and improved market access.

关键词： Support vector machines machine learning algorithms Convolution Reviews Systems architecture machine learning Feature extraction Belts Vectors Sorting

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Classification of Healthy and Diseased Mulberry Leaves using machine learning

Classification of Healthy and Diseased Mulberry Leaves using...

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Smart Systems for applications in Electrical Sciences (ICSSES), International Conference on

作者： P S Shilpashree G D Sampath H S Pavankumaraswamy B E Sagar S K Chaithra Dept. of E&CE Siddaganga Institute of Technology Tumkur Karnataka

ISBN: (数字)9798350364040

ISBN: (纸本)9798350364057

Sericulture, an essential agricultural sector in India, depends greatly on the quality of mulberry leaves, serving as the primary nutrition source for silkworms crucial for silk protein synthesis. This study proposes a methodology utilizing K-Nearest Neighbor (KNN) and Naive Bayes (NB) algorithms with VGG-16 to classify healthy and diseased mulberry leaves. Both KNN and NB models utilize auto features extracted by VGG-16 during training to classify healthy and diseased mulberry leaves. NB models achieve slightly lower recall and precision rates of 89% and 93%, respectively, with a 91% F1-score, while KNN models achieve 90% recall, 100% precision and 95% F1-score for diseased leaves. In the healthy category, NB exhibits 89% precision, 93% recall, and 91% F1-score, whereas KNN showcases 91% precision, 100% recall, and 95% F1-score. On average, NB models achieve 91% accuracy, whereas KNN models exhibit a 95% accuracy. This comprehensive approach offers the potential for effective disease management, ensuring consistent high-quality silk production in sericulture.

关键词： Training Proteins Accuracy machine learning algorithms Production Propulsion Feature extraction

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Categorization and Detection of Sound in Crime Environment using machine learning

Categorization and Detection of Sound in Crime Environment u...

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International Conference on Computing and Networking Technology (ICCNT)

作者： D. Sungeetha Priya Sethuraman A. Gnana Soundari R. Dhanalakshmi M. Amanullah S. Pushpalatha Saveetha School of Engineering SIMATS Saveetha University Chennai St. Joseph’s Institute of Technology OMR Chennai

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

With today’s technology, it is evident how important it is to incorporate artificial intelligence into the applications that are being used. Audio is a crucial component of this integration since it is essential vital surveillance systems. The purpose of the paper is to present a sound classification model that accurately classifies various sounds in crime environment. When implemented in the environment, the efficiency and architecture of the majority of currently available technologies may raise concerns. The many sounds in an environment setting are classified using machine learning algorithms. Since the functioning of the Mel Frequency Cepstral Coefficient is similar to that of the proposed Artificial Neural Network model, the coefficients derived using the Mel Frequency Cepstral Coefficient technique utilized to train model.

关键词： machine learning algorithms Surveillance Computational modeling Artificial neural networks Computer architecture machine learning Real-time systems Classification algorithms Mel frequency cepstral coefficient Spectrogram

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Tennis match outcome prediction using machine learning and in-match statistics

Tennis match outcome prediction using machine learning and i...

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IEEE MIT Undergraduate Research Technology Conference (URTC)

作者： Yash Gupta Rida Assaf Magnet for Math and Science Morris Hills High School Rockaway United States of America Department of Computer Science American University of Beruit Beruit Lebanon

ISBN: (数字)9798331531003

ISBN: (纸本)9798331531010

machine learning has become a cornerstone of sports analytics, including tennis, which is an ideal arena for its application due to the abundance of comprehensive tennis data. This study investigates machine learning algorithms in forecasting tennis match results, comparing their performance against traditional methods. Using a dataset spanning from 2000 to 2022, encompassing a wide range of player statistics and match details, we employed multiple prediction models, Logistic Regression, Random Forests, TabNet, and XGBoost. We explored the role of in-match statistics as key predictors, unveiling their significance in determining match outcomes. Rather than deploy feature selection and feature engineering techniques, we relied on machine tree-based learning models that can handle high-dimensional data while mitigating the risk of overfitting. We compared our results against various baselines, including the Glicko model, and demonstrate a superior performance. This highlights the potential of machine learning techniques, particularly XGBoost, in elevating predictive accuracy in tennis match forecasting. Our study contributes to the growing body of research in sports analytics by emphasizing the importance of in-game performance data in predicting tennis match outcomes. By integrating machine learning approaches, we demonstrate the potential for more accurate and insightful match predictions, benefiting both fans and players alike.

关键词： Logistic regression Accuracy machine learning algorithms High dimensional data Predictive models Feature extraction Forecasting Random forests Sports Overfitting

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