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检索条件"主题词=Machine Learning Algorithms"

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Identifying of Student Succes Based on Grades of State Madrasah Aliyah Students in Bandung City Using machine learning

Identifying of Student Succes Based on Grades of State Madra...

引用

International Conference on Telecommunication Systems, Services, and Applications (TSSA)

作者： Eki Ahmad Zaki Hamidi Nana Rachmana Syambas Muhammad Ali Ramdhani School of Electrical Engineering and Informatics Institut Teknologi Bandung Bandung Indonesia Department of Informatics UIN Sunan Gunung Djati Bandung Bandung Indonesia

ISBN: (数字)9798331527686

ISBN: (纸本)9798331527693

This research aims to identify the values of madrasa students in Bandung City using machine learning techniques. The application of technology in the education sector, primarily through machine learning, can provide significant benefits in analyzing complex and large amounts of academic data. In this study, several machine learning algorithms are applied to analysis student grade data from various subjects and data that influence student success in getting good grades. The analysis results show that the Naïve Bayes algorithm is the most effective in predicting and classifying based on student grades and other supporting data, with an accuracy of 98.5%. Compared with other algorithms used in this research, the results were Random Forest 98.4%, Decision Tree 95.3% and Logistic Regression 71.9%. These findings indicate that using machine learning in the academic assessment process in madrasas increases efficiency and provides deeper insights for better decision-making in the field of education.

关键词： Logistic regression Accuracy machine learning algorithms Urban areas Education Predictive models Prediction algorithms Bayes methods Classification algorithms Random forests

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Inertia Forecasting using Hybrid machine learning

Inertia Forecasting using Hybrid Machine Learning

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Innovative Sustainable Technologies for Energy, Mechatronics, and Smart Systems (ISTEMS), International Conference on

作者： Rabina Ningombam Chandransh Singh Sreenu Sreekumar Electrical Engineering Department National Institute of Technology Silchar Silchar India

ISBN: (数字)9798350384246

ISBN: (纸本)9798350384253

High penetration of low-inertia Renewable Energy Sources (RESs) like wind and solar in power systems results in frequency stability issues. This necessitates the deployment of a Frequency Containment Reserve (FCR) to ensure frequency stability. The optimal deployment of FCR necessitates accurate inertia forecasts. machine learning models like ANN are used for inertia forecasting. However, the accuracy of these models highly relies on their hyperparameters. This necessitates a suitable hybrid model to improve the forecasting accuracy. A hybrid model uses a machine learning algorithm to obtain forecasts and hyperparameters of the model are optimized by a suitable metaheuristic algorithm. Further, the missing values in the input data significantly affect the forecasting accuracy. The missing values can be easily replaced by a suitable data preprocessing technique. Therefore, this paper proposes two novel hybrid inertia forecasting models (ANN_ WaOA-CleanTS and SVR_ WaOA-CleanTS) using two powerful machine learning forecasting algorithms (ANN and SVR), a new metaheuristic optimization algorithm, Walrus Optimization Algorithm (WaOA), and a data preprocessing tool (cleanTS). ANN and SVR models are capable of obtaining accurate inertia forecasts due to their capability to identify nonlinear patterns in the data. The WaOA shows superior performance compared to other optimization techniques in various forecasting problems. The proposed work uses an R package, cleanTS, to replace the missing values as it shows superior performance in various forecasting problems. The performance analysis shows that the proposed hybrid models have an annual inertia forecasting accuracy above 97%.

关键词： Analytical models Accuracy machine learning algorithms Data preprocessing Metaheuristics Predictive models Power system stability

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Electrical Fault Detection And Localization Using machine learning

Electrical Fault Detection And Localization Using Machine Le...

引用

Expert Clouds and Applications (ICOECA), International Conference on

作者： Beethi Vivek B.Hareesh Teja Balasubbareddy Mallala G. Srinitha Chaitanya Bharathi Institute of Technology Hyderabad India

ISBN: (数字)9798350385793

ISBN: (纸本)9798350385809

Electrical faults pose significant challenges in power systems, often leading to operational disruptions and safety concerns. Timely detection and localization of these faults are crucial for ensuring uninterrupted power supply and preventing potential hazards. In this context, machine learning techniques have emerged as promising tools for enhancing fault diagnosis systems. This study presents a comprehensive approach to electrical fault detection and localization, leveraging machine learning algorithms. Drawing inspiration from real-world scenarios, such as power grid failures and industrial equipment malfunctions, we address the pressing need for reliable fault detection methods. The proposed methodology focuses on the utilization of decision tree classifier and contrasts its performance with the random forest algorithm. Through preprocessing steps, including feature engineering and normalization, the data is prepared for training. Additionally, this study addresses the issue of imbalanced classes by employing the Synthetic Minority Over-sampling Technique (SMOTE), which enhances the robustness of our analysis. Using a diverse dataset encompassing various fault types, we conduct extensive experimentation and evaluation. Our results demonstrate the superiority of decision tree-based methodologies in achieving high accuracy rates in fault detection and localization tasks. By presenting practical insights and performance metrics, this research contributes to the ongoing discourse in the field of electrical engineering. Overall, this study not only highlights the effectiveness of machine learning approaches in fault diagnosis but also underscores their potential for real-world implementation in power systems and industrial settings. These findings hold significant implications for engineers and practitioners seeking reliable solutions for mitigating electrical faults and ensuring operational resilience.

关键词： Location awareness Fault diagnosis Training Electric potential machine learning algorithms Fault detection Electrical fault detection

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Leveraging IoT and machine learning for Efficient Rice Stock Monitoring and Prediction

Leveraging IoT and Machine Learning for Efficient Rice Stock...

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Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA)

作者： Nana Sutisna Aditya Prawira Nugroho Christopher Jeffrey Patrick Amadeus Irawan Rizky Ramadhana Ronggur Mahendra Michael Jonathan Infall Syafalni Trio Adiono School of Electrical Engineering and Informatics Institut Teknologi The University Center of Excellence on Microelectronics Bandung Indonesia

ISBN: (数字)9798350367331

ISBN: (纸本)9798350367348

Recent technological advancements have made agricultural industry easier through the use of the Internet of Things (IoT). Many studies have been done to increase the adaptability of IoT in agricultural industries. IoT is mainly used as a tool to monitor and report data to a cloud server remotely. Those efforts are important to solve global challenges to produce 70% more foods, as the United Nation stated. To further solve those challenges, this paper tries to propose a comprehensive system to monitor the rice stock and forecast it for future use. This paper also shows that multivariable linear regression is an appropriate machine learning algorithm to forecast the rice stock. Furthermore, the forecast result is displayed in a web-based application built with *** that exhibits a wonderful score when tested with Lighthouse.

关键词： Weight measurement Productivity Performance evaluation machine learning algorithms Scalability Time measurement Internet of Things Time factors Servers Monitoring

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A Data-Driven Prediction Method of Short-Term Cooling Load Based on machine learning and Regression Analysis

A Data-Driven Prediction Method of Short-Term Cooling Load B...

引用

Energy Engineering and Power Systems (EEPS), International Conference on

作者： Jie Guo Qiang Zhao University of Jinan School of Physics and Technology Jinan China

ISBN: (数字)9798350366914

ISBN: (纸本)9798350366921

The modeling and prediction of air conditioning load in residential buildings has been an indispensable step in energy conservation and cost reduction. This paper proposes a data-driven load prediction model based on machine learning technology. Firstly, the input data is processed by feature engineering according to the Pearson Correlation Coefficient (PCC) between load and features. Secondly, three machine learning algorithms (SVR, MLP, and RF) and Pearson Correlation Coefficient (GTO) optimization algorithm are used for prediction. Finally, the predicted load is output by linear analysis and weighted regression. The model is verified by using residential building simulation data generated by Dest software. The influence of model integration on the overall prediction results and the error distribution of predicted load in different periods are analyzed and discussed. The Mean Absolute Error (EMAE) of predicted load is 1.52kW, and the Mean Absolute Percentage Error (EMAPE) is 10.7%.

关键词： Radio frequency Correlation coefficient machine learning algorithms Buildings machine learning Predictive models Prediction algorithms Data models Optimization Load modeling

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Fault Analysis for Grid Connected WECS Using machine learning Algorithm

Fault Analysis for Grid Connected WECS Using Machine Learnin...

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Smart Power Control and Renewable Energy (ICSPCRE), IEEE International Conference on

作者： A. Sree Sowmi P. Preethi Santhosam U. Sowmmiya Department of EEE SRM Institute of Science and Technology Chennai India

ISBN: (数字)9798350377002

ISBN: (纸本)9798350377019

In the realm of fault analysis, the integration of intelligent machine learning (ML) techniques has emerged as a transformative approach, ushering in unprecedented capabilities for identifying and understanding faults. Many research works are reported on machine learning based aero dynamical fault diagnosis. However, the identification of electrical faults using machine learning is rarely reported and also involves the conventional approaches. With this need and evolving trend of ML, this work reports the system disturbances that occur during all possible grid side faults in a grid connected Doubly fed Induction Generator (DFIG) based Wind Energy Conversion System (WECS). The system employs a Grid Side Converter (GSC) and a Rotor Side Converter (RSC) connected back-to-back creating a dc-link. The GSC aims at load compensation and dc-link voltage regulation whereas the RSC aims at speed and torque regulation through cascaded control schemes. During weak grid conditions, the grid is subjected to severe faults such as symmetrical (three phase fault LLL and three phase to ground fault LLLG) and asymmetrical faults (Phase-to-phase fault LL, Phase-to-Ground fault LG, Two-Phase-to-Ground fault LLG) whose existence beyond the allowable time might lead to blackouts demanding the necessity to identify the faults in a swift, intelligent way. The whole system is modeled in MATLAB to gather fault data using Discrete Wavelet Transform (DWT) The processed data is then used with different machine learning algorithms to detect the faults. Of which, Logistic Regression (LR) Model seems to produce less cost function and better metrics.

关键词： Fault diagnosis Analytical models machine learning algorithms Doubly fed induction generators machine learning Mathematical models Data models

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Heart Disease Prediction Using machine learning Models

Heart Disease Prediction Using Machine Learning Models

引用

Emerging Trends in Electrical, Electronic and Communications Engineering (ELECOM), 2020 3rd International Conference on

作者： Rameshwar Jugurnauth Shillia Madhoo Department of Electrical and Electronic Engineering Faculty of Engineering University of Mauritius Réduit Mauritius

ISBN: (数字)9798331532475

ISBN: (纸本)9798331532482

Heart disease is a condition that poses a serious and potentially deadly challenge encompassing a spectrum of heart-related issues ranging from irregular heartbeats and congenital defects to conditions such as atherosclerosis and heart failure. Accurately predicting heart disease is critical for enabling early detection and allowing for prompt treatment thereby preventing more severe complications and possibly saving multiple lives. The aim of this study is to identify the most accurate classifier for predicting heart disease using a dataset of 918 records and 11 features that was derived from five different sources: Stalog, Cleveland, Switzerland, Hungarian and Long Beach VA. In the study, the machine learning algorithms that are employed include the k-nearest neighbours, Decision Tree, Random Forest, Support Vector machine and Artificial Neural Network. Techniques such as feature selection as well as hyperparameter tuning were applied to improve model performance which were assessed using different performance metrics. The most accurate model was Random Forest that demonstrated an accuracy of 88.59%. A web-based interface was also developed using the Flask library to allow users to input patient attributes and to obtain predictions from the pre-trained models.

关键词： Heart Support vector machines Accuracy machine learning algorithms Recurrent neural networks Artificial neural networks Predictive models Reliability Random forests Tuning

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Mitigating Credit Card Fraud: A machine learning Perspective

Mitigating Credit Card Fraud: A Machine Learning Perspective

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Recent Trends in Microelectronics, Automation, Computing and Communications Systems (ICMACC), International Conference on

作者： Shivam Bhushan Indrajeet Gupta Angayarkanni S A Balakiruthiga B Pandimurugan V Rajaram V Department of Networking and Communications School of Computing SRM Institute of Science and Technology Kattankulathur India Associate Professor Computer Science and engineering Acharya Institute of Technology Bangalore India

ISBN: (数字)9798350366570

ISBN: (纸本)9798350366587

In this day and age of online and digital transactions, credit cards have come up as a popular option of payment. This has led to the increase in the fraudulent credit card transactions and as a result the banks and the customers have to bear the loss. It is very vital for the credit card companies to identify and stop the fraud transactions. The main aim of this project is to create a model that can distinguish fraudulent credit card transactions from real ones. The proposed model uses the transaction patterns of previous credit card transaction data which contains both genuine and fraud data to train itself using suitable ML algorithms and identify the fraudulent transactions and nip it in the bud. We will be using using SMOTE to process the data considering the imbalance between legitimate and fraudulent transactions, hyperparameter tuning the model for increased effectiveness and using random forest classifier and decision tree classifier to compare results. By using the above mentioned techniques we saw an increase in accuracy and recall. These models offer a scaleable solution to the problem of real time fraud detection in an ever growing and vast financial landscape

关键词： machine learning algorithms Accuracy Credit cards Data models Real-time systems Fraud Microelectronics Random forests Tuning Testing

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People Analytics Using Deep learning

People Analytics Using Deep Learning

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International Conference on Advances in Information Technology (ICAIT)

作者： Anshuman Bisht Utkarsh Mishra Santosh Saraf Electronics & Communication Engineering Graphic Era Deemed University Dehradun India

ISBN: (数字)9798350383867

ISBN: (纸本)9798350383874

The real-time tracking and analysis of foot traffic in public areas, specifically shops, buildings, and malls, is the main focus of this research project. By utilizing current technologies such as the Centroid Tracker and SSD (Single Shot Detector), the research endeavours to enhance and optimize the process of counting the number of people in particular region. The system include an alert mechanism designed to notify personnel when number of people surpasses predetermined thresholds, guaranteeing effective management and compliance with safety procedures. The project prioritizes real-time stream performance optimization in addition to crowd counting. To improve the video stream's frames per second (fps) and decrease lag, threading techniques are used. In addition to advancing computer vision and video analysis, the research provides a workable solution to the problems of monitoring people in desired region. The pressing need for efficient crowd management and adherence to safety guidelines has prompted the integration of cutting-edge technologies, like SSD (Single Shot Detector) and Centroid Tracker, to count and analyse the number of individuals present in these spaces. The system optimizes public spaces in the post-pandemic era by offering a comprehensive measure for footfall analysis, thereby helping to maintain a balance between customer service and safety. The approach increases the precision and effectiveness of the system by combining people counting, tracking, and object detection into a single algorithm. Despite testing models like YOLOv8 and ResNet, their accuracy was nearly similar. We used MobileNet due to its compact size, low parameters, fast processing, and good accuracy, making it ideal for real-time people analysis. The results indicate that the suggested method can successfully track and count people in real time, offering insightful information for efficiently managing public areas.

关键词： Deep learning machine learning algorithms Accuracy Instruction sets Heuristic algorithms Buildings Detectors Streaming media Real-time systems Testing

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Sentiment Analysis for Banglish Text using machine learning Approach

Sentiment Analysis for Banglish Text using Machine Learning ...

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International Conference on Computer and Information Technology (ICCIT)

作者： Musfique Ahmed Fardin Hasan Siam Neamul Islam Fahim Md. Awinul Haque Utsha Md. Mahin Khan Mohammad Nurul Huda Department of Computer Science and Engineering United International University Dhaka Bangladesh

ISBN: (数字)9798331519094

ISBN: (纸本)9798331519100

Sentiment analysis is a new area of study that has gained many achievements in the processing of Banglish, or Bengali-English, which is a type of language used for informal communication. Detecting sentiment in users’ comments is actually very important both for businesses and researchers as the number of online interactions increases exponentially. The machine learning models that we considered in this study for Banglish sentiment classification included Logistic Regression, Decision Tree, Random Forest, Support Vector machine (SVM), Naive Bayes, and XGBoost. Although these models are quite good, there is another ensemble technique that is based on using Logistic Regression, Mini Batch K-Means, and Gradient Boosting. It utilizes the uniqueness of each model to enhance accuracy and to gain more insights into the subtleties of Banglish sentiment analysis. Our ensemble method does way better than any other tested model we tried. Through this study, it has been shown that ensemble techniques are very effective in sentiment analysis and this is the basis for the future research in the area of mixed-language context user sentiment understanding that is very important for the general consumer behavior analysis and for the extracting of information about the social trends.

关键词： Support vector machines Sentiment analysis Logistic regression machine learning algorithms Accuracy Social networking (online) Boosting Robustness Ensemble learning Standards

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