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检索条件"主题词=Machine Learning Algorithms"

共 25808 条记录，以下是611-620 订阅

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OBE-Based Course Outcomes Prediction Using machine learning algorithms

OBE-Based Course Outcomes Prediction Using Machine Learning ...

引用

Intelligent Cybernetics Technology & Applications (ICICyTA), International Conference on

作者： Ellysa Tjandra Ridi Ferdiana Noor Akhmad Setiawan Department of Informatics Engineering University of Surabaya Surabaya Indonesia Department of Electrical and Information Engineering Gadjah Mada University Yogyakarta Indonesia

ISBN: (数字)9798331506490

ISBN: (纸本)9798331506506

Educational data mining has emerged as a powerful tool for exploring hidden patterns in student data, predicting academic success, and reducing the dropout rate, especially in higher education. The OBE approach has been widely used in almost all educational institutions to ensure that students have the competencies determined by the study program and to measure student achievement. This study proposes a prediction model using machine learning algorithms using Regression to predict student course outcomes in an integrated OBE attainment system utilizing midterm component scores. This study identifies the most potential machine learning techniques for predicting learning outcomes in an OBE-based framework. By predicting student course outcomes early, it is possible to identify at-risk students who fail to meet desired outcomes early so that lecturers can take preventive actions. The performance of the decision trees, random forests, support vector, and K-nearest neighbor algorithms was computed and compared to predict the students' course outcome results. This study uses a dataset of 2423 students enrolled in 30 courses in 6 study programs at a private university in Indonesia during the first semester of 2023–2024. The predictions were made using mid-term course components' scores defined in the course learning plan, such as assignments, class engagement, quizzes/exams, and case-based projects. This research finds support vector regression recommended for use in large classes, while decision trees and random forests are more suitable for smaller classes.

关键词： Support vector machines machine learning algorithms Education Predictive models Nearest neighbor methods Prediction algorithms Vectors Decision trees Data mining Random forests

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Analysis and Ranking Resume using machine learning algorithms and NLP

Analysis and Ranking Resume using Machine Learning Algorithm...

引用

Advances in Computing, Communication and Networking (ICAC2N), International Conference on

作者： Pratik Chandrasen Deshmukh M. R. Bendre Dept. of Data Science Engineering Pravara Rural Engineering College Loni India Dept. of Computer Engineering Pravara Rural Engineering College Loni India

ISBN: (数字)9798350356816

ISBN: (纸本)9798350356823

Specialized recruiting firms have emerged as a result of the expansion of the Indian employment market. These agencies use machine learning models to expedite the hiring process and provide their clients with the best candidates. Thus, the suggested methodology offers the potential to improve the hiring process by incorporating the advantages of machine learning in addition to natural language processing(NLP). Employing NLP techniques, one may accurately grade resumes according to the company's criteria and extract useful information from them. After thorough testing and improvement, the web portal might prove to be an invaluable tool for hiring managers and job seekers ***, one might think about adding features to the CV classifier and ranker, such as customized feedback and recommendations for enhancements that might assist candidates in understanding where they might be lacking and increase their chances of getting recruited.

关键词： machine learning algorithms Resumes Employment Natural languages machine learning Software Portals Recruitment Testing Qualifications

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Early Screening of Breast Cancer Using machine learning algorithms: A Comparative Study

Early Screening of Breast Cancer Using Machine Learning Algo...

引用

Future machine learning and Data Science (FMLDS), IEEE International Conference on

作者： Md Aminul Haque Moname Majumder Syed Mohammed Shamsul Islam The Discipline of Computing and Security Edith Cowan University Joondalup Australia The School of Science Edith Cowan University Joondalup Australia

ISBN: (数字)9798350391213

ISBN: (纸本)9798350391220

Breast cancer is one of the most typical types of cancer in women. It is the second greatest cause of death for women worldwide. Early detection and treatment can raise the likelihood of a full recovery and decrease the risk of cancer spreading. Therefore, the advancement in breast cancer illness prediction and detection is crucial for living a healthy life. As a result, high cancer prognostic accuracy is crucial for updating therapy aspects and patient survivability standards. machine learning techniques are now a top area of research because of their significant impact on the early diagnosis of breast cancer. To detect breast cancer, we applied seven machine learning algorithms: Random Forest (RF), Naïve Bayes (NB), Extreme Gradient Boost (XGB), Decision Tree (DT), Support Vector machine (SVM), Logistic Regression (LR), and K-Nearest Neighbors (KNN). We have also performed to-fold cross-validation method to detect breast cancer. The main goal of this study is finding the most effective machine learning algorithms for the prediction and diagnosis of breast cancer through confusion matrices, accuracy, and precision as well as ROC-AUC curves and scores. The study is performed by applying machine learning algorithms through feature scaling and two different splits of the training and testing data sets as well as 10-fold cross-validation methods. In this study, it has been seen that while all the selected classifiers have performed well in detecting breast cancer, the RF exceeds all other classifiers and obtains the best accuracy (97.9%) when the datasets are divided into 75% training and 25% testing data. On the other hand, SVM was found to beat all other classifiers with an accuracy of 98.20% when the datasets are divided into 80% training and 20% testing. Although the average accuracy decreased slightly (97.40%) when we performed to-fold cross-validation technique, SVM was still showing the best performance. This demonstrates that the separation of training and test

关键词： Support vector machines Training Radio frequency Accuracy machine learning algorithms Nearest neighbor methods Breast cancer Random forests Testing Classification tree analysis

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Research on Gradual Fault Prediction of Current Transformers Based on machine learning algorithms

Research on Gradual Fault Prediction of Current Transformers...

引用

IEEE International Conference on Civil Aviation Safety and Information Technology (ICCASIT)

作者： Jianbang Lin Yang Zhang Junwei Cao Zijian Deng Dingquan Tan Yangjiang Power Supply Bureau of Guangdong Power Grid Limited Liability Company Yangjiang Guangdong China

ISBN: (数字)9798350389418

ISBN: (纸本)9798350389425

Current transformers (CTs) are critical devices in power systems. Their gradual faults (such as insulation aging and core loss) are often hidden and progressive, making them difficult to detect and predict using traditional methods. This paper proposes a gradual fault prediction model for CTs based on machine learning algorithms. By constructing high-dimensional feature vectors, the model extracts, analyzes, and models CT fault characteristics and uses optimized machine learning algorithms to effectively predict gradual faults. First, the study extracts typical fault features from historical operational data of CTs using feature engineering methods and enhances the representation capability of these features through time-series analysis. Subsequently, various machine learning algorithms, including support vector machines (SVM), random forests (RF), and neural networks, are employed to train the fault prediction model. The model’s prediction accuracy and robustness are further improved through ensemble learning strategies. Experimental results show that the proposed model significantly improves the prediction accuracy of gradual faults in CTs, providing better robustness and generalization compared to traditional methods. This research can serve as a technical support for the safe and stable operation of power systems. Finally, the paper discusses the model’s application scenarios and deployment strategies in real-world power systems, providing insights and references for future studies.

关键词： Support vector machines machine learning algorithms Current transformers Predictive models Feature extraction Data models Robustness Power systems Data mining Long short term memory

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A Comparative Review of machine learning algorithms: Current State, Challenges and Future Perspectives

A Comparative Review of Machine Learning Algorithms: Current...

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Innovations in High Speed Communication and Signal Processing (IHCSP), International Conference on

作者： Anurag Choudhary

ISBN: (数字)9798350368949

ISBN: (纸本)9798350368956

The fundamental way to acquire information is through learning, which is the key indicator of human intellect. The primary method for making computers intelligent is machine learning. An important part of machine learning involves simulating human learning, studying, and improving performance and achievement using computers to acquire new skills, identify existing skills, and improve performance. Through the use of machine learning algorithms, which allow computers to see patterns and draw wise conclusions, we have completely changed how we handle and analyze data. Comparative studies are increasingly necessary to evaluate the current state of various algorithms, comprehend the challenges they encounter, and investigate their potential futures as the area of machine learning continues to grow quickly. machine learning algorithms are studied in this paper from a comparative perspective, covering their current state, challenges, and future prospects. This study seeks to offer academics, practitioners, and decision-makers insightful information and recommendations for choosing the most effective algorithms and overcoming related difficulties by analyzing and contrasting various algorithms. A quick overview of the state of machine learning algorithms is provided in the review’s first part. It shows a wide context of learning algorithms, such as supervised, unsupervised, semisupervised, and reinforcement. Each algorithm is displayed in accordance with its definition, scalability, performance, applications, advantages, and disadvantages. It is also exhibited in relation to its challenges, which are also mandatory portions that are detailed in a distinct section. Furthermore, the review paper also offers predictions for future perspectives and emerging trends in the machine-learning industry.

关键词： Computers Technological innovation machine learning algorithms Reviews Supervised learning Signal processing algorithms machine learning Prediction algorithms Market research Unsupervised learning

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Prediction of Chronic Liver Disease Using machine learning algorithms and Interpretation with SHAP Kernels

Prediction of Chronic Liver Disease Using Machine Learning A...

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Information Technology, Electronics and Intelligent Communication Systems (ICITEICS), IEEE International Conference on

作者： Harini Raj Niharika G Ashwini Kodipalli Trupthi Rao Dept. Artificial Intelligence & Data Science Global Academy of Technology Bangalore Karnataka

ISBN: (数字)9798350382693

ISBN: (纸本)9798350382709

The study below shows that we have mainly focused on predicting liver disease. The dataset used was obtained from Kaggle which contained 11 columns and 583 rows, amongst which there were 142 females and 441 males. The dataset contained various features which helped our model to do better predictions. We have used machine learning algorithms for the purpose which included classification algorithms like Random-Forest, Logistic-Regression, Decision-Tree, SVM, KNN. The results obtained from all these classifiers were then given to an ensemble model to arrive at exceptional results. In the paper below we obtained the highest accuracy using Random Forest classifier which gave us a result of 72%. We have then used LIME & SHAP kernels. SHAP kernels allowed us to explain the underlying decision-making-process in a better way, it also enhanced the interpretability and trustworthiness of the models. These findings highlight the effectiveness of the ML models and their contribution to the field of liver disease diagnoses and prediction.

关键词： Support vector machines machine learning algorithms Accuracy Liver diseases Medical services Predictive models Classification algorithms

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Malware Detection Technique for Android Devices Using machine learning algorithms

Malware Detection Technique for Android Devices Using Machin...

引用

Computing, Sciences and Communications (ICCSC), International Conference on

作者： Asif Iqubal Ashish Payal USICT Guru Gobind Singh Indraprastha University New Delhi India

ISBN: (数字)9798350353648

ISBN: (纸本)9798350353655

The increasing popularity of Android devices has raised concerns about the growing risk of malware. Malicious apps can compromise data security, disrupt device functionality, and invade user privacy. Traditional methods of detecting malware based on signatures need help to keep up with the evolving software landscape. This study introduces an approach to identifying Android malware using machine learning (ML) techniques. By extracting features of Android apps such as permissions requested by Android applications the method trains ML models to distinguish between benign and malicious applications. The effectiveness of these models is ensured using a comprehensive dataset. The paper also explores the performance of ML algorithms like Logistic Regression (LR), Support Vector machines (SVM), Decision Trees (DT), Random Forest (RF), k-Nearest Neighbors (k-NN), Neural Networks (NN), Gradient Boosting machines (GBM), Naive Bayes (NB), AdaBoost (AdaB), XGBoost (XGB) in detecting malware. Practical aspects like efficiency and scalability are discussed, emphasizing how these methods can seamlessly integrate into Android security solutions. This research represents a step in detecting Android malware offering improved protection against emerging security risks for Android users. The results show that RF obtained the highest accuracy of 97.20 % using the NATICUSdroid (Android Permission) Dataset for malware detection for Android devices.

关键词： Support vector machines Radio frequency Logistic regression machine learning algorithms Scalability Nearest neighbor methods Feature extraction Malware Protection Random forests

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Analysis of Physiological Parameters for Assessing the Risk Level of Cardiovascular Diseases Using machine learning algorithms

Analysis of Physiological Parameters for Assessing the Risk ...

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International Conference on Control, Decision and Information Technologies (CoDIT)

作者： José L. López-Saynes Elías N. Escobar-Gómez Sabino Velázquez Trujillo Carlos Venturino De Coss Pérez Sergio F. Marroquín-Cano Eduardo Chandomi-Castellanos Carlos A. Hernández-Gutiérrez The National Technological Institute of Mexico

ISBN: (数字)9798350373974

ISBN: (纸本)9798350373981

This article presents a machine learning approach to assess the risk level of developing cardiovascular disease. We employed various machine learning algorithms, like random forest, decision tree, MLP, CNN, and SVM, to improve the accuracy of risk assessment. Utilizing a dataset consisting of 12 features, including the target variable, sourced from IEEE, we aimed to surpass the 73% accuracy reported in state-of-the-art. To achieve this, we applied a Variational Autoencoder-Generative Adversarial Network (VAE-GAN) to generate additional synthetic data, which underwent rigorous validation procedures to ensure coherence and reliability. Our results demonstrate a significant improvement in precision and reliability in cardiovascular risk assessment, with an accuracy of 90.50% achieved thus far.

关键词： Support vector machines Accuracy machine learning algorithms Transfer learning Data models Cardiovascular diseases Risk management Decision trees Random forests Synthetic data

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Lungcanary- Pioneering Early Lung Cancer Detection using machine learning algorithms

Lungcanary- Pioneering Early Lung Cancer Detection using Mac...

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

作者： Priya Govindarajan Abhishek M Niharika K Abebe Tesfahun Department of Computer Science Amrita School of Computing Amrita Vishwa Vidyapeetham Mysuru Campus School of Electrical & Computer Engineering Debre Markos Institute of Technology Debre Markos University Ethiopia

ISBN: (数字)9798331505462

ISBN: (纸本)9798331505479

Lung cancer, a malignant tumour originating in the lung cells, is notoriously challenging to detect early through conventional clinical procedures, which often require invasive techniques. This study introduces LungCanary, an innovative approach leveraging machine learning algorithms to address the global health challenge of early lung cancer detection. By employing advanced computational methods and utilizing transfer learning principles, LungCanary effectively extracts meaningful features from medical imaging data. The model incorporates multiple decision-making components to capture diverse data patterns, significantly enhancing diagnostic precision. Through rigorous experimentation, LungCanary demonstrated superior performance with an accuracy of 98.36%, a precision of 96%, and an error rate of just 1.64%. These findings highlight LungCanary's potential to outperform existing models, marking a breakthrough in the accuracy and reliability of lung cancer diagnostics. The significance of this research lies in its potential to revolutionize early detection methodologies, ultimately improving patient outcomes.

关键词： Adaptation models Accuracy machine learning algorithms Error analysis Lungs Biological system modeling Lung cancer Feature extraction Biomedical imaging Tumors

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Rainfall Prediction using machine learning algorithms

Rainfall Prediction using Machine Learning Algorithms

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

作者： Pallem Sumanth Sai T.M. Thiyagu Pallem Geetha Soumya Division of CSE Karunya Institute of Technology and Sciences Coimbatore India Division of AI and ML Karunya Institute of Technology and Sciences Coimbatore India Division of CSE Sumathi Reddy Institute of Technology for women Hasanparthy India

ISBN: (数字)9798350375190

ISBN: (纸本)9798350375206

In several industries, including agriculture, water resource management, and disaster planning, rainfall prediction is essential. Because machine learning algorithms can examine historical rainfall data and provide precise projections, they have attracted a lot of interest. In this work, several meteorological characteristics including temperature, humidity, wind speed, and pressure are used to forecast rainfall patterns using machine learning algorithms. To determine which method best fits the prediction goal, a series of steps including feature selection, model training, assessment, and data pretreatment are involved. The models may be used to provide precise and timely forecasts by using historical rainfall data to study the intricate correlations between meteorological factors and rainfall patterns. Decision-making in a variety of weather-dependent industries is supported by the use of machine learning approaches into rainfall prediction, which provide a data-driven approach that improves forecast accuracy and dependability.

关键词： Rain machine learning algorithms Accuracy Decision making machine learning Predictive models Prediction algorithms

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