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检索条件"主题词=Machine Learning Algorithms"

共 25367 条记录，以下是51-60 订阅

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Soft Alert Generation for student Dropout Mitigation and Proactive Management by machine learning algorithms

Soft Alert Generation for student Dropout Mitigation and Pro...

引用

Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE), International Conference

作者： S. Geetha Spandana A P Vijay D Vishruth M V Dept. of CSE B.N.M Institute of Technology Bangalore Karnataka India

ISBN: (数字)9798331515911

ISBN: (纸本)9798331515928

Proactive dropout mitigation is crucial for enhancing student success and retention in schools. Dropout rates pose significant challenges in education systems globally, impacting students, communities, and societies. This paper emphasizes the necessity of proactive measures to address dropout rates and support student achievement by building an educational network. Through proactive approaches, schools can identify and address risk factors early, preventing student disengagement and dropout. Various strategies such as early interventions, personalized support systems, academic enrichment programs, and community partnerships are examined for their efficacy in promoting student engagement and retention. Additionally, the role of data-driven decision-making and predictive analytics in identifying at-risk students and tailoring interventions is explored. Implementing proactive dropout mitigation requires collaboration among educators, administrators, families, and community stakeholders. Prioritizing proactive measures can foster a supportive learning environment, empowering students to excel academically. This paper highlights the significance of proactive dropout mitigation that has been enabled using a seemingly enhanced automated full-stack student management system integrated with ML algorithms used in enhancing student success and promoting educational equity. By investing in proactive interventions, educational institutions can reduce dropout rates and foster a culture of academic excellence and lifelong learning.

关键词： machine learning algorithms Prevention and mitigation Education Decision making Buildings Collaboration machine learning Stakeholders Predictive analytics

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A Comparative Analysis of Feature Selection and machine learning algorithms for Enhanced Anomaly Detection in 5G Core Networks

A Comparative Analysis of Feature Selection and Machine Lear...

引用

International Global Information Infrastructure Symposium (GIIS)

作者： Júnia Maísa Oliveira César Morais Daniel Macedo José Marcos Nogueira Computer Science Department Universidade Federal de Minas Gerais Department of Electrical Electronic and Information Engineering (DEI) University of Bologna Italy

ISBN: (数字)9798331520113

ISBN: (纸本)9798331520120

A critical challenge lies in choosing the appropriate processing techniques to achieve optimal evaluation performance in machine learning algorithms for the 5G core data analysis in anomaly detection use case. In this work, we address this challenge by focusing on the comparative analysis of machine learning (ML) algorithms combined with data processing techniques for anomaly detection in 5G core networks. ML algorithms are promising for detecting such anomalies, but their performance heavily depends on the quality of the features used. This work highlights the importance of feature selection, demonstrating that by applying techniques such as Pearson’s correlation and dimensionality reduction (PCA and LDA), we can enhance the detection accuracy of various ML models. We evaluate eight ML algorithms using the 5GAD-2022 cybersecurity dataset and show that Random Forest achieves the best performance in terms of F1-score, AUC, and accuracy, regardless of the preprocessing techniques applied. The results suggest that meticulous feature selection can significantly enhance the algorithms’ effectiveness in detecting anomalies.

关键词： machine learning algorithms Data analysis Accuracy 5G mobile communication Focusing Feature extraction Computer security Anomaly detection Random forests Principal component analysis

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Evaluating machine learning algorithms for Effective Credit Card Fraud Detection

Evaluating Machine Learning Algorithms for Effective Credit ...

引用

International Conference on Signal Processing and Communication (ICSC)

作者： Parth Saxena Independent Researcher Houston USA

ISBN: (数字)9798331511890

ISBN: (纸本)9798331511906

This study studies machine learning (ML) algorithms for tracing credit card fraud, including random forest, logistic regression, and Support Vector machines (SVMs), using a dataset of 287,807 transactions from European cardholders. To resolve data imbalance, resampling was done after applying data preparation techniques. Ten balanced subsets of data were produced by undersampling the majority (non-fraud) class, each of which concentrated on a different aspect of the majority. These subsets were used to train individual models, and predictions were produced by majority voting. For comparison, models were trained using the original unbalanced data in addition to resampling. Accuracy, recall, precision, F1 scores, ROC-AVC scores, and a practical business assessment to determine their practical relevance were among the evaluation criteria. Logistic regression, trained on unbalanced data, achieved a ROC-AUC of 99.06% with transparent decision-making, outperforming models trained on balanced subsets, which, despite high AUC-ROC scores, suffered low precision and reduced practical reliability in fraud detection.

关键词： Support vector machines Logistic regression machine learning algorithms Signal processing algorithms Signal processing Credit cards Data models Fraud Reliability Random forests

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Exploring machine learning algorithms for Sentiment Analysis in the Sphere of Cyberbullying Detection

Exploring Machine Learning Algorithms for Sentiment Analysis...

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Advances in Computer Science, Electrical, Electronics, and Communication Technologies (CE2CT), International Conference on

作者： Lalita Bisht Kamika Chaudhary Department of Computer Science MBGPG College Haldwani Kumaun University Nainital India

ISBN: (数字)9798331518578

ISBN: (纸本)9798331518585

The rise in cybercrimes targeting children is leading to devastating consequences like harmful behaviors and even suicide. While social media platforms offer reactive solutions like post deletion and blocking, a more proactive approach is needed. To combat cyber child abuse effectively we need technology-driven solutions that focus on detection and alerts to identify predators or protect victims. Various strategies have been proposed, including machine learning techniques that leverage sentiment analysis to detect patterns in the bully's language. This study explores the potential of machine learning to combat cyber child abuse by focusing on detection and alerts. We investigated the effectiveness of supervised, unsupervised, and ensemble learning algorithms for sentiment analysis in identifying abusive language. Analyzing a social media dataset for bullying detection, the research reveals that Support Vector machines achieved the highest accuracy (83.26%) among supervised algorithms, with K-Nearest Neighbor (82.97%) and Random Forest Classifier (87.74%) performing best in the unsupervised and ensemble categories, respectively. The paper also addresses limitations and proposes future research directions.

关键词： Support vector machines Computer science Sentiment analysis machine learning algorithms Focusing Cyberbullying Nearest neighbor methods Classification algorithms Ensemble learning Random forests

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Application of machine learning algorithms in Artificial Intelligence Image Recognition Technology

Application of Machine Learning Algorithms in Artificial Int...

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Intelligent Systems and Computational Networks (ICISCN), International Conference on

作者： Qi Ouyang Siyang Dai Kexin Xie Zeyu Sun Yao Wang Sustainable Development of Architecture and Landscape Polytechnic of Milan Milan Italy Faculty of Engineering and Information Technology The University of Melbourne Victoria Australia College of Business and Public Management Wenzhou-Kean University Wenzhou China School of Environment and Science Griffith University Brisbane Australia Nanjing Stomatological Hospital Affiliated Hospital of Medical School Institute of Stomatology Nanjing University Nanjing China

ISBN: (数字)9798331529246

ISBN: (纸本)9798331529253

In smart city applications such as medical image analysis, autonomous driving, and security monitoring, image recognition faces challenges like complex backgrounds, low-quality images, and diverse targets, affecting accuracy and robustness. This study explores machine learning algorithms to enhance image recognition performance by addressing noise interference, intricate backgrounds, and feature extraction difficulties. It combines Convolutional Neural Networks (CNN) with transfer learning, starting with data preprocessing to reduce noise, using pre-trained CNN models to extract high-level features, and fine-tuning with a ResNet transfer learning strategy for specific tasks. Additionally, ensemble learning methods are employed to further improve model robustness and accuracy. Experimental results show that the ensemble model maintains around 85% accuracy even with high background complexity, and transfer learning achieves 90% accuracy when the sample size reaches 1000. These findings demonstrate that transfer and ensemble learning effectively enhance image recognition accuracy and resilience in complex environments.

关键词： Image recognition Accuracy machine learning algorithms Transfer learning Noise Interference Feature extraction Robustness Ensemble learning Convolutional neural networks

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Alzheimer's Disease Detection Using Various machine learning algorithms

Alzheimer's Disease Detection Using Various Machine Learning...

引用

Mobile Computing and Sustainable Informatics (ICMCSI), International Conference on

作者： Parthvi Patel Krishi Thakkar Prince Valani Krishna Patel Parth Goel Vaishali Vadhavana Computer Science & Engineering Department Devang Patel Institude of Advance Technology and Research Charotar University of Science and Technology (CHARUSAT) Changa Anand India

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522674

Alzheimer's disease (AD) is a brain-degenerative mental illness defined by growing forgetfulness. It also includes the loss of cognitive and behavioural characteristics. This research aims at developing a machine learning based tool that can work on early recognition of AD. This paper intends to develop an accurate, non-invasive machine learning model in detecting AD. Classification for AD has been done using behavioural, clinical, and lifestyle characteristics. The idea is to get such patterns from the data in a model, which will help in early detection of AD. For the detection of the AD dataset has been taken from Kaggle. This paper delivers an in-depth comparison of various algorithms, including Support Vector machine (SVM), Random Forest, Decision Tree, Logistic Regression, and Artificial Neural Network (ANN), with the goal of improving the detection of Alzheimer's disease (AD). The algorithms were evaluated using various performance metrics, such as F1 score, Precision, Accuracy, and Recall. The results indicate that the Random Forest algorithm attained the highest Accuracy, reaching 95%.

关键词： Support vector machines Measurement Logistic regression machine learning algorithms Accuracy Artificial neural networks Alzheimer's disease Informatics Random forests Mobile computing

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Forecasting of Reliability Indicators of Base Stations of Cellular Communication Networks Using machine learning algorithms

Forecasting of Reliability Indicators of Base Stations of Ce...

引用

Systems of Signals Generating and Processing in the Field of on Board Communications

作者： D. S. Chirov N. D. Petrov Moscow Technical University of Communications and Informatics Moscow Russia

ISBN: (数字)9798331532635

ISBN: (纸本)9798331532642

The article discusses the issues of forecasting the reliability of base stations of cellular communication networks using machine learning algorithms. This task is relevant, as forecasting the reliability of base stations allows timely measures to be taken to prevent equipment failures. For example, replacing the corresponding base station nodes or performing maintenance. All this ensures an increase in QoS in cellular communication systems. An analysis of existing developments in this field has shown that the use of machine learning algorithms is a promising way to predict reliability indicators. The article discusses two of the most popular machine learning algorithms for solving forecasting problems: decision trees and recurrent neural networks. To assess the applicability of these algorithms, a special dataset has been developed that simulates the process of failures in the main elements of the base station: the power supply unit and the RF power amplifier. The simulation results showed high quality forecasting of base station reliability indicators using these algorithms. In particular, the LSTM network provides about 92% accuracy in correctly predicting base station failure.

关键词： Cellular networks Base stations machine learning algorithms Recurrent neural networks Prediction algorithms Reliability Decision trees Communication networks Forecasting Long short term memory

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Synergistic Integration of Blockchain Technology and machine learning algorithms for Optimization of Agriculture Supply Chain Management

Synergistic Integration of Blockchain Technology and Machine...

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Intelligent Data Communication Technologies and Internet of Things (IDCIoT), International Conference on

作者： Gayatri Asalkar Prema Sahane Atharv Pedde Samarth Hanse Tejas Ghondage Krishna Bhosale Department Of Computer Engineering JSPM's Rajarshi Shahu College of Engineering Pune Maharashtra India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

This research study focuses on the discussion of blockchain and machine learning optimization in the conventional agricultural supply chain. Optimization of both rising technologies has the possibility of addressing some of the complex and entrenched issues that confront the agricultural industry while receiving calls for more openness, honesty, and productivity. Blockchain, being the technological application that is based on secure and transparent transactions and enables the assurance of immutability and machine learning as a data driven power in optimization, leads to a significantly better traceability, erased inefficiencies, less substances and time to market. It is however not entirely without some few challenges that will have to be overcome for all these innovations to come into their own. Among the obstacles that need to be overcome for realization of these innovations some of the critical ones are Technology and Data issues and Stakeholder issues.

关键词： Technological innovation machine learning algorithms Supply chains Time to market machine learning Agriculture Blockchains Stakeholders Reliability Optimization

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A Comprehensive Review of machine learning algorithms in Optimizing Power Consumption in Smart Buildings

A Comprehensive Review of Machine Learning Algorithms in Opt...

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Power Engineering Conference (SAUPEC), Southern African Universities

作者： Bertille Auyane Ngouessy Ep Ovono Temidayo Otunniyi Trudy Sutherland Faculty of Engineering Vaal University of Technology Gauteng South Africa

ISBN: (数字)9798331535162

ISBN: (纸本)9798331535179

The increasing global energy demand and the need for sustainable solutions have driven the evolution of smart buildings equipped with advanced technologies to optimize energy consumption. machine learning (ML) algorithms are pivotal in enabling these technologies, facilitating energy consumption forecasting, anomaly detection, and autonomous control systems. This paper offers a comprehensive review of state-of-the-art ML algorithms in the context of energy optimization within smart buildings. It categorizes and evaluates supervised, unsupervised, and reinforcement learning methods, focusing on their applications, advantages, and challenges. Furthermore, it discusses recent advancements and outlines future research directions, emphasizing the integration of innovative techniques like federated learning and adaptive energy management systems to enhance efficiency, scalability, and sustainability.

关键词： Energy consumption Smart buildings machine learning algorithms Reviews Federated learning Scalability Reinforcement learning Sustainable development Forecasting Optimization

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Enhancing Intrusion Detection Systems with Deep learning and machine learning algorithms for Real-Time Threat Classification

Enhancing Intrusion Detection Systems with Deep Learning and...

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Multi-Agent Systems for Collaborative Intelligence (ICMSCI), International Conference on

作者： Farooque Kadri Priyanka Singla ODYSSEY CLOUD Pune India BPIT Delhi India

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509835

Computer network intrusion has surged dramatically over the past decade, fueled by a lucrative underground cybercrime economy and the proliferation of sophisticated tools for executing such attacks. For over forty years, researchers from both industry and academia have been developing methods and systems to detect and prevent these security breaches. machine learning (ML) techniques have emerged as a powerful tool among the various methods used to classify normal and abnormal behavior. This study presents an advanced preprocessing strategy combined with a comprehensive evaluation of traditional machine learning models and a deep neural network (DNN) for real-time network intrusion detection. Preprocessing includes univariate analysis, feature selection, outlier handling, data normalization, and PCA-based dimensionality reduction. Further, the dataset was divided into train and test sets in the ratio 80:20. Traditional models GB, DT, and ET have been tested; however, ET performs better than all concerning accuracy, precision, recall, Fl score, and kappa. The proposed DNN model further enhanced detection capabilities, achieving the highest metrics across all evaluation parameters, including an AUC of 1.00, an accuracy of 99.50% and the lowest misclassification rate. The performance of both traditional and deep learning models was assessed using precision, recall, kappa, accuracy, Fl score, ROC curve analysis, and confusion matrices. The results indicate that the proposed approach is efficient for real-time network intrusion detection and threat analysis.

关键词： Deep learning Measurement Industries Accuracy machine learning algorithms Biological system modeling Network intrusion detection Artificial neural networks Real-time systems Security

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