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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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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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Evaluating machine learning algorithms for Effective Credit Card Fraud Detection

Evaluating Machine Learning Algorithms for Effective Credit ...

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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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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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Anomaly Detection in Electronic Health Records Across Hospital Networks: Integrating machine learning With Graph algorithms

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IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 2025年第5期29卷 3723-3735页

作者： Niu, Haoran Omitaomu, Olufemi A. Langston, Michael A. Grady, Stephen K. Olama, Mohammed Ozmen, Ozgur Klasky, Hilda B. Laurio, Angela Ward, Merry Nebeker, Jonathan Oak Ridge Natl Lab Computat Sci & Engn Div Oak Ridge TN 37830 USA Univ Tennessee Dept Elect Engn & Comp Sci Knoxville TN 37996 USA US Dept Vet Affairs Washington DC 20001 USA

In a large hospital system, a network of hospitals relies on electronic health records (EHRs) to make informed decisions regarding their patients in various clinical domains. Consequently, the dependability of the health information technology (HIT) systems responsible for collecting EHR data is of utmost importance for patient safety. Recently, novel methods and tools aimed at identifying anomalies in EHR data to bolster the reliability of HIT systems have been introduced. However, these existing methods and tools primarily concentrate on individual hospitals, which limits our understanding of system-wide anomalous events and their potential impact on patient safety across multiple hospitals. In this article, we introduce a new approach to detecting anomalies in EHR data within a network of hospitals. This is achieved by combining advanced machine learning techniques with graph algorithms to create a tool capable of swiftly identifying and responding to deviations. Our proposed approach employs a combination of five machine learning models, harnessing the unique strengths of each model to provide a more robust detection system. The detected anomalies are then represented as graphs, allowing us to recognize patterns across the hospital network. This aids in identifying anomalies that span multiple medical facilities, potentially indicating broader system-level risks. Extensive real-world testing of our approach demonstrated its ability to offer actionable insights compared to existing methods. Additionally, its scalable design ensures seamless integration into existing HIT infrastructures.

关键词： Anomaly detection Hospitals machine learning algorithms machine learning Principal component analysis Wavelet analysis Monitoring Correlation Clustering algorithms Solid modeling Health information technology system-level anomaly detection high-dimensional time-series data machine learning graph algorithms

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Fortifying Network Security: A Comparative Study of machine learning algorithms for Enhanced DDoS Attack Classification

Fortifying Network Security: A Comparative Study of Machine ...

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International Conference on Electrical, Computer and Communication Engineering (ECCE)

作者： Md Anamul Kabir Md Ekramul Kabir Jareer Bhuiyan Dhaka Power Distribution Company Limited (DPDC) Khulna University Bangladesh Encore Performance Seating (A Palliser Company) Canada Bangladesh University of Textiles Bangladesh Department of Computer Science Ontario Tech University Oshawa Canada

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

DDoS (Distributed Denial of Service) creates a threat to a network for its security, which causes disrupting services and results in financial losses. With this study, we will be exploring classifications of various algorithms of machine learning (ML) like Decision Trees (DT), Logistic Regression (LR), Random Forest (RF), Support Vector machines (SVM), K-Nearest Neighbors (KNN) and Gradient Boosting (GB) to expand the finding of DDoS occurrences expending the dataset of CICDDoS2019. Our models showed a extraordinary accuracy (99.95%) with Random Forest (RF), which is turning into the best performer.

关键词： Support vector machines Radio frequency machine learning algorithms Computational modeling Network security Nearest neighbor methods Denial-of-service attack Turning Computer crime Random forests

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Optimizing machine learning algorithms for Accurate Heart Disease Detection

Optimizing Machine Learning Algorithms for Accurate Heart Di...

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Device Intelligence, Computing and Communication Technologies, (DICCT), International Conference on

作者： SM Faizanut Tauhid Safdar Tanweer Md. Tabrez Nafis Mohd Abdul Ahad Syed Mohd Faisal Malik Syed Sibtain Khalid Department of Computer Science and Engineering Jamia Hamdard Delhi India

ISBN: (数字)9798331543358

ISBN: (纸本)9798331543365

Heart disease is a leading global health challenge, emphasizing the need for effective diagnostic solutions. This study evaluated the accuracy of machine learning algorithms in heart disease diagnosis with the aim of finding the most reliable method. The algorithms examined contain Random Forest, Support Vector Classifier, Gradient Boosting, AdaBoost, Optimized Logistic Regression, SVM, Naive Bayes, K-Nearest Neighbors (KNN), Decision Trees, and SGD Classifier. Among them, optimized logistic regression and naive Bayes have the highest accuracy of up to 88.52%, demonstrating their predictive capabilities. The result of this work is optimized logistic regression and naive Bayes are capable of early detection of cardiovascular disease. Offering major perspective for integration into clinical practice. Future work will explore the refinement and real-world application of these models to further improve healthcare outcomes.

关键词： Heart Support vector machines Logistic regression Accuracy machine learning algorithms Translation Nearest neighbor methods Vectors Bayes methods Random forests

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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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Model for Endometriosis Detection Using machine learning algorithms

Model for Endometriosis Detection Using Machine Learning Alg...

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Software Engineering and Computer Science (CSECS), International Conference on

作者： Alexis Bautista Jahir Tardillo José Luis Castillo-Sequera Lenis Wong Information Systems Engineering Program Universidad Peruana de Ciencias Aplicadas Lima Peru Department of Computer Science Universidad de Alcalá Alcalá de Henares Spain

ISBN: (数字)9798331522216

ISBN: (纸本)9798331522223

Endometriosis is a chronic disease that affects a considerable percentage of women of reproductive age and is characterized by the presence of endometrial tissue outside the uterine cavity, leading to symptoms such as pelvic pain and dysmenorrhea. The aim of this study is to develop a predictive model for the classification of endometriosis using four machine learning algorithms: Random Forest, LASSO, SVM, and Naive Bayes. For this purpose, a dataset from the Global Health Data Exchange was utilized, consisting of 1,000 cases of patients with endometriosis. The methodology included data cleaning and preprocessing, as well as the evaluation of each algorithm's performance using four metrics: precision, recall, F1-Score, and accuracy. The findings revealed that the Random Forest algorithm was the most effective in identifying endometriosis, outperforming the other algorithms with a precision of 0.99 for the “endometriosis” class and an overall accuracy of 0.98.

关键词： Support vector machines Measurement Training machine learning algorithms Accuracy Software algorithms Predictive models Bayes methods Random forests Software engineering

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Intelligent Heart Disease Prediction using Ant Colony Optimization based machine learning algorithms

Intelligent Heart Disease Prediction using Ant Colony Optimi...

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machine learning and Autonomous Systems (ICMLAS), International Conference on

作者： Shaik Shafi Abhilash Ch Manoj A Snehil B Nandan TPK Anusha M Electronics and Communication Engineering B V Raju Institute of Technology Narsapur Medak India ECE BVRIT Narsapur Medak India

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

Cardiovascular diseases continue to be the primary cause of death worldwide, responsible for around 31% of all deaths each year, as reported by the World Health Organization (WHO). Although significant progress has been made in their diagnosis and treatment, heart disease remains highly prevalent. It has risen significantly due to unhealthy dietary habits and reduced physical activity. Predicting heart diseases using patient-related data, such as blood pressure, cholesterol levels, and other health metrics, has emerged as one of the most complex tasks for human analysts in modern times. As a result, there is a growing global demand for timely and accurate predictive models in identifying heart disease in healthcare industry, predominantly in the field of cardiology. In this paper, an intelligent heart disease prediction using ant colony optimization using machine learning algorithms is proposed. It includes two phases namely: 1) Feature Selection and 2) Classification. Ant Colony Optimization (ACO) is used to reduce the redundancy in feature selection from the large data set and helps to upgrade the performance of the classifier in the next stage. Then, we exploit the different classification algorithms such as K-Nearest Neighbors (KNN), Support Vector machine (SVM), Decision Tree (DT), Random Forest (RF) and XGBoost (XGB). These algorithms are chosen for their ability to deliver high accuracy, computational efficiency, and optimized memory usage, making them well-suited for this application. Furthermore, performance of the machine learning algorithms has been evaluated using accuracy, precision, F 1 score, recall and support parameters. The results illustrate that the amalgamation of the ACO algorithm and SVM classifier has the utmost performance for use in heart disease estimation.

关键词： Heart Support vector machines Ant colony optimization machine learning algorithms Accuracy Nearest neighbor methods Predictive models Feature extraction Classification algorithms Classification tree analysis

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