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125 articles for “support vector machine learning”
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AI-Driven Prediction of Mechanical and Thermal Properties in Polymer-Based Functionally Graded Composites
Abstract: The proposed architecture of the current paper is an artificial intelligence (AI)-driven model of forecasting mechanical and thermal aspects of polymer-based functionally-graded composites (FGCs). Traditional micromechanical and finite element models, which are practical in homogeneous composites, might not be able to account in nonlinear interaction that is caused by compositional gradient. To overcome the challenge, machine learning (ML) models like artificial neural network (ANN), support vectors regression (SVR), and gradient-boosted …
Published in Journal of Polymer & Composites · Vol. 14, Issue 3, 2026 · pp. 70–89 Read article
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Literature Review and Discussion of Machine Learning Algorithms for Predicting Chronic Kidney Disease
Abstract: Being one of the most serious and most occurring diseases in our era, chronic kidney disease requires a fast and correct diagnosis. The usage of machine learning in medicine has now grown to such a level that it could be a means of diagnosis. The doctor can be the first one to get the ailment by using machine learning classifier algorithms. This has been the data science sector’s new horizons, …
Published in Journal of Computer Technology & Applications · Vol. 15, Issue 3, 2024 · pp. 34–39 Read article
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Customer Churn Prediction Using ML Algorithms
Abstract: Comprehending customer churn is essential for businesses aiming to enhance and sustain customer relationships. This study introduces a machine learning approach aimed at forecasting customer churn by leveraging demographic and behavioral data. Our research involved developing predictive models using support vector machines (SVM), random forests, and decision trees, evaluating their efficacy using real-world data from the telecom industry. Our findings underscore that random forests consistently outperform SVM and decision trees …
Published in Journal of Computer Technology & Applications · Vol. 15, Issue 2, 2024 · pp. 70–75 Read article
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Machine Learning Driven Mobile Price Prediction Using Feature Selection and Parameter Optimization
Abstract: Machine learning calculations are utilized in many fields like money, training, industry, medication, and online business. Machine learning calculations show execution contrasts relying upon the dataset and handling steps. Picking the right calculation, preprocessing and post-handling techniques have incredible significance in accomplishing great outcomes. The Random Forest classifier, K-nearest neighbor classifier, and support vector machine methods are evaluated to forecast mobile phone price categories. The “prediction” dataset which is taken …
Published in Current Trends in Information Technology · Vol. 14, Issue 3, 2024 · pp. 18–25 Read article
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Prediction of Customer Churn Using Machine Learning Classification Models
Abstract: Customer churn prediction is a critical task in both the telecommunication and medical industries, where retaining customers or patients is essential for ensuring long-term profitability and maintaining high-quality service. To address this, a range of machine learning models—including logistic regression, decision trees, random forests, gradient boosting machines, and support vector machines—were employed to accurately forecast churn behavior. Prior to model training, the dataset underwent thorough preprocessing, which included handling missing …
Published in Journal of Computer Technology & Applications · Vol. 16, Issue 2, 2025 · pp. 86–92 Read article
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Prediction of Mechanical Properties for Advanced Engineering Applications utilizing Polymer Composite Materials by Machine Learning
Abstract: Polymer composites show great promise as engineering materials because of their mechanical performance, resistance to corrosion, lightweight nature, and adaptability in design. Aerospace, automotive, biomedical, maritime, and civil engineers all rely on mechanical property prediction to cut down on trial expenses, expedite product development, and optimize material selection. Speedy design optimization is not possible using traditional numerical and experimental methods due to the high costs associated with material characterisation, computational …
Published in Journal of Polymer & Composites · Vol. 14, Issue 3, 2026 Read article
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Evaluation of Machine Learning Classifiers for Sentiment Analysis
Abstract: Sentiment in social media refers to users’ emotions and opinions through their posts and interactions. Sentiment analysis (SA) refers to relating and classifying the sentiments expressed as engagement and interactions between users. When analyzed, tweets frequently produce a large source of clustered data. These data help determine people’s opinions about a variety of motifs. Thus, this study presents an Automated Machine Learning (ML) Sentiment Analysis Model to detect media sentiment. …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 11, Issue 2, 2024 · pp. 141–154 Read article
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Predictive Analytics and Adaptive Learning: A Machine Learning Framework for Reducing Learning Gaps
Abstract: Most contemporary digital learning environments encounter persistent challenges when it comes to accurately identifying students who are at-risk of academic underperformance. These challenges often arise due to limited visibility in learners’ engagement levels and gaps in conceptual understanding, particularly during the early stages of a course. To address this issue, the present study proposes an early prediction framework that leverages comprehensive student-related data through the application of machine learning techniques. …
Published in International Journal of Algorithms Design and Analysis Review · Vol. 4, Issue 1, 2026 · pp. 16–21 Read article
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Genomic Selection for Grain Yield in Wheat Using Machine Learning on DArT Molecular Markers: A Comparative Evaluation Across Multi-Environment Trials
Abstract: Genomic selection (GS) predicts complex quantitative traits directly from genome-wide molecular markers, bypassing the need for extensive phenotypic trials and accelerating plant breeding cycles. We conducted a comparative evaluation of seven regression approaches — ridge regression (the machine-learning equivalent of RR-BLUP), Lasso, Elastic Net, Partial Least Squares, linear Support Vector Regression, Random Forest, and Gradient Boosting — for predicting grain yield from 1,279 Diversity Array Technology (DArT) molecular markers genotyped …
Published in Research and Reviews : Journal of Computational Biology · Vol. 15, Issue 2, 2026 Read article
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A Web Application for Predicting Diabetes Using Machine Learning Methods
Abstract: Diabetes is a long-term disease caused by high glucose quantity in the blood. It has the potential to result in serious health complications like heart disease, hypertension, and ocular damage. It is good to identify any health issues as early as possible to get the right medical treatment and make necessary lifestyle adjustments. One makes use of machine learning techniques to predict diabetes and develop treatment options using actual cases. …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 11, Issue 3, 2024 · pp. 92–102 Read article
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ML Model Comparison for Sentiment Analysis Across Diverse Datasets
Abstract: Analyzing sentiment is crucial for understanding public opinion on various issues in marketing, politics, and social sciences. This study compares the performance of seven different machine learning algorithms for sentiment classification, focusing on their effectiveness, accuracy, and complexity. The research is conducted on a pre-processed dataset with balanced text samples, utilizing feature extraction methods such as Term Frequency-Inverse Document Frequency (TF-IDF). The performance assessment criteria consist of accuracy, precision, recall, …
Published in Journal of Operating Systems Development & Trends · Vol. 12, Issue 2, 2025 · pp. 26–33 Read article
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Cutting-edge Deep Learning Methods for Predicting and Detecting Cardiovascular Diseases
Abstract: Cardiovascular diseases (CVDs) remain a major global health issue, highlighting the need for improved early detection and risk assessment methods. This research investigates the efficacy of both deep learning and traditional machine learning methods in forecasting cardiovascular diseases (CVDs). We evaluate a variety of models, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Multilayer Perceptrons (MLPs), Long Short-Term Memory (LSTM) networks, as well as Logistic Regression (LR), Decision Trees …
Published in Recent Trends in Parallel Computing · Vol. 11, Issue 2, 2024 · pp. 36–42 Read article
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Timestamp Extraction and Log Classification Using Supervised Machine Learning: A Comparative Study
Abstract: In modern software systems, logs are vital for monitoring application behavior, diagnosing issues, and analyzing performance. Timestamps are especially important for sequencing events, identifying anomalies, and understanding system failures. However, detecting timestamps in logs is challenging due to inconsistent formatting across systems and the presence of timestamp-like strings in non-timestamp fields. Traditional rule-based methods often fail in such cases. This study proposes a supervised machine learning approach to accurately classify …
Published in Journal of Software Engineering Tools & Technology Trends · Vol. 12, Issue 3, 2025 · pp. 26–38 Read article
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Facial Recognition System Utilizing Real-time Deep Learning Techniques
Abstract: This research introduces an openly accessible deep learning-based framework designed for facial recognition. The system encompasses five key stages: face segmentation, detection of facial features, face alignment, embedding, and classification. Deep learning methods are employed for the extraction of fiducial points and embedding within the system. For the classification task, a Support Vector Machine (SVM) is utilized due to its efficiency in both training and inference phases. Notably, the system …
Published in Journal of Image Processing & Pattern Recognition Progress · Vol. 11, Issue 1, 2024 · pp. 14–20 Read article
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A Survey of Several Machine Learning (ML) Algorithms for Security Solution in Internet of Things (IoT) Networks
Abstract: The Internet of Things (IoT) refers to the integration of physical objects with the Internet, allowing for connectivity and monitoring. This idea has garnered immense attention from researchers and users alike, driven by the widespread accessibility of the Internet. It spans a wide range of devices, including smart versions of conventional appliances, innovative tools tailored for Internet-enabled ecosystems, and sensors that leverage connectivity to revolutionize industries such as manufacturing, healthcare, …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 12, Issue 1, 2025 · pp. 1–11 Read article
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Enhancing Credit Card Fraud Detection Using Device Fingerprinting and Behavioral Biometrics
Abstract: Credit card fraud is a growing global concern, with financial losses projected to reach $ 43.47 billion by 2028. Credit card fraud poses a major challenge in the financial industry, resulting in substantial financial losses and security risks. This research introduces a Machine Learning-based Credit Card Fraud Detection System designed to improve the accuracy of fraud identification. Due to the imbalanced nature of fraud datasets, SMOTE (Synthetic Minority Over-sampling Technique) …
Published in Journal Of Network security · Vol. 13, Issue 2, 2025 · pp. 40–50 Read article
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ML Analysis of Factors Affecting Vaccination in Rural Children: A Machine Learning Approach
Abstract: Vaccination remains one of the most effective public health interventions for preventing childhood diseases, yet rural regions in India continue to experience uneven immunization coverage due to multiple socioeconomic and geographic barriers. This research applies machine learning techniques to identify and analyze the major determinants influencing childhood vaccination uptake in rural communities. The study utilizes survey-based demographic, socioeconomic, and healthcare-related parameters to build predictive models that classify children as vaccinated …
Published in International Journal of Vaccines · Vol. 3, Issue 2, 2026 Read article
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Diabetes Prediction Using ML Techniques
Abstract: Diabetes mellitus, commonly referred to as diabetes, denotes a cluster of prevalent endocrine disorders characterized by persistent elevated levels of blood sugar. Diabetes is classified into two main types: type 1 and type 2. Type 1 diabetes arises when the body is unable to produce insulin, while type 2 diabetes involves either insulin resistance or insufficient insulin production. Early detection and intervention are essential to reduce its harmful impacts. The …
Published in Research & Reviews: A Journal of Bioinformatics · Vol. 11, Issue 3, 2024 · pp. 1–9 Read article
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Multimodal Disease Detection Using Deep Learning
Abstract: Artificial Intelligence (AI) is playing an increasingly pivotal role in modern healthcare, particularly in improving the speed and accuracy of disease detection. With the evolution of Machine Learning (ML), Deep Learning (DL), and high-performance computing, AI-based solutions are now capable of processing extensive medical datasets, ranging from patient records to diagnostic images, with remarkable efficiency. These systems offer immense potential for early intervention, improved clinical decision-making, and alleviating pressure on …
Published in Journal of Computer Technology & Applications · Vol. 16, Issue 2, 2025 · pp. 129–139 Read article
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Support Vector Machine Inspired Load Forecasting of a State University in Haryana
Abstract: Estimating the possible environmental impact and determining probable capital requirements are made easier with a solid grasp of electricity demand. Beginning in the middle of the 20th century, demand forecasting for electric power networks was studied theoretically. Prior to that, the study of demand forecasting had not developed because of the small scale of power networks. With the use of statistical prediction techniques, plans for the electric power industry have …
Published in Trends in Electrical Engineering · Vol. 15, Issue 2, 2025 · pp. 33–40 Read article