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104 articles for “and SVM models”
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Cardiovascular Illness Detection and Categorization with Innovative Neural Networks
Abstract: Health-related problems are increasingly prevalent in modern-day societies and are significantly shaped by a multitude of factors encountered in everyday life. Among these, cardiovascular diseases have emerged as one of the primary causes of death on a global scale, posing serious challenges to public health systems. In response to this growing concern, the present study proposes a machine learning-based framework that is not only highly effective but also reliable and …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 12, Issue 2, 2025 · pp. 21–30 Read article
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Smart Polymer Composites with Multifunctional Capabilities Integrating Electroactive Polymers Conductive Nanofillers and Flexible Electronics for Advanced Sensing and Actuation Systems
Abstract: Smart polymer composites have gained significant attention to their ability to integrate polymer matrices with conductive nanofillers, offering tunable electrical, mechanical, and electroactive properties. These composites are highly responsive to external stimuli such as electrical fields, mechanical stress, and temperature variations, making them ideal for applications in flexible electronics, soft robotics, and adaptive sensing systems. This research investigates the effect of nanofiller dispersion on the performance of polymer composites, optimizing …
Published in Journal of Polymer & Composites · Vol. 13, Issue 6, 2025 · pp. 946–965 Read article
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Image Processing Based Analysis of Fundus Images for Glaucoma Detection
Abstract: Precise and prompt identification of glaucoma is essential for its treatment. Fundus images provide important information, which shall be extracted using carefully designed image processing algorithms. The paper discusses a methodology for automatic detection of glaucoma from fundus images. The technique makes use of an elaborate image segmentation process, wavelet decomposition for feature selection and a proven classification model.Keywords: Glaucoma, fundus image, discrete wavelet transform, SVM classifierCite this ArticleShibina M.P, …
Published in Journal of Multimedia Technology & Recent Advancements · Vol. 5, Issue 1, 2018 · pp. 33–39 Read article
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Nail Image Processing for Early Symptom Detection of Diseases based on Supervised Learning
Abstract: Digital Image Processing of human nail can be used for the prediction of various systemic and dermatological diseases. The proposed system – Nail Image Processing System using SVM (NIPS-S) helps us to create a model for the analysis of human nail and predict various diseases. The input to the proposed system is the Human Palm Image. The nail portion is segmented and a combination of nail color, shape and texture …
Published in Journal of Computer Technology & Applications · Vol. 8, Issue 3, 2017 · pp. 49–61 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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Machine Learning Based Sentiment Analysis of Student Feedback in Higher Education
Abstract: Educational institutions routinely collect feedback from students to understand their perceptions of academic programs, infrastructure, and campus facilities, to improve the overall quality of the college environment. In current practice, feedback is often gathered using numerical or grade-based rating systems, which tend to oversimplify student opinions and may overlook important details related to their level of satisfaction. In contrast, open-ended textual feedback allows students to clearly express their views, concerns, …
Published in International Journal of Data Structure Studies · Vol. 4, Issue 1, 2026 · pp. 01–10 Read article
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Brain Tumor Detection
Abstract: The MRI, or magnetic resonance imaging, is one of the most common practices in detecting a brain tumor since it contains important information used to extensively scan the human brain’s internal anatomy. The treatment of any tumor depends entirely on the knowledge and expertise of the physician. This paper reflects the identification of brain tumors using different machine learning algorithms along with the convolution neural network (CNN) and transfer learning. …
Published in Research & Reviews: A Journal of Bioinformatics · Vol. 8, Issue 1, 2021 · pp. 45–52 Read article
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Eye Disease Classification Using K-means Clustering Algorithm and Ensemble Classification Approach
Abstract: In this study, we present a comprehensive approach for the classification of eye diseases, specifically targeting normal, cataract, glaucoma, and diabetic retinopathy conditions. This research uses a dataset from Kaggle, which provides a wide and varied collection of retinal images to ensure good representation. The methodology encompasses advanced image processing and machine learning techniques to ensure accurate diagnosis and prediction. The preprocessing phase involves a series of image enhancement techniques …
Published in International Journal of Bioinformatics and Computational Biology · Vol. 3, Issue 2, 2025 · pp. 15–27 Read article
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A Review of AI-Based Intrusion Detection Systems for Mobile Ad Hoc Networks (MANETs)
Abstract: Mobile Ad Hoc Networks (MANETs) comprise wireless networks that lack any conventional infrastructure . Their chief features include highly changing network topologies, lack of centralized administration, and open nature of communication, which collectively result in making MANETs of the wireless kind very susceptible to a diverse range of cyber-attacks like blackhole, greyhole, wormhole, flooding, Sybil and denial-of-service (DoS) among others. Conventionally, Intrusion Detection Systems (IDS) relying on static rule-based methods …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 13, Issue 3, 2025 Read article
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Evaluation and Scientific Investigation: Stock Market Forecasting Techniques
Abstract: Analysts and scholars have consistently shown interest in predicting stock market trends, a complex task given the multitude of variables influencing stock values. This article includes a thorough analysis of 50 research papers that propose methodology for stock market prediction, including Bayesian models, fuzzy classifiers, artificial neural networks (ANNs), support vector machines (SVMs) classifiers, neural networks (NNs), and machine learning techniques. The collected papers are categorized using various prediction, clustering …
Published in Journal of Computer Technology & Applications · Vol. 14, Issue 3, 2023 · pp. 26–40 Read article
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Fixing Broken Link in Linked Data (LOD) from Source Point and Destination Point
Abstract: The advent of the Linked Open Data (LOD) initiative gave birth to a variety of open knowledge bases freely accessible on the web. The LOD activity has propelled various establishments to distribute their information on the web and to interlink them with those of other information sources. Since LOD sources are liable to change, interfaces between resources can break and prompt preparing mistakes in applications that expend linked data. The …
Published in Journal of Web Engineering & Technology · Vol. 6, Issue 2, 2019 · pp. 1–9 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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AI-Driven Multi-Objective Optimization of Conductive Polymer Composites for High-Performance Flexible Electronics
Abstract: The development of conductive polymer composites (CPCs) is critical for advancing flexible and wearable electronic technologies. However, the conventional trial-and-error approach to material formulation is time-consuming and often inefficient due to the high-dimensional nature of the design space. This study introduces a novel AI-driven framework that integrates machine learning (ML) with multi-objective optimization to accelerate the discovery of high-performance CPCs. A dataset of 1,000 experimentally reported formulations was compiled, capturing …
Published in Journal of Polymer & Composites · Vol. 13, Issue 6, 2025 · pp. 734–745 Read article
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Evaluation of Credit Risk of Bank Customers with a Hybrid Approach of Data Mining Techniques
Abstract: Credit risk poses the most significant threat to financial and monetary institutions. Banks strive to offer loans that generate high returns while minimizing risk. Achieving this requires the ability to accurately identify and classify credit customers, both individuals and legal entities, according to their likelihood of fully meeting their obligations. This classification is done using relevant financial and non-financial criteria. The primary goal of this study is to assess the …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 11, Issue 3, 2024 · pp. 63–81 Read article
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Vacant Parking Lot Detection
Abstract: AbstractAn issue looked in significant metropolitan regions, is the scan for parking spot. This study proposes a novel path for programmed parking garages’ location. In this methodology, first separate highlights by creating location patches and building Gaussian ground model from info video outlines. At that point, this model is utilized to prepare an eight-class multi-SVM classier. At last, the classification is upgraded internationally by means of Markov Random Field. This …
Published in Journal of Image Processing & Pattern Recognition Progress · Vol. 6, Issue 2, 2019 · pp. 11–21 Read article
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Heart Disease Prediction Using ANN Based Ensemble Framework (ANNEF) Approach
Abstract: The heart is the most important organ in the human body. One of the deadliest illnesses, heart disease is to blame for countless deaths worldwide. Because of the formation of fatty plaques in arteries, the heart fails to supply enough blood to other body areas to carry out its usual functions in heart disease (atherosclerosis). Age, a sedentary lifestyle, a family history of heart disease, stress, and other factors are …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 9, Issue 3, 2022 · pp. 38–45 Read article
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Direct Torque Control Based on Discrete-Time PI Controller of an SVPWM Inverter Fed Induction Motor Using Energy Model
Abstract: A direct torque control (DTC) of an induction motor (IM) with constant switching frequency has been presented in this work which is performed using space-vector modulation (SVM). The developed DTC scheme is based on the discrete-time PI controller strategy and this has been used to achieve direct control of torque of IM. To design the discrete-time PI (DTPI) controller the energy model of induction motor is simplified at the beginning …
Published in Journal of Power Electronics and Power Systems · Vol. 2, Issue 2, 2012 · pp. 45–55 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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Multiple Disease Prediction Using Machine Learning Algorithms
Abstract: The incorporation of machine learning algorithms into healthcare has transformed disease prediction and diagnosis. This research introduces a method for predicting various diseases using machine learning techniques. A comprehensive dataset, consisting of patient records, medical histories, and key disease-related features, was utilized to build predictive models. Data preprocessing methods, including feature selection and normalization, were implemented to clean and prepare the dataset. Several machine learning algorithms, such as Decision Trees, …
Published in Research and Reviews : A Journal of Immunology · Vol. 14, Issue 3, 2024 · pp. 34–38 Read article
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Support Vector Machine Based Supervised Machine Learning Algorithm for Finding ROC and LDA Region
Abstract: Breast Cancer is one of the most dangerous forms of conditions in the world, according to the Breast Cancer Institute (BCI). Breast cancer is a leading cause of death for women. Several empirical studies have explored the use of machine and predictive systems for breast cancer. Breast cancer can be the major cause of women’s death. Many claim their algorithms are faster, easier or more precise, as according to cancer. …
Published in Journal of Operating Systems Development & Trends · Vol. 7, Issue 1, 2020 · pp. 26–33 Read article