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Journal articles on the topic 'Support Vector Classifier (SVC)'

Author: Grafiati

Published: 5 June 2025

Last updated: 2 August 2025

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1

Hashemi, H., D. M. J. Tax, R. P. W. Duin, A. Javaherian, and P. de Groot. "Gas chimney detection based on improving the performance of combined multilayer perceptron and support vector classifier." Nonlinear Processes in Geophysics 15, no. 6 (2008): 863–71. http://dx.doi.org/10.5194/npg-15-863-2008.

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Abstract. Seismic object detection is a relatively new field in which 3-D bodies are visualized and spatial relationships between objects of different origins are studied in order to extract geologic information. In this paper, we propose a method for finding an optimal classifier with the help of a statistical feature ranking technique and combining different classifiers. The method, which has general applicability, is demonstrated here on a gas chimney detection problem. First, we evaluate a set of input seismic attributes extracted at locations labeled by a human expert using regularized di

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Ramdas, Soorya, and Neenu N. T. Agnes. "Leveraging Machine Learning for Fraudulent Social Media Profile Detection." Cybernetics and Information Technologies 24, no. 1 (2024): 118–36. http://dx.doi.org/10.2478/cait-2024-0007.

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Abstract Fake social media profiles are responsible for various cyber-attacks, spreading fake news, identity theft, business and payment fraud, abuse, and more. This paper aims to explore the potential of Machine Learning in detecting fake social media profiles by employing various Machine Learning algorithms, including the Dummy Classifier, Support Vector Classifier (SVC), Support Vector Classifier (SVC) kernels, Random Forest classifier, Random Forest Regressor, Decision Tree Classifier, Decision Tree Regressor, MultiLayer Perceptron classifier (MLP), MultiLayer Perceptron (MLP) Regressor, N

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Anindya, Florentina Pramita, Dyah Erni Herwindiati, and Novario Jaya Perdana. "Pengenalan Suara Manusia Menggunakan Support Vector Classifier(SVC) Untuk Proses Otentikasi." Computatio : Journal of Computer Science and Information Systems 7, no. 1 (2023): 28–36. http://dx.doi.org/10.24912/computatio.v7i1.16230.

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Sistem pengenalan suara SEAUI merupakan salah satu aplikasi website untuk melakukan proses verifikasi suara sebagai salah satu teknik pengamanan tingkat lanjut dengan mengenali pemilik suara setelah melakukan proses login. Sistem ini dibuat untuk diterapkan pada aplikasi website. Sistem ini dibuat dengan menggunakan bahasa pemrograman Python dengan modul framework Flask dan basis data SQL Server Management Studio untuk penyimpanan data. Proses pengenalan suara pada sistem ini menggunakan metode Median Filter, metode ekstraksi fitur suara Mel Frequency Cepstral Coefficients dan metode klasifika

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Hekmatian, Mohammad E., Vahid E. Ardestani, Mohammad A. Riahi, Ayyub M. K. Bagh, and Jalal Amini. "Estimating the Shapes of Gravity Sources through Optimized Support Vector Classifier (SVC)." Acta Geophysica 63, no. 4 (2015): 1000–1024. http://dx.doi.org/10.1515/acgeo-2015-0022.

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Saputri, Rezi Iwardani, Siti Khomsah, and Novian Adi Prasetyo. "Perbandingan Metode Naïve Bayes Classifier Dan Support Vector Machine Untuk Klasifikasi Cyber Harassment Pada Twitter." Algoritma: Jurnal Ilmu Komputer dan Informatika 8, no. 1 (2024): 10. http://dx.doi.org/10.30829/algoritma.v8i1.16601.

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<em>Cyber Harassment dapat disebut juga dengan pelecehan online dapat berupa mengancam atau melecehkan melalui email, pesan instan, media sosial atau memposting informasi secara online. Kasus ini kerap terjadi di media sosial seperti salah satunya adalah Twitter. Untuk itu dibutuhkan sebuah metode klasifikasi yang tepat agar mengatasi kasus Cyber Harassment dari data Twitter. Pada penelitian ini menggunakan bahasa pemrograman Python dan menggunakan dua metode yaitu Naïve Bayes Classifier dan Support Vector Machine untuk membandingkan metode yang memiliki akurasi yang baik dan mengetahui

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Cho, Byeong-Hyo, Yong-Hyun Kim, Ki-Beom Lee, Young-Ki Hong, and Kyoung-Chul Kim. "Potential of Snapshot-Type Hyperspectral Imagery Using Support Vector Classifier for the Classification of Tomatoes Maturity." Sensors 22, no. 12 (2022): 4378. http://dx.doi.org/10.3390/s22124378.

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It is necessary to convert to automation in a tomato hydroponic greenhouse because of the aging of farmers, the reduction in agricultural workers as a proportion of the population, COVID-19, and so on. In particular, agricultural robots are attractive as one of the ways for automation conversion in a hydroponic greenhouse. However, to develop agricultural robots, crop monitoring techniques will be necessary. In this study, therefore, we aimed to develop a maturity classification model for tomatoes using both support vector classifier (SVC) and snapshot-type hyperspectral imaging (VIS: 460–600

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Shahin, Makubhai, R. Pathak Ganesh, and R. Chandre Pankaj. "Comparative analysis of explainable artificial intelligence models for predicting lung cancer using diverse datasets." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 2 (2024): 1980–91. https://doi.org/10.11591/ijai.v13.i2.pp1980-1991.

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Lung cancer prediction is crucial for early detection and treatment, and explainable artificial intelligence (XAI) models have gained attention for their interpretability. This study aims to compare various XAI models using diverse datasets for lung cancer prediction. Clinical, genomic, and imaging data from multiple sources were collected, preprocessed, and used to train models such as logistic regression (LR), support vector classifier (SVC)-linear, SVC-radial basis function (RBF), decision tree (DT), random forest (RF), adaboost classifier, and XGBoost classifier. Preliminary results indica

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Setiawan, Assegaff, Rasywir Errissya, and Pratama Yovi. "Experimental of vectorizer and classifier for scrapped social media data." TELKOMNIKA 21, no. 04 (2023): 815–24. https://doi.org/10.12928/telkomnika.v21i4.24180.

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In this study, we used several classifiers and vectorizers to see their effect on processing social media data. In this study, the classifiers used were random forest, logistic regression, Bernoulli Naive Bayes (NB), and support vector clustering (SVC). Random forests are used to reduce spatial complexity, and also to minimize errors. Logistic regression is a method with a statistical model whose basic form uses a logistic function to represent the binary dependent variable. Then, the Naive Bayes function uses binary elements and SVC which has so far given good results rivals other guided lear

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G. Deena, K. Raja, M. Azhagiri, W. A. Breen, and S. Prema. "Application of support vector classifier for mango leaf disease classification." Scientific Temper 14, no. 04 (2023): 1163–69. http://dx.doi.org/10.58414/scientifictemper.2023.14.4.16.

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In India, Mango is the fruit of high economic and ecological importance as it exports in large quantities. 1000 varieties of mangoes are cultivated and mostly supported commercially. Among all the Indian fruits, mangoes are highly demand. In majority of the Indian region, mango crops are suffering from several diseases that reduce both the production and the quality and parallel reduces its value on the international market. Mangoes are highly affected by number of diseases, which hamper its appearance, taste and has huge impact on the economy the Indian commercial growth rate has not raised.

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Ghaziasgar, Sepideh, Amirhossein Masoudnezhad, Atefeh Javadi, Jacco Th van Loon, Habib G. Khosroshahi, and Negin Khosravaninezhad. "Spectral identification and classification of dusty stellar sources using spectroscopic and multiwavelength observations through machine learning." Proceedings of the International Astronomical Union 19, S368 (2023): 67–72. https://doi.org/10.1017/s1743921323001308.

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AbstractWe proposed a machine learning approach to identify and distinguish dusty stellar sources employing supervised methods and categorizing point sources, mainly evolved stars, using photometric and spectroscopic data collected over the IR sky. Spectroscopic data is typically used to identify specific infrared sources. However, our goal is to determine how well these sources can be identified using multiwavelength data. Consequently, we developed a robust training set of spectra of confirmed sources from the Large and Small Magellanic Clouds derived from SAGE-Spec Spitzer Legacy and SMC-Sp

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Mutaz Rasmi, Abu Sara, Khaled Sabarna, and Jawad H. Alkhateeb. "The Analysis of Breast Cancer Classification Involves Utilizing Machine Learning (Ml) Techniques and Hyperparameter Adjustment - A Comparative Study." Ahliya Journal of Allied Medico-Technology Science 1, no. 2 (2024): 10–15. https://doi.org/10.59994/ajamts.2024.2.10.

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This study aims to analyze and classify breast cancer (BC) cases using machine learning (ML) techniques and hyperparameter tuning. The BC dataset from the University of California (UCI) was utilized, which comprises 569 cases classified as malignant (M) and benign (B), with 32 features. The algorithms employed in the study included Logistic Regression (LR), Support Vector Classifier (SVC), K-Nearest Neighbors (KNN), Decision Tree (DT), and Gaussian Naive Bayes (NB). The results indicated that the SVC algorithm performed the best, achieving an accuracy of 98% on the test set, along with a preci

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Shaik, Riyaaz Uddien, Aiswarya Unni, and Weiping Zeng. "Quantum Based Pseudo-Labelling for Hyperspectral Imagery: A Simple and Efficient Semi-Supervised Learning Method for Machine Learning Classifiers." Remote Sensing 14, no. 22 (2022): 5774. http://dx.doi.org/10.3390/rs14225774.

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A quantum machine is a human-made device whose collective motion follows the laws of quantum mechanics. Quantum machine learning (QML) is machine learning for quantum computers. The availability of quantum processors has led to practical applications of QML algorithms in the remote sensing field. Quantum machines can learn from fewer data than non-quantum machines, but because of their low processing speed, quantum machines cannot be applied to an image that has hundreds of thousands of pixels. Researchers around the world are exploring applications for QML and in this work, it is applied for

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Sanchan, Nattapong. "Intent Mining of Thai Phone Call Text Using a Stacking Ensemble Classifier with GPT-3 Embeddings." ECTI Transactions on Computer and Information Technology (ECTI-CIT) 19, no. 1 (2025): 135–45. https://doi.org/10.37936/ecti-cit.2025191.258239.

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Intent mining has recently attracted Natural Language Processing (NLP) research communities. Despite the extensive research on English and other widely spoken languages, intent mining in Thai remains unexplored. This paper proposes an extended framework for mining intentions in Thai phone call text. It utilized a stacking ensemble method with GPT-3 embeddings, constructed by systematically determining based and meta-classifiers using Q-statistic and F1 scores. Overall, the based classifiers consisting of Support Vector Classier (SVC), k-nearest Neighbors (KNN), and Random Forest (RF) were deri

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Bhushan, Shashi, Mohammed Alshehri, Ismail Keshta, Ashish Kumar Chakraverti, Jitendra Rajpurohit, and Ahed Abugabah. "An Experimental Analysis of Various Machine Learning Algorithms for Hand Gesture Recognition." Electronics 11, no. 6 (2022): 968. http://dx.doi.org/10.3390/electronics11060968.

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Nowadays, hand gestures have become a booming area for researchers to work on. In communication, hand gestures play an important role so that humans can communicate through this. So, for accurate communication, it is necessary to capture the real meaning behind any hand gesture so that an appropriate response can be sent back. The correct prediction of gestures is a priority for meaningful communication, which will also enhance human–computer interactions. So, there are several techniques, classifiers, and methods available to improve this gesture recognition. In this research, analysis was co

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Darapureddy, Nagadevi, Nagaprakash Karatapu, and Tirumala Krishna Battula. "Comparative Analysis of Texture Patterns on Mammograms for Classification." Traitement du Signal 38, no. 2 (2021): 379–86. http://dx.doi.org/10.18280/ts.380215.

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Breast cancer is a cancerous tumor that arrives within the tissues of the breast. Women are mostly attacked than men. To detect early cancer medical specialists, suggest mammography for screening. Algorithms in Machine learning were executed on mammogram images to classify whether the tissues are deleterious or not. An analysis is done based on the texture feature extraction using different techniques like Frequency decoded local binary pattern (FDLBP), Local Bit-plane Decoded Pattern (LBDP), Local Diagonal Extrema Pattern (LDEP), Local Directional Order Pattern (LDOP), Local Wavelet Pattern (

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Nascimento, Pedro Gomes do, Pidge Witiak, Tucker MacCallum, Zachary Winterfeldt, and Rushit Dave. "Your Device May Know You Better Than You Know Yourself-Continuous Authentication on Novel Dataset using Machine Learning." International Journal of Computer Science and Information Technology 16, no. 1 (2024): 53–65. http://dx.doi.org/10.5121/ijcsit.2024.16105.

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This research aims to further understanding in the field of continuous authentication using behavioural biometrics. We are contributing a novel dataset that encompasses the gesture data of 15 users playing Minecraft with a Samsung Tablet, each for a duration of 15 minutes. Utilizing this dataset, we employed machine learning (ML) binary classifiers, being Random Forest (RF), K-Nearest Neighbors (KNN), and Support Vector Classifier (SVC), to determine the authenticity of specific user actions. Our most robust model was SVC, which achieved an average accuracy of approximately 90%, demonstrating

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Yaseen, Muhammad Waseem, Muhammad Awais, Khuram Riaz, Muhammad Babar Rasheed, Muhammad Waqar, and Sajid Rasheed. "Artificial Intelligence Based Flood Forecasting for River Hunza at Danyor Station in Pakistan." Archives of Hydro-Engineering and Environmental Mechanics 69, no. 1 (2022): 59–77. http://dx.doi.org/10.2478/heem-2022-0005.

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Abstract Floods can cause significant problems for humans and can damage the economy. Implementing a reliable flood monitoring warning system in risk areas can help to reduce the negative impacts of these natural disasters. Artificial intelligence algorithms and statistical approaches are employed by researchers to enhance flood forecasting. In this study, a dataset was created using unique features measured by sensors along the Hunza River in Pakistan over the past 31 years. The dataset was used for classification and regression problems. Two types of machine learning algorithms were tested f

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Islam, Kazi Samiul, Gourab Roy, Nafiz Nahid, et al. "Advancing Bangla typography: machine learning and transfer learning based font detection and classification approach using the ‘Bang-laFont45’ dataset." Journal of Computer Sciences Institute 35 (June 30, 2025): 166–74. https://doi.org/10.35784/jcsi.7120.

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This paper presents a dataset for detecting and classifying Bangla fonts, consisting of 28,000 images across 45 classes, aimed at supporting font users and typography researchers. Four traditional machine learning models— Support Vector Classifier (SVC), Logistic Regression (LR), K-Nearest Neighbors (KNN), and Random Forest—achieved accuracies of 93.43%, 92.37%, 84.71%, and 81.48%, respectively, with SVC performing best. Six transfer learning models—VGG-16, VGG-19, ResNet-50, MobileNet-v3, Xception, and Inception—were trained, yielding accuracies of 87.74%, 80.00%, 87.26%, 80.55%, 82.30%, and

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Huang, Tangsen, Xiangdong Yin, and Ensong Jiang. "Decision-making in clinical diagnostic for brain tumor detection based on advanced machine ‎learning algorithm‎." International Journal for Simulation and Multidisciplinary Design Optimization 16 (2025): 1. https://doi.org/10.1051/smdo/2024021.

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Brain tumors, abnormal growths in the brain or spinal canal, can be benign or malignant, causing symptoms like headaches, seizures, and cognitive decline by disrupting brain function. Therefore, developing reliable predictive models for diagnosis and prognosis is crucial. In this paper, the prediction of brain tumors is made using machine learning models enhanced by an optimizer, namely Escaping Bird Search Optimization. Optimized models incorporate Ada Boost Classifier (ADEB), Gaussian Process Classifier (GPEB), and Support Vector Classifier (SVC) which, after being tested on a few databases,

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Wu, Yuehong, Shen Zhuang, Lihong Ma, and Zixian Feng. "Universal Steganalysis for image Based on Genetic Algorithm and Grey-SVC." Transactions on Economics, Business and Management Research 8 (August 8, 2024): 211–16. http://dx.doi.org/10.62051/ajeb9466.

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The isolated samples can produce some effect on distinguishing the best classifying plane, which becomes one of causes of less performance of universal steganalysis that uses Support Vector Machines (SVM) as classifier. This paper proposes a new universal steganalysis algorithm for image based on Genetic Algorithm (GA) and Grey Support Vector Machines (GSVM). The algorithm firstly catches characteristic of noise signal in wavelet domain of image, then utilizes GA search samples which are used to train, and finds the best characteristic of species, finally makes grey relational degree between s

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Hudon, Alexandre, Kingsada Phraxayavong, Stéphane Potvin, and Alexandre Dumais. "Comparing the Performance of Machine Learning Algorithms in the Automatic Classification of Psychotherapeutic Interactions in Avatar Therapy." Machine Learning and Knowledge Extraction 5, no. 3 (2023): 1119–30. http://dx.doi.org/10.3390/make5030057.

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(1) Background: Avatar Therapy (AT) is currently being studied to help patients suffering from treatment-resistant schizophrenia. Facilitating annotations of immersive verbatims in AT by using classification algorithms could be an interesting avenue to reduce the time and cost of conducting such analysis and adding objective quantitative data in the classification of the different interactions taking place during the therapy. The aim of this study is to compare the performance of machine learning algorithms in the automatic annotation of immersive session verbatims of AT. (2) Methods: Five mac

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R, Dr Sivakumar,. "PHISHING DETECTION SYSTEM USING MACHINE LEARNING." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 05 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem32513.

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This study focuses primarily on phishing attacks, a prevalent form of cybercrime conducted over the internet. Despite originating in 1996, phishing has evolved into one of the most severe threats online. It relies on email deception, often coupled with fraudulent websites, to trick individuals into divulging sensitive information. While various studies have explored preventive measures and detection techniques, there remains a lack of a comprehensive solution. Hence, leveraging machine learning is crucial in combating such cybercrimes effectively. The study utilizes a phishing URL-based datase

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Dessai, Prathamesh Pradeep, Nikhil B, and Dr S. Senthil. "Enhancing Integrity of Toll Gates:Fastag Fraud Detection." International Journal of Advanced Networking and Applications 16, no. 01 (2024): 6304–10. http://dx.doi.org/10.35444/ijana.2024.16110.

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The FastTag Fraud Detection System employs a machine learning model to identify fraudulent activities in FastTag transactions. Key features such as 'Transaction_Amount,' 'Amount_paid,' 'Vehicle_Type,' 'Lane_Type,' and'Geographical_Location' are used to differentiate betweenlegitimate and potentially fraudulent transactions. The modelconsiders various classifiers including Stochastic Gradient Descent (SGD), K-Nearest Neighbors (KNN), XGBoost, Logistic Regression, and Support Vector Machines (SVM). The SGD Classifier emerges as the most effective, demonstrating high accuracy, perfect precision,

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Luo, Linkai, Qiaoling Yang, Hong Peng, Yiding Wang, and Ziyang Chen. "MaxMin-L2-SVC-NCH: A novel approach for support vector classifier training and parameter selection." Neurocomputing 623 (March 2025): 129438. https://doi.org/10.1016/j.neucom.2025.129438.

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Cizmic, Dea, Dominik Hoelbling, René Baranyi, Roland Breiteneder та Thomas Grechenig. "Smart Boxing Glove “RD α”: IMU Combined with Force Sensor for Highly Accurate Technique and Target Recognition Using Machine Learning". Applied Sciences 13, № 16 (2023): 9073. http://dx.doi.org/10.3390/app13169073.

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Emerging smart devices have gathered increasing popularity within the sports community, presenting a promising avenue for enhancing athletic performance. Among these, the Rise Dynamics Alpha (RD α) smart gloves exemplify a system designed to quantify boxing techniques. The objective of this study is to expand upon the existing RD α system by integrating machine-learning models for striking technique and target object classification, subsequently validating the outcomes through empirical analysis. For the implementation, a data-acquisition experiment is conducted based on which the most common

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Noah, Naheem, Abebe Tayachew, Stuart Ryan, and Sanchari Das. "PhisherCop: Developing an NLP-Based Automated Tool for Phishing Detection." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 66, no. 1 (2022): 2093–97. http://dx.doi.org/10.1177/1071181322661060.

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Phishing poses a major security risk to organizations and individuals leading to loss of billions of dollars yearly. While risk communication serves as a tool to mitigate phishing attempts, it is imperative to create automated phishing detection tools. Numerous Natural Language Processing (NLP) approaches have been deployed to tackle phishing. However, phishing attempts continue to increase exponentially, which reiterates the need for more effective approach. To address this, we have developed an anti-phishing tool called; PhisherCop. PhisherCop is built upon Stochastic Gradient Descent classi

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Mondal, Dr Dipannita, Harshad Shinde, Sarang Baghele, Pratham Kadam, and Darshan Thengal. "Machine Learning in Healthcare: Developing an AI Assistant Doctor for Symptom-Based Disease Prediction." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 11 (2024): 1–9. http://dx.doi.org/10.55041/ijsrem38474.

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The rapid advancements in data science and machine learning have revolutionized various sectors, particularly healthcare. This research introduces an innovative AI Assistant Doctor system that utilizes machine learning techniques, specifically a Support Vector Classifier (SVC), to predict potential diseases based on user-reported symptoms. The system aims to bridge the gap between symptom recognition and understanding their significance, promoting early intervention and improved health outcomes. The AI Assistant Doctor offers a comprehensive healthcare tool that goes beyond disease prediction,

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G.Karthikeyan, K.Saroja, and S.Prasath. "A Performance Assessment on Various Data mining Tool Using Support Vector Machine." Journal of Information Sciences and Computing Technologies 6, no. 1 (2016): 562–67. https://doi.org/10.5281/zenodo.3968244.

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Data mining is essentially the discovery of valuable information and patterns from huge chunks of available data. Two indispensable techniques of data mining are clustering and classification, where the latter employs a set of pre-classified examples to develop a model that can classify the population of records at large, and the former divides the data into groups of similar objects. In this paper we have proposed a new method for data classification by integrating two data mining techniques, viz. clustering and classification. Then a comparative study has been carried out between the simple

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Elgeldawi, Enas, Awny Sayed, Ahmed R. Galal, and Alaa M. Zaki. "Hyperparameter Tuning for Machine Learning Algorithms Used for Arabic Sentiment Analysis." Informatics 8, no. 4 (2021): 79. http://dx.doi.org/10.3390/informatics8040079.

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Machine learning models are used today to solve problems within a broad span of disciplines. If the proper hyperparameter tuning of a machine learning classifier is performed, significantly higher accuracy can be obtained. In this paper, a comprehensive comparative analysis of various hyperparameter tuning techniques is performed; these are Grid Search, Random Search, Bayesian Optimization, Particle Swarm Optimization (PSO), and Genetic Algorithm (GA). They are used to optimize the accuracy of six machine learning algorithms, namely, Logistic Regression (LR), Ridge Classifier (RC), Support Vec

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May, Zazilah, M. K. Alam, Nazrul Anuar Nayan, Noor A’in A. Rahman, and Muhammad Shazwan Mahmud. "Acoustic emission corrosion feature extraction and severity prediction using hybrid wavelet packet transform and linear support vector classifier." PLOS ONE 16, no. 12 (2021): e0261040. http://dx.doi.org/10.1371/journal.pone.0261040.

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Corrosion in carbon-steel pipelines leads to failure, which is a major cause of breakdown maintenance in the oil and gas industries. The acoustic emission (AE) signal is a reliable method for corrosion detection and classification in the modern Structural Health Monitoring (SHM) system. The efficiency of this system in detection and classification mainly depends on the suitable AE features. Therefore, many feature extraction and classification methods have been developed for corrosion detection and severity assessment. However, the extraction of appropriate AE features and classification of va

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Dangi, Dr Abhilasha. "Educational Data Classification using Different Classifiers for Real Time Student Applications." International Journal for Research in Applied Science and Engineering Technology 13, no. 5 (2025): 5667–71. https://doi.org/10.22214/ijraset.2025.71502.

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The application of data mining techniques to educational datasets is gaining increasing attention due to the growing availability of student-related information. However, organizing and interpreting this data effectively poses a significant challenge because of its high dimensional and complexity. This study explores the use of the Linear Support Vector Classifier (Linear - SVC), SVM, and naive bias known for its computational efficiency and robustness, in categorizing educational data. The model's output can reveal actionable insights into student performance, offering valuable support for re

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Mounir, Mounir, Samir G. Sayed, and Mohamed M. El El-Dakroury. "Smart Grid intrusion detection system based on AI techniques." Journal of Cybersecurity and Information Management 15, no. 2 (2025): 195–207. https://doi.org/10.54216/jcim.150215.

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Smart grids (SGs) are integral to modern utility systems, managing power generation, energy consumption, and communication networks. However, as these systems become increasingly interconnected, they are exposed to sophisticated cyber threats that can compromise their functionality and security. To address these challenges, this paper presents an AI-driven detection framework designed to significantly enhance cybersecurity in smart grids. The proposed system combining Recurrent Neural Networks (RNNs) with Support vector classifier to improve detection accuracy, recognition capabilities, and sy

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Chen, Kelvin, R. A. Fattah Adriansyah, Carles Juliandy, Frans Mikael Sinaga, Frederick Liko, and Aswin Angkasa. "Classification of Big Data Stunting Using Support Vector Regression Method at Stella Maris Medan Maternity Hospital." Indonesian Journal of Artificial Intelligence and Data Mining 7, no. 2 (2024): 497. http://dx.doi.org/10.24014/ijaidm.v7i2.31112.

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This study aims to classify big data related to stunting using the Support Vector Regression (SVR) method at Stella Maris Maternity Hospital, Medan. Stunting, a condition of impaired growth in children due to chronic malnutrition and repeated infections, affects physical and cognitive development. With increasing health data, big data processing methods are essential for accurate information. SVR was chosen for handling high-dimensional and non-linear data, providing precise results. The study uses medical information, nutritional history, and socio-economic factors collected from hospital pat

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Praticò, Filippo Giammaria, Rosario Fedele, Vitalii Naumov, and Tomas Sauer. "Detection and Monitoring of Bottom-Up Cracks in Road Pavement Using a Machine-Learning Approach." Algorithms 13, no. 4 (2020): 81. http://dx.doi.org/10.3390/a13040081.

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The current methods that aim at monitoring the structural health status (SHS) of road pavements allow detecting surface defects and failures. This notwithstanding, there is a lack of methods and systems that are able to identify concealed cracks (particularly, bottom-up cracks) and monitor their growth over time. For this reason, the objective of this study is to set up a supervised machine learning (ML)-based method for the identification and classification of the SHS of a differently cracked road pavement based on its vibro-acoustic signature. The method aims at collecting these signatures (

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Naveena, M., and Hemantha Kumar G. "Classification of Birds Using KNN and SVM Classifier." International Journal of Computer Science Issues 17, no. 1 (2020): 27–31. https://doi.org/10.5281/zenodo.3987114.

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This paper aims to develop a bird’s classification system based on classifiers fusion to easily identify the birds. It is based on image processing, which can control the classification, qualification and segmentation of images and hence recognize the birds. Usually from the captured images multiple shape features like area, centroid, angle at centroid, maximum angle and minimum angle can be extracted and analyzed to classify and recognize the birds. And then the extracted features are classified using KNN (K-Nearest Neighbor) Classifier and SVM (Support vector machine) classifiers.

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Ammar Oad, Zulfikar Ahmed Maher, Imtiaz Hussain Koondhar, Karishima Kumari, and Hammad Bacha. "Optimizing Cardiovascular Risk Assessment with a Soft Voting Classifier Ensemble0." Sir Syed University Research Journal of Engineering & Technology 14, no. 2 (2024): 101–7. https://doi.org/10.33317/ssurj.649.

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According to the latest data from the World Health Organization (WHO), heart disease has been the leading cause of death worldwide for the past several decades. It includes a variety of conditions that affect the heart. In Pakistan heart disease claims the lives of at least thirty people every hour. The best-known application of artificial intelligence is machine learning (ML). It is linked to numerous heart disease risk factors and the necessity of time to acquire sensitive accurate and dependable methods in order to make an early diagnosis. Experimental options have included the UCI reposito

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S., Brindha, and Ajisha T. "An Efficient Ensemble Classifier for Heart Disease Diagnosis and early Prediction." International Journal of Recent Technology and Engineering (IJRTE) 9, no. 4 (2020): 109–14. https://doi.org/10.35940/ijrte.D4824.119420.

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Heart Disease is one of the most significant causes of mortality in the world today. Prediction and Diagnosis of Cardiovascular disease is considered as one of the major challenges in the Medical Field especially for Cardiologists. Artificial Intelligence and Machine learning (ML) was popularly employed for pattern prediction and it was noticed that these Intelligent Mechanisms were used in Medical Feld for better Heart Disease Pattern Prediction. Thus more researchers were focusing Machine Learning based Data Mining Classifiers for Heart Disease Pattern Prediction and Diagnosis in the healthc

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Feng, Yifan. "Support Vector Machine for Stroke Risk Prediction." Highlights in Science, Engineering and Technology 38 (March 16, 2023): 917–23. http://dx.doi.org/10.54097/hset.v38i.5977.

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Nowadays, there are many paralytic patients sent to the hospital, and it is impossible for hospitals to search out those particular patients at once because these patients share the same symptoms such as unconsciousness as those hypoglycemic patients. Nowadays, most patients need to conduct blood tests to ensure whether they have a stroke or not. However, this will take a lot of time and resources. The investigation aims at stroke prediction. By using machine learning, the model will be used to train and test data. Support vector machine (SVM) will be used in this project. By using a support v

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Mahalakshmi, K. V., P. Vishnu, R. Venkateshkumar, Harshini Raja, and Kaustubh Lakshmi Narayanan. "Data Analysis in Healthcare Automation Using Computer Vision." Indian Journal Of Science And Technology 18, Sp1 (2025): 37–44. https://doi.org/10.17485/ijst/v18si1.icamada27.

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Objectives: To apply computer vision techniques in healthcare automation and analysis and assist medical professionals. Methods: The proposed work encapsulates computer vision techniques; such as traditional computer vision technique using contour detection, Machine Learning-Based Computer vision techniques and clustering techniques. The Machine Learning (ML)-based Computer vision techniques include; Support Vector Machine (SVM) and Logistic Regression (LR). The methodology uses Magnetic Resonance imaging scans of brain, Ovarian and hepatic tumors, focusing on parameters like region of interes

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Guru, Chinmayee, and Walaa Bajnaid. "Prediction of Customer Sentiment Based on Online Reviews Using Machine Learning Algorithms." International Journal of Data Science and Advanced Analytics 5, no. 5 (2023): 272–79. http://dx.doi.org/10.69511/ijdsaa.v5i5.200.

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Customer opinions and feedback play a pivotal role in enhancing business operations and decision-making processes. Sentiment analysis is a crucial technique used to decipher customer opinions from their feedback and thus provide valuable insights for businesses. However, analysing and understanding reviews is an intricate process and prone to be misleading if not conducted meticulously. This study aims to extract and classify customer emotions from e-commerce reviews of women’s clothing in terms of polarity of sentiment, enhancing sentiment analysis accuracy by means of machine learning (ML) c

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P Venkata Kishan Rao,. "Advanced Predictive Modeling of Diabetic Readmissions Using Machine Learning and Essential Health Metrics." Journal of Information Systems Engineering and Management 10, no. 19s (2025): 453–61. https://doi.org/10.52783/jisem.v10i19s.3057.

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The rising prevalence of Type 2 Diabetes Mellitus (T2DM) has underscored the need for predictive models that can effectively assess readmission risks among diabetic patients, thereby supporting improved healthcare management and resource allocation. This study presents an advanced approach for predicting diabetic patient readmissions by incorporating critical health metrics—Body Mass Index (BMI), HbA1c, cholesterol (new-Chol), and triglycerides (new-TG)—and employing a variety of machine learning algorithms. Using a dataset of diabetic patients with readmission intervals between 2 and 30 days,

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Bhatnagar, Shweta, and Rashmi Agrawal. "Value group classifier model for ethical decision-making." Indonesian Journal of Electrical Engineering and Computer Science 37, no. 3 (2025): 1899. https://doi.org/10.11591/ijeecs.v37.i3.pp1899-1907.

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Decision-makers refer to ethics or moral philosophy during times of ethical dilemma. Dilemmas are situations of inner conflict, which require a methodical approach. Diversity in viewpoints on moral decisions ensures there cannot be a fixed solution for ethical dilemmas as in the case of numerical problems. Existing ethical and sustainable decision models for businesses are not automated because of a lack of a comprehensive list of dilemmas. To resolve this gap, an AI model was trained to classify all dilemmas into three value groups by using a support vector classifier (SVC). The model provide

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Shweta, Bhatnagar Rashmi Agrawal. "Value group classifier model for ethical decision-making." Indonesian Journal of Electrical Engineering and Computer Science 37, no. 3 (2025): 1899–907. https://doi.org/10.11591/ijeecs.v37.i3.pp1899-1907.

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Decision-makers refer to ethics or moral philosophy during times of ethical dilemma. Dilemmas are situations of inner conflict, which require a methodical approach. Diversity in viewpoints on moral decisions ensures there cannot be a fixed solution for ethical dilemmas as in the case of numerical problems. Existing ethical and sustainable decision models for businesses are not automated because of a lack of a comprehensive list of dilemmas. To resolve this gap, an AI model was trained to classify all dilemmas into three value groups by using a support vector classifier (SVC). The model provide

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Shao, Yan Ming, Feng Ji, Shu An Zhao, Mu Chun Zhou, Yan Ru Chen, and Qi Zhao. "Applying Flame Spectrum on SVC-RVM Modeling for BOF Endpoint Prediction." Advanced Materials Research 631-632 (January 2013): 870–74. http://dx.doi.org/10.4028/www.scientific.net/amr.631-632.870.

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A new non-contact method for predicting the basic oxygen furnace(BOF) end point carbon content is proposed in this study. A model applying the flame spectrum of the converter vessel mouth is constructed to carry out the prediction. This model consists two parts, viz. a classifier based on support vector classification to classify the whole period of one BOF heat into two main phases, and a relevance vector machine working at the posterior phase to predict the carbon content. Compared with current non-contact methods of end point carbon content prediction, the proposed method can make better us

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Dr Mekala R, Balachandar S, and Dr Chinnaiyan R. "Machine learning approaches for optimal prediction of liver fibrosis cruelty." South Asian Journal of Engineering and Technology 12, no. 1 (2022): 132–41. http://dx.doi.org/10.26524/sajet.2022.12.20.

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   Noninvasive assessment of severity of liver fibrosis is crucial for understanding histology and making decisions on antiviral treatment for chronic HBV in view of the associated risks of biopsy. aimed to develop a computer-assisted assessment system for the evaluation of liver disease severity by using machine leaning classifier based on physical-layer with serum markers. The retrospective data set, including 920 patients, was used to establish Decision Tree classifier (DTC), Random Forest Classifier (RFC), Logistic Regression Classifier (LRC), and Support Vector Classifie

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Minaev, Georgy, Philipp Müller, Katri Salminen, Jussi Rantala, Veikko Surakka, and Ari Visa. "A Comparison of Various Algorithms for Classification of Food Scents Measured with an Ion Mobility Spectrometry." Sensors 21, no. 2 (2021): 361. http://dx.doi.org/10.3390/s21020361.

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The present aim was to compare the accuracy of several algorithms in classifying data collected from food scent samples. Measurements using an electronic nose (eNose) can be used for classification of different scents. An eNose was used to measure scent samples from seven food scent sources, both from an open plate and a sealed jar. The k-Nearest Neighbour (k-NN) classifier provides reasonable accuracy under certain conditions and uses traditionally the Euclidean distance for measuring the similarity of samples. Therefore, it was used as a baseline distance metric for the k-NN in this paper. I

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Minaev, Georgy, Philipp Müller, Katri Salminen, Jussi Rantala, Veikko Surakka, and Ari Visa. "A Comparison of Various Algorithms for Classification of Food Scents Measured with an Ion Mobility Spectrometry." Sensors 21, no. 2 (2021): 361. http://dx.doi.org/10.3390/s21020361.

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The present aim was to compare the accuracy of several algorithms in classifying data collected from food scent samples. Measurements using an electronic nose (eNose) can be used for classification of different scents. An eNose was used to measure scent samples from seven food scent sources, both from an open plate and a sealed jar. The k-Nearest Neighbour (k-NN) classifier provides reasonable accuracy under certain conditions and uses traditionally the Euclidean distance for measuring the similarity of samples. Therefore, it was used as a baseline distance metric for the k-NN in this paper. I

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Alghamdi, Hisham A. "A Time Series Forecasting of Global Horizontal Irradiance on Geographical Data of Najran Saudi Arabia." Energies 15, no. 3 (2022): 928. http://dx.doi.org/10.3390/en15030928.

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Environment-friendly and renewable energy resources are the need of each developed and undeveloped country. Solar energy is one of them, thus accurate forecasting of it can be useful for electricity supply companies. This research focuses on analyzing the daily global solar radiation (GSR) data of Najran province located in Saudi Arabia and proposed a model for the prediction of global horizontal irradiance (GHI). The weather data is collected from Najran University. After inspecting the data, I we found the dependent and independent variables for calculating the GHI. A dataset model has been

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Kristanti, Farida Titik, Mochamad Yudha Febrianta, Dwi Fitrizal Salim, Hosam Alden Riyadh, and Baligh Ali Hasan Beshr. "Predicting Financial Distress in Indonesian Companies using Machine Learning." Engineering, Technology & Applied Science Research 14, no. 6 (2024): 17644–49. https://doi.org/10.48084/etasr.8520.

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Predicting financial distress is essential in Indonesia's rapidly evolving economy, characterized by diverse business environments and regulatory challenges. This study evaluates four machine learning classifiers, XGBoost, Random Forest (RF), Support Vector Classification (SVC), and Long Short-Term Memory (LSTM), to predict financial distress among Indonesian companies. Two sampling methods, Random Under-Sampling (RUS) and Synthetic Minority Over-Sampling Technique (SMOTE), were used to address class imbalance. Empirical results indicate that the RF model trained with SMOTE sampling was the mo

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Sachinkumar Harshadbhai Makwana. "Optimizing Breast Cancer Diagnosis with Machine Learning Algorithms." Journal of Electrical Systems 20, no. 10s (2024): 4046–54. http://dx.doi.org/10.52783/jes.5970.

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Breast cancer is the most common disease among women worldwide, and it continues to pose a serious threat to global health [2]. Early and accurate diagnosis is critical for effective treatment and improved patient outcomes. Traditional diagnostic methods, while effective, have limitations in consistency and speed. Medical diagnostics have undergone a revolution with the introduction of machine learning (ML), which provides tools to analyze complex data and increase diagnosis accuracy. Using the Breast Cancer Wisconsin (Diagnostic) Dataset[1], this investigation dives into the application of se

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