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14 articles for “Ensemble Methods Classifier”
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Research ArticleOpen accessPhrase in the titleNonnegative Matrix Factorization Features Extraction and Ensemble Methods Classifier for ECG Image ClassificationHussain K. Ibrahim, Shawkat K. Guirguis, Hend A. ElsayedThis study is a novel proposed classification system for ECG image classification. This proposed system uses nonnegative matrix factorization for feature extraction and ensemble methods for classification, and the results are compared with the features extracted by the principal component analysis, kernel principal component analysis, and independent component analysis. Although algorithms find image classification challenging, humans typically find it easy. … …Journal of Computer Science, Vol. 21, Issue 4 · 15 Mar 2025 · 4,863 accessesView PDFCite
Research ArticleOpen accessIn the abstractEnhancing DevOps Pipelines With Viral Spread Optimization and Hybrid Deep LearningDileepkumar S R, Juby MathewModern software teams rely on Continuous Integration and Continuous Deployment (CI/CD) pipelines to automate testing and delivery. However, these pipelines often waste resources by running unnecessary tests and failing to predict which builds will fail. This study presents a new framework that addresses three key challenges: (1) deciding which tests to run first, (2) predicting which builds will fail, and … …Journal of Computer Science, Vol. 22, Issue 6 · 3 Jul 2026 · 935 accessesView PDFCite
Research ArticleOpen accessIn the abstractEnsemble Learning-Based Hybrid Explainable Intrusion Detection System With SMOTE for Enhanced Detection and Interpretability in CybersecurityMohammad Subhi Al-Batah, Taqieddin AldrousWith cyberattacks becoming increasingly sophisticated, there is a growing need for Intrusion Detection Systems (IDS) that are both accurate and interpretable. This study introduces a hybrid IDS framework that integrates Random Forest (RF) and Light Gradient Boosting Machine (LGBM) classifiers with the Synthetic Minority Over-sampling Technique (SMOTE) to address class imbalance. To enhance transparency and trust, the system also incorporates … …Journal of Computer Science, Vol. 22, Issue 5 · 19 May 2026 · 1,277 accessesView PDFCite
Research ArticleOpen accessIn the abstractPredictive Mathematical Modeling and Classification of Retail Sales Orders Using AI Machine Learning TechniquesMohammad Subhi Al-Batah, Mowafaq Salem Alzboon, Hamzeh ZureigatThis study presents a systematic mathematical model known as a low-code approach to classifying retail sales orders by size using AI machine learning techniques within the Orange Data Mining platform. Leveraging a real-world sales dataset sourced from Kaggle, we implemented and evaluated ten classification models, including ensemble learners (AdaBoost, Gradient Boosting), probabilistic classifiers (Naïve Bayes), distance-based methods (kNN), and interpretable … …Journal of Computer Science, Vol. 22, Issue 3 · 10 Mar 2026 · 1,567 accessesView PDFCite
Research ArticleOpen accessIn the abstractDeveloping an Effective Churn Prediction Model for Telecommunications: Enhancing Customer Retention through Advanced Machine Learning TechniquesAshu Goyal, Anuj Gupta, Sharad Kumar et al.Customer churn poses a significant challenge for the telecommunications sector, resulting in substantial revenue losses and increased customer acquisition costs. This research creates an efficient churn prediction model that combines state-of-the-art machine learning with ensemble learning to maximize customer retention. With the IBM Telco Customer Churn dataset, several baseline models, including Gradient Boosting, AdaBoost, Logistic Regression, Random Forest, and Support … …Journal of Computer Science, Vol. 22, Issue 1 · 3 Feb 2026 · 3,164 accessesView PDFCite
Research ArticleOpen accessIn the abstractOnline ML Streaming-Based Leading Ensemble Classifier for Intrusion Detection in IoTNeeraj Sharma, Neelu NihalaniIn the present decade, preventing IoT networks from malicious attacks and to ensure the privacy and integrity of data by strengthening their cybersecurity perspective is a critical concern. Traditional methods alone are not sufficient to detect multiple attacks. IDS-based system plays a crucial role to maintain security due to continuous expanding nature of IoT networks. Traditional methods alone are often … …Journal of Computer Science, Vol. 21, Issue 10 · 16 Dec 2025 · 2,078 accessesView PDFCite
Research ArticleOpen accessIn the abstractOptimization-Based Feature Selection and Ensemble Machine Learning Algorithms for Breast Cancer ClassificationSatyabrata Patro, Jyotirmaya Mishra, Bhavani Sankar PandaBreast cancer, which originates in a woman's breast tissue, is acknowledged to be a significant study topic in the medical field. For a long time, there has been a serious concern with the classification of breast cancer. Thus, to effectively categorize the breast cancer dataset, machine learning methods are designed and implemented. In previous research, the algorithms have classification accuracy … …Journal of Computer Science, Vol. 21, Issue 7 · 17 Jul 2025 · 3,172 accessesView PDFCite
Research ArticleOpen accessIn the abstractOptimizing N-linked Glycosylation Site Prediction in Human Proteins with Ensemble Stacking and Cross-ValidationMubina Malik, Jaimin UndaviaThe most frequent post-translational modification of proteins in all territories is glycosylation which impacts many biological activities. The most significant and critical of these modifications is N-linked glycosylation which is associated with various human diseases including diabetes cancer Inflammation Alzheimers and atherosclerosis. This article illustrates recent advances in knowledge of biology that are eventually targeting the computer science sector. Moreover-identification … …Journal of Computer Science, Vol. 20, Issue 12 · 20 Nov 2024 · 4,999 accessesView PDFCite
Research ArticleOpen accessIn the abstractPM2.5 Prediction Using Homogenous and Heterogenous Ensemble Learning: A Comprehensive EvaluationShrabani Medhi, Minakshi GogoiAir pollution is a global issue. PM2.5 is considered to be the most dangerous pollutant. Prediction of PM2.5 concentration is important so that effective measures can be taken beforehand. A multitude of machine learning methodologies have been employed in forecasting PM2.5 levels, utilizing diverse combinations of ensemble classifiers and regressors. However, there are three important issues that need to be … …Journal of Computer Science, Vol. 20, Issue 9 · 20 Jun 2024 · 5,176 accessesView PDFCite
Research ArticleOpen accessIn the abstractCombining SMOTE and OVA with Deep Learning and Ensemble Classifiers for Multiclass ImbalancedRattanawadee PanthongThe classification of real-world problems alwaysconsists of imbalanced and multiclass datasets. A dataset having unbalanced andmultiple classes will have an impact on the pattern of the classification modeland the classification accuracy, which will be decreased. Hence,oversampling method keeps the class of dataset balanced and avoids theoverfitting problem. The purposes of the study were to handle multiclassimbalanced datasets and to improve … …Journal of Computer Science, Vol. 18, Issue 8 · 23 Aug 2022 · 6,096 accessesView PDFCite