Research ArticleOpen accessIn the keywordsImproving Clustering Robustness through Fuzzy Ensemble of K-Means and Mean ShiftLNC. Prakash K., Palamakula Ramesh Babu, Shaik Thaseentaj et al.Ensemble clustering has emerged as a powerful strategy to improve the robustness and accuracy of unsupervised learning, particularly when individual algorithms struggle with noisy, heterogeneous, or high-dimensional data. This study introduces a fuzzy-based ensemble approach that integrates the complementary strengths of K-Means and Mean Shift clustering, followed by fuzzy membership assignment for data points that remain ambiguous. The inclusion of … …Journal of Computer Science, Vol. 22, Issue 5 · 20 May 2026 · 1,257 accessesView PDFCite
Research ArticleOpen accessIn the abstractA Machine Learning-Based Sentiment Analysis of Article 370 Tweets to Support Government Policy DecisionsSubhasis Mohapatra, Sudhir Kumar Mohapatra, Sweta Samantaray et al.This study proposes a robust sentiment analysis framework to evaluate public opinion on the abrogation of Article 370 using Twitter data. The methodology begins with tweet collection through the Twitter API, followed by systematic preprocessing. Sentiment labels were generated using the Text Blob lexicon-based polarity scoring approach to facilitate the construction of a large-scale sentiment dataset. Features are extracted, and … …Journal of Computer Science, Vol. 22, Issue 6 · 2 Jul 2026 · 914 accessesView PDFCite
Review ArticleOpen accessIn the abstractA Systematic Review of Adversarial Attacks: ML Techniques, Classification and CountermeasuresBhavesh Kumar Sharma, Sanatan Ratna, Rajiv KumarMachine Learning (ML) and Deep Learning (DL)-based technologies have made significant strides in areas such as computer vision, natural language processing, and autonomous systems. Yet they have been applied in high-stake applications, and have been found to be fragile through adversarial attacks-, maliciously crafted small distortions that fool models into making mistakes. From the pioneering work by Szegedy et al. … …Journal of Computer Science, Vol. 22, Issue 5 · 19 May 2026 · 1,497 accessesView PDFCite
Research ArticleOpen accessIn the abstractAssessing the Comparative Effectiveness of Ensemble CatBoost Versus XGBoost Models in Predicting Heart Disease OutcomesManivannan D., G. Gifta Jerith, G. Chandra Sekhar et al.Cardiovascular Diseases (CVDs) are a common form of heart disease that remains a significant global cause of mortality, responsible for more than 30% of all fatalities. Without intervention, the global fatality count is projected to reach 22 million by 2030. Arterial plaque buildup may impede blood flow, potentially causing heart attacks or strokes. A combination of risk causes, including lack … …Journal of Computer Science, Vol. 22, Issue 4 · 4 May 2026 · 1,544 accessesView PDFCite
Research ArticleOpen accessIn the abstractIntelligent Multi Model Ensemble for Engagement PredictionFahmida Begum, K Ulaga PriyaFor intelligent educational systems, the ability to monitor and respond to student engagement in real time is essential for enhancing learning outcomes. However, existing models often lack adaptability and practical deployment potential, as they depend on single data modalities, rigid ensemble mechanisms, and post-session analysis. This study introduces an intelligent multimodal ensemble framework designed to address these challenges by predicting … …Journal of Computer Science, Vol. 22, Issue 4 · 28 Apr 2026 · 1,844 accessesView PDFCite
Research ArticleOpen accessIn the abstractUncertainty-Aware Ensemble Models for Improved Defect Detection in Noisy DataMadhavi Perla, Gadi Lava Raju, A Radha Krishna et al.Software defect prediction plays a crucial role in ensuring software quality and reliability, especially as modern systems become more complex and data rich. This study introduces an uncertainty-aware ensemble learning framework aimed at improving defect classification performance in noisy and imbalanced datasets, particularly those from the PROMISE and NASA KC1 repositories. The proposed model integrates multiple classifiers in a multi-learner … …Journal of Computer Science, Vol. 22, Issue 4 · 27 Apr 2026 · 1,542 accessesView PDFCite
Review ArticleOpen accessIn the abstractMachine Learning Advancements for Lung Cancer Detection: An In-Depth Review and Future ProspectsAanchal Vij, Kuldeep Singh Kaswan, Anand NayyarLung cancer is a major cause of cancer-related mortality globally, underscoring the necessity for efficient diagnostic instruments. The review paper summarizes the role of ML and DL in detection, staging, and prognostication of lung cancer. We evaluate the relative efficacy of several models, such as CNNs, SVMs, and ensemble approaches, by analyzing publically accessible imaging and molecular information. We emphasize … …Journal of Computer Science, Vol. 22, Issue 1 · 1 Mar 2026 · 1,794 accessesView PDFCite
Research ArticleOpen accessIn the abstractEvaluating Online Learning in India: Insights from StudentsRitu Gupta, Anuradha Khattar, Meetu Bhatia Grover et al.As technology has advanced, the educational system has undergone numerous modifications. The benefits of online teaching and learning include increased accessibility, reduced costs, and the opportunity to learn at any time and from any location. However, a few drawbacks of this approach include a decline in student involvement, a lack of face-to-face interaction with classmates and teachers, and technical difficulties. … …Journal of Computer Science, Vol. 22, Issue 1 · 10 Feb 2026 · 1,595 accessesView PDFCite
Research ArticleOpen accessIn the abstractComparative Analysis of Five Decomposition Techniques for Monthly Rainfall Time Series from 1991 to 2020 in Guinea (West Africa)Noukpo Médard Agbazo, Oumar Keita, Alpha Oumar Baldé et al.The selection of an optimal time series decomposition’ technique is crucial for enhancing rainfall prediction by using sophisticated methods as deep learning hybrid approaches. This study evaluates the effectiveness of five times series decomposition techniques in analyzing monthly rainfall datasets across Guinea geographical regions (Lower-Guinea (Boké, Conakry, Kindia), Middle-Guinea (Labé, Mamou), Upper-Guinea (Faranah, Kankan) and Forest-Guinea (Nzérékoré)) and identifies the … …American Journal of Applied Sciences, Vol. 22, Issue 1 · 27 Jan 2026 · 3,040 accessesView PDFCite