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105 results for “sampling technique”
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Research ArticleOpen accessPhrase in the titleAn Efficient Implementation of Re-Sampling Technique for High Performance Multiple Classifier SystemsS. Sathiyabama, K. Thyagarajah, D. AyyamuthukumarDue to the large size of the database, the entire training dataset could not be used to construct the classifiers. One popular solution is to separate stream data into chunks, learn a base classifier from each chunk and then integrate all base classifiers to form Multiple classifier system (MCS). Sometimes this data streams does not include all the classes in … …Journal of Computer Science, Vol. 3, Issue 4 · 30 Apr 2007 · 7,014 accessesView PDFCite
Research ArticleOpen accessIn the keywordsA Comparative Analysis of Smote and CSSF Techniques for Diabetes Classification Using Imbalanced DataBashar Hamad Aubaidan, Rabiah Abdul Kadir, Mohamad Taha IjabDiabetes, a prevalent chronic metabolic disorder, poses a significant burden on healthcare systems worldwide. Accurate and timely diagnosis is crucial for effective management and complication prevention. Machine learning presents a promising solution but often faces challenges due to class imbalance within datasets, particularly the underrepresentation of diabetic cases. To address this issue, we introduce Cluster-based Synthetic Sample Filtering (CSSF), a … …Journal of Computer Science, Vol. 20, Issue 9 · 22 Jul 2024 · 4,998 accessesView PDFCite
Research ArticleOpen accessIn the keywordsStudy and Analysis of Prediction Model for Heart Disease Data Using Machine Learning TechniquesB. Santhi, K. RenukaHeart disease is the number one cause of death for all communities of individuals in advanced countries and a major problem for emerging nations too. Doctors’ availability to care for the general population could not catch up with the present demand for healthcare. So, there is a severe need for a support system to assist save individuals. With novel ML … …Journal of Computer Science, Vol. 16, Issue 3 · 26 Mar 2020 · 7,784 accessesView PDFCite
Research ArticleOpen accessIn the keywordsA Questionnaire Approach for User Trust Adoption in Palestinian E-Government nitiativeMohannad Moufeed Ayyash, Kamsuriah Ahmad, Dalbir SinghProblem statement: The present study attempts to identify the construct that impacts the user's trust in e-Government initiative and its adoption in Palestinian public sector organizations. Approach: It utilized a quantitative approach using survey based questionnaire as its primary research methodology. Results: The related constructs were: Information quality, system quality and service quality, perceived ease of use, perceived usefulness, security … …American Journal of Applied Sciences, Vol. 8, Issue 11 · 10 Oct 2011 · 7,439 accessesView PDFCite
Research ArticleOpen accessIn the abstractOptimizing Mobile Edge Computing Efficiency through Multidimensional Generative Adversarial NetworkAnitha Premkumar, Sowmya Vemagal Lakshminarayana, Shankar Rajagopal et al.Big data Processing and the IoT have recently gained popularity as study topics in MEC and thorough research is sought for well-informed decisions. Mobile edge equipment, such as base stations, routers, and edge servers, usually have limited processing capabilities compared to traditional data centers. According to this perspective, this research constructs MEC using Intelligent Outlier Detection with a Multidimensional Generative … …Journal of Computer Science, Vol. 21, Issue 2 · 18 Jan 2025 · 4,686 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 abstractUnveiling the Factors Influencing Social Media Usage Among A'Sharqiyah University Students: Exploring Impacts and Pathways to OptimizationHamed Al Hajri, Salim Al Hajri, Alycia SebastianSocial media networks facilitate global connectivity and communication by providing platforms for users to interact, share information, and build communities online. This study aims to explore the impact of utilizing Social Media Networks (SMNs) on the academic attainment of A’Sharqiyah University students in the Sultanate of Oman. It recognizes the global significance of SMNs in modern society, emphasizing their role … …Journal of Computer Science, Vol. 20, Issue 11 · 5 Sep 2024 · 4,867 accessesView PDFCite
Case ReportOpen accessIn the abstractGully Morphological Assessment and Anthropogenic Determinants in Urban and Rural Settlements of Upper Imo River Basin (UIRB) South Eastern NigeriaNnamdi Christian ChiboGully erosion have been proven to vary both in terms of size and number in urban and rural settlements and is caused by factors related to both human and natural phenomenon. This study assessed the gully morphological development in an urban and rural settlement with the view of finding the settlement that is more vulnerable to gully erosion development. It … …American Journal of Environmental Sciences, Vol. 18, Issue 6 · 19 Dec 2022 · 5,950 accessesView PDFCite
Research ArticleOpen accessIn the abstractForecasting Corporate Financial Distress Using Explainable Machine Learning: Implications for Risk Management TechnologyKanjana Hinthaw, Warawut NarkbunnumFinancial distress prediction is a critical task in financial risk management, yet it remains challenging due to class imbalance, complex financial structures, and limited model interpretability. This study develops an integrated machine learning framework that combines feature selection, class imbalance handling, threshold optimization, and Explainable Artificial Intelligence (XAI) to improve both predictive performance and decision-making reliability. Using a dataset of … …Journal of Computer Science, Vol. 22, Issue 7 · 22 Jul 2026 · 1,130 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