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19 results for “AUC-ROC”
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Research ArticleOpen accessIn the keywordsAssessment of Soil Loss Using GIS and RUSLE Model in Bakiya River Basin (BRB), Central NepalSujan Bista, Naresh Kazi Tamrakar, Kishor Rimal, Arju BhattaraiSoil erosion is the major geological hazard in the fragile landscape of Sub-Himalaya. This study examines the susceptibility of the Bakiya River Basin (BRB) in Central Nepal to soil erosion because of its steep topography, fragile geology, the impact of intense monsoon rainfall, and the lack of a direct monitoring mechanism available for rainfall-runoff. This study examines the spatial variations … …Current Research in Geoscience, Vol. 14, Issue 1 · 20 Oct 2025 · 3,384 accessesView PDFCite
Research ArticleOpen accessIn the keywordsA Permutation Test for Comparing Two Correlated Receiver Operating Characteristic CurvesOkeh Uchechukwu Marius, Onyeagu SidneyThe area under the Receiver Operating Characteristic (ROC) curve (AUC) is a summary measure when comparing two ROC curves. However, this summary measure is less informative when two ROC curves cross and have the same AUCs. In order to detect differences between ROC curves and to be able to tackle the problem of exchangeability of the labels between two diagnostic … …Journal of Mathematics and Statistics, Vol. 16, Issue 1 · 24 May 2020 · 8,129 accessesView PDFCite
Research ArticleOpen accessIn the keywordsA Distribution-Free Bayesian Approach for Indirect ComparisonsJohanna S. van Zyl, Jack D. TubbsThe objective of this paper is to consider an indirect comparison between treatments B and C when each have been compared directly to treatment A in separate studies. Problems of this type are common in Network Meta-Analysis (NMA). A commonly used method assumes that the underlying data, Y, are normally distributed and µ = E(Y) is the measure of clinical … …Journal of Mathematics and Statistics, Vol. 15, Issue 1 · 3 Jul 2019 · 6,687 accessesView PDFCite
Research ArticleOpen accessIn the keywordsInterface between the Ratio β with Area Under the ROC Curve and Kullback-Leibler Divergence Under the Combination of Half Normal and Rayleigh DistributionsSaebugari Balaswamy, Rudravaram Vishnu VardhanClassifying objects/individuals is common problem of interest. Receiver Operating Characteristic (ROC) curve is one such tool which helps in classifying the objects/individuals into one of the two known groups or populations. The present work focuses on proposing a Hybrid version of the ROC model. Usually the test scores of the two populations namely normal and abnormal tend to follow some … …Current Research in Biostatistics, Vol. 5, Issue 2 · 12 Nov 2015 · 7,655 accessesView PDFCite
Research ArticleOpen accessIn the keywordsEnsemble Learning for Proactive Detection of Network-Intrusion-Based Insurance FraudBenjamin Asubam Weyori, Selorm Kofi Tagbo et al.We propose an ensemble learning pipeline that proactively integrates Stacking Feature Embedding (SFE) with Principal Component Analysis (PCA) and tree-based ensembles to proactively detect insurance fraud originating from network intrusions. The main contributions are: (1) the novel integration of SFE-PCA as a meta-feature construction step for tabular network flow data; (2) a sensitivity analysis that justifies PCA reduction ratios used … …Journal of Computer Science, Vol. 22, Issue 7 · 16 Jul 2026 · 935 accessesView PDFCite
Research ArticleOpen accessIn the abstractUMPM-Net: A Multimodal Deep Learning Framework for Colorectal Cancer PrognosisK. Muthuchamy, S K. Piramu PreethikaColorectal cancer (CRC) is one of the most common cancers worldwide, for which early prediction of patient outcomes is important for personalised treatment planning and to improve survival. However, current prediction systems are limited in the fact that they are based on single-modality data and cannot capture the complex interactions between clinical, imaging, and genomic factors. To overcome these shortcomings, … …Journal of Computer Science, Vol. 22, Issue 9 · 20 Sep 2026 · 613 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 abstractApplication of Machine Learning for the Detection of Depression and Mindfulness as a Mitigation MethodSantiago Domingo Moquillaza Henriquez, Liliana Ruth Huamán Rondón et al.Depression is a global mental health problem with various causes. Nowadays, with Artificial Intelligence, it can be predicted in order to take preventive measures, whether at a psychotherapeutic or pharmacological level. This research, based on a survey conducted, detects depression using logistic regression with a 91% fit to the ROC-AUC model, accuracy of 0.839188, precision 0.851354. In addition, a study … …Journal of Computer Science, Vol. 22, Issue 6 · 16 Jun 2026 · 1,216 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
Research ArticleOpen accessIn the abstractA Context-Aware Temporal Convolutional Network for Water Replacement Prediction in Catfish Biofloc Ponds With Imbalanced Event HandlingYaddarabullah, Inanpi Hidayati Sumiasih, Mutiara Dewi PuspitawatiWater replacement is a biologically critical yet under-automated decision in biofloc-based aquaculture systems. Mistimed actions can destabilize microbial ecosystems, elevate fish mortality, and compromise sustainability through excessive water usage. Traditional rule-based heuristics often fail to account for the nonlinear and multiscale dynamics of pond environments. To address this, we propose a Context-Aware Temporal Convolutional Network (CA-TCN), a rare-event classification framework … …Journal of Computer Science, Vol. 22, Issue 3 · 5 Mar 2026 · 1,664 accessesView PDFCite