https://journalajrcos.com/index.php/AJRCOS/issue/feed 2026-11-06T13:33:35+00:00 Asian Journal of Research in Computer Science [email protected] Open Journal Systems <p style="text-align: justify;"><strong>Asian Journal of Research in Computer Science (ISSN: <a href="https://nsl.niscpr.res.in/ISSNPROCESS/issnassignedinfo.jsp">2581-8260</a> )</strong> aims to publish high-quality papers in all areas of 'computer science, information technology, and related subjects'. By not excluding papers based on novelty, this journal facilitates the research and wishes to publish papers as long as they are technically correct and scientifically motivated. The journal also encourages the submission of useful reports of negative results. This is a quality controlled, OPEN peer-reviewed, open-access INTERNATIONAL journal.</p> https://journalajrcos.com/index.php/AJRCOS/article/view/929 2026-11-06T12:54:20+00:00 K. W. I. P. Dissanayaka [email protected]

<p><strong>Aim:</strong> To appraise the documented performance and limitations of a masked/unmasked facial-recognition prototype and to specify a testable deep-learning architecture for subsequent evaluation.<br><strong>Design:</strong> A technical appraisal of thesis-reported prototype outcomes and a proposed but unimplemented architecture; no new comparative experiment is reported.<br><strong>Methods:</strong> The historical system combined an OpenCV deep-learning face detector with support vector machine (SVM)-based identity classification. The proposed system combines landmark-assisted face detection and alignment, ArcFace-style embeddings, cosine-similarity matching, an unknown-identity decision and optional ONNX Runtime inference. Evaluation requirements are defined for identity partitioning, mask conditions, multiple-person scenes and error reporting.<br><strong>Results:</strong> The thesis account attributes a face-detection accuracy value of 99% and approximately 80% live identity-classification accuracy to the prototype, while also presenting values of 97% for masked faces and 87% for unmasked faces elsewhere. Underlying test counts and predictions are unavailable, and these figures cannot be reconciled or recalculated. No accuracy or latency findings are available for the proposed architecture.<br><strong>Conclusion:</strong> A detector–embedding–matching workflow offers a technically justified route for further study, but its effectiveness, fairness, operating speed and suitability for deployment remain unestablished without reproducible evaluation.</p>

2026-11-06T00:00:00+00:00 Copyright (c) 2026 Author(s). The licensee is the journal publisher. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. https://journalajrcos.com/index.php/AJRCOS/article/view/930 2026-11-06T13:33:35+00:00 Joseph Kasiama Ngi Onkor Camile Likotelo Binene [email protected] Glad Lufimpu Mampuya

<p>Epidemiological surveillance systems produce decision-making indicators from data that may be affected by under-reporting, misclassification and notification delays. These errors can propagate through the extraction, transformation, loading and multidimensional aggregation stages. This article proposes a mathematical framework that integrates measurement error correction into an ETL–data warehouse–OLAP decision architecture. The central problem is to determine whether the correction of a misclassification error can be performed before or after aggregation without changing the resulting indicator. We show that the Rogan–Gladen estimator commutes with the roll-up operator only when the sensitivity and specificity parameters are homogeneous across the aggregated strata. A numerical instantiation based on a synthetic dataset calibrated on published parameters of the Ebola virus disease epidemic in North Kivu and Ituri (DRC, 2018–2020) illustrates this non-commutation and its consequences for the estimation of incidence and the reproduction number. These results lead to a design rule: when surveillance quality is heterogeneous, correction must be performed at the finest level at which the error parameters can be regarded as homogeneous, before any aggregation, because this is the only strategy that remains unbiased whatever the spatial distribution of cases. A bias–variance analysis (Theorem 4) and a Monte Carlo uncertainty analysis (5,000 replicates, seven scenarios) further show that aggregate-level correction can have a slightly smaller variance but becomes biased when cases concentrate in poorly covered zones, and that the corrected indicators are highly sensitive to the assumed specificity when test positivity is close to the false-positive floor (a misspecification of 0.005 in specificity changes cumulative incidence by more than 100%). No individual-level surveillance data were used: the synthetic data are fully specified by a generation algorithm and a parameter table provided for reproducibility. The proposed framework thus provides a formal basis for designing surveillance decision-support systems that explicitly account for data quality.</p>

2026-11-06T00:00:00+00:00 Copyright (c) 2026 Author(s). The licensee is the journal publisher. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.