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All Journal Indonesian Journal of Information System JOURNAL OF APPLIED INFORMATICS AND COMPUTING IJID (International Journal on Informatics for Development) Journal of Information Systems and Informatics Indonesian Journal of Innovation and Applied Sciences (IJIAS) The Indonesian Journal of Computer Science
Ndlovu, Belinda
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Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mathematics Social Sciences

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Early Detection of Diabetic Retinopathy Through Explainable AI Models: A Systematic Review Ngwazi, Tinashe; Ndlovu, Belinda; Maguraushe, Kudakwashe
IJID (International Journal on Informatics for Development) Vol. 14 No. 2 (2025): IJID December
Publisher : Faculty of Science and Technology, Universitas Islam Negeri (UIN) Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/ijid.2025.5200

Abstract

Diabetes, if not detected early, can lead to serious complications such as vision loss, known as diabetic retinopathy. Explainable Artificial Intelligence (XAI) can enhance traditional Machine Learning methods, which are not understandable and transparent in diagnostic tasks. This Systematic Literature Review explores data inputs that influence the performance of XAI models in detecting diabetic retinopathy, how XAI techniques can enhance early detection outcomes in diabetic retinopathy, the challenges in implementing these techniques and the ethical implications of using these models in clinical practice. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses approach guided the search in 4 databases, Springer, Science Direct, PubMed and IEEE Xplore. The findings reveal that XAI techniques like Local Interpretable Model-agnostic Explanations (LIME), SHapley Additive exPlanations (SHAP) and Gradient-weighted Class Activation Mapping (GRAD-CAM) offer opportunities like early detection outcomes, integration with existing clinical processes, enhancing trust in AI systems, improving accuracy and personalised treatment. XAI can also facilitate collaboration among clinicians, maintaining fairness in AI systems and supporting adherence to ethical standards. However, research on clinical validation of these models, as well as standardised performance evaluation metrics, is lacking.
Quantum Computing Cryptography: A Systematic Review of Innovations, Applications, Challenges, and Algorithms Maitireni, Peter; Ncube, Vusumuzi; Ndlovu, Belinda; Sibanda, Thando
Journal of Information System and Informatics Vol 7 No 4 (2025): December
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v7i4.1331

Abstract

This study explores how to build quantum-resistant systems to safeguard digital infrastructure in the post-quantum era by uncovering the innovations, applications, algorithms, and challenges of Quantum Computing cryptography. Utilizing the Preferred Reporting Items for Systematic Reviews and Meta-Analyses approach a search was conducted across the following databases for the years 2021–2025: PubMed, IEEE Xplore, ScienceDirect, SpringerLink, and Google Scholar. We shortlisted 15 studies from 519 screened articles for a comprehensive evaluation based on their relevance. Findings show strong adoption in finance, healthcare, IoT, cybersecurity, and e-government, with lattice-based PQC emerging as the most dominant cryptographic family, followed by QKD and hybrid PQC–QKD models. The review highlights key obstacles, including transition complexity, lack of global standards, high implementation costs, and integration difficulty. The study contributes by providing the first sector-aligned synthesis of innovations, identifying algorithmic trends, and mapping global research disparities through a conceptual model. It also presents a structured set of future research directions to guide policymakers, cryptographers, and practitioners preparing for quantum-enabled threats.
A Systematic Review of Post-Quantum Cryptography for Healthcare Data Protection: Performance, Readiness, and Deployment Challenges Ngwenya, Taboka; Ndlovu, Belinda
Journal of Applied Informatics and Computing Vol. 10 No. 1 (2026): February 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i1.11836

Abstract

The traditional cryptographic methods used to protect healthcare data, especially for the long-term storage of medical imaging records, are becoming increasingly threatened by the quick development of quantum computing. The purpose of this study is to assess the challenges, efficacy, and preparedness of integrating Post-Quantum Cryptography (PQC) into healthcare information systems. Twenty peer-reviewed studies published between 2020 and 2025 were analysed following the Preferred Reporting Items for Systematic Reviews and Meta Analyses (PRISMA) protocol. The review was conducted using a systematic research design that included qualitative thematic synthesis, predetermined eligibility criteria, and database searching. According to the results, lattice-based PQC schemes, specifically, CRYSTALS-Kyber for encryption and CRYSTALS-Dilithium for authentication, show great promise because of their effectiveness, resilience, and suitability for decentralized architectures like blockchain and Internet-of-Medical-Things environments. Nonetheless, the review points out a notable deficiency of empirical assessment in actual healthcare settings, particularly with regard to cloud-based platforms and Picture Archiving and Communication Systems utilized in medical imaging processes. Scalability limitations, intricate key-management specifications, system interoperability restrictions, and the requirement for conformity with regulatory and compliance frameworks are some of the major issues noted. The results indicate that lattice-based PQC schemes have great promise, deployment readiness remains largely at the conceptual and experimental stage, particularly for cloud-based PACS environments. Real-world implementation validation in a healthcare setting has not been achieved.
Explainable Transformer and Machine Learning Models in Predicting Tuberculosis Treatment Outcomes. A Systematic Review Sibanda, Shumirai; Ndlovu, Belinda
Journal of Applied Informatics and Computing Vol. 10 No. 1 (2026): February 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i1.11846

Abstract

Tuberculosis (TB) remains a major health challenge, and predicting treatment outcomes continues to be difficult in real-world settings. Recent advances in Artificial Intelligence (AI), particularly transformer-based models, have shown promise in modelling longitudinal, multimodal, and heterogeneous TB data. However, their clinical adoption is constrained by limited interpretability, fairness concerns, and deployment challenges. This study presents a systematic literature review of explainable transformer and machine learning models used for TB prognosis. Following PRISMA guidelines, searches across ACM, IEEE Xplore, PubMed, and ScienceDirect identified 17 peer-reviewed studies published between 2020 and 2025 that met the inclusion criteria. The review synthesises evidence on predictive performance, explainability techniques, and deployment considerations. Findings indicate that transformer-based and deep learning models generally outperform conventional machine learning approaches on longitudinal and multimodal data. In contrast, traditional models remain competitive for tabular clinical datasets. Explainability approaches are dominated by feature importance methods and SHAP, with limited use of intrinsic transformer interpretability mechanisms. Persistent challenges include data scarcity, limited generalisability, computational overhead, insufficient evaluation of fairness, and weak alignment with real-world TB care workflows. Building on these findings, the study proposes the Explainable Transformer Adoption Model for TB Prognosis (ETAMTB) as a conceptual clinical adoption framework integrating multimodal transformers, explainability layers, clinician-facing interfaces, and deployment enablers. Overall, the review concludes that effective AI adoption in TB care requires balancing predictive performance, interpretability, and equity, and that explainable transformers should currently be viewed as promising but largely experimental tools rather than deployment-ready solutions.
Transformer-Based Models for Electronic Health Records and Omics in Healthcare: A Systematic Literature Review Machemedze, Joshua; Ndlovu, Belinda
Journal of Applied Informatics and Computing Vol. 10 No. 1 (2026): February 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i1.11893

Abstract

Electronic Health Records (EHRs) have become central to modern healthcare. The emergence of transformer-based models has profoundly influenced how EHRs are used for modelling complex, longitudinal data. Integration with omics technologies improves the precision of disease identification and risk assessment during modelling. While several reviews have examined transformers in healthcare broadly, a systematic synthesis focused on their architectural design, empirical performance and integration of EHRs with omics data remains limited. This study presents a systematic literature review of transformer-based models applied to electronic health records (EHRs) and omics data, and of their integration into healthcare. Following PRISMA guidelines, peer-reviewed studies were retrieved from IEEE Xplore, ACM Digital Library, PubMed, and ScienceDirect, resulting in 14 eligible empirical studies published between 2020 and 2025. The review analyses transformer architectures, submodules, application domains, comparative performance, interpretability mechanisms, and limitations. Findings indicate that architectural design drives task-specific advantages in disease prediction, phenotyping, medication recommendation, and omics analysis. The integration of self-attention with deep learning, temporal modelling, and a pre-trained biomedical transformer improves performance. However, most studies remain centred on EHR, with limited empirical integration of omics data. Persistent challenges include limited generalisability, high computational cost, data quality issues, and insufficient interpretability for clinical deployment. The primary contribution of this review lies in synthesising architectural trends and methodological gaps. By consolidating current evidence, the study provides clear directions for the development of explainable, generalisable, and multimodal transformer-based systems in precision healthcare.
Transformer-based Models for Cardiovascular Disease Predictions from Electronic Health Records: A Systematic Review Chikumo, Onayi Theresa; Ndlovu, Belinda
Journal of Applied Informatics and Computing Vol. 10 No. 1 (2026): February 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i1.11899

Abstract

This systematic literature review (SLR) analyses 16 studies published between 2020 and 2025 that applied transformer-based or other machine learning models to predict cardiovascular disease (CVD) using electronic health records (EHRs). Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, the review ensures transparency in the identification, screening, and quality appraisal of eligible studies. The key findings reveal a rapid shift from traditional machine learning models, such as Random Forest, toward transformer architectures like the Bidirectional Encoder Representation from Transformers for Electronic Health Record (BEHRT) and its variants. These models demonstrate a superior discrimination (Area Under Curve:0.84 to 0.93) due to their capacity to model long-term temporal dependencies. Explainable AI (XAI) tools, such as attention visualisation, were frequently employed, yet clinical interpretability and integration into decision support remain underexplored. The review also highlights opportunities in federated and privacy-preserving learning, multimodal data fusion, and hybrid architectures that integrate transformers with traditional machine learning methods. This review addresses a gap in the past literature by being the first SLR to compare transformer variants for the prediction of CVDs. Other SLRs examined general CVD risk models, but the present SLR analyses interpretability, external validation and methodological limitations to transformer models. The findings of the recent SLR reported challenges that include data-shift limitations, model-poor population generalisation and their limitations to clinical adoption, which highlights the need for more evaluation protocols and clinicians’ interpretability frameworks.
Multi-Agent Retrieval Augmented Generation for Clinical Decision Support: A Systematic Review and Integrative Conceptual Framework Mugambiwa , Tarisai; Ndlovu, Belinda
Journal of Applied Informatics and Computing Vol. 10 No. 1 (2026): February 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i1.11900

Abstract

Multi agent retrieval augmented generation (RAG) systems are increasingly explored as advanced architectures for clinical decision support combining information retrieval, reasoning and verification through coordinated agent interactions. This study systematically reviews applications of agentic and multi agent RAG in clinical decision support systems (CDSS) and synthesizes an integrative conceptual framework linking technical design to technology adoption considerations. Following PRISMA guidelines, searches were conducted from PubMed, IEEE Xplore and ScienceDirect using structured Boolean strings combining terms for multi agent architectures, RAG and CDSS.The search yielded 12 studies published between 2020 and 2025 that met the inclusion criteria. The review synthesises evidence on multi agent role configurations retrieval and reasoning strategies, verification mechanisms and reported clinical contexts. Across studies, dominant challenges include data and corpus limitations retrieval quality dependency, limited clinical validation and computational overhead, alongside governance concerns such as privacy, bias and accountability. Building on the synthesis, we propose a four-agent CDSS framework retriever, reasoner, verifier, safety and map its deployment determinants to Technology Acceptance Model constructs perceived usefulness, perceived ease of use, trust and diffusion of Innovations attributes. The review concludes with design-oriented recommendations for safer, explainable, and adoption-ready multi-agent RAG CDSS, particularly for low-resource contexts.
Quantum Computing in Molecular Design and Drug Discovery: A Systematic Literature Review Razo, Charnelle; Ndlovu, Belinda
Journal of Information System and Informatics Vol 8 No 1 (2026): February
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i1.1380

Abstract

This study examines how quantum computing (QC) is being applied to molecular design and drug discovery. This study aims to investigates how QC surpasses classical limitations, focusing on empirical performance in precision, accuracy, and optimisation tasks. Study design use PRISMA 2009 guidelines, 15 empirical studies (2020-2025) were included. Data were extracted on the drug-discovery stage, the algorithm used, evaluation metrics, benefits, and limitations. The findings show QC outperforms classical methods particularly through hybrid quantum–classical models. Thirteen studies reported superior gains, including AUC–ROC values of 0.80–0.95, +30% improvement in drug-likeness (QED), +6% increase in prediction accuracy, and up to 99% accuracy in drug–target interaction tasks. However, noisy intermediate-scale quantum (NISQ) hardware limitations and poor scalability limit real-world deployment, due to noise, and limited qubit counts. Consequently, current performance results are largely simulation-based rather than hardware-validated. In contrast to prior algorithm-centric reviews, this study provides a consolidated empirical synthesis and proposes a hybrid quantum–classical pipeline that maps high-performing algorithms across the drug discovery workflow under NISQ-era constraints. These findings inform pharmaceutical research and development by identifying realistic adoption pathways and the boundaries of current technological readiness.
A Systematic Review of Agentic AI for Threat Detection and Mitigation in 5G Networks Chizengwe, Kudzaishe Lawal; Ndlovu, Belinda
Journal of Information System and Informatics Vol 8 No 1 (2026): February
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i1.1382

Abstract

Fifth-generation (5G) networks face escalating security challenges driven by decentralised architectures, stringent ultra-low-latency requirements, and rapidly evolving threat landscapes. Agentic Artificial Intelligence (agentic AI) autonomous systems that perceive network conditions, decide on countermeasures, and act in real time offers a promising route toward adaptive defence. This systematic review examines how agentic AI is being applied to detect and mitigate threats within 5G networks. Following PRISMA 2009 guidelines, four databases (IEEE Xplore, ACM Digital Library, SpringerLink, and ScienceDirect) were searched, yielding 22 eligible peer-reviewed studies published between 2020 and 2025, selected for explicit 5G relevance and empirical evaluation. The reviewed evidence clusters into four primary security areas: anomaly detection, DDoS mitigation, network slicing security, and intrusion detection. Across these domains, approaches based on federated learning, deep reinforcement learning, and multi-agent systems generally report stronger detection performance and/or more adaptive response behaviour than conventional, reactive baselines, while supporting privacy-preserving intelligence at the edge. However, key deployment barriers remain: 86% of studies rely on simulation-based validation, scalability beyond 100 nodes is insufficiently characterised, and reported coordination delays (120–180 ms) may conflict with 5G latency constraints in time-critical settings. To consolidate findings, this review proposes a Perception–Decision–Action–Feedback conceptual framework and highlights priorities for real-world validation and deployment-oriented evaluation.
Co-Authors Chamboko, Hazel Chikumo, Onayi Theresa Chipfumbu, Colletor T. Chizengwe, Kudzaishe Lawal Dube, Sibusisiwe Dube, Yangekile Hadebe, Sanele Kiwa, Fungai Jacqueline Mabikwa, Otis Machemedze, Joshua Maguraushe, Kudakwashe Maitireni, Peter Marambi, Sheltar Muderere, Vimbai Alice Muduva, Martin Mugambiwa , Tarisai Muzenda, Hillary Ncube, Vusumuzi Ndlovu, Sharmaine Ngwazi, Tinashe Ngwenya, Taboka Nyamupaguma, Tanyaradzwa Razo, Charnelle Sibanda, Shumirai Sibanda, Thando Vareta, Passmore