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All Journal Journal of Information Systems and Informatics
Belinda Ndlovu
National University of Science and Technology
Computer Science & IT

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Quantum Computing in Molecular Design and Drug Discovery: A Systematic Literature Review Charnelle Razo; Belinda Ndlovu
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 Kudzaishe Lawal Chizengwe; Belinda Ndlovu
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.
Small Language Models for Drug-Drug Interaction Extraction from Biomedical Text: A Systematic Literature Review Fortunate Mutanda; Belinda Ndlovu
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.v8i1.1430

Abstract

Drug–drug interaction (DDI) extraction from biomedical text is central to pharmacovigilance but remains challenging in resource-constrained clinical environments. While large language models have shown promise, their computational cost and deployment complexity limit practical adoption. This study systematically reviews the role of small language models (SLMs) for DDI extraction and examines their effectiveness, efficiency, and deployability. A systematic literature review was conducted following PRISMA guidelines, covering empirical studies published between 2020 and 2025 in PubMed, IEEE Xplore, ACM and  SpringerLink. Eligible studies were analysed with respect to model architectures, datasets, evaluation metrics, and deployment considerations. Quality assessment was applied to ensure methodological robustness. The synthesis indicates that SLM-based approaches, including CNN-, LSTM-, and lightweight transformer models, can achieve competitive F1-scores on benchmark DDI datasets while requiring substantially fewer computational resources than large language models. However, performance varies across datasets, and real-world clinical evaluations remain limited. These findings support the feasibility of deploying SLM-based DDI extraction systems in resource-constrained clinical and pharmacovigilance settings and provide a baseline for future benchmarking and comparative research in clinical natural language processing.
Towards Cloud-Based Electronic Health Records in Healthcare Systems: Security, Scalability, and Migration Strategies: A Systematic Literature Review Musawenkosi Moyo; Belinda Ndlovu
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.v8i1.1431

Abstract

Cloud-based Electronic Health Records (EHRs) are being adopted rapidly worldwide, but implementation still encounters recurring obstacles in security assurance, elastic scalability, and migration readiness. Prior reviews often treat these issues separately, leaving limited practical guidance for organizations planning end-to-end deployment. This study synthesizes recent evidence on cloud EHR adoption by examining how security controls, scalability claims, and migration strategies interact in real implementation contexts. A systematic literature review following PRISMA guidelines was conducted across ACM Digital Library, PubMed, IEEE Xplore, and ScienceDirect, covering peer-reviewed studies published from 2021 to 2025. Results show that the literature is technically mature in proposing encryption, access control, auditing, and performance optimization, and frequently reports scalability advantages. In contrast, evidence on complete migration pathways—data mapping, interoperability, validation, cutover planning, and post-migration assurance—remains sparse, with many studies relying on simulations rather than longitudinal deployments. The review also identifies geographic concentration in high-income settings, limiting generalizability to resource-constrained health systems. By integrating security, scalability, and migration readiness within a socio-technical, implementation-oriented perspective, this review provides actionable directions for secure and scalable cloud EHR transitions.
Co-Authors Charnelle Razo Fortunate Mutanda Kudzaishe Lawal Chizengwe Musawenkosi Moyo