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All Journal IJISCS (International Journal of Information System and Computer Science) Scientific Journal of Computer Science
Oluwagbemi, Johnson Bisi
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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DIAGNOSTIC ACCURACY OF DEEP NEURAL NETWORKS FOR PNEUMONIA AND COVID-19 DETECTION ON MEDICAL IMAGING: A SYSTEMATIC REVIEW AND META-ANALYSIS Oluwagbemi, Johnson Bisi; Akinbo, Racheal Shade; Mesioye, Ayobami Emmanuel
IJISCS (International Journal of Information System and Computer Science) Vol 9, No 3 (2025): IJISCS (International Journal of Information System and Computer Science)
Publisher : Bakti Nusantara Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56327/ijiscs.v9i3.1857

Abstract

Pneumonia and COVID-19 remain leading causes of universal morbidity and mortality, with timely and precise diagnosis essential for effective patient management. This systematic review and meta-analysis assessed the diagnostic accuracy of deep neural networks in detecting pneumonia and COVID-19 across main medical imaging modalities. Comprehensive searches of PubMed, Scopus, Web of Science, IEEE Xplore and Cochrane Library identified 80 eligible studies published between 2017 and 2025. Included studies used chest X-ray (CXR), computed tomography (CT) and lung ultrasound (LUS) images analyzed through convolutional neural networks, transformer-based and hybrid deep models. Pooled diagnostic performance was synthesized using a bivariate random-effects model and hierarchical summary receiver operating characteristic analysis. Overall pooled sensitivity and specificity were 0.88 (95% CI: 0.84-0.91) and 0.90 (95% CI: 0.86-0.92), respectively, with an area under the curve of 0.93, indicating high discriminative capability. Subgroup analyses revealed CT-based models outperformed CXR and LUS, while transformer architectures marginally exceeded CNNs. In addition, external validation studies steadily reported lower accuracy than internal validations, reflecting limited model generalizability. Risk of bias assessment using QUADAS-2 emphasized concerns related to patient selection, data leakage and non-standardized reference criteria. Despite moderate heterogeneity (I² = 39-52%) and potential publication bias, findings confirm the substantial potential of DNNs as decision-support tools for fast, scalable and reliable respiratory disease diagnosis. However, broader clinical adoption demands multicenter validation, transparency and adherence to ethical AI standards. This study provides evidence-based insights into the current performance and translational readiness of AI-driven diagnostic imaging for pneumonia and COVID-19.
IOT AND ML-POWERED CYBER-PHYSICAL FRAMEWORK FOR REAL-TIME URBAN FLOOD RESILIENCE WITH GEOSPATIAL VISUALIZATION Mesioye, Emmanuel Ayobami; Oluwagbemi, Johnson Bisi; Akinbo, Shade Racheal; Esan, Mathew Oluwatosin
IJISCS (International Journal of Information System and Computer Science) Vol 10, No 1 (2026): IJISCS (International Journal of Information System and Computer Science)
Publisher : Bakti Nusantara Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56327/ijiscs.v10i1.1877

Abstract

Urban flooding remains a disastrous challenge for rapidly expanding cities in developing nations. Despite the fact deep learning models and IoT sensing are individually established in hydrology, their seamless integration into a unified, cost-effective Cyber-Physical System (CPS) specifically architected for data-scarce and infrastructure challenged environments remains a critical research gap. This research contributes a novel, end-to-end framework that bridges this divide by harmonizing three distinct pillars: a low-cost, energy-autonomous IoT sensor network, a hybrid CNN-LSTM predictive model, and a dynamic geospatial visualization dashboard. Unlike conventional systems designed for data-rich environments, our framework is contextually adapted for the unique topographical and socio-technical realities of Nigerian urban centers. Validated through a six-month deployment in the high-density Ajeromi-Ifelodun region of Lagos, the system achieved a Nash-Sutcliffe Efficiency (NSE) of 0.89 and a critical 4.5-hour forecast lead time.
Hybrid Neuro-Fuzzy Deep Learning with Genetic Optimization for Explainable Stock Price Forecasting in Emerging Markets Adewale, Olumide Sunday; Ibam, Emmanuel Onwuka; Oluwagbemi, Johnson Bisi
Scientific Journal of Computer Science Vol. 2 No. 2 (2026): December Article in Process
Publisher : PT. Teknologi Futuristik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64539/sjcs.v2i2.2026.377

Abstract

Precise stock price forecasting is vital for economic stability and capital allocation, yet it remains a tenacious challenge in emerging economies due to the inherent uncertainty and non-linearity of financial time series. Despite advances in deep learning, existing models often lack linguistic interpretability, fail to adapt to rapid market shifts, or exhibit look-ahead bias due to static validation splits. Moreover, empirical research focused on African financial systems, such as the Nigerian market, remains sparse, limiting the practical utility of conventional black-box architectures. This study proposes a Hybrid Neuro-Fuzzy and Deep Learning (HNFDL) framework that integrates fuzzy inference systems with Long Short-Term Memory (LSTM) networks and Genetic Algorithms (GA). The objective is to unify semantic reasoning with temporal learning to improve forecasting accuracy while maintaining high model transparency through explainable AI (XAI). Empirical validation using data from the Nigerian Exchange Group (NGX) (Dangote Cement, Zenith Bank, and the NSE All-Share Index) shows that the HNFDL model achieved a directional accuracy of 68.4% and a Mean Absolute Percentage Error (MAPE) as low as 4.36%. An ablation study confirmed that GA-driven optimization reduced the Root Mean Square Error (RMSE) by 8.4%, while the Diebold-Mariano test () statistically confirmed the model's superiority over standalone LSTM and fuzzy baselines. These results demonstrate that combining explainable fuzzy reasoning with adaptive deep neural architectures significantly enhances decision-making confidence. The framework provides a robust, statistically validated decision-support tool for investors and policy makers operating within volatile, information-asymmetric financial environments.
Co-Authors Adewale, Olumide Sunday Akinbo, Racheal Shade Akinbo, Shade Racheal Esan, Mathew Oluwatosin Ibam, Emmanuel Onwuka Mesioye, Ayobami Emmanuel Mesioye, Emmanuel Ayobami