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All Journal Journal of Computing Theories and Applications IJISCS (International Journal of Information System and Computer Science) Scientific Journal of Computer Science
Johnson Bisi Oluwagbemi
McPherson University
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering

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Multimodal Deep Learning for Pneumonia Detection Using Wearable Sensors: Toward an Edge-Cloud Framework Emmanuel Onwako Ibam; Johnson Bisi Oluwagbemi
Journal of Computing Theories and Applications Vol. 3 No. 3 (2026): JCTA 3(3) 2026
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jcta.14944

Abstract

Pneumonia remains a leading cause of morbidity and mortality worldwide, particularly in resource-limited settings and among elderly populations, where timely diagnosis and continuous monitoring are often constrained by limited clinical infrastructure. This study presents an edge–cloud–integrated framework for early pneumonia risk monitoring, leveraging multimodal wearable sensors and deep learning to support continuous short-duration monitoring. The proposed system is designed to operate in near real time under simulated deployment conditions, continuously acquiring and analyzing physiological signals (respiratory rate, heart rate, SpO₂, and body temperature) alongside event-driven acoustic biomarkers (cough sounds) within a distributed architecture. A lightweight edge module performs local signal preprocessing and anomaly triage, selectively transmitting salient information to a cloud-based multimodal deep learning model for refined risk estimation and interpretability analysis. The framework was evaluated using a multi-source dataset comprising public repositories (MIMIC-III and Coswara) and a clinically supervised wearable study conducted in two Nigerian hospitals, resulting in 718  hours of quality-controlled multimodal monitoring data. In a pooled multi-source evaluation, the system achieved an AUC of 0.95, while in a clinically realistic local-only evaluation, the AUC was 0.86, reflecting a consistent but preliminary diagnostic signal. These results highlight the importance of local data adaptation for real-world applicability and suggest that multimodal AI can provide meaningful early risk indicators under resource constraints. Beyond predictive performance, this work demonstrates the feasibility of integrating multimodal learning, edge–cloud computation, and explainable analytics into a deployment-aware, privacy-preserving monitoring framework for low-resource healthcare environments.
Hybrid Neuro-Fuzzy Deep Learning with Genetic Optimization for Explainable Stock Price Forecasting in Emerging Markets Olumide Sunday Adewale; Emmanuel Onwuka Ibam; Johnson Bisi Oluwagbemi
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.
IOT AND ML-POWERED CYBER-PHYSICAL FRAMEWORK FOR REAL-TIME URBAN FLOOD RESILIENCE WITH GEOSPATIAL VISUALIZATION Emmanuel Ayobami Mesioye; Johnson Bisi Oluwagbemi; Shade Racheal Akinbo; Mathew Oluwatosin Esan
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.
Co-Authors Emmanuel Ayobami Mesioye Emmanuel Onwako Ibam Emmanuel Onwuka Ibam Mathew Oluwatosin Esan Olumide Sunday Adewale Shade Racheal Akinbo