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All Journal Techno Nusa Mandiri : Journal of Computing and Information Technology Journal of Technology and Informatics (JoTI)
Muhala Luhepa, Blaise
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Mechanical Engineering

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Semantic Knowledge Fusion in Healthcare: A Hybrid Approach for Connected Medicine Muhala Luhepa, Blaise; Bukasa Kakamba, John; Munduku Munduku, Deo; Mazono Magubu, Daniel; Ntumba Nkongolo, Albert; Matondo Mananga, Herman; Munene Asidi, Djonive
Journal of Technology and Informatics (JoTI) Vol. 7 No. 2 (2025): Vol. 7 N. 2 (2025)
Publisher : Universitas Dinamika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37802/joti.v7i2.1182

Abstract

In a context where connected medicine requires increasingly explainable, accurate, and responsive systems, this paper presents an applied experimental research focusing on the development and evaluation of a hybrid intelligent assistant for healthcare data fusion. The study is based on the parallel combination of two data paradigms: classical tabular structures and their ontological equivalent. Using an intelligent assistant, we simultaneously query a medical dataset on diabetes in tabular form and the same dataset translated into an OWL ontology that can be queried using SPARQL. The aim is to demonstrate that the synchronised combination of these two models not only provides a more complete response but also one that is better contextualised and clinically exploitable. The research follows an experimental methodology, involving the implementation, testing, and comparative evaluation of both models on 300 questions classified by increasing complexity (simple, complex, and very complex). The results reveal a relevance rate above 99%, with an average response time suited to medical use. This work highlights the potential of hybrid architectures in connected health and paves the way for new decision-making assistants that fully exploit the semantic richness of medical knowledge.
AUTONOMOUS AND EXPLAINABLE DETECTION OF SUSPICIOUS BEHAVIORS IN CONNECTED VEHICLE ENVIRONMENTS THROUGH MULTI-SENSOR VISION Gihonia Abraham, Senghor; Mabela Makengo Matendo, Rostin; Masakuna, Felicien; Muluba Mfumudimbu Lireh, Celeste; Muhala Luhepa, Blaise
Jurnal Techno Nusa Mandiri Vol. 23 No. 1 (2026): Techno Nusa Mandiri : Journal of Computing and Information Technology Period o
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/4z0rn547

Abstract

The safety of connected and autonomous vehicles requires intelligent systems capable of detecting suspicious behaviors in real time while providing clear explanations to human operators. This paper presents an innovative framework for the autonomous and explainable detection of suspicious activities around connected vehicles, combining multi-sensor vision, multi-agent reinforcement learning (MARL), and explainable artificial intelligence (XAI). The system relies on lightweight deep learning models (YOLO-tiny, MobileNet) for perception, along with spatio-temporal reasoning to identify abnormal events such as prolonged parking, restricted area crossings, or the placement of suspicious objects. Cooperative decision-making between vehicles and roadside units (RSUs) is managed through MARL. In parallel, an XAI module generates visual and textual explanations to enhance transparency and user trust. The framework has been implemented and evaluated in simulation (CARLA, SUMO/Veins) and on embedded platforms (Jetson Nano/Orin). Results demonstrate an F1-score of 0.91, real-time performance at 7.5 FPS, and a 40% reduction in false positives, confirming the robustness of the proposed system for the cyber-physical security of intelligent transportation systems.
Co-Authors Bukasa Kakamba, John Gihonia Abraham, Senghor Mabela Makengo Matendo, Rostin Masakuna, Felicien MATONDO MANANGA, Herman Mazono Magubu, Daniel Muluba Mfumudimbu Lireh, Celeste Munduku Munduku, Deo Munene Asidi, Djonive Ntumba Nkongolo, Albert