Jordan Junior Kamese

Mr. Jordan Junior Kamese

Convergence Engineering, Kitara Neural, Kampala Central Division, Uganda
orcid
Wealth is measured by how many lives you save.

Research Focus & Metrics

Academic Career Length

3Years (2024-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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14 September 2024 in Applied Sciences
Verified
Journal Article
Open access
PDF file
Jordan Junior KameseJordan Junior Kamese
Jordan Junior Kamese
KCkouayep sonia carole
kouayep sonia carole
TATagne Poupi Theodore Armand
Tagne Poupi Theodore Armand
Hee-Cheol KimHee-Cheol Kim
Hee-Cheol Kim
TIThe Alzheimer’s Disease Neuroimaging Initiative
The Alzheimer’s Disease Neuroimaging Initiative
In this study, we address the early detection challenges of Alzheimer’s disease (AD) using explainable artificial intelligence (XAI) techniques. AD, characterized by amyloid plaques and tau tangles, leads to cognitive decline and remains hard to diagnose due to genetic and environmental factors. Utilizing deep learning models, we analyzed brain MRI scans from the ADNI database, categorizing them into normal cognition (NC), mild cognitive impairment (MCI), and AD. The ResNet-50 architecture was employed, enhanced by a channel-wise attention mechanism to improve feature extraction. To ensure model transparency, we integrated local interpretable model-agnostic explanations (LIMEs) and gradient-weighted class activation mapping (Grad-CAM), highlighting significant image regions contributing to predictions. Our model achieved 85% accuracy, effectively distinguishing between the classes. The LIME and Grad-CAM visualizations provided insights into the model’s decision-making process, particularly emphasizing changes near the hippocampus for MCI. These XAI methods enhance the interpretability of AI-driven AD diagnosis, fostering trust and aiding clinical decision-making. Our approach demonstrates the potential of combining deep learning with XAI for reliable and transparent medical applications.

1 December 2025 in Virtual Worlds
Verified
Journal Article
Open access
PDF file
KBKamdem Poupi Arnold brice
Kamdem Poupi Arnold brice
ADAratrika De
Aratrika De
WNWiysenyuy Louis Nyuydzeran
Wiysenyuy Louis Nyuydzeran
Jordan Junior KameseJordan Junior Kamese
Jordan Junior Kamese
TATagne Poupi Theodore Armand
Tagne Poupi Theodore Armand
Geopolitical disruptions such as trade wars, sanctions, and political instability threaten global supply chain (SC) resilience. As a result, multinational corporations face financial losses, operational delays, and strategic uncertainties, creating an urgent demand for innovative risk management and scenario-planning strategies. Traditional risk management methods struggle to keep pace with the complexity of these events. This study explores the metaverse, combining VR, AR, digital twins, AI, and blockchain, as a tool for enhancing SC risk management. By enabling immersive scenario planning, real-time risk visualization, and collaborative decision-making, the metaverse supports agile and resilient supply chains. This research proposes a conceptual framework integrating key fourth industrial revolution (4IR) technologies to address geopolitical SC disruptions systematically. This model fosters digital preparedness, simulation-based learning, and adaptive coordination. While technological, organizational, and regulatory challenges persist, the study demonstrates that metaverse-enabled systems can support future-ready SC resilience strategies.

28 October 2024 in Journal of Alzheimer's Disease & Reports
Verified
Journal Article
Closed access
Jordan Junior KameseJordan Junior Kamese
Jordan Junior Kamese
TATagne Poupi Theodore Armand
Tagne Poupi Theodore Armand
HKHee-Cheol Kim
Hee-Cheol Kim
Alzheimer's Disease (AD) is a leading cause of cognitive decline in older adults, yet early diagnosis remains a major challenge. Diffusion Tensor Imaging (DTI) has emerged as a promising neuroimaging technique for assessing white matter integrity, a key factor in Alzheimer's progression. This commentary explores the intersection of DTI and Artificial Intelligence (AI), highlighting how advanced machine learning (ML) and deep learning (DL) algorithms are being utilized to process DTI data and improve AD diagnosis. AI models, including Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), have demonstrated significant success in predicting AD progression, identifying disease subtypes, and differentiating Alzheimer's from other dementias. Automated Fiber Quantification (AFQ) also plays a critical role in analyzing white matter disruptions associated with AD. This synergistic approach between DTI and AI is advancing early detection efforts and paving the way for more precise and personalized AD diagnoses, ultimately enhancing clinical outcomes and improving our understanding of Alzheimer’s pathology.