Research Publications
Discover our cutting-edge research contributions to the scientific community through peer-reviewed publications and conference presentations in AI for agriculture and healthcare.
CARA-FMs: A Framework for Context-Aware and Responsible AI in East Africa
2025
Rectifying the extremely weakened signals for cassava leaf disease detection
Advanced signal processing techniques for enhancing weakened signals in cassava leaf disease detection, improving diagnostic accuracy for agricultural AI applications.
How Can Innovation Modernize Public Service Delivery and Create an Inclusive Digital Economy
Invited presentation exploring how innovation can transform public service delivery and foster inclusive digital economic growth in East Africa.
CARA-FMs: A Framework for Context-Aware and Responsible AI in East Africa
Framework for developing context-aware and responsible AI solutions specifically designed for East African contexts, addressing ethical considerations and cultural awareness in AI development.
Prime Directives for Responsible AI for Africa: A Manifesto for Inclusive Technology
Manifesto establishing prime directives for responsible AI development in Africa, focusing on inclusive technology development and ethical AI frameworks.
Integration of Multi-Omics and Meta Data for Metabolic Modelling
Research on integrating multi-omics data with metadata for advanced metabolic modeling in healthcare applications, presented at the Gates Foundation consortium.
Prime Directives for Responsible AI for Africa: A Manifesto for Inclusive Technology
2024
A salient feature establishment tactic for cassava disease recognition
Novel feature establishment tactics for improving cassava disease recognition accuracy using advanced machine learning and computer vision techniques.
Online chicken carcass volume estimation using depth imaging and 3-D reconstruction
Advanced 3D reconstruction techniques for real-time chicken carcass volume estimation, enhancing food processing efficiency and quality control in poultry industry.
MAIANet: Signal modulation in cassava leaf disease classification
MAIANet framework for signal modulation in cassava leaf disease classification, improving diagnostic accuracy through advanced neural network architectures.
Research Profiles
Google Scholar
Complete publication list with citation metrics and h-index tracking
ResearchGate
Research network with full-text papers and collaboration opportunities
ORCID
Persistent digital identifier for researchers and scholarly works