University of Malawi

Staff

Search other staff

Profile

Dr. Dackson Masiyano

SENIOR LECTURER
Physics and Electronics

Research Areas

Research Interest: AI-Enhanced Stethoscope Technology for Cardiovascular Screening My research focuses on adapting artificial intelligence (AI)–powered stethoscope technology for early detection of cardiovascular disease in low-resource healthcare settings. AI stethoscopes combine high-fidelity h ... Read More

Profile

Dr. Dackson Masiyano joined the Department of Physics and Electronics at the University of Malawi as a Senior Lecturer in August 2024. Before joining academia, he spent over 10 years in research and development (R&D) roles within industry, where he worked on the design, prototyping, and deployment of electro-optical systems for real-world applications. His research and professional interests span Artificial Intelligence (AI), Internet of Things (IoT), embedded systems, and remote sensing, with an emphasis on developing practical and context-appropriate technological innovations. His current academic work focuses on AI-enabled diagnostic tools for healthcare, sensor networks for environmental monitoring, and data-driven systems for climate-smart agriculture. Dr. Masiyano brings to UNIMA a strong background in applied research, system innovation, and technology transfer. He is particularly interested in bridging the gap between academic research and industrial application, fostering collaborations that support evidence-based innovation, capacity development, and sustainable technology solutions in Malawi and the broader region.

Publications

More on Research

Research Interest: AI-Enhanced Stethoscope Technology for Cardiovascular Screening My research focuses on adapting artificial intelligence (AI)–powered stethoscope technology for early detection of cardiovascular disease in low-resource healthcare settings. AI stethoscopes combine high-fidelity heart-sound recording with machine-learning analysis to identify heart failure, valve disease, and arrhythmias within seconds. By developing and validating locally relevant AI models and low-cost prototypes, this work aims to make advanced cardiac diagnostics accessible at the primary-care level in Malawi. The research integrates biomedical engineering, signal processing, and clinical science to create scalable, affordable diagnostic solutions. It also contributes to the generation of Malawian heart-sound datasets for global AI training and supports national efforts to strengthen health-system capacity through digital innovation. Active project in Collaboration with Global Health Informatics Institute,AI and IoT for Food Safety and Quality Assurance — Application of artificial intelligence, machine vision, and IoT-based sensing to detect aflatoxin contamination, optimize processing conditions, and reduce waste in Malawi’s small-scale maize value chains. The research integrates deep learning models (YOLO, CNN, LSTM) with real-time environmental monitoring for predictive control and food safety compliance. Active Project: Funded by NCST ,Research Area: AI-Driven Prediction of Effective Maize Planting Rains for Climate-Resilient Agriculture in Malawi Active project-Pilot funded by CRAFS Summary: This research focuses on developing and validating Artificial Intelligence (AI) models to predict the onset and reliability of effective planting rains for maize in Malawi. The work combines satellite and ground-based rainfall data through machine learning (LSTM, CNN, and ensemble models) to capture complex spatio-temporal rainfall patterns influenced by local topography, convective systems, and Lake Malawi effects. By defining planting rains dynamically based on agronomic thresholds and farmer observations, the study aims to generate ward-level planting advisories that reduce false starts and improve crop establishment. The approach bridges data science, meteorology, and agronomy to support localized decision-making and strengthen national early warning systems through partnerships with DCCMS, NASFAM, and CRAFS.,