Makda Fekadie Tewelgne
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Makda Fekadie Tewelgne has a total of 22 co-authors. Here are the top co-authors by number of shared publications:
- Tirualem Zeleke Yehuala
Department of Health Informatics, Institute of Public Health, College of Medicine and Health Sciences, University of Gondar, Gondar, Ethiopia
3 shared publications - Gebrie Getu Alemu
Affiliation Department of Epidemiology and Biostatistics, Institute of Public Health, College of Medicine and Health Sciences, University of Gondar, Gondar, Ethiopia
2 shared publications - Habtamu Wagnew Abuhay
Department of Epidemiology and Biostatistics, Institute of Public Health, College of Medicine and Health Sciences, University of Gondar, Gondar, Ethiopia
2 shared publications - Mekuriaw Nibret Aweke
Department of Human Nutrition, Institute of Public Health, College of Medicine and Health Sciences, University of Gondar, Gondar, Ethiopia
2 shared publications - Miteku Andualem Limenih
Department of Epidemiology and Biostatistics, Institute of Public Health, College of Medicine and Health Sciences, University of Gondar, Gondar, Ethiopia
2 shared publications - Nebebe Demis Baykemagn
Department of Health Informatics, Institute of Public Health, College of Medicine and Health Sciences, University of Gondar, Gondar, Ethiopia
2 shared publications
Latest Publications
- Open Access
- 2 September 2026
- Published by Springer Nature in Discover Artificial Intelligence
- Vol. 6 (1) , 1025
Explainable machine learning for predicting preterm birth and associated factors in Sub Saharan Africa
- 2 September 2026
- Published by Springer Nature in Discover Artificial Intelligence
- Vol. 6 (1) , 1025
Open AccessExplainable machine learning for predicting preterm birth and associated factors in Sub Saharan Africa
- Open Access
- 25 April 2026
- Published by Springer Nature in Discover Artificial Intelligence
- Vol. 6 (1)
Applying machine learning algorithms and explainable AI to predict stunting and identify determinants among children under five in Ethiopia
- 25 April 2026
- Published by Springer Nature in Discover Artificial Intelligence
- Vol. 6 (1)
Open AccessApplying machine learning algorithms and explainable AI to predict stunting and identify determinants among children under five in Ethiopia
- Open Access
- 27 February 2026
- Published by Springer Nature in Discover Applied Sciences
- Vol. 8 (5)
Energy efficient modified TDMA schedule for reducing energy consumption in Wireless Sensor Networks
Wireless Sensor Networks comprise low-cost, low-power, lightweight, and multifunctional sensor nodes capable of sensing and transmitting data from remote or hazardous environments to a central base station. These networks are widely deployed across various domains, including environmental monitoring, healthcare, and military applications. However, designing energy-efficient routing protocols remains a challenge due to limited battery capacity, constrained computational resources, absence of global addressing, and the dynamic topology of self-organizing nodes. The Low-Energy Adaptive Clustering Hierarchy (LEACH) protocol, a widely used cluster-based routing scheme, organizes nodes into clusters where cluster heads (CHs) manage intra-cluster communication using a Time Division Multiple Access (TDMA) schedule. Despite its advantages, LEACH’s TDMA mechanism becomes inefficient under unbalanced clustering, resulting in increased energy consumption at CHs. To overcome this issue, the Modified TDMA (MTDMA) approach was introduced, which adapts scheduling to the largest cluster size. However, MTDMA increases energy usage due to frequent inter-CH communication for determining cluster capacities. This study proposes the Energy-Efficient Modified Time Division Multiple Access (EEMTDMA) algorithm, where the base station centrally determines maximum cluster capacity, eliminating CH-to-CH communication and significantly reducing energy consumption. The algorithm was implemented using the Castalia 3.3 simulator in the OMNeT + + framework. Simulation results show that EEMTDMA outperforms both LEACH with MTDMA and LEACH with standard TDMA, achieving an average energy consumption of 48.4 J and delivering 4.75 packets per node to the base station. These results represent improvements of 7.9 J and 1.62 packets compared to LEACH with MTDMA, and 13.29 J and 2.33 packets compared to LEACH with TDMA.
- 27 February 2026
- Published by Springer Nature in Discover Applied Sciences
- Vol. 8 (5)
Open AccessEnergy efficient modified TDMA schedule for reducing energy consumption in Wireless Sensor Networks
Wireless Sensor Networks comprise low-cost, low-power, lightweight, and multifunctional sensor nodes capable of sensing and transmitting data from remote or hazardous environments to a central base station. These networks are widely deployed across various domains, including environmental monitoring, healthcare, and military applications. However, designing energy-efficient routing protocols remains a challenge due to limited battery capacity, constrained computational resources, absence of global addressing, and the dynamic topology of self-organizing nodes. The Low-Energy Adaptive Clustering Hierarchy (LEACH) protocol, a widely used cluster-based routing scheme, organizes nodes into clusters where cluster heads (CHs) manage intra-cluster communication using a Time Division Multiple Access (TDMA) schedule. Despite its advantages, LEACH’s TDMA mechanism becomes inefficient under unbalanced clustering, resulting in increased energy consumption at CHs. To overcome this issue, the Modified TDMA (MTDMA) approach was introduced, which adapts scheduling to the largest cluster size. However, MTDMA increases energy usage due to frequent inter-CH communication for determining cluster capacities. This study proposes the Energy-Efficient Modified Time Division Multiple Access (EEMTDMA) algorithm, where the base station centrally determines maximum cluster capacity, eliminating CH-to-CH communication and significantly reducing energy consumption. The algorithm was implemented using the Castalia 3.3 simulator in the OMNeT + + framework. Simulation results show that EEMTDMA outperforms both LEACH with MTDMA and LEACH with standard TDMA, achieving an average energy consumption of 48.4 J and delivering 4.75 packets per node to the base station. These results represent improvements of 7.9 J and 1.62 packets compared to LEACH with MTDMA, and 13.29 J and 2.33 packets compared to LEACH with TDMA.
- Open Access
- 13 February 2026
- Published by Springer Nature in Discover Artificial Intelligence
- Vol. 6 (1)
Machine learning models for predicting caesarean section delivery among women in Ethiopia
- 13 February 2026
- Published by Springer Nature in Discover Artificial Intelligence
- Vol. 6 (1)
Open AccessMachine learning models for predicting caesarean section delivery among women in Ethiopia
- 12 September 2025
- Published by Elsevier in International Emergency Nursing
- Vol. 83, 101676
Poor treatment outcome and its determinants among patients with burn injuries in Ethiopia. A systematic review and meta-analysis
- 12 September 2025
- Published by Elsevier in International Emergency Nursing
- Vol. 83, 101676
Poor treatment outcome and its determinants among patients with burn injuries in Ethiopia. A systematic review and meta-analysis
- Open Access
- 2 July 2025
- Published by Springer Nature in Discover Applied Sciences
- Vol. 7 (7) , 1-22
Energy efficient inter-cluster multi-hop communication routing protocol for wireless sensor network based on centralized energy efficient clustering routing protocol
- 2 July 2025
- Published by Springer Nature in Discover Applied Sciences
- Vol. 7 (7) , 1-22
Open AccessEnergy efficient inter-cluster multi-hop communication routing protocol for wireless sensor network based on centralized energy efficient clustering routing protocol