Dr Emmanuel Kwabena Gyasi
Profile
Dr. Gyasi's commitment to education and research is exemplary. He served as a teaching and research associate at VIT University-India, nurturing the next generation of computer scientists. His niche areas include artificial intelligence (machine learning, deep learning, neural networks, etc.), Internet of Things (IoT), computer vision and image processing, software engineering, and atmospheric physics.He has developed courses like "Computational Meteorology" and "Weather Data Analytics," emphasizing the integration of computer science techniques in meteorological research.
Dr. Emmanuel Gyasi has demonstrated exceptional academic achievement through his groundbreaking research and contributions to the field of Artificial Intelligence. His work, published in renowned peer-reviewed journals, has advanced the understanding of machine learning and neural networks.
Dr. Gyasi's dedication to excellence is reflected in his role as a lecturer and his ongoing commitment to mentoring the next generation of scholars and professionals in his field. His innovative approach and critical insights have earned him numerous accolades, solidifying his reputation as a leading expert in Artificial Intelligence and a peer reviewer for Scopus index journals such as Journal of water and Climate Change (JWC).
Academic Qualification
| # | Qualifications |
|---|---|
| 1 | Ph.D. in Computer Science and Engineering VIT University, India 2024 |
Publications
| Publications |
|---|
| Emmanuel, G. K. & Swarnalatha, P. (2022). Smart City Technology and Its Implementation: 5G as a Catalyst. In P. Swarnalatha & S. Prabu (Eds.), Blockchain Technologies for Sustainable Development in Smart Cities (pp. 1-18). IGI Global. https://doi.org/10.4018/978-1-7998-9274-8.ch001. |
| Kwabena, G. E., Ramamurthy, M., Wijethunga, A., & Swarnalatha, P. (2021). Wearable Technology as a Source of Data-Generation Tool for Artificial Intelligence. In P. Swarnalatha & S. Prabu (Eds.), Applications of Artificial Intelligence for Smart Technology (pp. 17-34). IGI Global. https://doi.org/10.4018/978-1-7998-3335-2.ch002. |
| Kwabena E., Asamoah, D., Ofori, E., & Opoku, S. (2018). The Scalability Metrics Based on Cost Effectiveness in Distributed Systems. International Journal of Applied Information Systems (IJAIS) 12(15) https://doi.org/10.5120/ijais2018451773 |
| Gyasi, E.K, Purushotham S. (2023). Advancements in Soil Classification: An In-Depth Analysis of Current Deep Learning Techniques and Emerging Trends. Air, Soil and Water Research, 16(2). https://doi:10.1177/11786221231214069 Impact Factor 3.8; Cite Score 6.1 |
| Gyasi, E. K., & Swarnalatha, P. (2023). Soil-MobiNet: A Convolutional Neural Network Model Based Soil Classification to Determine Soil Morphology and Its Geospatial Location. Sensors, https://doi.org/10.3390/s23156709 Impact factor 3.9; Cite Score 6.8 |
| Gyasi, E. K., & Swarnalatha, P. (2023). Cloud-MobiNet: An Abridged Mobile-Net Convolutional Neural Network Model for Ground-Based Cloud Classification. Atmosphere, 14(2), 280. http://dx.doi.org/10.3390/atmos14020280. Impact factor 3.11; Cite Score 4.1 |