Dr. Salvin Sanjesh Prasad

Dr. Salvin Sanjesh Prasad

Position: Assistant Professor
School: School of Sciences
Email: salvin.prasad@fnu.ac.fj
Campus: Derrick Campus – Samabula
Phone: 3381044 ext. 4361

Biography:

Dr. Salvin Sanjesh Prasad serves as an Assistant Professor in the School of Sciences at the College of Engineering and Technical Vocational Education and Training (CETVET), Fiji National University (FNU). With expertise in advanced artificial intelligence (AI), solar ultraviolet radiation, renewable energy, meteorology, and environmental Physics, he is deeply committed to teaching both undergraduate and postgraduate courses. Dr Salvin has made significant contributions towards research that integrates AI-driven predictive technology into the realms of health, meteorology, climatology, energy, environment, hydrology, applied engineering, image processing & computer vision, and science education. The postgraduate courses he teaches are designed to swiftly prepare students towards the aforementioned research domains.

Qualification:

  • Doctor of Philosophy (Physics integrated with Artificial Intelligence), University of Southern Queensland (USQ), Toowoomba, Queensland, Australia.
  • Master of Science in Physics, University of the South Pacific (USP) – Laucala Campus, Suva, Fiji.
  • Post Graduate Diploma in Renewable Energy, University of the South Pacific (USP) – Laucala Campus, Suva, Fiji.
  • Post Graduate Certificate in Education, University of the South Pacific (USP) – Laucala Campus, Suva, Fiji.
  • Bachelor of Science (Physics & Mathematics), University of the South Pacific (USP) – Laucala Campus, Suva, Fiji.

Area of Expertise:

  • Environmental/atmospheric Physics
  • Meteorological/climatological Physics
  • Artificial intelligence (AI) – machine learning
  • Advanced AI – deep learning
  • Data science
  • Image processing
  • Signal/data decomposition
  • Ground instrumentation & satellite data acquisition/analysis
  • Solar ultraviolet radiation and health
  • Air quality
  • Renewable energy (solar photovoltaic, hydro, biofuel, biogas and biomass)
  • Mitigating climate change
  • Science and education
  • Economic analysis and feasibility studies
  • Python programming
  • GIS with Python programming
  • R programming
  • MATLAB
  • RETScreen, HOMER softwares

Journals:

  • Ghimire, S., Deo, R. C., Hopf, K., Liu, H., Casillas-Pérez, D., Helwig, A., Prasad, S. S., Pérez-Aracil, J., Barua, P. D., & Salcedo-Sanz, S. (2025). Half-hourly electricity price prediction model with explainable-decomposition hybrid deep learning approach. Energy and AI, 100492. (Impact Factor: 9.6. Scopus Rank: Q1).
  • Ghimire, S., Deo, R. C., Jiang, N., Ahmed, A. M., Prasad, S. S., Casillas-Pérez, D., Salcedo-Sanz, S., & Yaseen, Z. M. (2025). Explainable deep learning hybrid modeling framework for total suspended particles concentrations prediction. Atmospheric Environment, 121079. (Impact Factor: 4.2, Scopus Rank: Q1).
  • Prasad, S. S., Joseph, L. P., Ghimire, S., Deo, R. C., Downs, N. J., Acharya, R., & Yaseen, Z. M. (2025). Explainable hybrid deep learning framework for enhancing multi-step solar ultraviolet-B radiation predictions. Atmospheric Environment, 343, 120951. (Impact Factor: 4.2, Scopus Rank: Q1).
  • Briceno Medina, L., Joehnk, K., Deo, R. C., Ali, M., Prasad, S. S., & Downs, N. (2024). Forecasting River Water Temperature Using Explainable Artificial Intelligence and Hybrid Machine Learning: Case Studies in Menindee Region in Australia. Water, 16(24), 3720. (Impact Factor: 3.0, Scopus Rank: Q1).
  • Ghimire, S., Abdulla, S., Joseph, L. P., Prasad, S., Murphy, A., Devi, A., Barua, P. D., Deo, R. C., Acharya, R., & Yaseen, Z. M. (2024). Explainable artificial intelligence-machine learning models to estimate overall scores in tertiary preparatory general science course. Computers and Education: Artificial Intelligence, 7, 100331. (Scopus Rank: Q1).
  • Prasad, S. S., Deo, R. C., Downs, N. J., Casillas-Pérez, D., Salcedo-Sanz, S., & Parisi, A. V. (2024). Very short-term solar ultraviolet-A radiation forecasting system with cloud cover images and a Bayesian optimized interpretable artificial intelligence model. Expert Systems with Applications, 236, 121273. (Impact Factor: 8.665. Scopus Rank: Q1).
  • Prasad, S. S., Deo, R. C., Salcedo-Sanz, S., Downs, N. J., Casillas-Pérez, D., & Parisi, A. V. (2023). Enhanced joint hybrid deep neural network explainable artificial intelligence model for 1-hr ahead solar ultraviolet index prediction. Computer Methods and Programs in Biomedicine, 241, 107737. (Impact Factor: 7.027, Scopus Rank: Q1).
  • Prasad, S. S., Deo, R. C., Downs, N., Igoe, D., Parisi, A. V., & Soar, J. (2022). Cloud affected solar UV prediction with three-phase wavelet hybrid convolutional long short-term memory network multi-step forecast system. IEEE Access, 10, 24704-24720. (Impact Factor: 3.9. Scopus Rank: Q1).
  • Prasad, S. S., Singh, A., & Prasad, S. (2020). Degummed Pongamia oil–Ethanol microemulsions as novel alternative CI engine fuels for remote Small Island Developing States: Preparation, characterization, engine performance and emissions characteristics. Renewable energy, 150, 401-411. (Impact Factor: 9.0. Scopus Rank: Q1).
  • Prasad, S. S., & Singh, A. (2020). Economic feasibility of biodiesel production from Pongamia Oil on the Island of Vanua Levu. SN Applied Sciences, 2, 1-9. (Unranked).

Book and Book Chapters:

  • Prasad, S. S. (2020). Pongamia biodiesel production potential in Vanua Levu: A full LCA of emissions reduction. Translating the Paris Agreement into Action in the Pacific, 233-256.

Conference:

  • Prasad S. S. and Singh A. (2019). The Economics of Pongamia Biodiesel Production in Vanua Levu. International Symposium on Renewable Energy, Environment and Climate Change, (ISEECC). 20th – 21st August, 2019. Tanoa International Hotel, Nadi, Fiji.
  • Prasad S. S. and Singh A. (2015). Feasibility Study for the Production of Pongamia Biodiesel on the Island of Vanua Levu. International Conference on Energy, Environment and Climate Change, (ICEECC). 8th – 9th July, 2015. Resort Le Meridien Ile Maurice, Mauritius.

Teaching interest:

  • Environmental & meteorological Physics
  • Advanced artificial Intelligence (AI) with machine learning and deep learning algorithms
  • Data science
  • Renewable energy
  • Research methods in science

Research interest:

  • Applications of advanced AI (machine learning and deep learning framework) in environmental/meteorological/climatological sciences
  • AI integration in health sector (neuroinformatics)
  • Data science
  • Data mining with GIS & satellite data
  • Computer vision
  • Natural language processing (NLP)
  • Internet of things (IoT)
  • Renewable energy
  • Emissions analysis & estimation
  • AI applications in science education