All Outputs (2)

Optimizing energy efficiency in underwater acoustic networks through machine learning classifiers. (2024)
Presentation / Conference Contribution
STEWART, C., FOUGH, F., FOUGH, N. and PRABHU, R. 2024. Optimizing energy efficiency in underwater acoustic networks through machine learning classifiers. In Proceedings of the 31st IEEE (Institute of Electrical and Electronics Engineers) International conference on electronics, circuits, and systems (IEEE ICECS 2024), 18-20 November 2024, Nancy, France. Piscataway: IEEE [online], 10848718. Available from: https://doi.org/10.1109/ICECS61496.2024.10848718

Among many challenges in establishing an Underwater Wireless Sensor Network, is the challenge of resource constraints, battery and bandwidth being limited which renders acoustic networks limited in life and application. One identified application of... Read More about Optimizing energy efficiency in underwater acoustic networks through machine learning classifiers..

Predicting and identifying antimicrobial resistance in the marine environment using AI and machine learning. (2024)
Presentation / Conference Contribution
FOUGH, F., ZHAO, Y. and SHAH, F.M. 2024. Predicting and identifying antimicrobial resistance in the marine environment using AI and machine learning. In Proceedings of the 31st IEEE (Institute of Electrical and Electronics Engineers) International conference on electronics circuits and systems 2024 (IEEE ICECS2024), 18-20 November 2024, Nancy, France. Piscataway: IEEE [online], article 10849269. Available from: https://doi.org/10.1109/ICECS61496.2024.10849269

Antimicrobial resistance (AMR) poses a serious threat to public health and serves as a vital reservoir for resistant microorganisms. Antimicrobial resistance (AMR) is an increasingly critical public health issue that requires precise and efficient me... Read More about Predicting and identifying antimicrobial resistance in the marine environment using AI and machine learning..