Machine learning-based design and monitoring of algae blooms: Recent trends and future perspectives–A short review
Abstract Machine learning (ML) models are widely used methods for analyzing data from
sensors and satellites to monitor climate change, predict natural disasters, and protect …
sensors and satellites to monitor climate change, predict natural disasters, and protect …
About Machine Learning Techniques in Water Quality Monitoring
C Saab, GP Zéhil - … on Advances in Computational Tools for …, 2023 - ieeexplore.ieee.org
Water Quality Monitoring (WQM) faces significant challenges posed by emerging
contaminants, non-point source pollutants, and climate change. The continued development …
contaminants, non-point source pollutants, and climate change. The continued development …
Prediction of Harmful Algal Blooms Severity Using Machine Learning and Deep Learning Techniques
N Karthikeyan, M Bhargav, SH krishna… - … Conference on Data …, 2023 - Springer
The prediction of harmful algal blooms is most important because harmful algal blooms are
having a high impact on marine ecosystems and human health. In this research, proposed a …
having a high impact on marine ecosystems and human health. In this research, proposed a …
Algae Recognition and Usage Prediction Using Deep Convolutional Neural Network
Algae species are essential to many environmental processes, such as the sequestration of
carbon, the creation of oxygen, and the cycling of nutrients. Precise identification and …
carbon, the creation of oxygen, and the cycling of nutrients. Precise identification and …
Active Genetic Learning with Evidential Uncertainty for Identifying Mushroom Toxicity
Mushroom's classification as edible or poisonous is an important problem that can have a
direct impact on hu-man life. However, most of the existing works do not in-clude model …
direct impact on hu-man life. However, most of the existing works do not in-clude model …