• A.YUGANDHARA REDDY
• ATLURI KUSUMA NAGA SRI
• BONDALAPATI NAVYA SRI
• YANIKAPATI SAI VAISHNAVI
• VUNNAM PRASANNA

The paper will discuss a new approach to the classification of water quality for fish using transformer networks, which is the state-of-the-art deep learning architecture considered efficient for processing sequential data. The methodology involves extensive data accumulation and preprocessing of water quality data containing parameters like pH, temperatureand turbidity. In this work, time-series data is fed into a transformer network to take advantage of the mechanism of attention for the identification of important features affecting water quality. This model has shown high accuracy in the classification of water conditions after rigorous training and validation, thereby facilitating real-time monitoring and earlier warning of hostile environments for fish.

Water Quality, PH, Transformer Network, Deep learning,Real-time monitoring,Rigorous training

"WATER QUALITY CLASSIFICATION FOR FISH USING TRANSFORMER NETWORK", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 10, page no.98-105, October-2024, Available :http://www.jetir.org/papers/JETIRGN06012.pdf

"WATER QUALITY CLASSIFICATION FOR FISH USING TRANSFORMER NETWORK", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 10, page no. pp98-105, October-2024, Available at : http://www.jetir.org/papers/JETIRGN06012.pdf