Drinking water safety is a critical global issue, as pathogenic bacteria in water can cause various severe diseases, including diarrhea and systemic infections. Rapid and accurate detection of hazardous bacteria is key to ensuring water quality, especially in regions with limited access to water treatment facilities. can cause diseases such as cholera, dysentery, typhoid and polio and is expected to cause 485,000 annual diarrheal deaths. For the treatment & prevention of waterborne diseases, smart & diligent identification of pathogens is therefore very important. The proposed system combines Artificial Neural Networks (ANNs) and Recurrent Neural Networks (RNNs) to optimize the detection of harmful bacteria in drinking water. The ANN component is used to analyze spatial features from microscopic images of bacteria, allowing for accurate identification of pathogens. These spatial features, such as shape and size, help the model distinguish between different bacterial types. The RNN component processes temporal patterns, enabling the system to track changes in bacterial growth over time, which improves detection accuracy and provides insights into the progression of bacterial contamination.This hybrid model has several advantages, including the ability to detect bacteria effectively even without the use of bacterial image staining.
Keywords - Bacterial Identification, Spatial features,Temporal patterns ,Microscopic images, Bacterial growth tracking, Hybrid model , Cost-effective solution, Non-staining detection Water quality assurance Harmful bacteria detection ,ANN water monitoring.