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The identification of E. coli bacteria is critical for the prevention of health risks. According to EPA-approved gold standard methods, 24–48 h are required to count viable cells in water. Manual counting of viable bacteria colonies on agar plates is time-consuming and can be prone to human error. The method requires experts to identify and count colonies on agar plates using a microscope. Hence, the bacterial counting procedure must be automated in order to decrease error. The main objective of this study was to develop an automatic system for bacteria colony counting. A total of 1,301 groundwater samples were collected from eight districts in Rajasthan, India, for a field investigation. The results were validated using artificial intelligence (AI) methods on this experimental data set. We automated the process of E. coli bacteria identification using a convolutional neural network (CNN). We developed a smartphone application for the rapid detection of E. coli bacteria on agar plates using CNN. We also automated the process of bacteria colony counting using faster region-based convolutional neural network (R-CNN) to overcome manual cell counting process limitations. A graphical user interface (GUI) application was created to rapidly count bacteria colony–forming units on agar plates using faster R-CNN. The developed faster R-CNN model achieved an overall accuracy of 97% and an error (loss) of 0.10. The performance of the CNN and faster R-CNN models was validated using F-score, precision, sensitivity, and accuracy statistical measures. The comparative analysis showed that the faster R-CNN model is reliable and effective in E. coli cell counting. The study developed a system for identifying and counting viable cells of E. coli bacteria in water that can be used to forecast hotspots of water contamination. [ABSTRACT FROM AUTHOR]

Fluoride plays an essential role in terms of the health of human beings. Persistent exposure to fluoride, which is present in drinking water mainly, may result in dental, skeletal, and non-skeletal fluorosis. However, drinking water, being consumed presently, is not sufficient to indicate the degree of exposure to fluoride. The existing literature indicates that nails can be used as indicative (biomarkers) of not only to exposure of fluoride but also the degree of the same. However, because of differential metabolism rate depending on a number of factors like age, gender, nutritional status, water characteristics, etc., exposure to fluoride is not easily detectable in human beings by just testing the fluoride content in nails. Moreover, due to sensitive chemical analysis and lack of facilities, it is difficult to identify the exact concentration of fluoride in nails. The objective of this study is to identify the significant parameters that affect the fluoride content in nail samples. Apart from laboratories test, the application of different artificial intelligence (AI) methods are used for the prediction of fluoride in nails, which will help to identify the degree of fluoride exposure to children, females, and males. The field study covers a collection of 2401 nail samples from eight districts of Rajasthan, India. The samples were taken of different age groups and genders. 1024 water samples were also collected from the same households from where the nails samples were collected. These water and nail samples were tested in laboratories at Birla Institute of Technology and Science, Pilani, India. This data set was used to train, test, and validate the results using AI techniques. The proposed hybrid model (HM) combines Principal Component Analysis (PCA), Firefly Algorithm (FA), and Artificial Neural Network (ANN) to predict fluoride concentration in nails. The model performance was assessed by using different errors and coefficient of relationship. A comparative analysis of the proposed HM was done against ANN and PCA–ANN models. Sensitivity analysis was performed to study the importance of input parameters on the output. Results show that the PCA–FA–ANN–AFpE model having four input parameters (age, fluoride, pH, and electrical conductivity), displayed the best performance with high R2 values of 0.97. The study generates a novel methodology for predicting fluoride concentration in nails, which can be used for the prediction of hotspots in terms of continuous exposure to fluoride. [ABSTRACT FROM AUTHOR]