Reliable water quality prediction and parametric analysis using explainable AI models.

The consumption of water constitutes the physical health of most of the living species and hence management of its purity and quality is extremely essential as contaminated water has to potential to create adverse health and environmental consequences. This creates the dire necessity to measure, control and monitor the quality of water. The primary contaminant present in water is Total Dissolved Solids (TDS), which is hard to filter out. There are various substances apart from mere solids such as potassium, sodium, chlorides, lead, nitrate, cadmium, arsenic and other pollutants. The proposed work aims to provide the automation of water quality estimation through Artificial Intelligence and uses Explainable Artificial Intelligence (XAI) for the explanation of the most significant parameters contributing towards the potability of water and the estimation of the impurities. XAI has the transparency and justifiability as a white-box model since the Machine Learning (ML) model is black-box and unable to describe the reasoning behind the ML classification. The proposed work uses various ML models such as Logistic Regression, Support Vector Machine (SVM), Gaussian Naive Bayes, Decision Tree (DT) and Random Forest (RF) to classify whether the water is drinkable. The various representations of XAI such as force plot, test patch, summary plot, dependency plot and decision plot generated in SHAPELY explainer explain the significant features, prediction score, feature importance and justification behind the water quality estimation. The RF classifier is selected for the explanation and yields optimum Accuracy and F1-Score of 0.9999, with Precision and Re-call of 0.9997 and 0.998 respectively. Thus, the work is an exploratory analysis of the estimation and management of water quality with indicators associated with their significance. This work is an emerging research at present with a vision of addressing the water quality for the future as well.

Differential transcriptional expression following thidiazuron-induced callus differentiation developmental shifts in rice.

Very little is known about molecular events associated with callus differentiation in indica rice. The genes expressed differentially during shoot meristem initiation were identified on genomic arrays applied to efficiently regenerating rice calli. A thidiazuron (TDZ; N-phenyl-N-thiadiazol-1,2,3-5,ylurea)-dependent regeneration protocol was developed for efficient embryogenesis in indica rice. The regenerating embryogenic calli induced by TDZ for 10 days showed transcriptional modulation of a number of genes associated with photosynthesis, hormone metabolism, plant development, signal transduction, light response, and plant defense. Eighteen candidate miRNAs were predicted to target the genes expressed differentially in the embryogenic calli grown in TDZ-containing medium. The majority of the photosynthesis-related genes up-regulated in differentiating calli were not expressed or were down-regulated in developing seeds and inflorescences. Most of the genes down-regulated in differentiating calli were up-regulated in developing seeds. The transcriptome of differentiating callus most closely resembled that of the germinating whole seed.