Appraisal of groundwater suitability for drinking and irrigation purposes in highly vulnerable coastal area of Bangladesh.

Groundwater contamination is a significant threat to water security in Bangladesh, especially in coastal areas influenced by salinity intrusion and geogenic pollutants. The study has collected 48 groundwater samples and analyzed 16 water quality parameters to assess the suitability of groundwater for drinking and irrigation purposes in a highly vulnerable coastal area of Bangladesh. The Canadian Council of Ministers of the Environment Water Quality Index (CCME WQI), Metal Index (MI), and Heavy Metal Pollution Index (HPI) were used to assess drinking water quality.

A novel approach for assessing fairness in deployed machine learning algorithms.

Fairness in machine learning (ML) emerges as a critical concern as AI systems increasingly influence diverse aspects of society, from healthcare decisions to legal judgments. Many studies show evidence of unfair ML outcomes. However, the current body of literature lacks a statistically validated approach that can evaluate the fairness of a deployed ML algorithm against a dataset.

Quantification of differences in resistance to gastrointestinal nematode infections in sheep using a multivariate blood parameter.

Breeding for resistance to gastrointestinal nematodes (GIN) in sheep relies largely on the use of worm egg counts (WEC) to identify animals that are able to resist infection. As an alternative to such measures of parasite load we aimed to develop a method to identify animals showing resistance to GIN infection based on the impact of the infection on blood parameters. We hypothesized that blood parameters may provide a measure of infection level with a blood-feeding parasite through perturbation of red blood cell parameters due to feeding behaviour of the parasite, and white blood cell parameters through the mounting of an immune response in the host animal.

Machine learning approach for pooled DNA sample calibration.

Background: Despite ongoing reduction in genotyping costs, genomic studies involving large numbers of species with low economic value (such as Black Tiger prawns) remain cost prohibitive. In this scenario DNA pooling is an attractive option to reduce genotyping costs. However, genotyping of pooled samples comprising DNA from many individuals is challenging due to the presence of errors that exceed the allele frequency quantisation size and therefore cannot be simply corrected by clustering techniques.

Allele frequency calibration for SNP based genotyping of DNA pools: A regression based local-global error fusion method.

Background: The costs associated with developing high density microarray technologies are prohibitive for genotyping animals when there is low economic value associated with a single animal (e.g. prawns).

Novel layered clustering-based approach for generating ensemble of classifiers.

This paper introduces a novel concept for creating an ensemble of classifiers. The concept is based on generating an ensemble of classifiers through clustering of data at multiple layers. The ensemble classifier model generates a set of alternative clustering of a dataset at different layers by randomly initializing the clustering parameters and trains a set of base classifiers on the patterns at different clusters in different layers.