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Purpose This paper aims to understand the relationships of complementary specialization, cognitive trust, affective trust, tie strength and similarity with group formation intention. Design/methodology/approach The data have been collected from 30 management students from a batch of 110 students of a premiere Indian business school. To assess the proposed relationship, multiple hierarchical regression was performed on collected data by using SPSS© 20. Findings The obtained results exhibited cognitive trust, affective trust and tie strength as significant predictors of dyadic group formation intention, whereas similarity and complementary specialization were not found. Originality/value The research on group formation is limited, and more particularly the functions of the above-mentioned factors on the group formation intentions of management graduates are yet to generalize. Therefore, present research is an early approach which tries to address the mentioned gap from a social network perspective and considers the group formation and social network literature.

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Over the last few years, different contaminants have posed a danger to the quality of the water. Hence modelling and forecasting water quality are very important in the management of water contamination. The paper proposes an ensemble machine learning-based model for assessing water quality. The results of the proposed model are compared with several machine learning models, including k-nearest neighbour, Naïve Bayes, support vector machine, and decision tree. The considered dataset contains seven statistically important parameters: pH, conductivity, dissolved oxygen, Biochemical Oxygen Demand, nitrate, total coliform, and fecal coliform. The water quality index is calculated for assessing water quality. To utilize an ensemble approach, a voting classifier has been designed with hard voting. The highest prediction accuracy of 99.5% of the water quality index is presented by the voting classifier as compared to the prediction accuracy of 99.2%, 90%, 79%, and 99% presented through k-nearest neighbour, Naïve Bayes, support vector machine, and decision tree, respectively. This was further enhanced to 99.74% using particle swarm based optimization.

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