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Keyphrases facilitate finding the right information in digital sources. Keyphrase assignment is the alignment of documents or text with keyphrases of any standard taxonomy/classification system. Kea++ is an automatic keyphrase assignment tool using a machine learning-based technique. However, it does not effectively exploit the hierarchical relations that exist in its input taxonomy and returns noise in its results. The refinement methodology was designed as a top layer of Kea++ in order to fine tune its results. It was an initial step and focused on a single Computing domain. It was neither validated on multiple domains nor evaluated to determine whether the improvement in the results is significant or not. The aim of this task was to solidify the refinement methodology. The main contributions of this work are (a) to extend the methodology for multiple domains and (b) to statistically verify that the improvement in the Kea++ results is significant. [ABSTRACT FROM PUBLISHER]

Water makes up about 70% of the earth's surface and is one of the most important sources vital to sustaining life. Rapid urbanization and industrialization have led to a deterioration of water quality at an alarming rate, resulting in harrowing diseases. Water quality has been conventionally estimated through expensive and time-consuming lab and statistical analyses, which render the contemporary notion of real-time monitoring moot. The alarming consequences of poor water quality necessitate an alternative method, which is quicker and inexpensive. With this motivation, this research explores a series of supervised machine learning algorithms to estimate the water quality index (WQI), which is a singular index to describe the general quality of water, and the water quality class (WQC), which is a distinctive class defined on the basis of the WQI. The proposed methodology employs four input parameters, namely, temperature, turbidity, pH and total dissolved solids. Of all the employed algorithms, gradient boosting, with a learning rate of 0.1 and polynomial regression, with a degree of 2, predict the WQI most efficiently, having a mean absolute error (MAE) of 1.9642 and 2.7273, respectively. Whereas multi-layer perceptron (MLP), with a configuration of (3, 7), classifies the WQC most efficiently, with an accuracy of 0.8507. The proposed methodology achieves reasonable accuracy using a minimal number of parameters to validate the possibility of its use in real time water quality detection systems. [ABSTRACT FROM AUTHOR]