  For fair academic recruitment at universities and research institutions,
determination of the right measure based on globally accepted academic quality
features is a highly delicate, challenging, but quite important problem to be
addressed. In a series of two papers, we consider the modeling part for
academic quality quantification in the first paper, in this paper, and the case
studies part in the second paper. For academic quality quantification modeling,
we develop two computational frameworks which can be used to construct a
decision-support tool: (i) an optimization-based framework and (ii) a Siamese
network (a type of artificial neural network)-based framework. The output of
both models is a single index called Academic Quality Index (AQI) which is a
measure of the overall academic quality. The data of academics from first-class
and average-class world universities, based on Times Higher Education World
University Rankings and QS World University Rankings, are assumed as the
reference data for tuning model parameters.
