Ashok Sahai

Work place: Department of Mathematics and Statistics, The University of the West Indies, St. Augustine Campus @ TRINIDAD



Research Interests: Statistics


Ashok Sahai is working presently at The University of The West Indies; St. Augustine Campus (TRINIDAD & TOBAGO) as a Professor of Statistics in the Department of Mathematics & Statistics (Faculty of Science & Agriculture) since February 2006. Dr. Sahai started his teaching-n-research career as a Lecturer in Statistics Department at Lucknow University (INDIA) in July 1966, and continued thereat till April 1980. He has, up-to-date, published more than one hundred research papers in peer-reviewed journals of international repute & in the peer-reviewed proceedings of international conferences.

He worked as Reader in Statistics and as Professor of Statistics in the Department of Mathematics at University of Roorkee (Now IIT Roorkee) during the period: April 1980- July 1995. Prof. Sahai had also worked as an Assoc. Professor of Statistics at University of Dar-Es-Salaam; TANZANIA (East Africa) during the period: July 1982- June 1984, and as a Professor of Statistics at University of Swaziland (Southern Africa) during the period: July 1993- June 2003. He worked as a Guest Scholar @ PharmacoEconomic Research Centre; University of Arizona, TUCSON during the period from July 2003 to October 2003 and as Visiting Professor @ Hyderabad; INDIA during December 2003 to January 2006 in ICFAI Tech. University, Medchel Rd.; in Aurora School of Management at Chikkadpally; and in St. Ann’s P.G. (Management) College For Women at Mallapur.

Author Articles
A Glowworm Optimization Method for the Design of Web Services

By Koffka Khan Ashok Sahai

DOI:, Pub. Date: 8 Sep. 2012

A method for adaptive usability evaluation of B2C eCommerce web services is proposed. For measuring eCommerce usability a checklist integrating eCommerce quality and usability is developed. By a Glowworm swarm optimization (GSO) neural networks-based model the usability dimensions and their checklist items are adaptively selected. A case study for usability evaluation of an eCommerce anthurium retail website is carried out. The experimental results show that GSO with neural networks supports the allocation of usability problems and the defining of relevant improvement measures. The main advantage of the approach is the adaptive selection of most significant checklist dimensions and items and thus significant reduction of the time for usability evaluation and design.

[...] Read more.
Decision-Making Using Efficient Confidence-Intervals with Meta-Analysis of Spatial Panel Data for Socioeconomic Development Project-Managers

By Ashok Sahai Clement K. Sankat Koffka Khan

DOI:, Pub. Date: 8 Aug. 2012

It is quite common to have access to geospatial (temporal/spatial) panel data generated by a set of similar data for analyses in a meta-data setup. Within this context, researchers often employ pooling methods to evaluate the efficacy of meta-data analysis. One of the simplest techniques used to combine individual-study results is the fixed-effects model, which assumes that a true-effect is equal for all studies. An alternative, and intuitively-more-appealing method, is the random-effects model. A paper was presented by the first author, and his co-authors addressing the efficient estimation problem, using this method in the aforesaid meta-data setup of the ‘Geospatial Data’ at hand, in Map World Forum meeting in 2007 at Hyderabad; INDIA. The purpose of this paper had been to address the estimation problem of the fixed-effects model and to present a simulation study of an efficient confidence-interval estimation of a mean true-effect using the panel-data and a random-effects model, too in order to establish appropriate ‘confidence interval’ estimation for being readily usable in a decision-makers’ setup. The present paper continues the same perspective, and proposes a much more efficient estimation strategy furthering the gainful use of the ‘Geospatial Panel-Data’ in the Global/Continental/ Regional/National contexts of “Socioeconomic & other Developmental Issues’. The ‘Statistical Efficient Confidence Interval Estimation Theme’ of the paper(s) has a wider ambit than its applicability in the context of ‘Socioeconomic Development’ only. This ‘Statistical Theme’ is, as such, equally gainfully applicable to any area of application in the present world-order at large inasmuch as the “Data-Mapping” in any context, for example, the issues in the topically significant area of “Global Environmental Pollution-Mitigation for Arresting the Critical phenomenon of Global Warming”. Such similar issues are tackle-able more readily, as the impactful advances in the “GIS & GPS” technologies have led to the concept of “Managing Global Village” in terms of ‘Geospatial Meta-Data’. This last fact has been seminal to special zeal-n-motivation to the authors to have worked for this improved paper containing rather a much more efficient strategy of confidence-interval estimation for decision-making team of managers for any impugned area of application.

[...] Read more.
A Comparison of BA, GA, PSO, BP and LM for Training Feed forward Neural Networks in e-Learning Context

By Koffka Khan Ashok Sahai

DOI:, Pub. Date: 8 Jun. 2012

Training neural networks is a complex task of great importance in the supervised learning field of research. We intend to show the superiority (time performance and quality of solution) of the new metaheuristic bat algorithm (BA) over other more “standard” algorithms in neural network training. In this work we tackle this problem with five algorithms, and try to over a set of results that could hopefully foster future comparisons by using a standard dataset (Proben1: selected benchmark composed of problems arising in the field of Medicine) and presentation of the results. We have selected two gradient descent algorithms: Back propagation and Levenberg-Marquardt, and three population based heuristic: Bat Algorithm, Genetic Algorithm, and Particle Swarm Optimization. Our conclusions clearly establish the advantages of the new metaheuristic bat algorithm over the other algorithms in the context of eLearning.

[...] Read more.
Swarm-Optimization-Based Affective Product Design Illustrated by a Mobile Phone Case-Study

By Koffka Khan Ashok Sahai

DOI:, Pub. Date: 8 May 2012

This paper presents a new approach of user-oriented design for transforming users’ perception into product elements design. An experimental study on mobile phones is conducted to examine how product form and product design parameters affect consumer’s perception of a product. The concept of Kansei Engineering is used to extract the experimental samples as a data base for neural networks (NNs) with particle swarm optimization (PSO) analysis. The result of numerical analysis suggests that mobile phone makers need to focus on particular design parameters to attract specific target user groups, in addition to product forms. This paper demonstrates the advantage of using KE-PSO for determining the optimal combination of product design parameters. Based on the analysis, we can use KE-PSO to suggest customers’ preferences for mobile phone design attributes that would be considered optimal by various user groups of all surveyed. They can be used for improvement and development of new future products.

[...] Read more.
Other Articles