Ashok Sahai

Work place: Department of Mathematics & Statistics, The University of the West Indies St. Augustine, TRINIDAD AND TOBAGO (W.I.)



Research Interests: Statistics


Dr. 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 & Computer Science(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 journals of international repute & in the 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
Neural-Based Cuckoo Search of Employee Health and Safety (HS)

By Koffka Khan Ashok Sahai

DOI:, Pub. Date: 8 Jan. 2013

A study using the cuckoo search algorithm to evaluate the effects of using computer-aided workstations on employee health and safety (HS) is conducted. We collected data for HS risk on employees at their workplaces, analyzed the data and proposed corrective measures applying our methodology. It includes a checklist with nine HS dimensions: work organization, displays, input devices, furniture, work space, environment, software, health hazards and satisfaction. By the checklist, data on HS risk factors are collected. For the calculation of an HS risk index a neural-swarm cuckoo search (NSCS) algorithm has been employed. Based on the HS risk index, IHS four groups of HS risk severity are determined: low, moderate, high and extreme HS risk. By this index HS problems are allocated and corrective measures can be applied. This approach is illustrated and validated by a case study. An important advantage of the approach is its easy use and HS index methodology speedily pointing out individual employee specific HS risk.

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