Neural-Based Cuckoo Search of Employee Health and Safety (HS)

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Koffka Khan 1,* Ashok Sahai 2

1. Department of Computing and Information Technology, The University of the West Indies St. Augustine, TRINIDAD AND TOBAGO (W.I.)

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

* Corresponding author.


Received: 1 May 2012 / Revised: 8 Sep. 2012 / Accepted: 9 Nov. 2012 / Published: 8 Jan. 2013

Index Terms

Health and Safety, Risk, Employee, Checklist, Neural-Swarm, Cuckoo Search Algorithm


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.

Cite This Paper

Koffka Khan, Ashok Sahai, "Neural-Based Cuckoo Search of Employee Health and Safety (HS)", International Journal of Intelligent Systems and Applications(IJISA), vol.5, no.2, pp.76-83, 2013. DOI:10.5815/ijisa.2013.02.09


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