Junli Yang  , David J. Edwards  , P.E.D. Love  , 
    Classifying Plant Operator Productivity Using Computational Science,
    Asian Journal of Information Technology,
    Volume 3,Issue 5,
    2004,
    Pages 336-346,
    ISSN 1682-3915,
    ajit.2004.336.346,
    (https://makhillpublications.co/view-article.php?doi=ajit.2004.336.346)
    Abstract: This paper presents a conceptual model with which to classify plant operator productivity using the artificial intelligent technique, neural networks (ANN). Specially, an artificial network model is proposed that uses factors such as: operator`s motivation, management role, maintenance task taken, stress and fatigue, education and training. Within these broad ‘generic` factors, a comprehensive range of variables exist. The ANN system design proposes a feed-forward multiplayer perceptron with back-propagation algorithm that will predict three levels of operator` productivity (namely high, medium and low). It is then proposed that the maths and algorithms developed be incorporated into a web-based software solution that connects databases of information, held on a server with dual connectivity capabilities, to users using Active Server Pages (ASP) programming code. Using this approach, it is anticipated that a user-friendly package will be developed that will enable the widest possible practitioner audience to access the software, anywhere on the planet, anytime of day.
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