ITRU - 2015

Permanent URI for this collectionhttp://192.248.9.226/handle/123/14727

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Browsing ITRU - 2015 by Subject "Machine Learning, Self-Organizing Maps, Project Management"

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    Analyzing the Healthiness of an IT Project Using Self Organizing Maps
    Thilakarathne, HHTD; Wellage, CH; Rupasinghe, JAPNS; Nimantha, KC; Karunaratne, PM; Fernando, S
    This research paper discusses a solution for a problem we have identified that IT companies face when managing their projects. Project managers often find themselves in a tough situation when deciding the current status of a project and making decisions based on the evaluation. But similar situations have happened earlier in other projects and the knowledge about the measures that were taken at those situations and their effect on the success of the project can be used to evaluate similar situations in new projects. Our approach in this regard is analyzing the past data of IT projects using different machine learning techniques to identify the major factors that have affected the success of a project, understand how strongly each factor is bound to success and then training a model with the data. Where it can be used to analyze situations that arise in new projects and identify how like the current situation is to lead the project in to a success or a failure. The machine learning technique we have likely used in this study is Self- Organizing Maps (SOM) and the system was implemented using Python language