Browsing by Author "Wimalasena, L"

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    Decisionai: a framework to automate the decision making process
    (Computer Science & Engineering Society c/o Department of Computer Science and Engineering, University of Moratuwa., 2011) Buddhika, KT; Jayamanne, DP; Kohomban, U; Wimalasena, L; WIjegunawardana, PP; Gunasekera, RC; Perera, AS; Weerawardhana, S; Madusanka, A; Dilrukshi, T; Aravinda, H
    accurate decision making is the key to make a business profitable. Decision support systems are used to make the decision making process accurate and easy. Even though there are many business specific decision support systems they cannot be used for general purpose decision making or outside their domain. DecisionAI provides a framework which can be used in any decision making domain with similar decision types. DecisionAI provides a good solution to many decision making problems in the industry allowing detailed analysis of data with the integrated intelligence.
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    Prophetia: artificial intelligence for travelbox technology
    (Computer Science & Engineering Society c/o Department of Computer Science and Engineering, University of Moratuwa., 2009-07) Geethal, BBA; Gunasekara, RC; Hassan, M; Mathew, CDT; Perera, AS; Subasinghe, H; Welikala, Y; Wimalasena, L; Nanayakkara, V; Gunasinghe, UP
    In today’s fiercely competitive and dynamic market scenario, business enterprises are facing many problems due to the increasing complexity and latency of the decision making process. In addition, the amount of data to be analyzed has increased substantially. This has resulted in AI stepping in to assist decision makers to make better business decisions, reduce latency and enhance revenue opportunities. Prophetia, is a research project carried out to integrate Artificial Intelligence capabilities into TravelBox® technology - a range of solutions developed by CodeGen. IT for tour operators. This research paper discusses three main areas that were researched on for the above purpose. These are Probability Prediction - the of Feed Forward Neural Networks for calculating the probability of selling a particular vacation package, Package Recognition - the use of Self Organizing Maps for recognizing patterns in past vacation package records, and Customer Interest Prediction - the use of association rule mining for determining the influence of customer characteristics on the vacation destination.