Faculty of Computing

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Author: search.filters.author.Wijekoon, JSubject: search.filters.subject.prediction

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    Effectiveness of artificial intelligence, decentralized and distributed systems for prediction and secure channelling for Medical Tourism
    (IEEE, 2020-11-04) Subasinghe, M; Magalage, D; Amadoru, N; Amarathunga, L; Bhanupriya, N; Wijekoon, J
    Good health and wellbeing, a sustainable development goal introduced by the United Nations to be achieved by 2030. Sri Lanka is a country that highly depends on tourism. A healthcare system which consists of high quality and low-cost services and an abundance of tourist attractions makes Sri Lanka to be one of the best medical tourism destinations. Tourism and travel have contributed to the GDP of Sri Lanka by 11.1 billion USD by 2018. Lack of technological advancements within the medical sector has drawn back the ability to smoothly cater medical tourism. The proposed system aims for an advanced technological improvement that would help in further developing and contributing to medical tourism. To this end, this paper introduces an Intelligent System for Secure Channeling platform that aids medical tourism with the help of artificial intelligence and blockchain technologies. System proposes a treatment prediction and suggesting the best doctor for it and a secured network to store and access electronic health records (EHR). The yielded results show that the proposed method successfully performed treatment prediction with 79-88% accuracy.
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    A router-based management system for prediction of network congestion
    (IEEE, 2014-03-14) Harahap, E; Wijekoon, J; Tennekoon, R; Yamaguchi, F; Ishida, S; Nishi, H
    Network Management System (NMS) plays an important role in networks to maintain the best performance of a network. It employs variety of tools, applications, and devices in order to support network administrators to monitor and maintain the stability of a network. Fault management is part where the NMS dealing with problems and failures, such as congestion, in the network. Generally, most NMSs use Simple Network Management Protocol (SNMP) to monitor and map network availability, performance, and error rates. In the existing NMS process, an SNMP agent is deployed to get information about the network condition and then send them to the administrator for taking further action on solving the problems. However, deploying such agent to the network may increase the traffic density. On the other hand, packet latency and RTT will increase as well. In this paper, we implemented a prototype of the proposing novel system that no need to deploy such agent to obtain network information. Our system analyze the streaming traffic by implementing a Service-oriented Router (SoR). Our objective is to predict a congestion in the specific link in the network through a router-based data traffic analysis using a Bayesian network model. The purpose of the prediction is to support the network administrator to notify the early warning regarding to the fault in the network as long as possible before it actually happening. By this prediction, the network administrator can immediately taking action to avoid the problems.We provided simulation experiment to demonstrate the performance of the proposed system. Our simulation results show that the proposed system can predict a congestion link caused by a particular problem, before hand it is getting congested.