Research Papers - Dept of Computer Systems Engineering
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Publication Open Access A Framework for Macroeconomic Stress Testing on Credit Risk Management in Banking Sector(researchgate.net, 2017-08) Kandawala, D. S. A; Jayakody, J. A. D. C. A- In Sri Lanka the financial System composes the leading financial institutions which are associated with increased growth of an economy and contributes to the nation’s growth as well. Hence Financial system stability has become an integral part which safeguards financial system which is able to hold off external and internal shocks. This Process implements a healthy environment for investors and encourages financial markets and institutes for the efficient and effective functioning. The maintenance of financial system stability leads to analyze and emphasize potential vulnerabilities and risks to the financial system. Among the potential vulnerabilities and risks, Credit risk can be introduced as the dominant and leading macroeconomic risk factor in many banking sectors which has been introduced as the problem towards the financial system stability. Therefore Credit risks must be managed from financial crisis to enhance the performance, sustainable growth and consistent profitability for the betterment of financial system stability. The purpose of this research project is to propose a framework to investigate the relationship between credit risk management and its impact on performance of the Sri Lankan Banking Sector.Publication Open Access Experimental study on an efficient dengue disease management system: Planning and optimizing hospital staff allocation(The Science and Information (SAI) Organization, 2018-01) Jayasuriya, M. M. C; Galappaththi, G. K. K. T; Sampath, M. A. D; Nipunika, H. N; Rankothge, WDengue has become a serious health hazard in Sri Lanka with the increasing cases and loss of human lives. It is necessary to develop an efficient dengue disease management system which could predict the dengue outbreaks, plan the countermeasures accordingly and allocate resources for the countermeasures. We have proposed a platform for Dengue disease management with following modules: (1) a prediction module to predict the dengue outbreak and (2) an optimization algorithm module to optimize hospital staff according to the predictions made on future dengue patient counts. This paper focuses on the optimization algorithm module. It has been developed based on two approaches: (1) Genetic Algorithm (GA) and (2) Iterated Local Search (ILS). We are presenting the performances of our optimization algorithm module with a comparison of the two approaches. Our results show that the GA approach is much more efficient and faster than the ILS approach.Publication Embargo Quantification of machine effectiveness in building and construction industry(IEEE, 2018-02-08) Weerasinghe, H. M; De Silva, D. HBuilding and Construction industry highly depends on different kinds of heavy machines and machinery vehicles when it comes to performing different construction tasks. Since the current industry is not heavily concerned about measuring the effectiveness of machines, a considerable amount of budget is wasted due to the misuse of machines. Hence, there should be a proper method to monitor their effectiveness and to eradicate their misuse. Effectiveness can be achieved by analyzing the matrices, Availability, Performance, and Quality. Availability measurements can be acquired by analyzing the machine's on/off times and also the idle times, and by introducing a GPS and sensor equipped tracking device, the availability of machines can be tracked. The device is capable of acquiring the geographical location and the on/off state of the machine. Performance can be acquired by analyzing the task and the details of the machine. Excavating, Digging, Scraping, Spreading, and Loading are some of the construction activities and different machines are used for different purposes. This paper reports on a reliable way of calculating the performance by using specially designed algorithms for each type of machine. The algorithm uses a data driven questionnaire for each and every machinery vehicle to evaluate the quality factor. Each question has a weightage value where questions can be rated based on the value. By analyzing and calculating the outputs of availability, performance and quality factors, the system is able to calculate the effectiveness of the machine.Publication Embargo Learning platform for visually impaired children through artificial intelligence and computer vision(IEEE, 2018-02-19) Balasuriya, B. K; Lokuhettiarachchi, N. P; Ranasinghe, A. R. M. D. N; Shiwantha, K. D. C; Jayawardena, CThe topic Visual Disabilities and Computer Vision are the most researched topics of recent years. Researchers have been trying to combine two topics to create most usable systems to the visually disabled to aid them in their day to day tasks. In this research, we are trying to create an application which is targeting children between the age of 6-14 who suffers from visual disabilities to aid them in their primary learning task of learning to identify objects without a supervision of a third-party. We are trying to achieve this task by combining latest advancements of Computer Vision and Artificial Intelligence technologies by using Deep Region Based Convolutional Networks (R-CNN), Recurrent Neural Networks (RNN) and Speech models to provide an interactive learning experience to such individuals. The paper discusses.Publication Embargo TreeSpirit: Illegal logging detection and alerting system using audio identification over an IoT network(IEEE, 2018-02-19) Kalhara, P. G; Jayasinghearachchi, V. D; Dias, A. H. A. T; Ratnayake, V. C; Jayawardena, C; Kuruwitaarachchi, NIllegal logging has been identified as a major problem in the world, which may be minimized through effective monitoring of forest covered areas. In this paper, we propose and describe the initial steps to build a new three-tier architecture for Forest Monitoring based on Wireless Sensor Network and Chainsaw Noise Identification using a Neural Network. In addition to detection of chainsaw noises, we also propose methodologies to localize the origin of the chainsaw noise.Publication Embargo Towards a Smart City: Application of Optimization for a Smart Transportation Management System(IEEE, 2018-12) Thiranjaya, C; Rushan, R; Udayanga, P; Kaushalya, U; Rankothge, WIntelligent traffic planning, the efficiency of public transport and the improved connectivity of all road users in a city, comprise the mobility characteristics of a smart city. In the era of smart cities, efficient and well managed public transportation systems play a crucial role. The planning and allocation of public transportation systems, especially the public bus scheduling is one of the major resource allocation problems where the optimal resource allocation increases the passenger's as well as bus owner's satisfaction. In this research, we have proposed a platform for public transportation management, especially for optimal planning and scheduling of buses. We have used two approaches for our algorithms: Iterated Local Search (ILS) and Genetic Algorithm (GA). In this paper, we are presenting our optimization algorithms and their performances. Our results show that, using our algorithms, we can decide the optimal allocations of buses and plan the bus schedules dynamically in the order of seconds.Publication Open Access Application Layer Challenges And Adoption Barriers to Internet Based Advanced Communication Technologies In SMEs(IEEE, 2018-10-12) Kuruwitaarachchi, NSuccessful integration of advance communication technologies with different business models are playing a significant role in business development in various industries today. This integration gives business agility other numerus benefits. Therefore, organizations are focus on adopting to well-known key technologies quickly and experience the main advantages. Out of many applications Electronic business (E-business) though Electronic commerce (E-commerce) transactions has special attention from all the industries as it has becoming one of the primary level requirements to industrial development in different domains. But when analyzing the empirical studies and similar projects, organizations are faced different incompatibility issues with stakeholders without having comprehensive e-business models. This makes various barriers for business development. In this study focus on how organizations should start moving to new advance communication technologies and how to address key technical challenges in the deployment process. As this is critical in developing countries than developed many studies required to analyze this issue and start e-business operations. This research present literature in developing countries with strong theoretical and empirical background analysis. Finally, this study suggests main identical barriers to move in to information and communication technological solutions or models and showing a research avenue to overcome those through modeling a testable framework.Publication Embargo Towards Smart Farming: Accurate Prediction of Paddy Harvest and Rice Demand(IEEE, 2019-01-31) Hashini Saranga, A. M; Weerakkody, W. A. N. D; Palliyaguru, S. T; Muthusinghe, R; Rankothge, WRice is the predominant staple food in Asian countries. It has a major impact on the social and economic development of these countries. Therefore, it is very important to keep the sustainability between paddy cultivation and consumer demand. Paddy crop yield and demand for rice of a country depend on numerous factors such as rainfall, humidity, citizen's life styles etc. Hence, the prediction of future harvest and demand is a complex process. There is a requirement for a platform that predicts on future harvest and demands based on all affecting factors. We have proposed a platform that targets the smart farming concepts for paddy, with following modules: (1) a prediction module to predict paddy harvest and (2) a prediction module to predict rice demand. We have developed the prediction modules using two machine learning algorithms: (1) Recurrent Neural Network (RNN) and (2) Long Short-Term Memory (LSTM). The performances of algorithms were evaluated using real data sets for the Sri Lankan context. Our results show that the prediction modules are giving accurate results in a short time.Publication Embargo Sustainable tourism: Application of optimization algorithms to schedule tour plans(IEEE, 2019-01-31) Perera, D; Rathnayaka, C; Siriweera, L; Dilan, S; Rankothge, WOne of the challenging problems in the tourism industry is to maintain the environmental sustainability of the tourists attracted locations while giving a better user experience for the tourists. The proposed platform for sustainable tourism management system consist with following modules: A prediction module to predict an approximate value on tourist arrival for each location, an optimization algorithm module to decide the number of tourists that can be accommodated in each location considering the environmental sustainability, and an optimal path generating module to show the best route to each location. The optimization algorithm module is developed to decide the number of tourists for each location based on two approaches: Genetic Algorithms and Iterated Local Search. Next the optimal path generating module is developed based on traveling salesman problem.In this paper, the performances of the optimization algorithm module and the optimal path generating module is presented. Results show that, using the suggestions given by the algorithms help the tourist to enjoy a better experience in travelling while ensuring the sustainability in the tourism industry.Publication Embargo On the scaling of virtualized network functions(IEEE, 2019-05-20) Rankothge, W; Ramalhinho, H; Lobo, JOffering Virtualized Network Functions (VNFs) as a service requires automation of cloud resource management to allocate cloud resources for the VNFs dynamically. Most of the existing solutions focus only on the initial resource allocation. However, the allocation of resources must adapt to dynamic traffic demands and support fast scaling mechanisms. There are three basic scaling models: vertical where re-scaling is achieved by changing the resources assigned to the VNF in the host server, horizontal where VNFs are replicated or removed to do rescaling, and migration where VNFs are moved to servers with more resources. In this paper, we present an Iterated Local Search (ILS) based framework for automation of resource reallocation that supports the three scaling models. We, then, use the framework to run experiments and compare the different scaling approaches, specifically how the optimization is affected by the scaling approach and the optimization objectives.