Research Papers - Dept of Computer Systems Engineering

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    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, W
    Intelligent 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.
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    Towards an Efficient and Secure Blood Bank Management System
    (IEEE, 2020-12-01) Sandaruwan, P. A. J; Dolapihilla, U.D.L; Karunathilaka, D. W. N. R; Wijayaweera, W. A. D. T. L; Rankothge, W. H; Gamage, N.D.U
    A blood bank plays an important role in a hospital as well as in a country, ensuring safe and timely blood transfusions. However, there are several challenges faced by blood banks around the world, specifically when securing the blood supply chain. Reducing the supply-demand imbalance, protecting the data privacy of donors as well as receivers, are some of them. Therefore, there is a timely requirement for an effective and secure management system for the blood bank. We have proposed a management platform for the blood bank operations with the following modules: (1) forecast blood demand, (2) suggest blood donation campaign locations and (3) secure blood supply chain. The proposed platform has been implemented using techniques such as Long Short-Term Memory (LSTM), k-means clustering, Geographic Information Systems (GIS), and blockchain. Our results show that using our proposed modules, we can minimize the imbalance between supply and demand of blood, find the most suitable donor in an emergency, and enhance the privacy of data.
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    Secured, Intelligent Blood and Organ Donation Management System-“LifeShare”
    (IEEE, 2020-12-10) Wijayathilaka, P. L; Gamage, P. H. P; De Silva, K. H. B; Athukorala, A. P. P. S; Kahandawaarachchi, K. A. D. C. P; Pulasinghe, K. N
    The scarcity and exigency for blood and organs has created many discrepancies in current approaches. These have created the criteria for malpractices such as organ trafficking and black market selling. This research presents a solution with a secured-smart blood and organ donation web developed system, allowing both patients and healthcare providers to access information about the blood and organ processing records. The database would be managed using the Blockchain technology which could be only accessed by authorized users. Finally, tracking all registered donors, the proposed system generates a smart identity developed by Ethereum Smart Contract (ESC). System predicts blood demand for the future ten years using Linear Regression Model with 0.998 of high R-squared accuracy value. This reduces shortages and wastage of blood. Also, using global positioning system and K-Nearest Neighbors Machine Learning algorithm, the system finds the best matches among donors and seekers according to the nearest location. Further, the system will automatically send questionnaires for registered users to identify and evaluate their awareness and issues about organ donation. Overall, this study aims for a secured and transparent web application. Thus, it facilitates an innovative and a productive blood donation and organ transplantation process in Sri Lankan healthcare sector.
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    Smart Human Resource Management System to Maximize Productivity
    (IEEE, 2020-12-17) Hewage, H. A. S. S; Hettiarachchi, K. U; Jayarathna, K. M. J. B; Hasintha, K. P. C; Senarathne, A. N; Wijekoon, J
    Human resource is one of the most valuable assets in an organization. They are bounded to develop the unique and dynamic aspects that strengthen their competitive advantage to persist in an always changing market environment. In order to recruit a quality candidate for an organization, reducing human involvement and verifying details of the candidate is important in recruitment process. Furthermore, having an idea about how well or poor the employees perform, and how likely the employee attrition can occur is vital in human resource management process. This paper is an attempt to introduce smart human resource management system that can maximize the productivity of an organizational environment using machine learning and blockchain technologies. The end goal of this research is a smart human resource management system that reduces human judgment, time in the candidate selection process and predicts employee performance and attrition to motivate current employers to maximize productivity with minimal financial loss in the workplace environment. Skill assessment and resume classification have been done using unsupervised learning algorithms and natural language processing after extracting raw data from employee resumes using Object Character Recognition. Candidate details verification is done by comparing the hashes of the records which are stored in the blockchain. Employee performance and attrition are predicted using supervised machine learning classification techniques with high accuracy and the result of the final performance is generated as a score for each employee considering the multiple attributes that has been standardized and regulated by some specifically considered e-competence frameworks.