Research Papers - Dept of Computer Systems Engineering

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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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    Efficient Agricultural Sensor Network with Disease Detection
    (IEEE, 2019-12-05) Gunathilaka, M. D. N; Lokuliyana, S; Udurawana, A. W. G. C; Dissanayaka, D. M. A. S; Jayakody, A
    The smart Agriculture concept is a new trending topic in making traditional agriculture task automation to make them more effective and efficient to suit current human requirements. With machine learning and image processing technologies those tasks are made more robust and accurate while maintaining the low cost made this research inspired to adopt Sri Lankan farmers to develop a real-time disease detection monitoring system with wireless sensor node for crops, so that would be able to harvest and store energy for battery-free operation using supercapacitors and technologies such as Maximum Power Point Tracking. The main outcomes of this nodes are to monitor the growth environment and also the crop for diseases by using image processing and machine learning techniques in order to cultivate a better fruit overall. The wireless sensor node can be adapted to be used on multiple types of remote farms. Pineapple (Ananas comosus) was selected as the test crop for the research which is a fruit grown widely in tropical countries in large fields. The texture, shape of the fruit and the taste of pineapple changes due to various conditions. The final system makes monitoring the crop for diseases a lot effective while making monitoring the growth conditions more efficient compared with what's available on the market.