Research Papers - Dept of Information Technology

Permanent URI for this collectionhttps://rda.sliit.lk/handle/123456789/593

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    Machine Learning Based Solution for Improving the Efficiency of Sugar Production in Sri Lanka
    (IEEE, 2022-12-26) Kulasekara, S; Kumarasiri, K; Sirimanna, T; Dissanayake, D; Karunasena, A; Pemadasa, N
    Although sugar is a popularly used commodity in Sri Lanka, sugar manufactured within the country fulfill only a very small portion of the demanded amount. Sugar production is an intricate process which requires a considerable amount of expertise especially in the areas of cultivation, production and revenue prediction which may not exist in novice farmers. This research proposes a methodology which provides novice sugarcane farmers with expert knowledge on four main areas related to farming including weather forecast, sugarcane maturity estimation, production forecast and prediction of return sugarcane amounts from lands. ARIMA model is used for weather forecast whereas machine learning methods and multiple regression models were used for sugarcane maturity estimation and production of forecasts and returns respectively. The final ARIMA time series model was validated with p-value greater than 0.05 for Ljung-Box test with three different lag values. The Support Vector Machines model was identified as the best model with an accuracy of 81.19% for the sugarcane maturity estimation. The SVM model was trained using the HSV and texture features extracted from sugarcane stalk images using image processing techniques. The prediction of sugar production received a testing R-squared score of 87.75% and mean squared error of 0. Prediction of yield received a mean squared error of approximately 0 and R squared score of 98% on test data. The methodology used in this research could be used by novice farmers to increase their cultivation as well as sugar production.
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    Techniques of Enhancing Synchronization Efficiency of Distributed Real Time Operating Systems
    (IEEE, 2022-02-23) Rajapaksha, S; Alagalla, H
    Distributed Operating Systems is one of the most modernizing concepts of the world. When it comes to Distributed Networks and Operating Systems, Real-Time Operating systems are highly crucial for the developers to achieve desired Tasks. To make the concept into the working functionality, Synchronization is playing a huge role. Synchronization has experimented with many techniques by researchers. Still, there is a lack of analysis to find the most optimized and most effective technique to achieve the goal of enhancing the accuracy and efficiency of Real-Time Operating Systems (RTOS) by synchronization context. Apart from that research is analytically produces the explanation about existing synchronization platforms in the computing world including backup Synchronization, On-Chip Memory Handling, Location-Based Network Systems Configuration, and Hardware Oriented Synchronization. Especially this research focuses on the problems and challenges of the existing RTOS and Distributed RTOS (DRTOS) Systems. Further, this paper will elaborate on the best-suited solutions and techniques to follow during the development of Distributed Node Network with Multi-Core Processors. It will propose multiple techniques to enhance the efficiency by using various algorithms comprising lock-based, lock-free, semaphore based, Mutex based, and Hardware related high accuracy criteria. Also, this will be highly beneficial for the people who are interested in Internet of Thing-based Distributed Networks to build more supportive and high-performance processing systems using desired featured objectives.