Research Papers - Dept of Information Technology

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    Sinhala Part of Speech Tagger using Deep Learning Techniques
    (IEEE, 2022-12-21) Sathsarani, M.W.A.R.; Thalawaththa, T.P.A.B.; Galappaththi, N.K.; Danthanarayana, J.N.; Gamage, A
    Natural Language Processing (NLP) is a sub-field of Artificial Intelligence (AI) that consists of a collection of computational methods motivated by theory for the automated classification and reflection of human languages. The foundation for many sophisticated applications of NLP, including named entity recognition, sentiment analysis, machine translation, in-formation retrieval, and information processing, is laid by Part of Speech (POS) tagging, which is part of the lexical layer of NLP systems. In contrast to English, French, German, and other languages from the same geographical region, the development of high-accuracy, stable POS taggers for the Sinhala language is still in its early stages. Hence, Sinhala is identified as a low-resource language. The main objective of this research is to create a POS tagger for the Sinhala language to solve this issue. An innovative and novel strategy that has never been used with the Sinhala language has been designed. This approach has been suggested specifically to evaluate the possibility of enhancing the accuracy compared to other methodologies. So, deep learning algorithms have been applied in this study, which has a significant impact on improving tagger performance. First, highly accurate individual classifiers for primary POS tags were implemented, and then they were combined into one composite model. As expected, all individual classifiers and the final composite model have achieved a higher accuracy level. Thus, it demonstrates that the proposed solution using deep learning algorithms outperformed other methods, such as rule-based and stochastic, in terms of accuracy.
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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.
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    PublicationOpen Access
    Tievs: Classified Advertising Enhanced Using Machine Learning Techniques
    (IEEE, 2021-12-06) Ranawake, D; Bandaranayake, S; Jayasekara, R; Madhushani, I; Gamage, G; Kumari, S
    The scarce use of tangible periodicals led to a consistently soaring popularity of online classified advertising. Nevertheless, existing platforms retain complications. Most recommendation systems are built with conventional technologies that are less scalable, less accurate, and having high latency processes. Moreover, customers find it tiring when clarifying a reliable, precise price value for items they are trying to sell through the classified advertising system. Additionally, strict validation techniques to identify and prevent fraudulent content or images from being published in the advertising portals have been neglected. Therefore, authors have inaugurated a superior classified advertising system, Tievs, as a solution, by appraising said predicaments. It wields a flexible, process-simplifying, concurrency-induced recommendation breakthrough implemented from Universal Sentence Encoding incurred Natural Language Processing and Deep Learning routines. Furthermore, an innovative price prediction system having a supervised regression-based ensemble model forged ensuing a comparative study, having excellent accuracy in proactively predicting item prices as to cater hassles faced by customers, was satisfied. Light Gradient Boosting classifier-driven fake description analysis and a Convolution Neural Network powered figure deception recognition system were introduced, which gained prodigious precision with moral clarity in fraud detection and prevention. Hence, the proposed solution's objective of surpassing former classified advertising systems in delivering customers' necessities, using the most lucrative, time-saving, human-centric, and error-preventive approaches, was accomplished. It was affirmative by the positively responded questionnaire regulated among prospective users by the authors.
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    Coconut Disease Prediction System Using Image Processing and Deep Learning Techniques
    (Institute of Electrical and Electronics Engineers Inc., 2020-12-09) Nesarajan, D; Kunalan, L; Logeswaran, M; Kasthuriarachchi, S; Lungalage, D
    Coconut production is the most important and one of the main sources of income in the Sri Lankan economy. The recent time it has been observed that most of the coconut trees are affected by the diseases which gradually reduces the strength and production of coconut. Most of the tree leaves are affected by pest diseases and nutrient deficiency. Our main intensive is to enhance the livelihood of coconut leaves and identify the diseases at the early stage so that farmers get more benefits from coconut production. This paper proposes the detection of pest attack and nutrient deficiency in the coconut leaves and analysis of the diseases. Coconut leaves monitorization has been taken place after the use of pesticides and fertilizer with the help of the finest machine learning and image processing techniques. Rather than human experts, automatic recognition will be beneficial and the fastest approach to identify the diseases in the coconut leaves very efficiently. Thus, in this project, we developed an android mobile application to identify the pests by their food behaviors, pest diseases and the nutrition deficiencies in the coconut trees. As an initial step, all datasets for image processing technology met pre-processing steps such as converting RGB to greyscale, filtering, resizing, horizontal flip and vertical flip. After completing the above steps, the classification was performed by analyzing several algorithms in the literature review. SVM and CNN were chosen as the best and appropriate classifier with 93.54% and 93.72% of accuracy respectively. The outcome of this project will help the farmers to increase the coconut production and undoubtedly will make a revolution in the agriculture sector.