Browsing by Author "Nonis, S"

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Open Access
Optimization of In-vitro Callus Inducti on and Cell Suspension Cultures of Gyrinops walla for Commercialization
(Faculty of Humanities and Sciences, SLIIT, 2024-12-04) Benaragama, R; Balasooriya, J; Yapa, C; Nonis, S; Athukorala, D; Kasturiarachchi, J
Gyrinops walla (G.walla), oft en referred to as ‘Walla Patt a’ in Sinhala, is an indigenous, economically important plant renowned for its producti on of agarwood, which is a highly valuable resin having high economical, religious and traditi onal values. G. walla trees take 5-7 years on average to grow naturally before being inoculated to produce resin. In-vitro callus culture approach will shorten the agarwood resin producti on process signifi cantly and be important for the industry. However, ti ssue culture methods are challenging due to explant contaminati on and low rate of callus producti on. Therefore, this study aims to opti mize the conditi ons for surface sterilizati on and enhance in-vitro callus inducti on from leaf explants, with the objecti ve of advancing the development of cell suspension cultures for commercializati on. The experiment for surface sterilizati on and callus inducti on was conducted using leaf explants obtained from two G.walla mother plants, a home garden plant and a wild plant. The results suggested that 100 mg/L silver nitrate (AgNO3) and Dett ol provide a bett er surface sterilizati on for callus producti on, especially in explants from a home garden mother plant exhibiti ng a low contaminati on rate (31%) compared to explants from wild plants (80%). Also, explants from home garden mother plant possessed bett er callus inducti on (65%) compared to explants from wild mother plants (13%). Furthermore, this study suggests that AgNO3 can be used as an alternati ve for hazardous chemicals such as mercuric chloride (HgCl2), which is commonly applied in surface sterilizati on and, introducing ground callus to suspension cultures will yield an improved callus proliferati on in suspension cultures.
Open Access
A Secure Framework for Detecting “Fake News in Social Media Networks”
(SLIIT, 2024-12) Nonis, S
In today's digital era, the proliferation of fake news poses significant challenges to societal trust, political stability, and public perception. This study develops a comprehensive framework for enhancing fake news detection, leveraging advanced machine learning techniques, privacy-preserving methods, and dynamic threat modeling. Key objectives include improving detection accuracy, ensuring user data privacy, and adapting to evolving misinformation tactics. By integrating ensemble learning methods such as Random Forests and Gradient Boosting, along with Natural Language Processing (NLP) techniques, the framework offers superior performance in identifying fake news. Additionally, privacy-preserving techniques like differential privacy and federated learning help address growing concerns over user data confidentiality. The research highlights the importance of ensuring compatibility with major social media platforms to maximize effectiveness and scalability. Comprehensive performance evaluations underscore the robustness of the proposed system. Recommendations include fostering collaboration among stakeholders, strengthening user engagement in evaluation processes, and advancing the framework's adaptability to dynamic misinformation tactics. This research contributes significantly to the ongoing fight against misinformation, promoting a more informed and resilient society through an efficient, privacy-focused detection system.