Browsing by Author "Perera, S."

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    PublicationOpen Access
    Automated Log Parsing and Anomaly Detection Using BERT and GPT-2: A Large Language Model Approach for IT Systems
    (Department of Mathematics and Statistics, Faculty of Humanities and Sciences, SLIIT, 2025-10-10) Sathyanjana, W. W. N. C.; Gunawardhane, H. M. K. T.; Kumara Samantha, B. T. G. S.; Perera, S.
    Logs are important for diagnosing and understanding the security and operations of IT systems. In these spectacles, the sheer volume of data and their inherent complexity do not allow for an isolated approach. Issues of scalability and adaptability majorly divest most rule-based systems in log analysis. This paper proposes an automatic approach that employs state-of-the-art Large Language Models to detect anomalies, suggest parsing templates, and improve log quality. The suggested system will try to integrate and analyse an Anomaly Detection module for identifying outliers and threats to security, a Pattern Recognition Engine for identifying semantic relations, and a Log Parsing Module for deriving structured patterns. All three collectively serve to enhance efficiency, adaptability, and real-time detection of the log analysis process. Before any LLM-based processing, the results of these preprocessing steps are put through tokenization and normalization. The system was evaluated in a combination of 16 log sources with over 32,000 entries. The model attained an accuracy of 96% in lassification; this shows that it performs well in identifying complex log structures and detecting anomalies. Compared to standard approaches, the framework reduces manual processes and increases interpretability on a large scale across diverse environments of IT. The paper describes a structured approach in AI-powered log analysis, which automates essential procedures to offer improved system reliability, as well as real-time security monitoring. Further directions include real-time streaminganalysis, addressing ethical concerns in log data processing, and enhancing explain ability.
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    PublicationOpen Access
    Formulation and Sensory Evaluation of a Herbal Tea Using Pomegranate Peel
    (Department of Applied Sciences. Faculty of Humanities and Sciences,SLIIT, 2025-10-10) Perera, S.; Kasturiarachchi, J; Mathangadeera, R.
    This study evaluates the sensory attributes and physicochemical properties of pomegranate peel-based herbal tea formulations to determine the most acceptable blend. Sensory evaluation was conducted with a semi-trained panel (n=36) using a 9-point hedonic scale to assess aroma, brew colour, taste, astringency, aftertaste, and overall acceptability across five formulations. Statistical analysis using the Friedman test indicated significant differences in sensory acceptability among formulations (p < 0.001). Formulation 567, containing a blend of pomegranate peel powder (PPP), lemongrass powder (LP), ginger powder (GP), and cinnamon powder (CP), exhibited the highest acceptability. In addition, a comparison was made between herbal teas prepared using pomegranate peel powder and those made with coarse pomegranate peel to identify the differences and determine which is most preferred. The findings suggest that formulation 567 offers an optimal balance of sensory qualities, supporting its potential for commercial herbal tea development. This study contributes valuable insights into the utilisation of pomegranate peel waste in functional beverage formulations.