http://repo.lib.jfn.ac.lk/ujrr/handle/123456789/128 2026-08-20T04:24:33Z 2026-08-20T04:24:33Z Sarveswaran, K. Ebhohimen, I.E. Karthika, K. Gopu, M. Kovalska, Y. Mayer, P. http://repo.lib.jfn.ac.lk/ujrr/handle/123456789/12883 2026-08-14T07:03:48Z 2025-01-01T00:00:00Z

Title: Publishing Journal Articles: 5 Tips for Success Authors: Sarveswaran, K.; Ebhohimen, I.E.; Karthika, K.; Gopu, M.; Kovalska, Y.; Mayer, P. Abstract: This paper provides practical guidance for early-career researchers seeking to succeed in academic publishing despite challenges related to time, pressure, and limited resources. To help scholars navigate these common obstacles, it presents five actionable tips: setting oneself up for publishing success, using effective tools to streamline the research and publishing process, improving writing skills through articles, books, and online platforms, building productive collaborations with fellow researchers, and focusing on making a meaningful impact through research. Additionally, the paper draws attention to the specific difficulties faced by researchers in countries with limited facilities and funding, emphasizing the importance of striving for international visibility and contributing high-quality research to the global academic community to achieve success in a highly competitive environment.

2025-01-01T00:00:00Z Sarveswaran, K. Ahrane, M. http://repo.lib.jfn.ac.lk/ujrr/handle/123456789/12882 2026-08-14T05:38:22Z 2025-01-01T00:00:00Z

Title: A survey and evaluation of text-to-speech systems for the Tamil language Authors: Sarveswaran, K.; Ahrane, M. Abstract: This survey provides a comprehensive review of existing Tamil Text-to-Speech (TTS) synthesis systems, synthesis approaches, evaluation approaches, and highlights state-of-the-art approaches and challenges in handling linguistic nuances. Voice-based interfaces are becoming part of life. Therefore, it is import to have an expensive TTS system which can make human experience better. Tamil, with its rich linguistic features and diagnostic nature, presents significant challenges to speech synthesis. In addition to the survey, importantly this work proposes a perceptual evaluation framework which consists of expressiveness, low listening fatigue, and overall quality, in addition to traditional intelligibility and naturalness, dimensions to evaluate better human experience. This study also uses the Comparative Mean Opinion Score (CMOS) for the subjective evaluation instead of the Mean Opinion Score. A dataset for the evaluation was also carefully prepared and six widely used Tamil TTS systems were evaluated using Word Error Rate and the subjective evaluation was done using the proposed evaluation framework with the support of 30 evaluators. The reliability of the subjective evaluation is also assessed using Krippendorff’s Alpha. The results indicate the existing systems have significant room for improvement in all perceptual dimensions. The study underscores the need for evaluation datasets and evaluation approaches that cater to subjective perceptual dimensions of speech synthesis for better human experience and lays a foundation for future research and development in Tamil and similar TTS systems.

2025-01-01T00:00:00Z Samantha Tharani, J. Arachchilage, N.A.G. http://repo.lib.jfn.ac.lk/ujrr/handle/123456789/9540 2026-06-12T09:27:41Z 2020-01-01T00:00:00Z

Title: Understanding phishers’ strategies of mimicking uniform resource locators to leverage phishing attacks: A machine learning approach Authors: Samantha Tharani, J.; Arachchilage, N.A.G. Abstract: Phishing is a type of social engineering attack with an intention to steal user data, including login credentials and credit card numbers, leading to financial losses for both organizations and individuals. It occurs when an attacker, pretending as a trusted entity, lure a victim into click on a link or attachment in an email, or in a text message. Phishing is often launched via email messages or text messages over social networks. Previous research has revealed that phishing attacks can be identified just by looking at uniform resource locator (URLs). Identifying the techniques which are used by phishers to mimic a phishing URL is rather a challenging issue. At present, we have limited knowledge and understanding of how cyber-criminals attempt to mimic URLs with the same look and feel of the legitimate ones, to entice people into clicking links. Therefore, this paper investigates the feature selection of phishing URLs (uniform resource locators), aiming to explore the strategies employed by phishers to mimic URLs that can obviously trick people into clicking links. We employed an information gain (IG) and Chi-Squared feature selection methods in machine learning (ML) on a phishing dataset. The dataset contains a total of 48 features extracted from 5000 phishing and another 5000 legitimate URL from web pages downloaded from January to May 2015 and from May to June 2017. Our results revealed that there were 10 techniques that phishers used to mimic URLs to manipulate humans into clicking links. Identifying these phishing URL manipulation techniques would certainly help to educate individuals and organizations and keep them safe from phishing attacks. In addition, the findings of this research will also help develop anti-phishing tools, framework or browser plugins for phishing prevention.

2020-01-01T00:00:00Z Manivannan, S. http://repo.lib.jfn.ac.lk/ujrr/handle/123456789/9045 2023-02-09T04:36:25Z 2022-01-01T00:00:00Z

Title: The Boost of Deep Learning: Why Now and Not Earlier? Authors: Manivannan, S.

2022-01-01T00:00:00Z