Research Publications Authored by SLIIT Staff

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This collection includes all SLIIT staff publications presented at external conferences and published in external journals. The materials are organized by faculty to facilitate easy retrieval.

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Author: search.filters.author.Perera, A

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Now showing 1 - 10 of 21
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    Addressing Child Labour in SAARC: The Synergy of Education, Health and Economic Growth Towards SDGs
    (John Wiley and Sons, 2025-11-09) Muthugala, H; Magammana, T; Perera, A; Bandara, A; Jayathilaka, R
    Child labour remains a critical socio-economic challenge in the SAARC region, closely linked to sustainable development goals (SDGs). This study investigates the determinants of child labour by examining the roles of education, health and economic growth using a robust methodological framework. The analysis captures the non-linear country-specific relationships between these variables and child labour, employing advanced methodological approaches, including multiple polynomials, stepwise and simple polynomial regression. The findings reveal a complex interplay of factors, with each variable showing positive and negative effects on child labour in country-specific contexts. Improved access to education generally reduces child labour, but disparities in quality and affordability can have the opposite effect. Health improvements significantly lower child labour rates, yet unequal healthcare access perpetuates exploitation among vulnerable groups. Economic growth shows dual effects: it promotes adult employment and alleviates poverty, yet unregulated expansion in specific sectors can heighten the demand for child labour. This study makes a novel contribution by integrating socio-economic determinants with child labour within a regional framework, providing actionable insights while aligning with SDGs 3, 4, 8 and 8.7. Key policy recommendations include fostering regional collaboration, ensuring access to free education, enacting and enforcing new laws, improving healthcare infrastructure and promoting inclusive and sustainable economic growth. These measures align with global SDG commitments but aim to secure a brighter future for the region's children by achieving these goals by 2030.
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    Unveiling the Economic Determinants of Child Labour in Africa: A Comprehensive Study of 37 Countries
    (Springer Science and Business Media, 2025-03-10) Muthugala, H; Magammana, T; Bandara, A; Perera, A; Jayathilaka, R
    This study investigates the impact of unemployment, household income and expenditure, globalisation, and foreign direct investment (FDI) on child labour across 37 African countries from 2010 to 2021, employing panel and multiple linear regression models. The findings reveal diverse impacts: rising unemployment significantly increased child labour in countries like Ethiopia and Niger, while in Cameroon and Kenya, it had a negative effect. Globalisation’s influence varied, strongly reducing child labour in Ghana but exacerbating it in Burundi. Household income and expenditure generally reduced child labour, particularly in Ethiopia and Zambia. The effect of FDI was also mixed, decreasing child labour in Madagascar but increasing it in countries with weaker governance. These insights underscore the necessity for tailored, country-specific policies that consider local economic conditions and governance quality. Future efforts to combat child labour must focus on developing sustainable solutions that address these complex dynamics.
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    PublicationOpen Access
    Breaking the cycle: long-term socio economic determinants of child labour in SAARC countries
    (BioMed Central Ltd, 2025-11-19) Magammana, T; Muthugala, H; Bandara, A; Perera, A; Jayathilaka, R
    Background: Child labour remains a critical issue in SAARC countries, driven by various socio-economic factors. While previous studies have explored individual determinants, limited research has been conducted on their collective long-term impact. Understanding how structural and economic conditions shape child labour trends is essential for designing effective policy interventions. Methods: This study engages panel cointegration techniques to examine the long-term relationship between child labour and key socio-economic drivers in SAARC countries. It assesses the impact of education, access to healthcare, economic conditions, labour market dynamics, foreign investment, and urbanisation on the prevalence of child labour. Results: The findings confirm a stable, long-term relationship between child labour and these determinants in each SAARC country. Improvements in education and health significantly reduce child labour. However, economic growth and urbanisation have complex, country-specific effects. Higher unemployment and increased FDI may also influence child labour, emphasising the need for targeted policy responses. Conclusions: The study highlights the significance of ongoing investments in education and healthcare. Labour market reforms are crucial to mitigate the impact of unemployment, while inclusive economic policies ensure that growth benefits vulnerable populations. Targeted strategies for FDI and urbanisation are necessary to prevent unintended consequences on child labour. Combating child labour in SAARC countries requires a multi-sectoral approach. Regional collaboration is crucial for sharing best practices, developing unified strategies, and enhancing cross-border initiatives. Holistic policies integrating education, health, and economic planning are key to reducing child labour.
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    PublicationOpen Access
    Unveiling the Economic Determinants of Child Labour in Africa: A Comprehensive Study of 37 Countries
    (Springer Nature, 2025-02-28) Muthugala, H; Magammana, T; Bandara, A; Perera, A; Jayathilaka, R
    This study investigates the impact of unemployment, household income and expenditure, globalisation, and foreign direct investment (FDI) on child labour across 37 African countries from 2010 to 2021, employing panel and multiple linear regression models. The findings reveal diverse impacts: rising unemployment significantly increased child labour in countries like Ethiopia and Niger, while in Cameroon and Kenya, it had a negative effect. Globalisation’s influence varied, strongly reducing child labour in Ghana but exacerbating it in Burundi. Household income and expenditure generally reduced child labour, particularly in Ethiopia and Zambia. The effect of FDI was also mixed, decreasing child labour in Madagascar but increasing it in countries with weaker governance. These insights underscore the necessity for tailored, country-specific policies that consider local economic conditions and governance quality. Future efforts to combat child labour must focus on developing sustainable solutions that address these complex dynamics.
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    GreenEye: Smart Consulting System for Domestic Farmers
    (IEEE, 2022-12-09) Mendis, O; Perera, A; Ranasinghe, S; Chandrasiri, S
    Always it is challenging for typical domestic farmers to maintain a good homestead in today’s world and with the ever-growing economic concerns. To save time, money, and energy, they must keep up with the advancements of incorporating technology in their farming practices to ensure that their crops are up to standard and optimized for the maximum yield. Domestic farmers may grow crops for economic gain, pleasure, stress relief, decorative purposes, Etc. However, regardless of the purpose, everyone must be aware of good farming practices. No matter the intention, challenges, and outcomes, everyone engaged with plant growth is the same. In today’s highly advanced technological world, a lot of domestic farmers are using modern technology in their growing practices. Experimenting with intelligent growth mechanisms and intend to use modern technologies to provide advice that is useful for all gardeners who prefer home gardening. Additionally, the most crucial aspects of plant care are recognizing the ideal plants for each season, identifying stress factors, identifying diseases, identifying soil moisture levels, and predicting the harvest based on the current environmental conditions. Green Eye mobile application aims to provide a comprehensive solution to technologized domestic farmers using image processing technologies for their most common concerns.
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    PublicationOpen Access
    CORPORATE GOVERNANCE AND FIRM INTEGRATED PERFORMANCE: A CONCEPTUAL FRAMEWORK
    (researchgate.net, 2022-05-09) Nagalingam, N; Kumarapperuma, C; Malinga, C; Gayanthika, K; Amanda, N; Perera, A
    Though the corporate governance has been studied from the viewpoint of first, accounting and financial performance (Khatib & Nour, 2021; Goel, 2018; Mohamed, Basuony, & Badawi, 2013), next, marketing performance (El Fawal & Mawlawi, 2018), and finally, logistic and supply chain performance (Hernawati & Surya, 2019) in isolation, moreover, literature on the first is comparatively higher than on the other two, it is further argued that it has not been studied from the viewpoint of firm integrated performance. The purpose of this study, therefore, is to conceptualize the relationship between corporate governance and firm integrated performance. The study adopted a rigorous literature review in forming critical arguments for the theme studied. Accordingly, the study embraced rigorous a priori knowledge in building the arguments for hypotheses development. The study proposes a conceptual framework for the relationship between corporate governance and firm integrated performance which has the potential of facilitating efficient decision-making on corporate governance and firm integrated performance. The study concludes with a foundation for the theoretical basis of the relationship between corporate governance and firm integrated performance.
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    Social media based personalized advertisement engine
    (IEEE, 2018-02-19) De Silva, H; Jayasinghe, P; Perera, A; Pramudith, S; Kasthurirathna, D
    Online advertising has become a global phenomenon that affects the retail market substantially. Advertisements engines are an effective solution to the mobile application market to push advertisements. This paper reports evidence that AdSeeker, User Preference Based Advertisement Engine Based on Social Media is an effective solution to improve the business value of the marketing and advertising. Since the internet is used by vast number of people, it essentially needs a comprehensive method to push personalized advertisements to the right people. Adseeker is a system built using ontological mapping and social media content based semantic analysis to direct personalized. Identifying personal relationship hierarchy, and ontological approach for advertisement classification helps to identify the most appropriate advertisement for each user. AdSeeker uses the tweets posted by users to capture the preference of each and every user. Each user pushed advertisements based on their individual preferences. Based on the social experiments done using Adseeker, we could demonstrate that the social media profile based advertising is effective in providing highly relevant advertisements.
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    The Next Gen Security Operation Center
    (IEEE, 2021-04-02) Perera, A; Rathnayaka, S; Che, C; Madushanka, W. W; Senarathne, A. N
    Due to the evolving Cyber threat landscape, Cyber criminals have found new and ingenious ways of breaching defenses in networks. Due to the sheer destruction these threat actors can cause to an organization, most modern-day organizations have focused their attention towards protecting their critical infrastructure and sensitive information through multiple methods. The main defense against both internal and external threats to an organization has been the implementation of the Security Operations Center (SOC) which is responsible for monitoring, analyzing and mitigating incoming threats. At the heart of the Security Operations Center, lies the Security Information and Event Management system (SIEM) which is utilized by SOC analysts as the centralized point where all security notifications from various security technologies including firewalls, IPS/IDS and Anti-Virus logs are collected and visualized. The effective operation of SOC in an organization is dependent on how well the SIEM filters log events and generates actual alerts. Here lies the major problem faced by SOC analysts in detecting threats. If proper alert correlation is not accomplished, analysts would have to deal with too much alert noise due to a high false positive count. This would ultimately cause analysts to miss critical security incidents, thus causing severe implications to the organization's security. The performance of a SIEM can be enhanced through adding various functionalities such as Threat Hunting, Threat Intelligence and malware identification and prevention in order to reduce false positive alarms, threat framework and machine learning which would increase the accuracy and efficiency of the overall Security Operations process of an organization. Even though many products which provide these additional functionalities exist in the current market, they can be too expensive for smaller scale organizations to handle. Our aim is to make security operations deliverable to any organization regardless of the size and scale without any financial implications and enhance its functionalities with the aid of Advanced Machine Learning Techniques.
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    PublicationOpen Access
    Comparison of different Artificial Neural Network (ANN) training algorithms to predict atmospheric temperature in Tabuk, Saudi Arabia
    (researchgate.net, 2020-06) Perera, A; Azamathulla, H; Rathnayake, U
    Use of Artificial neural network (ANN) models to predict weather parameters has become important over the years. ANN models give more accurate results in weather and climate forecasting among many other methods. However, different models require different data and these data have to be handled accordingly, but carefully. In addition, most of these data are from non-linear processes and therefore, the prediction models are usually complex. Nevertheless, neural networks perform well for non-linear data and produce well acceptable results. Therefore, this study was carried out to compare different ANN models to predict the minimum atmospheric temperature and maximum atmospheric temperature in Tabuk, Saudi Arabia. ANN models were trained using eight different training algorithms. BFGS Quasi Newton (BFG), Conjugate gradient with Powell-Beale restarts (CGB), Levenberg-Marquadt (LM), Scaled Conjugate Gradient (SCG), Fletcher-Reeves update Conjugate Gradient algorithm (CGF), One Step Secant (OSS), Polak-Ribiere update Conjugate Gradient (CGP) and Resilient Back-Propagation (RP) training algorithms were fed to the climatic data in Tabuk, Saudi Arabia. The performance of the different training algorithms to train ANN models were evaluated using Mean Squared Error (MSE) and correlation coefficient (R). The evaluation shows that training algorithms BFG, LM and SCG have outperformed others while OSS training algorithm has the lowest performance in comparison to other algorithms used.
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    PublicationOpen Access
    An empirical study of students’ satisfaction with professional accounting education programs, Sri Lanka
    (researchgate.net, 2020-07-29) Nadishana, G. S. W. Y; Ameen, Z; Kulatunga, K. A; Perera, A; Perera, C; Madhavika, W. D. N; Nagendrakumar, N
    This study aims to analyze the factors affecting students' satisfaction with professional accounting courses offered by Professional Accounting Education Institutions, and then aims to assess the impact of students' satisfaction and students' loyalty towards Professional Accounting Education Institutions in Sri Lanka. It is evident that a significant gap exists between student enrolment and the rate of students’ passing out as professional accountants as per the annual reports of the Institute of Chartered Accountants of Sri Lanka and the Institute of Certified Management Accountants of Sri Lanka (2014-2018). The study adopted a deductive methodology while employing a stratified random sampling technique and distributed 500 questionnaires which had a response rate of 80%. The data was analyzed using structural equation modeling via SPSS and AMOS versions 25. The study concludes that course assessment and institutional image, teaching methods, teaching staff, course organization and infrastructure facilities, and institutional administration and efficiency significantly impact the student satisfaction. And also, it concludes that the students’ satisfaction significantly impacts students’ loyalty. This study add value to the literature by focusing the students’ satisfaction from two extreme angles (i.e., students’ need and loyalty) and introduces a new model which would enhance the appropriate administration of the Professional Accounting Education Institutions