Scopus Index Publications
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This collection consists of all Scopus-indexed publications produced by SLIIT researchers. Scopus is recognized worldwide as a leading and reputable academic indexing database.
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Publication Open Access True nature of brain drain and brain gain: A MM-QR analysis on migration, remittances and FDI(Elsevier Ltd, 2026-12) Lokuliyana, A; Perera, J; Perera, R; Marina, N; Samarakkody, T; Kankanam Pathiranage, H.SWith ever-changing global dynamics, understanding which factors promote or hinder economic growth is crucial for any country. Although many studies have examined the variables migration, remittances, and FDI, fewer have examined their collective impact on economic growth in an income-level analysis. A noticeable limitation of the few existing studies in the area is the inability of the mean-based models to address the heterogeneity in real-life distributions. The main objective of the study was to bridge the knowledge gap and address the heterogeneity concerns in mean-based models using MM-QR, meanwhile providing quantile-based evidence for economic theories such as endogenous growth theory, structural transformation theory and most importantly, brain drain theory. The study used a panel dataset of 118 countries from 1995 to 2023, categorized into 4 income levels. Results of the study supported the brain drain theory, where migration negatively affected poorer economies and hinted at the possible existence of brain gain in HICs. Continuous non-linear behavior of FDI, remittances explained the existence of developmental asymmetries and supported endogenous growth theory and structural transformation theory and reveals development threshold effects. Finally, the study underlines the importance of placing tailored policies to absorb migration, remittances and FDIs for economic growth and provides policy recommendations accordingly.Item Embargo Intelligent Water Quality Monitoring and Prediction System(Institute of Electrical and Electronics Engineers Inc., 2025) Shiraz, S; Karunasena, K; Mudelige, H; Kumarasinghe, O; Nawinna, D; Perera, JThe paper aims to develop an integrated approach to improve water treatment processes using predictive modeling and SCADA integration in order to meet the specific needs of water purification systems in Sri Lanka. The current systems utilized for this need are outdated since these systems are based on traditional technologies and do not have the means for predictions or real-time data accessibility outside the system. The proposed solution will focus on raw water quality prediction, optimization of chemical usage to bring in efficiency, sustainability, and resource management, ensuring seamless access to all the relevant data required to manage and monitor. In order to achieve this, past data provided by the Meewatura water plant in Sri Lanka, sourced from the Mahawali river, is utilized for the relevant predictions alongside of the data gathered through the SCADA system. The data is not directly accessible since the SCADA system is mainly built for monitoring, and in order to get the data, a MODBUS connection through the PLC is utilized alongside of an IOT device. In addition to the extracted data, past data that was provided by the water plant is also incorporated. The combined data set is utilized for the predictions while continuously improving itself with new data. The present study contributes to the establishment of sustainable and adaptable water treatment frameworks for a wide range of operational needs within the water plants by addressing the gaps in the existing water quality management systems and improving upon them.Item Embargo Smart Agricultural Platform for Sri Lankan Farmers with Price Prediction, Blockchain Security, and Adaptive Interfaces(Institute of Electrical and Electronics Engineers Inc., 2025) Kuruppu K.A.G.S.R.; Kandambige S.T; Perera W.H.T.H; Cooray N.T.L; Nawinna, D; Perera, JImproper management of seed demand in Sri Lanka's agricultural sector can result in market imbalances, affecting farmers' decision-making and supply chain efficiency. This research introduces an integrated system for monitoring vegetable seed demand using digital technologies. The proposed system utilizes machine learning techniques to predict vegetable prices, a blockchain network for secure transactions, and a reward-based system to encourage user engagement. It also incorporates an adaptive user interface to accommodate different levels of digital literacy, ensuring accessibility for all farmers, especially senior citizens. Furthermore, the system features an AI Chatbot powered by Langchain and Pinecone, offering domain-specific responses and real-time support for farmers. The solution aims to combine advanced technology with agricultural practices to improve seed demand forecasting, promote transparency in transactions, and ensure a more efficient supply chain. This paper presents a multi-component agricultural platform that integrates predictive analytics, blockchain-secured transactions, gamified incentives, and adaptive user interfaces to support farming decision-making. The system combines machine learning for price forecasting, dynamic reward mechanisms to drive user engagement, and personalized UI/UX optimizations tailored for diverse user groups, including senior farmers. A multilingual AI-powered chatbot enhances accessibility and real-time support, enabling a robust, transparent, and inclusive digital solution for agricultural supply chain management.Publication Open Access A Graph Pointer Network-Based Multi-Objective Deep Reinforcement Learning Algorithm for Solving the Traveling Salesman Problem(MDPI, 2023-01-13) Perera, J; Liu, S.H; Mernik, M; Črepinšek, M; Ravber, MTraveling Salesman Problems (TSPs) have been a long-lasting interesting challenge to researchers in different areas. The difficulty of such problems scales up further when multiple objectives are considered concurrently. Plenty of work in evolutionary algorithms has been introduced to solve multi-objective TSPs with promising results, and the work in deep learning and reinforcement learning has been surging. This paper introduces a multi-objective deep graph pointer network-based reinforcement learning (MODGRL) algorithm for multi-objective TSPs. The MODGRL improves an earlier multi-objective deep reinforcement learning algorithm, called DRL-MOA, by utilizing a graph pointer network to learn the graphical structures of TSPs. Such improvements allow MODGRL to be trained on a small-scale TSP, but can find optimal solutions for large scale TSPs. NSGA-II, MOEA/D and SPEA2 are selected to compare with MODGRL and DRL-MOA. Hypervolume, spread and coverage over Pareto front (CPF) quality indicators were selected to assess the algorithms’ performance. In terms of the hypervolume indicator that represents the convergence and diversity of Pareto-frontiers, MODGRL outperformed all the competitors on the three well-known benchmark problems. Such findings proved that MODGRL, with the improved graph pointer network, indeed performed better, measured by the hypervolume indicator, than DRL-MOA and the three other evolutionary algorithms. MODGRL and DRL-MOA were comparable in the leading group, measured by the spread indicator. Although MODGRL performed better than DRL-MOA, both of them were just average regarding the evenness and diversity measured by the CPF indicator. Such findings remind that different performance indicators measure Pareto-frontiers from different perspectives. Choosing a well-accepted and suitable performance indicator to one’s experimental design is very critical, and may affect the conclusions. Three evolutionary algorithms were also experimented on with extra iterations, to validate whether extra iterations affected the performance. The results show that NSGA-II and SPEA2 were greatly improved measured by the Spread and CPF indicators. Such findings raise fairness concerns on algorithm comparisons using different fixed stopping criteria for different algorithms, which appeared in the DRL-MOA work and many others. Through these lessons, we concluded that MODGRL indeed performed better than DRL-MOA in terms of hypervolumne, and we also urge researchers on fair experimental designs and comparisons, in order to derive scientifically sound conclusions.