Scopus Index Publications
Permanent URI for this communityhttps://rda.sliit.lk/handle/123456789/2162
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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Item Embargo Predictive Policing with Neural Networks: A Big Data Approach to Crime Forecasting in Sri Lanka(Institute of Electrical and Electronics Engineers Inc., 2025) Nauzad, H; Dayawansa, D; Dias, N.Y; Haddela, P.S; Ratnayake, SThe surge in crime rates, particularly in urban regions, has underscored the importance of predictive policing within law enforcement strategies. This research introduces a neural network-based crime prediction model, specifically tailored to address the complexities of Sri Lanka's crime landscape. By combining big data analytics with advanced machine learning methods - including ensemble models such as Random Forest and Gradient Boosting, alongside Artificial Neural Networks (ANNs) - our study presents a robust framework to forecast crime incidents, locations, and time spans. While neural networks excel in predictive accuracy, their "black-box"nature can hinder practical applications in critical fields like law enforcement. To address this, our model integrates Explainable AI (XAI), making the decision-making process of the system transparent and interpretable for end-users. XAI helps break down complex neural network predictions, ensuring trust and clarity in the model's insights. With a prediction accuracy rate of 85%, this approach demonstrates substantial potential to improve crime prevention efforts and optimize resource allocation. Our research not only highlights the predictive strengths of neural networks but also showcases the essential role of interpretability for deploying these models effectively in real-world policing.Item Embargo UrbanGreen - E-Waste Detection and Analysis using YOLOv5(Institute of Electrical and Electronics Engineers Inc., 2025) Madusanka A.R.M.S; Nawaratne D.M.R.S.; Gamage, N; Attanayaka, BE-waste has become a global concern that challenges environmental sustain ability. The disposal of electronic devices is often poorly managed, especially in urban areas. This research aims to develop an innovative e-waste management system suitable for urban areas, focusing on accurately identifying electronic devices and their harmful components through advanced image processing techniques. (Y olov5) The system identifies various electronic devices, harmful components and materials and assesses their recyclability, improper disposal's environmental and health impacts, empowering users to make informed decisions about disposal and recycling. The system will integrate tools to identify E-waste, promote the reuse of electronic devices, educate the public through interactive educational platforms, and locate nearby e-waste collection centers. By addressing these critical aspects of e-waste management, the project aims to provide a useful platform to manage e-waste effectively in urban areas. This paper was developed to discuss E-waste detection and analysis using YOLOv5 object detection model.Item Open Access Intelligent Systems for Comprehensive Dog Management(Association for Computing Machinery, 2025-06-28) Katipearachchi, M.E; Sachethana, O; Gunawardena, G. N.A; Ruwanara, D.C; Krishara, J; Kasthurirathna, DIn recent years, the integration of advanced technologies with canine welfare has gained significant attention, leading to the development of comprehensive platforms for dog management. The "Research Pooch-Paw"initiative addresses the multifaceted needs of dog owners and stray dog populations through an innovative platform that incorporates machine learning, wearable sensors, and real-time data processing. The platform facilitates early disease detection, behaviour analysis, and health monitoring using IoT-enabled devices, and provides personalized care guidance. Additionally, it includes features for stray dog identification and emergency response using deep learning algorithms and image processing techniques. The research underscores the potential of leveraging modern technology to enhance the quality of life for dogs and improve the effectiveness of canine welfare strategies.Publication Embargo Assessing the Efficacy of Machine Learning Algorithms in Predicting Critical Properties of Gold Nanoparticles for Pharmaceutical Applications(Springer Nature Link, 2025-07-08) Fernando, H; Mohottala, S; Jayanetti, M; Thambiliyagodage, CAu nanoparticles are increasingly used in pharmaceuticals, but their synthesis is costly and time-intensive. Machine Learning can help optimize this process. In this research, eight distinct Machine Learning models were implemented and optimized on a dataset comprising 3000 records of gold nanoparticles. The performance of these models was assessed using four accuracy metrics and the time required for training and inference. The results are promising, with all seven models demonstrating high accuracy and low time requirements. Notably, the XGBoost and Artificial Neural Network models exhibited exceptional performance, with Mean Squared Error values of 0.0235 and 0.0098, Mean Absolute Error values of 0.1021 and 0.0674, Mean Absolute Percentage Deviation values of 0.4945 and 0.3590, R2 scores of 0.9995 and 0.9998, and inference times of 0.0029 and 0.4299 s, respectively. The Explainable Artificial Intelligence analysis of the resulting models revealed some interesting insights into how the models make the predictions and what factors heavily contribute to the nanoparticle AVG_R, allowing chemists to optimize the synthesis for gold nanoparticles better. The key contributions of the research include the design and development of eight Machine Learning models using industry-standard frameworks, the training, tuning, and evaluation of these eight models using five different metrics, and further assessment of these trained models using Explainable Artificial Intelligence. The findings indicate a substantial potential for applying neural networks in the design phase of nanoparticle synthesis, which could lead to significant reductions in both the time and cost required for synthesizing Au nanoparticles for pharmaceutical applications.Publication Open Access Exploring nontoxic perovskite materials for perovskite solar cells using machine learning(Discover, 2025-07-06) Pabasara, W.G.A.; Wijerathne, H.A.H.M; Karunarathne, M.G.M.M.; Sandaru, D.M.C.; Abeygunawardhana, Pradeep K. W.erovskite solar cells are promising renewable energy technology that faces significant challenges due to the Pb induced toxicity. The current study addresses this issue by leveraging machine learning techniques to explore Pb-free perovskite materials that ensure environmental sustainability and human safety. A highly accurate machine learning model was developed to predict Goldschmidt factor and the band gap, aiming to discover lead-free perovskites. Extreme Gradient Boost (XGBoost), Random Forest (RF), Gradient Boost Regression (GBR), and Ada Boost Regression (ABR) models were employed for this purpose. The findings exhibit that XGBoost delivers the most precise and reliable results for Goldsmith tolerance factor prediction with an accuracy of 98.5%. Furthermore, GBR model, combined with K-nearest neighbors (KNN) model delivers an impressive accuracy of 98.7% for the band gap predictions. 49 Pb-free perovskite materials were screened out considering the toxicity and the abundance. Utilizing Principal Component Analysis (PCA) and K-means clustering, six optimal materials (KBiBr3, KZnBr3, RbBiBr 3, RbZnBr3, MAGeI3, and FAGeI3null) were identified as the potential environment-friendly materials for photovoltaic applications. These results show the crucial role of machine learning and statistical analysis in discovering nontoxic and environmental-friendly perovskite materials, advancing the development of sustainable energy solutions.