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Publication Open Access Reshaping Sri Lankan Intellectual Property Law: The Influence of Artificial Intelligence(School of Law, Faculty of Humanities and Sciences, 2025-10-10) Fonseka, SAs Artificial Intelligence (AI) systems increasingly perform autonomous creative and inventive functions, the foundational concepts of authorship and inventorship in intellectual property (IP) law are being challenged. This Article explores the complex intersection between AI and intellectual property (IP) rights. Additionally, the article examines the impact of AI on copyright and patent regulations, specifically within the context of the Sri Lankan legal framework. It critically examines the challenges surrounding authorship and ownership of AI-generated works. The central research questions address whether AI-generated works can qualify for authorship and receive copyright protection; how inventorship is determined in AI-generated inventions; whether non-human creators can be legally recognized; the adequacy of the existing IP regime in Sri Lanka; and the reforms required to build a future-ready, balanced legal framework. A doctrinal and comparative approach was employed to analyse gaps in statutes and trends in judicial decisions across various jurisdictions. Therefore, the study highlights legal and ethical concerns arising from AI-driven innovation by analysing relevant case law, international legal instruments, and evolving policy frameworks. The results indicate an urgent requirement to revise definitions related to authorship, originality, and inventive step to incorporate contributions from non-human entities. The study concludes by suggesting a legal framework that is prepared for the future, aiming to harmonize innovation, ethical considerations, and the rights of human creators in the era of digital advancement. The paper ultimately proposes strategies to balance protecting intellectual property and fostering innovation in the era of artificial intelligence.Publication Open Access Challenges and Frontiers in Intellectual Property Rights Amidst the Rise of Artificial Intelligence(Faculty of Humanities and Sciences, SLIIT, 2024-05-25) Mahingoda, C.DThis article investigates the impact of artificial intelligence (AI) on intellectual property (IP) rights, addressing challenges in ownership and authorship of AI-generated creations while exploring legal and ethical dilemmas in traditional IP domains. It offers strategies for navigating these complexities, drawing on legal precedents, international agreements, and policy recommendations. The research emphasizes the urgent need for legislative updates to address these challenges effectively. Recommendations include the enactment of innovative constitutional provisions, updating IP legislation to encompass AI-related issues comprehensively, and advocating for effective judicial intervention. By implementing these strategies, Sri Lanka can foster a harmonious coexistence of AI and IP, ensuring the protection of intellectual property rights while stimulating innovation in the AI era.Publication Open Access Eco-friendly mix design of slag-ash-based geopolymer concrete using explainable deep learning(Elsevier, 2024-09) Ranasinghe, R.S.S.; Kulasooriya, W.K.V.J.B; Perera, U.S; Ekanayake, I.U.; Meddage, D.P.P.; Mohotti, D; Rathanayake, UGeopolymer concrete is a sustainable and eco-friendly substitute for traditional OPC (Ordinary Portland Cement) based concrete, as it reduces greenhouse gas emissions. With various supplementary cementitious materials, the compressive strength of geopolymer concrete should be accurately predicted. Recent studies have applied deep learning techniques to predict the compressive strength of geopolymer concrete yet its hidden decision-making criteria diminish the end-users’ trust in predictions. To bridge this gap, the authors first developed three deep learning models: an artificial neural network (ANN), a deep neural network (DNN), and a 1D convolution neural network (CNN) to predict the compressive strength of slag ash-based geopolymer concrete. The performance indices for accuracy revealed that the DNN model outperforms the other two models. Subsequently, Shapley additive explanations (SHAP) were used to explain the best-performed deep learning model, DNN, and its compressive strength predictions. SHAP exhibited how the importance of each feature and its relationship contributes to the compressive strength prediction of the DNN model. Finally, the authors developed a novel DNN-based open-source software interface to predict the mix design proportions for a given target compressive strength (using inverse modeling technique) for slag ash-based geopolymer concrete. Additionally, the software calculates the Global Warming Potential (kg CO2 equivalent) for each mix design to select the mix designs with low greenhouse emissions.Publication Open Access A comprehensive review to evaluate the synergy of intelligent food packaging with modern food technology and artificial intelligence field(Springer link, 2024-07-22) Abekoon A; Sajindra, H; Samarakoon, E. R. J.; Jayakody, J.A.D.C.A; Kantamaneni, K; Rathnayake, U; Buthpitiya, B. L. S. K.This study reviews recent advancements in food science and technology, analyzing their impact on the development of intelligent food packaging within the complex food supply chain. Modern food technology has brought about intelligent food packaging, which includes sensors, indicators, data carriers, and artificial intelligence. This innovative packaging helps monitor food quality and safety. These innovations collectively aim to establish an unbroken chain of food safety, freshness, and traceability, from production to consumption. This research explores the components and technologies of intelligent food packaging, focusing on key indicators like time–temperature indicators, gas indicators, freshness indicators, and pathogen indicators to ensure optimal product quality. It further incorporates various types of sensors, including gas sensors, chemical sensors, biosensors, printed electronics, and electronic noses. It integrates data carriers such as barcodes and radio-frequency identification to enhance the complexity and functionality of this system. The review emphasizes the growing influence of artificial intelligence. It looks at new advances in artificial intelligence that are driving the development of intelligent packaging, making it better at preserving food freshness and quality. This review explores how modern food technologies, especially artificial intelligence integration, are revolutionizing intelligent packaging for food safety, quality, reduced waste, and enhanced traceability.Publication Open Access Eco-friendly mix design of slag-ash-based geopolymer concrete using explainable deep learning(Elsevier, 2024-09) Ranasinghe, R.S.S.; Kulasooriya, W.K.V.J.B.; Perera, U S; Ekanayake, I.U.; Meddage, D.P.P.; Mohotti, D; Rathanayake, UGeopolymer concrete is a sustainable and eco-friendly substitute for traditional OPC (Ordinary Portland Cement) based concrete, as it reduces greenhouse gas emissions. With various supplementary cementitious materials, the compressive strength of geopolymer concrete should be accurately predicted. Recent studies have applied deep learning techniques to predict the compressive strength of geopolymer concrete yet its hidden decision-making criteria diminish the end-users’ trust in predictions. To bridge this gap, the authors first developed three deep learning models: an artificial neural network (ANN), a deep neural network (DNN), and a 1D convolution neural network (CNN) to predict the compressive strength of slag ash-based geopolymer concrete. The performance indices for accuracy revealed that the DNN model outperforms the other two models. Subsequently, Shapley additive explanations (SHAP) were used to explain the best-performed deep learning model, DNN, and its compressive strength predictions. SHAP exhibited how the importance of each feature and its relationship contributes to the compressive strength prediction of the DNN model. Finally, the authors developed a novel DNN-based open-source software interface to predict the mix design proportions for a given target compressive strength (using inverse modeling technique) for slag ash-based geopolymer concrete. Additionally, the software calculates the Global Warming Potential (kg CO2 equivalent) for each mix design to select the mix designs with low greenhouse emissions.Publication Open Access Intellectual Property Rights in the Era of Artificial Intelligence: Navigating the Challenges and Expanding the Boundaries(Faculty of Humanities and Sciences, SLIIT, 2023-11-01) Mahingoda, C.BThis article delves into the tangled web of artificial intelligence (AI) and intellectual property (IP) rights. It investigates the issues raised by AIgenerated works such as machine-generated art, music, and literature, as well as the issues of ownership and authorship in these cases. The essay also examines the influence of AI on conventional intellectual property domains such as patents, copyrights, and trademarks, as well as the legal and ethical consequences of AI-driven innovation. This article provides ways for balancing IP protection and supporting innovation in the AI age by studying case law, international treaties, and developing policy.Publication Embargo Calculating a health index for power transformers using a subsystem-based GRNN approach(IEEE, 2017-11-07) Islam, M; Lee, G; Hettiwatte, S. N; Williams, KA power transformer is one of the most crucial items of equipment in the electricity supply chain. The reliability of this valuable asset is strongly dependent on the condition of its subsystems such as insulation, core, windings, bushings and tap changer. Integration of various measured parameters of these subsystems makes it possible to evaluate the overall health condition of an in-service transformer. This paper develops an artificially intelligent algorithm based on multiple general regression neural networks to combine the operating condition of various subsystems of a transformer to form a quantitative health index. The model is developed using a training set derived from four conditional boundaries based on IEEE standards, the literature and the knowledge of transformer experts. Performance of the proposed method is compared with expert classifications using a database of 345 power transformers. This shows that the proposed method is reliable and effective for condition assessment and is sensitive to poor condition of any single subsystem.Publication Open Access Artificial intelligence based smart building automation controller for energy efficiency improvements in existing buildings(2015-08-01) Basnayake, B. A. D.J.C.K; Amarasinghe, Y.W.R; Attalage, R. A; Udayanga, T.D.I; Jayasekara, A.G.B.PThis paper presents the design and implementation details of an Artificial Intelligent based smart building automation controller (AIBSBAC). It has the capability to perform intelligently adaptive to user preferences, which are focused on improved user comfort, safety and enhanced energy performance. The design of AIBSBAC consists of subsystems of smart user identification, internal and external environment observation subsystems, an artificial intelligent decision making subsystem and also a universal infrared communication system. Furthermore, the design architecture of AIBSBAC facilitates quick install flexible plug and play concept for most of the residential and buildings automation applications without a barrier to infrastructure modifications in installation.