Research Papers - Dept of Computer Science

Permanent URI for this collectionhttps://rda.sliit.lk/handle/123456789/4595

Browse

Filters

20254

Advanced Search

Filter by

Settings

Search Results

Now showing 1 - 4 of 4
  • Thumbnail Image
    ItemOpen Access
    Optimizing Inotropic Infusion With Cluster Specific AI Decision Models and Digital TwinsNair, V
    (Institute of Electrical and Electronics Engineers, 2025-06-20) Nair, V. S; Niranga, G. D. H; Aryalakshmi C.S; Sathyapalan, D. T; Madathil, T; Pathinarupothi, R.K
    Inotropes are critical care medications essential for maintaining normal blood pressure (BP) in hospitalized patients. Titrating infusion rates of inotropes such as noradrenaline, vasopressin, and adrenaline based on fluctuating BP presents significant challenges in critical care settings. Typically, clinicians set a constant infusion rate for one hour, which may not accommodate the dynamic variability of BP inherent in critically ill patients, potentially leading to inadvertent hypotension or hypertension. Conventional feedback controllers, including fuzzy logic controllers (FLC), struggle to adapt to complex BP variations due to fixed algorithms and intracohort variability in drug responses. We propose an AI-enhanced closed-loop noradrenaline infusion control mechanism utilizing long short-term memory (LSTM) networks. This approach captures variability in drug responses through clustering of patients using LSTM autoencoders and K-means algorithms, subsequently developing LSTM-based decision models for infusion rates tailored to clusters. Additionally, a digital twin cardiac model serves as a simulation tool for validating the impact of inotropic infusion as indicated by the decision model. Comparative performance analyses demonstrate that our AI-enhanced closed-loop feedback method outperforms conventional systems like FLC regulators and pharmacokinetic-pharmacodynamic (PK-PD) models while ensuring patient safety as well as reducing the workload of clinicians.
  • Thumbnail Image
    ItemEmbargo
    Wet-Neuromorphic Computing: A New Paradigm for Biological Artificial Intelligence
    (Institute of Electrical and Electronics Engineers, 2025-03-31) Perera, J; Balasubramaniam, S; Somathilaka, S; Wen, Q; Li, X; Kasthurirathna, D; Roohi, A; Nelson, M. T
    As we delve into a life governed by artificial intelligence (AI), ongoing research continues to discover new forms of intelligence that are efficient and closely mimic an organism’s brain in terms of performance. This article presents a new concept termed wet-neuromorphic computing, in which biological cells or organisms are leveraged to perform computational tasks using their natural molecular functions. We map key neuromorphic properties to natural biological computing observed in bacteria, 3-D organoids, and Caenorhabditis elegans. To expand beyond the inspiration of the brain to create conventional neuromorphic computing, the study presents a case study that demonstrates bacterial AI computing using the gene regulatory neural network derived from Escherichia coli’s gene regulatory network for pattern recognition, validated through wet lab experiments. Finally, challenges and future directions are discussed.
  • Thumbnail Image
    ItemOpen Access
    Enhancing Cognitive and Metacognitive Domains of Autistic Children Using Machine Learning
    (Multidisciplinary Digital Publishing Institute (MDPI), 2025-08-21) Tharaki, D; Rupasinghe, Y; Ruhunage, P; Pehesarani, A; Rathnayake, S.C
    ASD poses special difficulty in both cognitive and metacognitive development, necessitating specialized educational strategies. This research proposes LearnMate, a web-based application powered by machine learning techniques that aims to improve the abilities of children with autism. Utilizing classification models learned from medical data, LearnMate forecasts skill acquisition and suggests personalized learning activities according to the strengths and developmental requirements of the child. The system permits instructors to monitor progress through real-time feedback, enabling adaptive learning approaches. Pilot application to more than 100 children showed significant gains in their skills. The results demonstrate the immense potential for change through machine learning in special education to facilitate data-driven, personalized learning opportunities that enhance the capabilities of both autistic students and teachers.
  • Thumbnail Image
    ItemOpen Access
    Exploring emergent topological properties in socio-economic networks through learning heterogeneity
    (2025-12-10) Karavita, C; Lyu, Z; Kasthurirathna, D; Piraveenan, M
    Understanding how individual learning behavior and structural dynamics interact is essential to modeling emergent phenomena in socio-economic networks. While bounded rationality and network adaptation have been widely studied, the role of heterogeneous learning rates–both at the agent and network levels–remains underexplored. This paper introduces a dual-learning framework that integrates individualized learning rates for agents and a rewiring rate for the network, reflecting real-world cognitive diversity and structural adaptability. Using a simulation model based on the Prisoner’s Dilemma and Quantal Response Equilibrium, we analyze how variations in these learning rates affect the emergence of large-scale network structures. Results show that lower and more homogeneously distributed learning rates promote scale-free networks, while higher or more heterogeneously distributed learning rates lead to the emergence of core-periphery topologies. Key topological metrics–including scale-free exponents, Estrada heterogeneity, and assortativity–reveal that both the speed and variability of learning critically shape system rationality and network architecture. This work provides a unified framework for examining how individual learnability and structural adaptability drive the formation of socio-economic networks with diverse topologies, offering new insights into adaptive behavior, systemic organization, and resilience.