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Publication Embargo iParking—Smart Way to Automate the Management of the Parking System for a Smart City(John Wiley & Sons, Inc., 2021-04-06) Jayakody, A; Edirisinghe, E. A. G. A; Karunanayaka, S. A. H. M; Ekanayake, E. M. C. S; Dikkumbura, H. K. T. M; Bandara, L. A. I. MDue to the highest usage of vehicles which occurs with developing economy and the improved city modernization level, traffic congestion and parking have become one of the main social problems. Despite the progress, there is still a need for an enhanced and distributed solution that can exploit data and provide an appropriate and real-time method on parking systems. This paper describes the design and implementation of a smart parking management system (SPMS) which can solve the previously mentioned parking problem. Huge traffic causes a considerable deal of damage, like the increment of accidents, the air pollution caused by a substantial amount of CO2 released by vehicles, and the unnecessary stress on drivers who must drive in often narrow and very busy roads and time wastage to find a free spot to park. Thus, to take care of the parking issue, present day advances have been made to furnish vehicle parks with smart devices that help street clients to identify the nearest available car park. In this paper, the development of the new fully automated parking system which is called iParking is presented. IParking is a design and a development of a smart parking system that will ensure the implementation of the outdoor parking system for the Smart City concept by using image processing (IP) and machine learning (ML). With connected to smart parking, a mobile app alerts the user about available spaces and guide user to the exact location with accuracy. IParking system aims to take the driver’s stress out while finding a parking space in city centers where all cars in the city are looking for parking slots in the same time.Item Embargo Bovitrack:Animal behavior monitoring using Machine learning and IoT(Institute of Electrical and Electronics Engineers Inc., 2025) Viraj, H; Wijesekara, S; Tharuka, K; Fernando, S; Jayakody, A; Wijesiri, PAnalyzing dairy cattle behavior and anomalies is a critical component of precision livestock farming, allowing farmers to remotely monitor animals for health and behavior. In order to accomplish this task better, the use of IoT technology and machine learning algorithms is more appropriate as per the time. The YOLO (you only look once) object recognition algorithm is more suitable for that, and the use of this algorithm allows these processes to be performed automatically and in real time with high accuracy. YOLO's ability to recognize multiple objects in images or videos makes Yolo ideal for cattle detection and tracking.Item Open Access Model Optimization for Personalized Health Metrics Analysis(Institute of Electrical and Electronics Engineers Inc., 2025) Perera, M; Wijesiriwardena, A; Pathirana, A; Gamaathige, L; Wijesiri, P; Jayakody, AThis paper investigates the development and application of four machine learning models designed to enhance personalized health management, specifically targeting young adults aged 15-30. The research addresses common health challenges, such as obesity and lifestyle-induced diseases, through data-driven methodologies that provide personalized meal plans, workout recommendations, and progress monitoring. The first model generates optimized personalized recommendations according to the user's health condition using Random Forest and Decision Tree algorithms. The second model utilizes an ensemble of Random Forest, LightGBM, and XGBoost, combined through a stacking technique with Linear Regression as the meta-model, to generate optimized personalized meal plans according to health condition. The third model generates optimized workout plans using Gradient Boosting and XGBoost classifiers, accounting for individual fitness objectives, body compositions, and medical conditions. A fourth model predicts goal achievement timelines by analyzing features such as caloric balance and hydration efficiency, providing users with actionable feedback using XGBoost. The integration of these AI-driven components into a scalable digital platform demonstrates the potential of machine learning in transforming health management. Future enhancements include improving model accuracy, enabling real-time feedback, and deploying the system as an accessible mobile application. ensemblePublication Open Access Smart-Camp Box: An Integrated IoT and Artificial Intelligence Framework for Safety, Communication, Learning Assistance, and Resource Optimization in Disaster Relief Camps(Sri Lanka Institute of Information Technology, 2026-05-21) Kashmira S S; Hansana G P; Deerasinghe A D S N S; Jayakody, A; Lokuliyana, SNatural disasters continue to inflict devastating consequences on communities across South Asia, with Sri Lanka ranking among the most frequently affected nations in the region. When disasters strike, temporary relief camps serve as critical shelters for displaced populations; however, existing systems fail to address three persistent operational challenges simultaneously: camp-level flood and landslide prediction, psychological and attentional readiness assessment for displaced children, and resilient communication under degraded network conditions. This paper presents the Smart Camp Box, an integrated, portable IoT-based framework that addresses all three dimensions through tightly coupled sub-systems. The first sub-system provides location-aware environmental risk monitoring using GPS/GNSS, IoT sensors, DEM integration, and a machine learning prediction model. The second introduces the Psycho-Attentional Gated Educational System (PAGES), an offline-first application for assessing and supporting displaced children's cognitive readiness. The third proposes a Semantic-Aware Adaptive Message Prioritization (SAAMP) framework for reliable MQTT communication under constrained network environments. Currently in active development, the Smart Camp Box represents a paradigm shift toward proac