Browsing by Author "Liyanage, V"

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Open Access
EcoSort: An Edge-Deployable Hybrid AI-IoT Framework with Decision Fusion for Automated Waste Sorting and Real-Time Bin Monitoring
(Sri Lanka Institute of Information Technology, 2026-05-21) Liyanage, V; Seneviratne, O
Abstract—Effective waste segregation remains a major challenge in urban environments because manual sorting, isolated sensor systems, and stand-alone vision models often fail to deliver the accuracy, integration, and operational visibility required for reliable deployment. This paper presents EcoSort, an edgedeployable hybrid AI-IoT framework that combines imagebased waste classification, sensor-assisted validation, decision fusion, automated sorting, and real-time bin monitoring within a single architecture. A MobileNetV3-based classifier performs lightweight visual recognition, while complementary sensor readings provide physical cues for validating ambiguous cases. The independent outputs are merged using a priority-based decision fusion layer that produces the final class label used to trigger the sorting actuator. The system is implemented as a lowcost prototype using embedded controllers, ultrasonic sensing, servo-based actuation, and a web dashboard for live fill-level visualization. In addition to end-to-end sorting, the monitoring layer generates threshold-based collection alerts when bin capacity approaches critical levels, improving operational responsiveness. The study contributes a unified design that addresses the fragmentation seen in prior waste management solutions, where classification, segregation, and monitoring are typically treated as separate subsystems. Prototype-level evaluation and implementation observations indicate that the hybrid pipeline improves classification dependability and sorting robustness compared with single-modality operation while remaining feasible for resource-constrained edge deployment. The proposed framework therefore offers a practical foundation for scalable, data-driven, and sustainable waste management in institutions and smart-city settings.
Embargo
Foody-Smart restaurant management and ordering system
(IEEE, 2018-12-06) Liyanage, V; Ekanayake, A; Premasiri, H; Munasinghe, P; Thelijjagoda, S
Customers play a vital role in the contemporary food industry when determining the quality of the restaurant and its food. Restaurants give considerable attention to customers’ feedback about their service, since the reputation of the business depends on it. Key factors of evaluating customer satisfaction are, being able to deliver the services effectively to lessen the time of consumption, as well as maintaining a high quality of service. In most cases of selecting a prominent restaurant, customers focus on their choice of favorite food in addition to available seating and space options. Long waiting times and serving the wrong order is a common mistake that happens in every restaurant that eventually leads to customer dissatisfaction. Objectives of this online application “Foody” is to address these deficiencies and provide efficient and accurate services to the customer, by providing unique menus to each customer considering their taste. This concept is implemented as a mobile application using latest IT concepts such as Business Intelligence, Data Mining, Predictive Analysis and Artificial Intelligence. This includes graphics and 3D modeling that provide existent physical information related to food such as colors, sizes and further user can view the ingredients of the meal as well as the available tables. In addition, the app shows the real-time map to the restaurant. Current table reservation status is indicated by the color change of the table. Unique food recommendation and it’s order for each customer is generated by analyzing their social media information and the system notifies the customer the wait time by calculating it. Preparation of food and allocation is done subjectively. The expected outcome of the research is to develop a fully automated restaurant management system with the mentioned features as well as to avoid confusions between orders, provide better view of food and allow the customer to choose the menu according to their taste in a minimum time.