SLIIT Conference and Symposium Proceedings
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All SLIIT faculties annually conduct international conferences and symposiums. Publications from these events are included in this collection.
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Publication Embargo VirtualPT: Virtual Reality based Home Care Physiotherapy Rehabilitation for Elderly(2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Heiyanthuduwa, T.A.; Amarapala, K.W.N.U.; Gunathilaka, K.D.V.B.; Ravindu, K.S.; Wickramarathne, J.; Kasthurirathna, D.This paper describes the development of Personal computer based Virtual Reality home-care Physiotherapy system aimed for rehabilitating full body function in elders. VirtualPT is a true virtual reality platform where the environment is completely replaced by a virtual reality platform based on the mental condition of the person at the time. While doing the home-based prescribed physiotherapy exercises, the key health metrics are continuously monitored and tracked by combining the immersive Virtual Reality with the wearable VirtualPT Sensor kit. Virtual Reality combined with 3D motion capture lets real time movements to be accurately translated onto the virtual reality avatar that can be viewed in a virtual environment to assist physiotherapist to add exercises to the system easily. This ultimate virtual reality Physiotherapy assistant avatar is used to provide guidance to elders at home, to demonstrate and assist elders in adhering to the prescribed exercises. As a significant aspect of social interactions, mirroring of movements has been added to focus on whether the elder is able to accurately follow the movements of avatar. Furthermore, the insightful dashboard offers the elders and physiotherapists an interactive platform through virtual reality capabilities. VirtualPT physiotherapy system is cost effective and makes recovery and more convenient to elders at home while the participatory and immersive nature of Virtual Reality offers a unique realistic quality that is not generally existing in clinical-based physiotherapy. When looking at the broader concept of VirtualPT; continuity of care, integration of services, quality of life and access are equally important criteria which add more value.Publication Embargo Real-Time Decision Optimization Platform for Airline Operations(2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Weerasinghe, P.S.R.; Ranasinghe, R.A.M.D.K.; Mahanthe, M.M.V.R.B.; Samarakoon, P.G.C.B.; Rankothge, W.H.; Kasthurirathna, D.With close to 4 billion origin-destination passenger journeys worldwide, airline operations have become a crucial factor in the global economy. With the increasing number of journeys and passengers, managing the daily operations of airlines have become a complicated task. We have proposed a real-time decision optimization platform for airline operations with the following subsystems: (1) determine the optimum path for a flight, (2) optimum fleet assignment, (3) optimum gate allocation, (4) optimum crew allocation. We have used an approximation (heuristics) based optimization approach: Genetic Programming (GP) to implement the modules. The results of our proposed platform illustrate that, the decision-making process of Airline Operations Control Center (AOCC) can be optimized, and dynamic change requirements can be accommodated.Publication Embargo Predictive Analytics Platform for Airline Industry(2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Tissera, P. H. K.; llwana, A.N.M.R.S.P.; Waduge, K.T.; Perera, M.A.l.; Nawinna, D.P.; Kasthurirathna, D.The research is to develop accurate demand forecasting model to control the availability in Airline industry. The primary outcome of the model is that the Airline organization can maximize the revenue by controlling the availability. The product in airline industry is the seat, which is an expensive, unstock able product. The demand for the seats is almost uncertain, the capacity is constraint and difficult to increase and the variable costs are very high. Hence the priority of the expected demand forecast is very high for airline industry. An accurate mechanism to predict the revenue for future months of ODs (Origin destinations) is done using fare and passenger data. The revenue is derived by the number of passengers and the fares they pay which vary for each flight. Airline travel is very susceptible to the social, political and economic changes. Therefore, passenger buying patterns change quite dynamically. Hence, it is challenging to develop an accurate method to project the revenue for each route. To overcome this, we are going to use semi-supervised learning mechanism. We have the current ticketed revenue plus we have the current booked passengers. We also have the ticketed passenger details of previous flights. Hence most of the information is available, however changing market conditions is an unknown variable which can have a significant impact on passenger travel patterns. Through this research We are going to design and develop the best fit model to forecast flight OD level passenger demand based on the historical data.Publication Embargo Domain Specific Conversational Intelligence: Voice Based E-Channeling System(2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Weerathunga, W.A.H.; Lokugamage, G.N.; Hariharan, V.; Yahampath, A.D.N.H.; Kasthurirathna, D.In this research the application of Automatic Speech Recognition, Natural Language Understanding, Neural Networks and Text To Speech Conversion is investigated to create a domain specific end to end voice based E-Channeling system. The novel idea in this research can be extended to any other domain(e.g.: Taxi Application) and build a conversational intelligence system. This system enables the user to avoid the shortcomings in the traditional doctor appointment channeling procedures. The system also have the ability to predict the doctor specialization according to the symptoms of the patient and can give emergency health tips by using the powerful Neural Network module. Domain-specific speech recognition model is created according to Sri Lankan accents and handles the context-specific to this domain(94% accuracy). Extracting the entities, handling e-channeling functions and selecting the most suitable API is done by the RASA backend. Neural Network will give the first aid and doctor specialization recommendations according to user input with a validation accuracy of 90%. Speech synthesis model will output the response in user preferred language(Sinhala, English or Tamil).Publication Embargo A Network Science-Based Approach for an Optimal Microservice Governance(2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Siriwardhana, G.S.; De Silva, N.; Jayasinghe, L.S.; Vithanage, L.; Kasthurirathna, D.In today's world of software application development, Kubernetes has emerged as one of the most effective microservice deployment technologies presently available due to its exceptional ability to deploy and orchestrate containerized microservices. However, a common issue faced in such orchestration technologies is the employment of vast arrays of disjoint monitoring solutions that fail to portray a holistic perspective on the state of microservice deployments, which consequently inhibit the creation of more optimized deployment policies. In response to this issue, this publication proposes the use of a network science-based approach to facilitate the creation of a microservice governance model that incorporates the use of dependency analysis, load prediction, centrality analysis, and resilience evaluation to effectively construct a more holistic perspective on a given microservice deployment. Furthermore, through analysis of the factors mentioned above, the research conducted, then proceeds to create an optimized deployment strategy for the deployment with the aid of a developed optimization algorithm. Analysis of results revealed the developed governance model aided through the utilization of the developed optimization algorithm proposed in this publication, proved to be quite effective in the generation of optimized microservice deployment policies.Publication Embargo Guided Vision: A High Efficient And Low Latent Mobile App For Visually Impaired(2021 3rd International Conference on Advancements in Computing (ICAC), SLIIT, 2021-12-09) Rizan, T.; Siriwardena, V.; Raleen, M.; Perera, L.; Kasthurirathna, D.This paper presents a novel solution for visually impaired individuals. A mobile app is connected to an ESP32CAM and a remote server to help visually impaired individuals to navigate around their environment safely. A deep learning model is deployed in the mobile app to detect obstacles in real-time without connecting to the internet. Other tasks such as reading texts, recognizing people, and describing objects are done in the remote server. We managed to connect the mobile app to the ESP32CAM and the remote server simultaneously. This was possible because the ESP32CAM is connected to the mobile app through Bluetooth. This gave the mobile the ability to connect to the remote server via the internet. To the best of our knowledge, no research has been done using Bluetooth to stream images to do object detection in a mobile app locally. Hence, our solution can detect obstacles locally and do other tasks mentioned previously in the remote server. This paper discusses how the ESP32CAM, obstacle detection module, face recognition module, text reading module, and object description module was implemented such that a low latent and highly efficient mobile app is created using minimal resources.Publication Embargo E-Learn Detector: Smart Behaviour MonitoringSystem to Analyze Student Behaviours DuringOnline Educational Activities(2021 3rd International Conference on Advancements in Computing (ICAC), SLIIT, 2021-12-09) Bamunuge, H.K.T.; Perera, H.M.; Kumarage, S.; Savindri, P.A.P.; Kasthurirathna, D.; Kugathasan, A.With the rise of online education more attention is being paid to the deficiencies in online learning platforms. Online Learning environments aim to deliver efficacious instructions, but rarely take providing a conventional classroom experience to the students into consideration. Efficient detection of students’ learning situations can provide information to teachers to help them identify students having trouble in real-time. This idea has been exploited several times for Intelligent Tutoring Systems, but not yet in other types of learning environments that are less structured. “E-Learn Detector is a web application solution to these existing issues in online learning which consists of unique features such as verifying the user during logging procedure and throughout an examination, detecting suspicious behaviors and presence of multiple users during online examinations and detecting low engagement levels of students during online lectures. “E-Learn Detector” is developed with the aim to provide guidance to students to improve their academic performance and behavior during classroom activities and to induce the best out of the educational activities.Publication Embargo Dynamic User Interface Personalization Based on Deep Reinforcement Learning(2021-12-09) Silva, K. G. G. H.; Abeyasekare, W. A. P. S.; Dasanayake, D.M. H. E.; Nandisena, T. B.; Kasthurirathna, D.; Kugathasan, A.Personalization is one of the most sought out and popular methods for brand recognition and consumer attraction. The usage of deep reinforcement learning due to its’ ability to learn actions the way humans learn from experience, if utilized and evaluated properly it can result in a revolutionary effect on personalization. The methodology proposed in this research utilizes deep reinforcement learning where an artificial agent may be trained by interacting with its environment. Utilizing the experience gathered, the agent is able optimize in the form of rewards. The approach explained, can be utilized across applications which can be personalized. Several scenarios ranging from changing the layout of webpages, to rearranging icons on mobile home screens are discussed. The main objective is to develop an API for the web developers and smartphone manufacturers to utilize so that depending on the application personalization can be achieved by enhancing saliency, minimizing selection time, increasing engagement, or an arrangement of these. The technique can manage a variety of adaptations, such as how graphical elements are shown and how they behave. An experiment was conducted which showcased improved user experience considering the position change of the