Browsing by Author "Abeysiri, L."

Now showing 1 - 5 of 5

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AI - Driven Smart Bin for Waste Management
(2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Abeygunawardhana, A. G. D. T.; Shalinda, R. M. M. M.; Bandara, W. H. M. D.; W. D. S. Anesta, D.; Kasthurirathna; Abeysiri, L.
With increasing urbanization, waste has become a major problem in the present world. Therefore, proper waste management is a must for a healthy and clean environment. Though government authorities in most countries provide various solutions for waste management, solid waste tends to make a significant impact on the environment as they do not decompose easily. This research focuses on AI (Artificial Intelligence)-driven smart waste bin that can classify the most widely available solid waste materials namely Metal, Glass, and Plastic. The smart waste bin performs the separation of waste using image processing and machine learning algorithms. The system also performs the continuous monitoring of the collected waste level by using ultrasonic sensors. A dedicated mobile application will generate the optimal routes for the available waste collectors to collect the filled bins. Moreover, with this smart bin, the challenge of recognizing each waste item is overcome by using visual data as the source. Therefore, the usage of expensive sensor devices and filtration techniques to determine the category is disregarded. The smart bin can recognize the category of solid waste, collect it to the specified container, and notify the garbage level in each container. So, it is a portable waste management system.
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DenGue CarB: Mosquito Identification and Classification using Machine Learning
(2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Mohommed, M.; Rajakaruna, P.; Kehelpannala, N.; Perera, A.; Abeysiri, L.
This research paper discusses a web-based application that assists Public Health Officers in the dengue identification process. The mosquito classification is done using image processing and machine learning techniques. The training models are developed using Convolutional Neural Networks Algorithm, Support Vector Machine Algorithm, and K-Nearest Neighbors Algorithm to validate the results to determine the most accurate and suitable algorithm. this paper discusses the previous related research work on its significance and drawbacks while highlighting design, methods, and implementation in the solution. We conclude that the CNN algorithm provides the highest accuracy among the machine learning techniques used.
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‘GAIMS’ — Gamified aid information management system to connect donor and requester
(Faculty of Graduate Studies and Researchz`, 2017-01-26) Weerawarna, N.T.; Abeysiri, L.; Madhushan, A.
Eradicating poverty remains one of the greatest challenges in the world. In recent years, governments of number of developing countries have begun to implement their own domestic aid tracking systems, generally known as Aid Information Management System (AIMS). An AIMS has an overwhelming effect on connecting donors and requester. Currently there is lack of studies on the usefulness of gamified AIMS applications. This research addresses that by investigating how to map real world actions of donation process into a platform as a game. Though there are protocols established to help needy people, including AIMS they did not shown success because of different reasons such as the communication gap between requester and donors, slow growth of donor base, fail to track donating activities and fail to maintain interest of donors. Using gamification to develop AIMS is exciting and it allows donors to be more productive while they engaged in donation process. The outcome of ‘GAIMS’ research is proposing a cloud based gamified social communication platform to connect donors and underprivileged people with necessities. This research effort is to improve donor engagement, motivation for donation activities, quality of information, tracking and monitoring donation records which should necessarily implemented in AIMS as its key success factors. This paper discusses the process and framework of gamification and proposes an approach for applying game mechanics and dynamics in AIMS development.
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Usability and user experience towards an experience economy
(Faculty of Graduate Studies and Research, 2017-01-26) Weerawarna, N.T.; Abeysiri, L.
Technological advances of the modern day have helped combine usability and user experience. Both factors contribute heavily towards a user's satisfaction which is essential in an `experience economy'. Proceeding with identified literature related to `usability' and `user experience', this research attempts to identify whether there is a significant impact on five different factors related to usability and user experience and benchmark them to suit user's overall satisfaction. The methodology for the research followed the identification of the theory involved in terms of five factors with Web application use in terms of user's satisfaction. A structured questionnaire based on the five factors was next drawn up and used on a sample of 88 Web application users. The collected data was analysed using a statistical tool. The results were further validated using a primary data collection with 20 Web application users. A structured interview process was used for the purpose. The use of a common factor `satisfaction', helped reveal that usability and user satisfaction only were affected as against the other three variables. Perhaps, a more detailed study may reveal the absorption of the other variables as related to user satisfaction.
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Workload management to optimize productivity in tertiary and quaternary education
(Faculty of Graduate Studies and Research, 2017-01-26) Abeysiri, L.; Weerawarna, N.T.; Abeygunawardhana, P.K.W.
The use of a common yardstick to determine `work hours' for different categories of workers cannot be accepted due to differences between types of work that demand mind and labour. In this connection, where labour only is considered, there are accepted `work hours' recognized internationally. However, for tertiary and quaternary education there is no such acceptance although similarities exist. Available frameworks in existing literature still fails to reveal any such. Yet, there needs to be some measurement. Taking advantage of the proportionate task assessments made so far, this research attempts to establish proportions between and among tasks carried out by members of the academia. Purpose of this study is to develop a comprehensive workload management system with weightage assignment which accounts all activities that academia are performing.