Faculty of Computing

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Author: search.filters.author.Jayalath, T

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    PublicationEmbargo
    E-tutor: Comprehensive Student Productivity Management System for Education
    (IEEE, 2022-12-09) Silva, K; Induwara, R; Wimukthi, M; Poornika, S; Samaratunge Arachchillage, U.S.S; Jayalath, T
    With the advancement of technology, e-learning has emerged as predominant in the education sector. As students, parents, and educators acknowledged, adopting e-learning can offer several benefits over traditional learning techniques. Since more individuals are becoming acclimated to online learning platforms, these online platforms can provide a simple, instructive, and efficient mode of delivery. This novel approach could be improved with the aid of Artificial Intelligence (AI) to comprehend consumers more thoroughly and provide valuable and better-suited services. Most sectors in education, including universities, swiftly adapted to new educational methodologies because of their flexibility and productivity. Nevertheless, there are some downsides that young demography experiences, such as less instructiveness, distraction due to the absence of teachers, and poor IT literacy. Consequently, these drawbacks would recede the capability of students to assimilate content during the lecture. Therefore, the main objective of this research is to implement an E-learning platform with AI learning analytics to enhance students’ performance regularly while reducing the significant drawbacks of the E-learning platforms. This research consists of students’ focus detection, essay-based answer evaluation, note summarization, mind map generation, and personalized guidance facilities.
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    Planning Marketing Strategies in Small-Scale Business Using Data Analysis
    (IEEE, 2022-12-29) Fernando, A.M.P.; Adhikari, A.M.T.T.; Wijesekara, W.H.A.T.K.; Vithanage, T.V.T.I.; Gamage, A; Jayalath, T
    The proposed research work develops a system focused on business opportunities to enhance market returns and improve marketing strategies and new strategies by identifying how customers interact with products and their behavior. Existing research efforts attempt to identify and market consumer attraction to products and marketplace in the marketplace. Current research focuses on the challenges of identifying consumer buying patterns and how consumers interact with products, Existing research has not, however, integrated the essential elements into a single system. Consequently, the recommended study has been conducted on a number of significant issues, such as determining the high-value client base and the number of sectors, understanding the purchasing pattern of products that comprise the customers' basket, identifying customer lifetime value, and Customer Trajectory Determination for identifying customer attractive shelf. This system focuses on various machine learning algorithms. Customer segmentation and value analysis using K Mean, Agglomerative, Clustering algorithm, and Arima model. Association rules are generated using the Apriori algorithm for market basket analysis, which is built on the idea that a set of frequently purchased items is a subset of a set of frequently purchased items. Also using RFM analysis to create and prepare our data frame by using BG/NBD model and the Gamma-Gamma model to calculate the customer lifetime value standardization. Using image processing algorithms and retail video analysis algorithms, background reduction technology clearly identifies moving objects/ tracks customer routes using different colors. Based on results from implementation and testing, it was determined that the suggested technique outperformed the use of CCTV to identify consumer behavior and satisfaction with the product in recognizing customer purchasing patterns. The proposed system can identify customers' buying patterns, how customers interact with product...
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    PublicationOpen Access
    Green Cloud Computing: A Review on Adoption of Green-Computing attributes and Vendor Specific Implementations
    (researchgate.net, 2019-03) Jayalath, T; Chathumali, E. J. A. P. C; Kothalawala, K. R. M; Kuruwitaarachchi, N
    With cloud computing emerging as a trending topic, it has been a major point o f discussion for the last few years. In regards to technological advancements, the associated shortcomings like environmental footprint caused by them also become an affair o f high significance. Cloud computing itself is a much greener alternative to individual data centers with lesser number o f servers being used and cloud data centers being far more efficient than those o f traditional thereby reducing the carbon impact. Nonetheless, it cannot be neglected the fact that the data centers utilized by the cloud vendors are still a major source o f carbon emissions due to the dirty energy usage. Therefore, the discussion o f the paper is based on how green the foremost cloud providers are and the implementations o f green IT attributes in the cloud infrastructure.
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    PublicationOpen Access
    Source Code based Approaches to Automate Marking in Programming Assignments
    (Science and Technology Publications, 2021) Kuruppu, T; Tharmaseelan, J; Silva, C; Samaratunge Arachchillage, U. S. S; Manathunga, K; Reyal, S; Kodagoda, N; Jayalath, T
    With the embarkment of this technological era, a significant demand over programming modules can be observed among university students in larger volume. When figures grow exponentially, manual assessments and evaluations would be a tedious and error-prone activity, thus marking automation has become fast growing necessity. To fulfil this objective, in this review paper, authors present literature on automated assessment of coding exercises, analyse the literature from four dimensions as Machine Learning approaches, Source Graph Generation, Domain Specific Languages, and Static Code Analysis. These approaches are reviewed on three main aspects: accuracy, efficiency, and user-experience. The paper finally describes a series of recommendations for standardizing the evaluation and benchmarking of marking automation tools for future researchers to obtain a strong empirical footing on the domain, thereby leading to further advancements in the field.
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    Innovative use of Collaborative Teaching in Conducting a Large Scale Online Synchronous Fresher’s Programming Course
    (IEEE, 2021-04-21) Kodagoda, N; Gamage, A; Suriyawansa, K; Jayasinghe, B; Rupasinghe, S; Ganegoda, D; Jayalath, T; Kurrupu, A
    The COVID-19 pandemic has forced educationist to come up with innovative solutions in delivering, engaging synchronous online academic modules. An innovative collaborative teaching approach was utilized in delivering programming concepts for freshers. A team of six academics functioned as a resource panel in delivering synchronous online lecture content. These interactive sessions were led by a moderator inquiring the resource panel on topics related to the content of the lecture. This was done in the same spirit on how a panel discussion would be conducted led by a moderator in a conference. Microsoft Teams Live was used in the delivery of the content to an audience of up to 800 students. Delivering a freshers programming course is known to be challenging in face- to-face delivery. A collaborative programming environment was used to engage students in live coding activities during the lectures. Students had opportunities to interact with the resource panel through quizzes, QA and through coding related activities. These lectures also introduced the innovative use of QR codes to get students engagement through a mobile device for the interactive sessions. Results based on a survey shared among the participated students, confirmed the collaborative teaching approach in conducting webinar was more effective over a traditional webinar that is conducted by one person. Interactive programming environment (Repl.it) allowed the resource personal to give feedback on the programs submitted by the students during synchronous sessions conducted. The best practices used in delivering this course can be easily adopted in delivering highly engaging online lectures for other courses.
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    Cognitive Rehabilitation based Personalized Solution for Dementia Patients using Reinforcement Learning
    (IEEE, 2021-04-15) Rathnayaka, M. H. K. R; Watawala, W. K. C. R; Manamendra, M. G; Silva, S. R. R. M; Kasthurirathna, D; Jayalath, T
    Dementia is one of the most challenging health problems faced globally with the increase in the ageing population. The estimated current prevalence of dementia is 47.5 million worldwide. This number will nearly double in every 20 years globally. Dementia is basically, a syndrome which cannot be cured by medicine, but non-pharmacological therapy can be used to treat Dementia patients, this is known as Cognitive Rehabilitation Therapy. According to the recommendations of the doctors, the use of a brain training application could be better than traditional approaches. There are number of Brain training mobile applications in the world that could be useful in improving human concentration, attention and all sorts of brain activities but there isn’t any customized software solution that has games or activities. Patients can be in different stages of Dementia. Therefore, for a better cognitive rehabilitation they need personalized therapies with the games and activities. Accordingly, developing this application is an actual global requirement for dementia patients. The world is evolving with new technologies and this application includes the mind games based on such technologies as Reinforcement Learning which predict the next level for patients based on user behavior. And there are some activities on speech recognition using Deep Neural Network as well. Patients, caregivers and doctors can view the progress reports of the patients. All the games have designed along with the supervision and recommendation from a Consultant Psychiatrist in Sri Lanka. The main objective is to help the Dementia patients in cognitive rehabilitation to improve the quality of life with best suited personalized games and activities.