Research Publications

Now showing 1 - 3 of 3

Open Access
Agent-Based Gamified Learning Environments for Data Science Education
(SLIIT Business School, 2023-12-14) Jayalath, N; Rajapakse, C
Because of the rapid advancement of technology and the increasing importance of the inferences that can be drawn from the big data available in organizations, modern organizations require managers and data Analysts who are capable of data-driven decision-making. But data science students need a natural environment when it comes to learning data-driven decisionmaking, especially when it comes to predictive and prescriptive analytics. Due to costs and other associated risks in a natural organisation setting, it is hard for educational institutions to teach these aspects of decision-making for data science students. Even Though gamification has been implemented in the data analysis domain in various forms, the field still requires a suitable environment to learn predictive analytics interactively for the students. Even though Researchers have identified that Gamified learning environments can improve Predictive analytics learning can be improved by 15.8%, still there is the lack of proper implementation of a suitable gamified learning environment. This research focused on identifying drawbacks of existing learning environments and whether Agent-Based Modeling can be used in modelling a suitable gamified learning environment. Therefore, an agent-based prototype model of a parameterized environment that enables data-driven decision-making in a simulated environment was modeled using Agentbased modeling, which depicts real-life donor interactions. Results suggest that fill in blanks This Agent-based model can be used as a learning environment for data analysis. Upon further modification, A game that applies this Agent-based model can be developed.
Open Access
Data Science to Determine Mechanical Properties of Low Carbon Steel During In-Process Inspections
(SLIIT, Faculty of Engineering, 2023-03-02) Alahapperuma, K. G.; Suraweera, D. D. D.; Nandhakumar, N.
Carbon steel is a widely used category of engineering metal, mainly due to its attractive mechanical and fabrication properties and low cost. The chemical composition, physical parameters, and mechanical properties of carbon steel are maintained as per the specified standards, and local steel should be complied with Sri Lankan Standard 375: 2009. Generally, the chemical composition is tested during melt stages, and mechanical properties are tested for finished products. Since it is necessary to ensure products comply with the standard, mechanical properties are tested during in-process inspections as well. When the results are not within the acceptable range, a considerable amount of production has to be rejected, causing a loss to the manufacturers. If the results of the in-process inspection are instant, it will help make suitable adjustments to process conditions and thereby prevent rejection of products, while reducing quality assurance costs, as well. Therefore, the objective of this study is to predict tensile properties with chemical composition, as input variables, to be used for in-process inspections. Forty mechanical test reports were collected from a steel manufacturing factory for 12 mm diameter, thermo-mechanically treated (TMT) steel bars. Each test report is of 15 samples from the respective batch, and consists of corresponding chemical composition and physical parameters. Multiple linear regression analysis was applied to each batch, using Statistical Package for the Social Sciences (SPSS) software, to predict yield strength (YS), ultimate tensile strength (UTS), elongation at break (EB) with carbon equivalent value (CEQ) and percentage of Sulphur as inputs. Relationships between variables were not significant, even though those relationships can be used to predict tensile properties. The predictions may not be reliable, due to the limited conditions of the study and assumptions made. It is therefore recommended to apply multivariate regression analysis or Artificial Neural Network (ANN) techniques, with other chemical elements, process temperature and water flow rate etc. also as input variables.
Embargo
Crime Analysis, Prediction and Simulation Platform Based on Machine Learning
(2021 3rd International Conference on Advancements in Computing (ICAC), SLIIT, 2021-12) Herath, I.S.; Dinalankara, R.; Wijenayake, U.
As a global social-economical problem, crime has shown complex correlations with spatial-temporal, socio-economical, and environmental factors. Understanding patterns and interactions in the crimes is essential to prepare better to respond to those criminal activities. This study is focused on research and development of crime analysis, prediction and simulation platform that provides descriptive analysis, predictive crime analysis, Reinforcement learning based crime entity simulations and safest route navigation services based on crime data from the city of San Francisco. Ultimately, the proposed crime analysis, prediction and simulation platform provides critical information on root causes and statistical patterns of crime and future crime predictions for the policymakers and security officials to create strategies to minimise the crimes.

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