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Subject: search.filters.subject.Decision Tree

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Now showing 1 - 4 of 4
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    PublicationEmbargo
    A Mobile Gamified Application to Improve Mathematics Skills of Students from Age 9 to 11 Years
    (Institute of Electrical and Electronics Engineers, 2022-09-16) Karunasekara, K.K.R; Olinka, P.L.T; Kodithuwakku, K.T.D; Jayashan, B.V.P; Kahandawaarachchi, C; Attanayaka, B
    The revolutionary change in technology has directly involved with e-learning systems. Mathematics is recognized as essential yet difficult for most of students. The main problem is considered as existing solutions were not provided a better solution for knowledge level enhancement of mathematics by detecting the exact knowledge levels of students. Current applications have offered permission to select students' knowledge levels by themselves or initiated from the initial level which has led to wrong identification of mathematics knowledge. As a solution for these problems gamified mobile application is provided by introducing a knowledge level detection with a whole syllabus coverage question classification and answer classification by using a Logistic Regression extra trees classifier. The knowledge improvement is performed from the exact knowledge level detected and positive emotions of students. Convolutional neural network is utilized for emotional recognition. Further mental health is improved via a Chabot as an educational encouragement and gamified application is developed based on psychology and preference around ten years by adhering to the best practices of Human-Computer Interaction. The proposed solution is considered as a multi-valued and cost-effective solution which will improve the mathematics knowledge level and good mental wellbeing of students with an appealing gamification environment.
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    PublicationOpen Access
    A comparative study of data mining algorithms in the prediction of auto insurance claims
    (European International Journal of Science and Technology, 2016) Weerasinghe, K. P. M. L. P; Wijegunasekara, M. C
    Insurance claims are a significant and costly problem for insurance companies. The prediction of auto insurance claims has been a challenging research problem for many auto insurance companies. Identifying the risk factors which are affected for the high number of claims and denying them may lead to increased corporate profitability and keep insurance premiums at a below rate. The key objective of conducting this study is to examine the data mining techniques in developing a predictive model in support of auto insurance claim prediction and a comparative study of them. The research was carried out by using Artificial Neural Network (ANN), Decision Tree (DT) and Multinomial Logistic Regression (MLR) to develop the prediction model. The results indicated that the ANN is the best predictor with 61.71% overall classifier accuracy. Decision tree came out to be the second with 57.05% accuracy and the logistic regression model indicated 52.39% accuracy. Parameters of optimal NN model gives 6 input neurons and 7 minimum hidden neurons with 0.15 learning rate. The comparative study of multiple prediction models provided us with an insight into the relative prediction ability of different data mining methods. The comparison of the results of the decision tree and neural network models showed an interesting pattern. Policies that are misclassified by one model are correctly classified by the other. This might be an indication that the combination of the models could result in a better classification performance.
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    PublicationEmbargo
    Smart Intelligent Troubleshooter to Solve Windows Operating System Specific Issues
    (2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Rajapakshe, D.I.K.; Shamil, M.P.P.; Paththinisekara, P.M.C.P.; Liyanage, S.K.; Samaratunge Arachchillage, U.S.S.; Kuruppu, A.
    While working on computers, people frequently confront with various kinds of problems, those beyond their extensive expertise. Microsoft Windows is the widely used Operating System running on numerous personal computers and the reason which gives more irritating problems that require to be addressed. Currently, troubleshooting is considered as a costly and time-consuming approach. The SAITA is an Artificial Intelligent Troubleshooting Agent that utilizes natural language generation, machine learning, and dependency resolving and ontology-based methodologies for solving most common Windows-specific issues within a short period of time than the traditional approach. The assistant learns from the accessible data and accomplishes the task for users as performed by human experts. The main objective of this exploration venture is to distinguish the constraints of existing troubleshooting software and create an AI troubleshooting assistant to provide solutions to fix the identified user issues. The use of this assistant would be economical as an IT help desk alternative in the industry. SAITA is developed to serve as a representative troubleshooter for fundamental user issues, service issues, application issues, and perform environment setup by analyzing software. This system will be able to solve the common Windows user’s issues as same as a human with less time.
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    PublicationEmbargo
    Cricket Shot Image Classification Using Random Forest
    (2021 3rd International Conference on Advancements in Computing (ICAC), SLIIT, 2021-12-09) Devanandan, M.; Rasaratnam, V.; Anbalagan, M.K.; Asokan, N.; Panchendrarajan, R.; Tharmaseelan, J.
    Cricket is one of the top 10 most played sport across the world regardless of age and gender. However, learning cricket has been quite challenging as the majority of the cricket-playing individuals are unable to afford quality infrastructure. While this has opened up many research opportunities to provide solutions to automatically learn cricket, very little work has been done in this era. In this paper, we focus on the batting skills of cricket players. We develop a Random Forest model to classify the cricket shot images using human body keypoints extracted with MediaPipe. Experiment results show the proposed model achieves an F1-score of 87% and outperforms the existing solution in a 5% margin. Further, we propose a similarity estimation approach to compare the user’s cricket image with popular international cricket players’ cricket shot images of the same type and retrieve the most similar one. The mobile application we developed based on our solution will enable cricket-playing individuals to analyze, improve and track their batting performances without the need of having a coach.