Research Papers - Dept of Information Technology

Permanent URI for this collectionhttps://rda.sliit.lk/handle/123456789/593

Browse

Filters

2016 - 20204

Advanced Search

Filter by

Settings

Subject: search.filters.subject.Data mining

Search Results

Now showing 1 - 4 of 4
  • Thumbnail Image
    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.
  • Thumbnail Image
    PublicationOpen Access
    User Awareness System to Diagnose Dermatological Diseases
    (Foundation of Computer Science, 2020-12-18) Chandrasiri, S; Weerasooriya, T; Pathivarathan, V; Thavabalasingham, N; Philipreman, K; Gunasekaran, S
    Nowadays, humans' health is deteriorating by dermatological diseases, and the spreading rate is high. Most people are not aware of skin diseases. As they do not realize these diseases' seriousness, they try to treat with some remedies by themselves, even without knowing what the actual disease is. Nevertheless, it is not a suitable way to cure the disease, leading to future complications. So still the dermatological diseases remain as one of the main categories of common health issues. A few people prefer to use computerized systems to evaluate the disease conditions these days. Moreover, it is essential to know about the diseases to manage that condition and prevent escalation. Therefore, the proposed system is implemented to give users some knowledge about dermatological diseases as much as possible. The users can get awareness and predict skin diseases and complications from the data mining technique. The user can identify the stage of the dermatological disease by applying the classification algorithm. Furthermore, this system will also scrap web pages related to that disease from known or system verified websites. The content analysis is based on the machine learning process, especially using Neural Language Processing. Hence, the system will undeniably be useful to the users to summarize skin diseases and get concerns from a dermatologist
  • Thumbnail Image
    PublicationOpen Access
    EduMiner-An Automated Data Mining Tool for Intelligent Mining of Educational Data
    (3rd International Conference on Advances in Computing and Technology (ICACT‒2018), Faculty of Computing and Technology, University of Kelaniya, Sri Lanka., 2018) Kasthuriarachchi, K. T. S; Liyanage, S. R
    Data mining is a computer based information system that is devoted to scan huge data repositories, generate information and discover knowledge. Data mining pursues to find out patterns in data, organize information of hidden relationships, structure association rules and many more operations which cannot be performed using classic computer based information systems. Therefore, data mining outcomes represent a valuable support for decisions making in various industries. Data mining in education is not a novel area but, lives in its summer season. Educational data mining emerges as a paradigm oriented to design models, tasks, methods, and algorithms for exploring data from educational settings. It finds the patterns and make predictions that characterize learners’ behaviors and achievements, domain knowledge content, assessments, educational functionalities, and applications. Educators and non-data mining experts are using different data mining tools to perform mining tasks on learners’ data. There are a few tools available to carry out educational data mining tasks. However, they have several limitations. Their main issue is difficulty to use by non- data mining experts/ educators. Therefore, an automated tool is required that satisfies the data mining needs of different users. The “EduMiner” is introduced to make important predictions about students in the education domain using data mining techniques. R studio, R Shiny, data mining algorithms and several key functionalities of Knowledge Discovery in Databases have been used in the development of “EduMiner”. The functionalities of the tool are very user-friendly and simple for novice users. The user has to configure the tool and provide the appropriate inputs for parameters such as the data set, the algorithms used for mining in advance to obtain the results of the analysis. The pre-processing will be done to clean the data prior to starting the analysis. The tool is capable of performing several analytical tasks. They are; student dropout prediction, student module performance prediction, module grade prediction, recommendations for students/ teachers, student enrollment criteria predictor and student grouping according to different characteristics. Apart from these features, the tool will consist of an intelligent execution of data analysis tasks with real time data as a background service. Finally, the results of the analysis are evaluated and visualized in order to easily understand by the user. Users of education industry can achieve a valuable gain by this tool since, it would be very user friendly to handle and easy to understand the mining results.
  • Thumbnail Image
    PublicationEmbargo
    A Data Mining Approach to Identify the Factors Affecting the Academic Success of Tertiary Students in Sri Lanka
    (Springer, Cham, 2018-02-11) Kasthuriarachchi, S; Bhatt, C. M; Liyanage, S. R
    Educational Data Mining has become a very popular and highly important area in the domain of Data mining . Application of data mining to education arena arises as a paradigm oriented to design models, methods, tasks and algorithms for discovering data from educational domain. It attempts to uncover data patterns, structure association rules, establish information of unseen relationships with educational data and many more operations that cannot be performed using traditional computer based information systems. It grows and adopts statistical methods, data mining methods and machine-learning to study educational data produced mostly by students, educators, educational management policy makers and instructors. The main objective of applying data mining in education is primarily to advance learning by enabling data oriented decision making to improve existing educational practices and learning materials. This study focuses on finding the key factors affecting the performance of the students enrolled for technology related degree programs in Sri Lanka. The findings of this study will positively affect the future decisions about the progress of the students’ performance, quality of the education process and the future of the education provider.