http://dr.lib.sjp.ac.lk/handle/123456789/11008 2026-01-07T04:26:50Z http://dr.lib.sjp.ac.lk/handle/123456789/11015 Coronary Heart Event Analysis with Association Rule Mining Gomathy, B.; Ramesh, S.M.; Shanmugam, A. Coronary heart disease (CHD) is one of the major causes of disability in adults as well as one of the main causes of death in the developed countries. Although significant progress has been made in the diagnosis and treatment of CHD, further investigation is still needed. The objective of this study was to develop the assessment of heart event-risk factors targeting in the reduction of CHD events using Association Rule Mining. The risk factors investigated were: 1) before the event: a) non modifiable—age, sex, and family history for premature CHD, b) modifiable—smoking before the event, history of hypertension, and history of diabetes; and 2) after the event: modifiable—smoking after the event, systolic blood pressure, diastolic blood pressure, total cholesterol, high density lipoprotein, low-density lipoprotein, triglycerides, and glucose. The events investigated were: myocardial infarction (MI), percutaneous coronary intervention (PCI), and coronary artery bypass graft surgery (CABG).Data-mining analysis was carried out using the Association Rule Mining for the afore mentioned three events using five different splitting criteria for larger datasets. The most important risk factors, as extracted from the classification rules analysis were: 1) for MI, age, smoking, and history of hypertension; 2) for PCI, family history, history of hypertension, and history of diabetes; and 3) for CABG, age, history of hypertension, and smoking. It is anticipated that data mining could help in the identification of high and low risk subgroups of subjects, a decisive factor for the selection of therapy, i.e., medical or surgical. 2013-01-01T00:00:00Z http://dr.lib.sjp.ac.lk/handle/123456789/11014 Finding Efficient Linguistic Feature Set for Authorship Verification Ranatunga, R.V.S.P.K. Authorship verification rely on identification of a given document to verify whether it is written by a particular author or not. Internally, analyzing the document itself with respect to variations in writing style of the author and identification of the author‟s own idiolect is the main context of the authorship verification. Mainly, the detection performance depends on the used feature set for clustering the document. Linguistic features and stylistic features have been utilized for author identification according to the writing style of a particular author. Disclosing the shallow changes of the author‟s writing style is the major problem which should be addressed in the domain of authorship verification. It motivates the computer science researchers to do research on authorship verification in the field of computer forensics and this research also focuses on this problem. The contributions from the proposed research are two folded: Former is introducing a new feature extracting method with Natural Language Processing (NLP) and latter is proposing a novel and more efficient linguistic feature set for verification of the author of the given document. Experiments were carried out on a corpus composed of freely downloadable genuine 19th century English text. Each word segment obtained from the corpus is subjected to feature extraction and 49 stylistic features are used for clustering the text. Other than the standard stylistic features, 19 linguistic features are used as new feature set for the experiments. Generated parse trees by the Stanford Parser are utilized for extracting these linguistic features. Self organizing maps have been used as the classifier to cluster the documents. Proper word segmentation is also introduced in this work which helps us to demonstrate that the proposed strategy can produce promising results. Finally, it is realized that more accurate classification is generated by the proposed strategy with the extracted linguistic feature set. 2013-01-01T00:00:00Z http://dr.lib.sjp.ac.lk/handle/123456789/11013 Free and Open Source Software Technology Adoption in the Sri Lankan Banking Industry Perera, K.A.C.; Weerawarne, S. This research explores Free and Open Source Software (FOSS) adoption in the Sri Lankan banking sector. It produces two types of results, which are unique. One is an 8-level abstract model based on the criticality for the business, which represents all the software application usage in Sri Lankan banks. Then it presents the adoption of FOSS in banks, in terms of two quantified indices, namely General FOSS Adoption Index and Category Specific FOSS Adoption Index. The first index modeled so that it represents the „strategic nature of usage of FOSS. as well as the „FOSS friendliness. within the bank. The second index represents the FOSS adoption in the bank, in terms of extraction of the „best technological features out of them. and the „level of adoption.. The results reveal that the Sri Lankan banks do not have good levels of FOSS adoption, though all the banks use FOSS applications for some purpose or another. By further drill down into the model, it was discovered that the lack of government policy initiative towards FOSS has had a causal effect on the poor adoption ratings in the Sri Lankan banking context. Further it will greatly helpful to have FOSS supportive software business in the country, which will influence banks to get better service for FOSS products 2013-01-01T00:00:00Z http://dr.lib.sjp.ac.lk/handle/123456789/11012 Dynamic Object Path Detection in a Network of Surveillance Cameras Weerasena, H.H.; Bandara, P. B. S.; Kulasekara, J. R. B. 2013-01-01T00:00:00Z