Research Publications Authored by SLIIT Staff
Permanent URI for this communityhttps://rda.sliit.lk/handle/123456789/4195
This collection includes all SLIIT staff publications presented at external conferences and published in external journals. The materials are organized by faculty to facilitate easy retrieval.
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Publication Embargo An Analysis on Different Distance Measures in KNN with PCA for Android Malware Detection(IEEE, 2022-11-30) Dissanayake, S; Gunathunga, S; Jayanetti, D; Perera, K; Liyanapathirana, C; Rupasinghe, LAs Majority of the market is presently occupied by Android consumers, Android operating system is a prominent target for intruders. This research shows a dynamic Android malware detection approach that classifies dangerous and trustworthy applications using system call monitoring. While the applications were in the execution phase, dynamic system call analysis was conducted on legitimate and malicious applications. Majority of relevant machine learning-based studies on detecting android malware frequently employ baseline classifier settings and concentrate on selecting either the best attributes or classifier. This study examines the performance of K Nearest Neighbor (KNN), factoring its many hyper-parameters with a focus on various distance metrics and this paper shows performance of KNN before and after performing Principal Component Analysis (PCA). The findings demonstrate that the classification performance may be significantly improved by using the adequate distance metric. KNN algorithm shows decent accuracy and improvement of efficiency such as decreasing the training time After PCA.Publication Embargo Linguistic features based personality recognition using social media data(IEEE, 2017-01-27) Sewwandi, D; Perera, K; Sandaruwan, S; Lakchani, O; Nugaliyadde, A; Thelijjagoda, SSocial media has become a prominent platform for opinions and thoughts. This stated that the characteristics of a person can be assessed through social media status updates. The purpose of this research article is to provide a web application in order to detect one's personality using linguistic feature analysis. The personality of a person has classified according to Eysenck's Three Factor personality model. The proposed technique is based on ontology based text classification, linguistic feature-vector matrix using LIWC (Linguistic Inquiry and Word Count) features including semantic analysis using supervised machine learning algorithms and questionnaire based personality detection. This is vital for HR management system when recruiting and promoting employees, R&D Psychologists can use the dynamic ontology for storage purposes and all the other API users including universities and sports clubs. According to the test results the proposed system is in an accuracy level of 91%, when tested with a real world personality detection questionnaire based application, and results demonstrate that the proposed technique can detect the personality of a person with considerable accuracy and a speed.