Browsing by Author "Perera, K"

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    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, L
    As 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.
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    PublicationOpen Access
    Application of Sentinel-2 Satellite Data to Map Forest Cover in Southeast Sri Lanka through the Random Forest Classifier
    (SLIIT, Faculty of Engineering, 2022-09) Gunawansa, T; Perera, K; Apan, A; Hettiarachchi, N
    Sentinel-2 satellite data has been used for forest cover monitoring for almost five years. Mapping with Sentinel data will be a cost-effective solution for Sri Lanka, where the lack of updated land cover maps with high spatial resolution is a significant challenge in the land resource management of the country. A study area of about 5,000 km2 located in southeast Sri Lanka was selected for this study. Agricultural lands, forests including Yala national park, and villages with perennial crops make up the region. A Level-2A Sentinel-2 image with less than 10 percent cloud cover was used in the European Space Agency's (ESA) SNAP software version 8.0.0 for image processing and the forest cover of the study area was mapped through the Random Forest classifier (RFC). Normalized Difference Vegetation Index (NDVI) is also calculated as a Sentinel product to support RFC output. For RFC, ground truth data were collected through the reference of Google Earth high-resolution data. The classification accuracy was assessed using the Google Earth image as the reference dataset. Furthermore, RFC results were compared with NVDI greenness values. The classification accuracy was calculated using a confusion matrix (error matrix) through randomly selected 100 sample points. The overall accuracy of the land cover map was 85 percent, with a 96 percent accuracy for forest cover identification. The study found RFC as an effective method to isolate forest cover in Sri Lanka.
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    Linguistic features based personality recognition using social media data
    (IEEE, 2017-01-27) Sewwandi, D; Perera, K; Sandaruwan, S; Lakchani, O; Nugaliyadde, A; Thelijjagoda, S
    Social 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.