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Subject: search.filters.subject.feature extraction

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    PublicationOpen Access
    NoFish; Total Anti-Phishing Protection System
    (Global Journals, 2020-12-05) Pabasara, R. A. H. D; Atimorathanna, D. N; Ranaweera, T. S; Perera, J. R
    Phishing attacks have been identified by researchers as one of the major cyber-attack vectors which the general public has to face today. Although software companies launch new anti-phishing products, these products cannot prevent all the phishing attacks. The proposed solution, “No Fish” is a total anti-phishing protection system created especially for end-users as well as for organizations.In this paper, a realtime anti-phishing system, which has been implemented using four main phishing detection mechanisms, is proposed. The system has the following distinguishing properties from related studies in the literature: language independence, use of a considerable amount of phishing and legitimate data, real-time execution, detection of new websites, detecting zero-hour phishing attacks and use of feature-rich classifiers, visual image comparison, DNS phishing detection, email client plug in and specially the overall system has designed to the levelbased security architecture to reduce the time-consumption.
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    PublicationEmbargo
    Plant Leaf Recognition: Comparing Contour-Based and Region-Based Feature Extraction
    (2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Donesh, S.; Ishanka, U.A.P.
    Plants play a vital role in the environment. Identifying them and classifying them is an important task for botanists. This study briefly points out- how to recognize plant species using image processing techniques that can help botanists and scientists, the appropriate features for plant species recognition in feature extraction, how can a classification help to increase the accuracy of the plant leaf classification. There are four major phases used in here for the recognition, and they are image input, image pre-processing, feature extraction, and SVM classification. This automatic recognition system is developed using python with Jupyter Notebook environment gives higher accuracy for the plant recognition for the botanists and comparing the feature extractions such as Contour-based and Region-based to get down more accurate results than previous researches is the main purpose of the proposed study. Contour-based and Region-based features were calculated through equations. SVM classification is used for both feature extraction methods. For individual feature extraction the Contour-based feature extraction is more efficient with 72.25% accuracy than Region-based feature extraction with 70.41% accuracy, and for combining both feature extraction SVM classification gives 68.58% accuracy. Contour-based feature is the most appropriate feature for a plant species recognition.