Faculty of Computing

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    An Approach To Weigh Cybersecurity Awareness Questions In Academic Institutions Based On Principle Component Analysis
    (INTERNATIONAL JOURNAL OF SCIENTIFIC & TECHNOLOGY RESEARCH, 2021-04-04) Abdullahi, Adamu Garba; Fathe Jeribi; Ibrahim Al-Shourbaji; Mohammed Alhameed; Faheem Reegu; Sophia Alim
    Cybersecurity knowledge is among the essential elements for both public and private organizations and individuals due to the advent of online activities that pose a threat to critical organizational information. Researchers have conducted much research to provide a solution on how to increase the level of cybersecurity awareness. The methods used by various researchers include qualitative, quantitative, or mixed-methods approaches to determine the level of cybersecurity awareness. This paper aims to identify the most critical questions asked using the quantitative approach as it is the most commonly used method. The paper examines a dataset used in the work of Al-Janabi and Al-Shourbaji using an unsupervised machine learning technique known as Principal Component Analysis (PCA) to identify the most critical questions. The result from the analysis indicates that only the first six PCs have eigenvalues greater than 1, which means that these components (i.e., questions) are the most crucial to be used in identifying the most accurate level of cybersecurity awareness. Furthermore, the result provides a new dimension of questions to be used in determining the awareness level as it has been verified using the PCA technique. The paper also gives further recommendations on how to increase the level of cybersecurity awareness among both the public and private sectors.
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    Comprehensive Evaluation Of Appearance-Based Techniques For Palmprint Features Extraction Using Probabilistic Neural Network, Cosine Measures And Euclidean Distance Classifiers
    (UNIVERSITY OF PITESTI SCIENTIFIC BULLETIN, 2018-08-08) Akande Oluwatobi Noah; Abikoye O. C; Adeyemo I. A; Ogundokun R. O; Aro T. O
    Most biometric systems work by comparing features extracted from a query biometric trait with those extracted from a stored biometric trait. Therefore, to a great extent, the accuracy of any biometric system is dependent on the effectiveness of its features extraction stage. With an intention to establish a suitable appearance based features extraction technique, an independent comparative study of Independent Component Analysis (ICA), Linear Discriminant Analysis (LDA) and Principal Component Analysis (PCA) algorithms for palmprint features extraction is reported in this article. Euclidean distance, Probabilistic Neural Network (PNN) and cosine measures were used as classifiers. Results obtained revealed that cosine metrics is preferable for ICA features extraction while PNN is preferable for LDA features extraction. Both PNN and Euclidean distance yielded a better recognition rate for PCA. However, ICA yielded the best recognition rate in terms of FAR and FRR followed by LDA then PCA