Scholarly work in the Department of Computer Science and Communication Technology
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Item Computer Anxiety and Computer Self-Efficacy in Computer-Based Tests in Selected Universities in South-West Nigeria(African Journal of Library, Archives & Information Science, 2016) Olukunle Adebayo; Nwagwu WilliamsThis study was designed to examine the influence of demographic and personality factors on computer anxiety and computer efficacy among first -year students admitted into three universities (CBT) in southwest Nigeria through computerbased test strategies. Using a questionnaire, data was collected from 892 students who were willing to participate in the study. In terms of the demographic characteristics of respondents in the study, the student populations from the three universities are homogenous as Chi Square analysis showed no significant differences among them. Logistics regression analysis shows that computer self-efficacy and computer attitude are considerably high while computer anxiety is relatively low. However, the number of students reporting low self-esteem (33.91%) and low exposure (31.10%) can be considered relatively high. None of the demographic factors predicted computer anxiety but male (â=0.742, p=0.000) and semi-urban residential status (â=-0.542,p=0.001) significantly predicted computer selfefficacy. Also, self-esteem and computer exposure did not predict computer anxiety and high computer self-efficacy, but they did computer selfefficacy. Basically, dealing with computer anxiety and improving self-efficacy of students in respect of CBT will require students being pre-exposed to CBT type of examinations for relatively long periods prior to the examination.Item Fostering E-Education in Nigeria(Crawford Journal of Natural & Applied Sciences, 2019) Akano Victoria AdebimpeThe world has become a global village where information could be obtained just by a click. While the use of electronic device is well recognised by the social world, it is still under-utilised by academes, especially in developing countries. There is a sharp contrast in the statistics of users on social media (more than half a million on monthly basis) with those on academic media. The variance is not only reflected in online academic media but also in the acquisition and transmission of E-Information in Nigerian schools. This paper examines the quality of E-Education in Nigeria. Expanding on what the quality of E-Education entails, the overview of education without electronic facilities is analysed. The availability, usage and the impact of inculcating Information and Communication Technology (ICT) in learning also form a part of the discussion. The paper concludes with recommendations on the way forward to maximise the use of electronic facilities and improve the standard of education in Nigeria.Item Conversion of Sign Language to Text and Speech Using Machine Learning Techniques(Journal of Research and Review in Science., 2018) Akano Victoria Adebimpe; Olamiti Adejoke O.Introduction: Communication with the hearing impaired (deaf/mute) people is a great challenge in our society today; this can be attributed to the fact that their means of communication (Sign Language or hand gestures at a local level) requires an interpreter at every instance. Conversion of images to text as well as speech can be of great benefit to the non-hearing impaired and hearing impaired people (the deaf/mute) from circadian interaction with images. To effectively achieve this, a sign language (ASL – American Sign Language) image to text as well as speech conversion was aimed at in this research. Aims: To convert ASL signed hand gestures into text as well as speech using unsupervised feature learning to eliminate communication barrier with the hearing impaired and as well provide teaching aid for sign language. Materials and Method: The techniques of image segmentation and feature detection played a crucial role in implementing this system. We formulate the interaction between image segmentation and object recognition in the framework of FAST and SURF algorithms. The system goes through various phases such as data capturing using KINECT sensor, image segmentation, feature detection and extraction from ROI, supervised and unsupervised classification of images with K-Nearest Neighbour (KNN)-algorithms and text-to-speech (TTS) conversion. The combination FAST and SURF with a KNN of 10 also showed that unsupervised learning classification could determine the best matched feature from the existing database. In turn, the best match was converted to text as well as speech. Results: The introduced system achieved a 78% accuracy of unsupervised feature learning. Conclusion: The success of this work can be attributed to the effective classification that has improved the unsupervised feature learning of different images. The pre-determination of the ROI of each image using SURF and FAST, has demonstrated the ability of the proposed algorithm to limit image modelling to relevant region within the image.Item Cyber-Security of Higher Institution Web Portals in Nigeria(Crawford Journal of Natural & Applied Sciences, 2019) Akano Victoria AdebimpeEvery institutional web portal should be treated as a “jewel in a crown”. The reason for this cannot be far-fetched in that, each institution has two basic assets to guard jealously, which is the identity of the university and its clienteles. The drive for this research on institutional portals can be attributed to the fact that the portal serves as the first point of entry in most cases to the clienteles in order to provide essential information and application resources in a secured, consistent and reliable mode. However, this treasure chest of high value information is vulnerable in most cases to cyber-attacks resulting from unintended disclosure of vitals through phishing, improper use of social media and supposed availability of services. There is therefore the need to do a better job not only of bolstering network defenses against cyber-attacks, but also of raising awareness of basic cyber security hygiene among the full spectrum of IT users: the university and its clienteles. This study gives different approaches on tools, tactics and procedures of minimizing the number of compromised networks and stolen data.