During the last decade, increasing attention has been focused on ontologies and Ontological Engineering. The application of ontological engineering technique to e-learning, though still in its early stage, has become a phenomena tool in the integration and adaptation of a dynamic and flexible elearning environment, where tutors and learners are provided with the functionality of personalizing their learning process, this has not fully considered the generality of an entire field of study. This paper presents an ontology-based framework for classification of the entire courses of computer science at the university level. A model of an e-learning domain in the context of material classification that enables students retrieve information about a particular course in personalized ways was presented the method adopted elicited e-learning knowledge using documented materials, observation, consultation, prototyping among others. With domain expertise developed, the knowledge elicited was analyzed and formally represented using OWL-Description Logic. The design was implemented using Protégé 5.0 editor. The validation for accuracy and completeness was carried out with the domain and ontology experts using consistency checking reasoned, DL-Query. The results of the implementation show that elearning materials can be searched and retrieved easily and timely. The study also established and formalised an effective computational approach for representing information content of educational resources thereby helping to improve the flexibility of content representation in an e-learning system.Keywords: e-learning, Ontological Engineering, Ontology, Ontology Web Language (OWL), Knowledge Management, Description LogicVol. 26, No 1, June 2019

For improved human comprehension and autonomous machine perception, optical character recognition has been saddled with the task of translating printed or hand-written materials into digital text files. Many works have been proposed and implemented in the computerization of different human languages in the global community, but microscopic attempts have also been made to place Yoruba Handwritten Character on the board of Optical Character Recognition. This study developed a novel available dataset for research on offline Yoruba handwritten character recognition so as to fill the gaps in the existing knowledge. The developed database contains a total of 12,600 characters being made up of 70 classes from a total number of 200 writers, in which 80 % (10,500) is regarded as the training and validation dataset while the remaining 20 % (2,100) is regarded as testing dataset. The dataset is available on https://github.com/oluwashina90/Yoruba-handwritten-character-database. Hence, it is the complete and largest dataset available for Yoruba Handwritten character research.

The use of computer technology has significantly advanced the medical sector, and many computer technologies have been used to develop healthcare, such as the patient management system, monitoring and control systems, and diagnostic systems. Technological advances in healthcare have also helped in saving numerous patients and are constantly improving our quality of life. Technology in the medical sector has also had a major effect on almost all healthcare professional techniques and practices. In order to facilitate rapid diagnosis and treatment of different skin diseases by the use of a deep learning model, this study developed a comprehensive framework to improve the decision-making of dermatologists in Nigeria in terms of the diagnosis of selected skin diseases. The developed system achieved the network accuracy of 98.44 % and the validation accuracy of the test set is 99.44 % as specified by the training results, further testing reveal that the developed system yielded rejection rate of 2.2 % and recognition accuracy of 97.8 %.

Multimodal biometric system combines more than one biometric modality into a single method in order, to overcome the limitations of unimodal biometrics system. In multimodal biometrics system, the utilization of different algorithms for feature extraction, fusion at feature level and classification often to complexity and make fused biometrics features larger in dimensions. In this paper, we developed a face-iris multimodal biometric recognition system based on convolutional neural network for feature extraction, fusion at feature level, training and matching to reduce dimensionality, error rate and improve the recognition accuracy suitable for an access control. Convolutional Neural Network is based on deep supervised learning model and was employed for training, classification, and testing of the system. The images are preprocessed to a standard normalization and then flow into couples of convolutional layers. The developed multimodal biometrics system was evaluated on a dataset of 700 iris and facial images, the training database contain 600 iris and face images, 100 iris and face images were used for testing. Experimental result shows that at the learning rate of 0.0001, the multimodal system has a performance recognition accuracy (RA) of 98.33% and equal error rate (ERR) of 0.0006%.

. In the field of deep learning, facial recognition belongs to the computer vision category. In various applications such as access control system, security, attendance management etc., it has been widely used for authentication and identification purposes. In deep learning, transfer learning is a method of using a neural network model that is first trained on a problem similar to the problem that is being solved. The most commonly used face recognition methods are mainly based on template matching, geometric features based, algebraic and deep learning method. The advantage of template matching is that it is easy to implement, and the disadvantage is that it is difficult to deal with the pose and scale changes effectively. The most important issue, regardless of the method used in the face recognition system, is dimensionality and computational complexity, especially when operating on large databases. In this paper, we applied a transfer learning model based on AlexNet Deep convolutional network to develop a real time face recognition system that has a good robustness to face pose and illumination, reduce dimensionality, complexity and improved recognition accuracy. The system has a recognition accuracy of 98.95 %.