On the Internet, where the number of choices is overwhelming, there is need to filter, prioritize and efficiently deliver relevant information in order to alleviate the problem of information overload, which has created a potential problem to many Internet users. Recommender systems solve this problem by searching through large volume of dynamically generated information to provide users with personalized content and services. This paper explores the different characteristics and potentials of different prediction techniques in recommendation systems in order to serve as a compass for research and practice in the field of recommendation systems.Ó 2015 Production and hosting by Elsevier B.V. on behalf

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E-learning is an innovative approach for delivering electronically mediated, well-designed, learner-centred interactive learning environments by utilizing internet and digital technologies with respect to instructional design principles. This paper presents the application of Software Development techniques in the development of a Mobile Based E-learning system that facilitates learning in a University environment. The developed application presents a system where a student after registration, has access to various functions that can improve the process of learning. Web pages were developed to serve as the user interface to the MLS and provide all the services needed for an E-learning portal including assessment and provision of feedback to learners. A portal exists for lecturers to upload learning contents and students’ examination results. The online portal uses Apache HTTP server as its web server, MySQL for relational database management and PHP as the scripting language to serve as a communication gateway between the back end and the users. The system was tested and evaluated with satisfactory results. This work, if adopted in schools to aid conventional learning, is expected to immensely improve the learning process and performance of students.

Our aim was to measure the level of metadata integrity emanating from textual documents in order to ascertain whether the data contained in the original document remained an accurate reflection of the metadata that emanated from the original document. The results of the study revealed that respondents have a better understanding of the content of textual documents at their second attempt than at their first attempt. Also, respondents were able to give better and clearer metadata for a text of a general nature than for a text of a specialized nature. Recommendations were made based on the findings of the study.

Biometrics usage is growing daily and fingerprint-based recognition system is among the most effective and popular methods of personality identification. The conventional fingerprint sensor functions on total internal reflectance (TIR), which is a method that captures the external features of the finger that is presented to it. Hence, this opens it up to spoof attacks. Liveness detection is an anti-spoofing approach that has the potentials to identify physiological features in fingerprints. It has been demonstrated that spoof fingerprint made of gelatin, gummy and play-doh can easily deceive sensor. Therefore, the security of such sensor is not guaranteed. Here, we established a secure and robust fake-spoof fingerprint identification algorithm using Circular Gabor Wavelet for texture segmentation of the captured images. The samples were exposed to feature extraction processing using circular Gabor wavelet algorithm developed for texture segmentations. The result was evaluated using FAR which measures if a user presented is accepted under a false claimed identity. The FAR result was 0.03125 with an accuracy of 99.968% which showed distinct difference between live and spoof fingerprint.