Fingerprint biometric is popularly used for protecting digital devices and applications. They are better and more reliable for authentication in comparison to the usual security tokens or password, which make them to be at the forefront of identity management systems. Though, they have several security benefits, there are several weaknesses of the fingerprint biometric recognition system. The greatest challenge of the fingerprint biometric system is theft or leakage of the template information. Also, each individual has limited and unique fingerprint which is permanent throughout their lifespan, hence, the compromise of the fingerprint biometric will cause a lifetime threat to the security and privacy of such an individual. Security and privacy risk of fingerprint biometric have previously been studied in the context of cryptosystem and cancelable biometric generation. However, these approaches do not obviously address the issue of revocability, diversity and irreversibility of fingerprint features to guard against the wrong use or theft of fingerprint biometric information.  In this paper, we proposed a model that harnesses the strength of Arnold transform and Bio-hashing on fingerprint biometric features to overcome the limitations commonly encountered in sole fingerprint biometric approaches. In the experimental analysis, the result of irreversibility showed 0% False Acceptance Rate (FAR), performance showed maximum of 0.2% FAR and maximum of 0.8% False Rejection Rate (FRR) at different threshold values. Also, the result of renewability/revocability at SMDKAB SMKADKB and SMKBDKA showed that the protection did not match each other. Therefore, the performance of the proposed model was notable and the techniques could be efficiently and reliably used to enforce protection on biometric templates in establishments/organizations so that their information and processes could be secured.

Context-aware recommender system (CARS) is a promising technique for recommending research resources to users (researchers) by predicting their preferences (resources) under different situations. If the contextual information given to such a system is inappropriate, it will certainly have a negative effect on the nature of recommendation output generated by the system as well as making the system to have high dimensionality complexity. Currently, several CARS recommendation algorithms have been developed but they have failed to bring to bear the means and importance of experimentally validating the contextual information used in different domains of application of CARS. Hence, this paper experimentally validates the contextual variables in the domain of research resources by splitting a research resource (article) into three major sections (introduction, review and methodology). These sections are the contextual variables validated in order to authenticate their viability as context that could be used in recommending research resources based on the specific section of an article a researcher is interested in. The result of our experiment shows that irrespective of the domain of articles, journal articles have higher variability in their citations at introduction, very significant variability between the articles in the review and high variability in the methodology contextual variable respectively than the articles in the proceeding under the three contextual variables. This experiment shows that these three variables could be used as context .It also shows the percentage of splitting that could be used within journals and proceedings for context-aware research resources recommendations.

In our present environment, heart diseases are very rampart and they describe the various types of diseases that affect the heart. They account for the leading cause of death word-wide especially, in Africa. It is therefore very important for individuals to have adequate knowledge about their heart health in order to avoid the risk of decreased life expectancy. The high mortality rate of heart (cardiovascular) diseases is attributed to the unequal ratio of patients to scarcity of medical experts who can provide medical care, also patients are not always warn to waiting long hours on queue in the hospital, especially in cases of emergency. This paper designed and implemented a Mobile Neuro-fuzzy System that uses the combination of the intelligence technique of Artificial Neural Networks (ANN) and the human-like reasoning style of Fuzzy Logic to diagnose and suggest possible treatments for cardiovascular diseases through interactivity with user. It employs programs like MySQL, PHP, JAVA (Android) and XML (Android Studio) while tools like XAMPP, PhpStorm and Android O/S were used to integrate these techniques together. The system, proved to be of enormous advantage in diagnosing heart diseases, as it diagnoses and learns about each user per time, to provide adequate and appropriate results and also makes reliable predictions to users.