The user confirmation systems witch using a biometric method are mostly encounter noisy data and infinite orders of error. To improve special matching in such situations, hybrid biometric systems are utilized. In this study we combined two biometric systems of ears and face to propose a multimodal biometric authentication system which is secure and reliable. To perform desired experiments three face databases including Cohen-Canad, Indian Institute of Technology and FEI new database are considered. We combined these databases separately with ear images database from PNU. Then, we tried to recognize people and evaluate the proposed system. Experimental results revealed that proposed hybrid biometric system is more reliable and more precise comparing to systems using a single biometric method. Owing to its fast recognition it is absolutely suitable for real-time systems.
Background: The next generation of learning called mobile learning (m-learning) provides education to people through mobile devices. The security of mobile systems is an important issue due to users' mobility and use of various devices and different ways of connecting to the network. Therefore, the current study aimed at providing a secure architecture as the basis for use in m-learning software to improve the security of such systems and investigate professors' satisfaction with this architecture. Methods: The current applied study was conducted in 2014. Statistical population included all professors using m-learning in Tabas, Firdaus, and Birjand cities, Southern Khorasan, Iran (26 participants). The purposive sampling method was used. The reliability of the questionnaire was measured using Cronbach's alpha (94%) and the validity based on factor validity (explained percentage of variance) was 63%. According to the use of the web-based e-learning service in Southern Khorasan Payam-e-Noor University (PNU), it was attempted to write a mobile application to provide the use of this service on mobile devices. The difference between the current study provided app and other mobile apps is based on using the proposed secure architecture. To measure the satisfaction of users with the study app, a researcher-made questionnaire was distributed among the professors using the app. Data analysis was performed with SPSS 16 by t test and the one-way analysis of variance (ANOVA). Results: Findings showed that the professors using m-learning application of Southern Khorasan PNU had a positive attitude towards it with the average satisfaction of 80%. The most important factor for this satisfaction was the safety of the presented architecture, while avoiding complex and time-consuming control mechanisms. Conclusions: In the current study a secure architecture was provided for mobile system that was simple, fast, and applicable on all mobile network operators. Given the positive perspectives of professors, this architecture can be a good solution to secure mlearning environments.
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