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Decision support systems (DSS) are increasingly demanded due that diagnosis is one of the main activities that physicians accomplish every day. This fact seems critical when primary care physicians deal with uncommon problems belonging to specialized areas. The main objective of this paper is the development and user evaluation of a mobile DSS for iOS named OphthalDSS. This app has as purpose helping in anterior segment ocular diseases' diagnosis, besides offering educative content about ophthalmic diseases to users. For the deployment of this work, firstly it has been used the Apple IDE, Xcode, to develop the OphthalDSS mobile application using Objective-C as programming language. The core of the decision support system implemented by OphthalDSS is a decision tree developed by expert ophthalmologists. In order to evaluate the Quality of Experience (QoE) of primary care physicians after having tried the OphthalDSS app, a written inquiry based on the Likert scale was used. A total of 50 physicians answered to it, after trying the app during 1 month in their medical consultation. OphthalDSS is capable of helping to make diagnoses of diseases related to the anterior segment of the eye. Other features of OphthalDSS are a guide of each disease and an educational section. A 70% of the physicians answered in the survey that OphthalDSS performs in the way that they expected, and a 95% assures their trust in the reliability of the clinical information. Moreover, a 75% of them think that the decision system has a proper performance. Most of the primary care physicians agree with that OphthalDSS does the function that they expected, it is a user-friendly and the contents and structure are adequate. We can conclude that OphthalDSS is a practical tool but physicians require extra content that makes it a really useful one. [ABSTRACT FROM AUTHOR]

People can use their web browser or mobile devices to access web services and applications which are built into these servers. Users have to input their identity and password to login the server. The identity and password may be appropriated by hackers when the network environment is not safe. The multiple secure authentication protocol can improve the security of the network environment. Mobile devices can be used to pass the authentication messages through Wi-Fi or 3G networks to serve as a second communication channel. The content of the message number is not considered in a multiple secure authentication protocol. The more excessive transmission of messages would be easier to collect and decode by hackers. In this paper, we propose two schemes which allow the server to validate the user and reduce the number of messages using the XOR operation. Our schemes can improve the security of the authentication protocol. The experimental results show that our proposed authentication protocols are more secure and effective. In regard to applications of second authentication communication channels for a smart access control system, identity identification and E-wallet, our proposed authentication protocols can ensure the safety of person and property, and achieve more effective security management mechanisms. [ABSTRACT FROM AUTHOR]

Energy profiling and estimation have been popular areas of research in multicore mobile architectures. While short sequences of system calls have been recognized by machine learning as pattern descriptions for anomalous detection, power consumption of running processes with respect to system-call patterns are not well studied. In this paper, we propose a fuzzy neural network (FNN) for training and analyzing process execution behaviour with respect to series of system calls, parameters and their power consumptions. On the basis of the patterns of a series of system calls, we develop a power estimation daemon (PED) to analyze and predict the energy consumption of the running process. In the initial stage, PED categorizes sequences of system calls as functional groups and predicts their energy consumptions by FNN. In the operational stage, PED is applied to identify the predefined sequences of system calls invoked by running processes and estimates their energy consumption. [ABSTRACT FROM AUTHOR]