Information fusion is a key step in multimodal biometric systems. The fusion of information can occur at different levels of a recognition system, i.e., at the feature level, matching-score level, or decision level. However, feature level fusion is believed to be more effective owing to the fact that a feature set contains richer information about the input biometric data than the matching score or the output decision of a classifier. The goal of feature fusion for recognition is to combine relevant information from two or more feature vectors into a single one with more discriminative power than any of the input feature vectors. In pattern recognition problems, we are also interested in separating the classes. In this paper, we present discriminant correlation analysis (DCA), a feature level fusion technique that incorporates the class associations into the correlation analysis of the feature sets. DCA performs an effective feature fusion by maximizing the pairwise correlations across the two feature sets and, at the same time, eliminating the between-class correlations and restricting the correlations to be within the classes. Our proposed method can be used in pattern recognition applications for fusing the features extracted from multiple modalities or combining different feature vectors extracted from a single modality. It is noteworthy that DCA is the first technique that considers class structure in feature fusion. Moreover, it has a very low computational complexity and it can be employed in real-time applications. Multiple sets of experiments performed on various biometric databases and using different feature extraction techniques, show the effectiveness of our proposed method, which outperforms other state-of-the-art approaches.

Virtual reality (VR) is being used for many applications, ranging from medicine to space and from entertainment to training. In this research paper, VR is applied in engineering education, the scope being to compare three major VR systems with the traditional education approach when we do not use any VR system (No-VR). The Corner Cave System (CCS) is compared with the Head Mounted Display (HMD) system. Both of these systems are using a tracking system to reflect the user movements in the virtual environment. The CCS uses only three coordinates: x-, y- and z-axis. The HMD system has six degrees of freedom, the x-, y- and z-axis, as well as the roll, pitch and yaw. Those two systems are also compared with HMD, as a standalone device (HMD-SA) without the tracking system where it has only roll, pitch and yaw. The objective of the study was to evaluate the impact of VR systems on the students’ achievements in engineering colleges. The research examined the effect of the four different methods and compared the scores of the students after each test. The experiments were ran over 48 students. Those systems show incredible results.

To promote sustainable development, the smart city implies a global vision that merges artificial intelligence, big data, decision making, information and communication technology (ICT), and the internet-of-things (IoT). The ageing issue is an aspect that researchers, companies and government should devote efforts in developing smart healthcare innovative technology and applications. In this paper, the topic of disease diagnosis in smart healthcare is reviewed. Typical emerging optimization algorithms and machine learning algorithms are summarized. Evolutionary optimization, stochastic optimization and combinatorial optimization are covered. Owning to the fact that there are plenty of applications in healthcare, four applications in the field of diseases diagnosis (which also list in the top 10 causes of global death in 2015), namely cardiovascular diseases, diabetes mellitus, Alzheimer’s disease and other forms of dementia, and tuberculosis, are considered. In addition, challenges in the deployment of disease diagnosis in healthcare have been discussed.