In general, humans are very complex organisms, and therefore, research on their various dimensions and aspects including personality has become an attractive subject of research works. With the advent of technology, the emergence of a new kind of communication in the context of social NETWORKS has also given a new form of social communication to the humans, and the recognition and categorization of people in this new space have become a hot topic of research that has been challenged by many researchers. In this paper, considering the Big Five personality characteristics of the individuals, first, a categorization of the related works is proposed, and then a hybrid framework based on the fuzzy NEURAL NETWORKS (FNN) and the deep NEURAL NETWORKS (DNN) is proposed, which improves the accuracy of personality recognition by combining different FNN-classifiers with DNN-classifier in a proposed two-stage decision fusion scheme. Finally, a simulation of the proposed approach is carried out. The suggested approach uses the structural features of a social NETWORKS analysis (SNA) along with a linguistic (LA) analysis feature extracted from the description of the activities of the individuals and comparison with the previous similar research works. The results obtained well-illustrate the performance improvement of the proposed framework up to 83. 2% of the average accuracy of the personality dataset.

In recent years applications of artificial NEURAL NETWORKS are extended to the analysis and design of structures. In this paper a general introduction is provided to the NEURAL NETWORKS, and their applications to the problems in the field of structural analysis. NETWORKS with single layer and multiple layers are considered for this purpose. The NETWORKS trained can be used as a fast structural analyzer in nonlinear analysis and optimal design of structures. Examples are included to illustrate the efficiency of the present nets.

In the present paper, an efficient method for three dimensional aircraft pattern recognition is introduced. In this method, a set of simple area based features extracted from silhouette of aerial vehicles are used to recognize an aircraft type from its optical or infrared images taken by a CCD camera or a FLIR sensor. These images can be taken from any direction and distance relative to the flying aircraft. A multilayer perceptron NEURAL network has been used for the purpose of aircraft classification. The network training has been carried out using a library of images generated by a 3D model of each aircraft. The NEURAL network is successfully trained and used to recognize and classify arbitrary real aircraft images. The results show more than 90% accuracy in ideal conditions and very good robustness in the presence of noise.