Most of the information is available in the form of unstructured textual documents due to the growth of information sources (the Web for example). In this respect, to extract a set of events from texts written in natural language in the management change event, we have been introduced an open information extraction (OIE) system. For instance, in the management change event, a PERSON might be either the new coming person to the company or the leaving one. As a result, the Adaptive CRF approach (A-CRF) has shown good performance results. However, it requires a lot of expert intervention during the construction of classifiers, which is time consuming. To palpate such a downside, we introduce an approach that reduces the expert intervention during the relation extraction. Also, the named entity recognition and the reasoning, which are automatic and based on techniques of adaptation and correspondence, were implemented. Carried out experiments show the encouraging results of the main approaches of the literature.

Associative classification is a supervised classification method. Many experimental studies have shown that associative classification is a promising approach. However, the latter suffer from a major drawback: the huge number of the generated classification rules which takes efforts to select the best ones in order to construct the classifier. To overcome such drawback, we propose in this paper a new direct associative classification method called IGARC, an improvement of GARC approach that extracts directly generic associative classification rules from a training set in order to reduce the number of associative classification rules without jeopardising the classification accuracy. Moreover, we propose an algorithm called PN-GARC that deals with negative classification rules. Considering negated items in classification framework provides additional information describing the data and reduces the conflicts while classifying new objects. Nevertheless, there are a sheer number of rules when considering negated items. That is why, we will explore generic classification rules both negative and positive ones in order to study their behaviour and their usefulness on the studied datasets. A detailed description of IGARC method is presented, as well as the experimentation study on 12 benchmark datasets proving that it is highly competitive in terms of accuracy in comparison with popular classification approaches.

Face recognition (FR) in unconstrained conditions remains an open research topic and an ongoing challenge. The facial images exhibit diverse expressions, occlusions, variations in illumination, and heterogeneous backgrounds. This work aims to produce an accurate and robust system for enhanced Security and Surveillance. A parallel hybrid deep learning model for feature extraction and classification is proposed. An ensemble of three parallel extraction layer models learns the best representative features using CNN and RNN. 2D LBP and 3D Mesh LBP are computed on face images to extract image features as input to two RNNs. A stacked autoencoder (SAE) merged the feature vectors extracted from the three CNN-RNN parallel layers. We tested the designed 2D/3D CNN-RNN framework on four standard datasets. We achieved an accuracy of $98.9\%$. The hybrid deep learning model significantly improves FR against similar state-of-the-art methods. The proposed model was also tested on an unconstrained conditions human crowd dataset, and the results were very promising with an accuracy of $95\%$. Furthermore, our model shows an 11.5% improvement over similar hybrid CNN-RNN architectures, proving its robustness in complex environments where the face can undergo different transformations.

Multimedia repository is helpful for educational activities since it offers several illustrations that facilitate the learning process and the text understanding. In this paper, we propose to build a multimedia repository from collected images using object extraction techniques. Then, we associate Arabic captions to all extracted objects. These extracted objects are used to compose new scenes that could illustrate efficiently the most important events in an Arabic story. We extend, thereby the notion of image composing to our approach as the task of constructing new images based on a set of toolboxes and a set of extracted objects as our multimedia repository of common animal behaviors. Our preliminary results show that the composed scenes using single objects provided a fair understanding of the main events of the stories as well as a coherent visual layout of all single objects. The diversity and the precision of the single objects images for the domain of animals have shown a great impact on composing new scenes either manually or dynamically.