Facial expressions constitute one of the most effective and instinctive methods that allow people to communicate their emotions and intentions. In this context, the both Machine Learning (ML) and Convolutional Neural Networks (CNNs) have been used for emotion recognition. Efficient recognition systems are required for good human-computer interaction. However, facial expression recognition is related to several methods that impact the performance of facial recognition systems. In this paper, we demonstrate a state-of-the-art of 65% accuracy on the FER2013 dataset by leveraging numerous techniques from recent research and we also proposed some new methods for improving accuracy by combining CNN architectures such as VGG-16 and Resnet-50 with auxiliary datasets such as JAFFE and CK. To predict emotions, we used a second approach based on geometric features and facial landmarks to calculate and transmit the feature vector to the SVM model. The results show that the ResNet50 model outperforms all other emotion prediction models in real time by maximizing.

Communications are required to operate in dynamic environments with random mobile devices in wireless networks, particularly in mobile ad hoc networks (MANETs). This unpredictable mobility is, in the most case, the principal cause of links breakage between nodes and their MPRs (Multi-point relays). To reduce the mobility effect in this kind of networks, we focus on the link lifetime. Therefore, we propose a new metric named RTTQ (Remaining Time To Quite) that uses distance and radio scoop to predict links remaining lifetime between nodes and their neighboring. Through extensive simulation using NS2 (Network Simulator), we proved that we can increase several performance namely MPRs lifetime, packet Delivered Ratio (PDR) and Average Throughput Traffic (ATT), by choosing as MPRs nodes with RTTQ greater than a given threshold.

There exist many problems encountered in Mobile Networks such as security, energy, bandwidth and routing problems. All these problems are caused by the high mobility of nodes. The objective of this paper is to propose a new way for nodes' mobility measuring based on OLSR Protocol. The parameter used in the formula of nodes mobility calculation in the Mob-OLSR protocol is automated witch introduce a new version of Mob-OLSR called Mob-2-OLSR. This version helps improving the network performance. The impact of mobility and pause time on the behavior of the three versions of OLSR protocol: standard OLSR, Mob-OLSR, and Mob-2-OLSR is also examined.

The radio link between a pair of wireless nodes is affected by a set of random factors such as transmission range, node mobility, and environment conditions. The properties of such radio links are continually experienced when nodes status balances between being reachable and being unreachable; thereby on completion of each experience the statistical distribution of link lifetime is updated. This aspect is emphasized in mobile ad hoc network especially when it is deployed in some fields that require intelligent processing of data information such as aerospace domain.

The most complex problems, in data science and more specifically in artificial intelligence, can be modeled as cases of the maximum stable set problem (MSSP). this article describes a new approach to solve the MSSP problem by proposing the continuous hopfield network (CHN) to build optimized link state protocol routing (OLSR) protocol cluster. our approach consists in proposing in two stages: the first acts at the level of the choice of the OLSR master cluster in order to quickly make a local minimum using the CHN, by modeling the MSSP problem. As for the second step, the objective is the improvement of the precision making a solution of efficient at the first rank of neighborhood as a linear constraint, and at the end, to find the resolution of the model using the CHN. We will show that this model determines a good solution of the MSSP problem. To test the theoretical results, we propose a comparison with a classic OLSR.