Mobile Ad-Hoc network is a collection of mobile nodes in communication without using infrastructure. Despite the importance of type of the exchanged data between the knots on the QoS of the MANETs, the mul-tiservice data were not treated by the larger number of previous researches. In this paper we propose an adaptive method which gives the best performances in terms of delay and throughput. We have studied the impact, respectively, of mobility models and the density of nodes on the performances (End-to-End Delay, Throughput and Packet Delivery ratio) of routing protocol (On-Demand Distance Vector) AODV by using in the first a multiservice VBR (MPEG-4) and secondly the Constant Bit Rate (CBR) traffic. Finally we com-pare the performance on both cases. Experimentally, we considered the three mobility models as follows Random Waypoint, Random Direction and Mobgen Steady-State. The experimental results illustrate that the behavior of AODV change according to the model and the used traffics

BACKGROUND: To combat COVID-19, curb the pandemic, and manage containment, governments around the world are turning to data collection and population monitoring for analysis and prediction. The massive data generated through the use of big data and artificial intelligence can play an important role in addressing this unprecedented global health and economic crisis. OBJECTIVES: The objective of this work is to develop an expert system that combines several solutions to combat COVID-19. The main solution is based on a new developed software called General Guide (GG) application. This expert system allows us to explore, monitor, forecast, and optimize the data collected in order to take an efficient decision to ensure the safety of citizens, forecast, and slow down the spread’s rate of COVID-19. It will also facilitate countries’ interventions and optimize resources. Moreover, other solutions can be integrated into this expert system, such as the automatic vehicle and passenger sanitizing system equipped with a thermal and smart High Definition (HD) cameras and multi-purpose drones which offer many services. All of these solutions will facilitate lifting COVID-19 restrictions and minimize the impact of this pandemic. METHODS: The methods used in this expert system will assist in designing and analyzing the model based on big data and artificial intelligence (machine learning). This can enhance countries’ abilities and tools in monitoring, combating, and predicting the spread of COVID-19. RESULTS: The results obtained by this prediction process and the use of the above mentioned solutions will help monitor, predict, generate indicators, and make operational decisions to stop the spread of COVID-19. CONCLUSIONS: This developed expert system can assist in stopping the spread of COVID-19 globally and putting the world back to work.

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Mobile Ad-Hoc network is a collection of mobile nodes in communication without using infrastructure. As the real-time applications used in today's wireless network grow, we need some schemes to provide more suitable service for them. We know that most of actual schemes do not perform well on traffic which is not strictly CBR. Therefore, in this paper we have studied the impact, respectively, of mobility models and the density of nodes on the performances (End-to-End Delay, Throughput and Packet Delivery ratio) of routing protocol (Optimized Link State Routing) OLSR by using in the first a real-time VBR and secondly the Constant Bit Rate (CBR) traffic. Finally we compare the performance on both cases. Experimentally, we considered the three mobility models as follows Random Waypoint, Random Direction and Mobgen Steady State. The experimental results illustrate that the behavior of OLSR change according to the model and the used traffics.

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The aim of this work is to present a meta-heuristically approach of the spatial assignment problem of human resources in multi-sites enterprise. Usually, this problem consists to move employees from one site to another based on one or more criteria. Our goal in this new approach is to improve the quality of service and performance of all sites with maximizing an objective function under some managers imposed constraints. The formulation presented here of this problem coincides perfectly with a Combinatorial Optimization Problem (COP) which is in the most cases NPhard to solve optimally. To avoid this difficulty, we have opted to use a meta-heuristic popular method, which is the genetic algorithm, to solve this problem in concrete cases. The results obtained have shown the effectiveness of our approach, which remains until now very costly in time. But the reduction of the time can be obtained by different ways that we plan to do in the next work.

A recent informational phenomenon has emerged as one of the considerable innovations in information systems, commonly referred to as "Big Data". The latter is currently trendy, both in academia and industry, and is used to describe a wide range of concepts, from data extraction, storage, and management, to data processing and analysis using well-known schemas, to extract patterns in hidden relationships in order to make better decisions and to derive new knowledge using analytical techniques and solutions. The technology that enables the potential of big data to be exploited is called "Big Data Analytics". Big data analytics is a major challenge that enables researchers, analysts and business users to make better decisions faster. Big Data became an important part of marketing research and marketing strategies. The e-commerce industry is one of the industries that currently benefits most from the potential of big data collection and analysis. This paper therefore aims to demonstrate the use of big data to understand customers and to improve and facilitate the decision making process. In this research, we apply multiple machine learning (ML) models on large dataset present in the e-commerce area by studying several practical cases on online markets.