Massive open online courses (MOOCs) expansion encounter many challenges related to different aspects, from learner’s perspective to teacher’s perspective, to technological aspects. Previous studies demonstrated the prevailing challenges pertaining to Academic contexts using both qualitative analysis and quantitative analysis of different samples from different countries. In our study, we have tried to look at practices from all over the world regarding MOOCs implementation, we presented a summary of the major challenges of MOOCs as well as their strengths vis as vis the open education in both general and academic contexts. Following the steps of Khan’s framework for online learning, our ultimate objective was to come up with an approach that can bridge the challenges that hinder current effective delivery of MOOCs and calls for metrics that respect the results of previous work in the implementation of blended learning in the Academic context.

The Covid-19 emergency has brought a mandatory shift to online systems in the education sector worldwide. This document gives an overview about the online teaching challenges encountered from the teachers’ point view, restitutes how the teacher’s role in online settings can be determining in the successfulness of the learning experience and more importantly provides insights into Artificial Intelli-gence techniques that can solve the equation of transferring the role of teachers in face-to-face settings to distance learning environments.

Information systems are becoming more and more complex and closely linked due to the exponential use of internet applications. These systems are encountering an enormous amount of traffic, this traffic can be a normal one destined for natural use or it may be a malicious one intended to violate the security of the system. Therefore, a defense method needs to be in place. One of the commonly used tools for network security is the Intrusion Detection System (IDS). An IDS, while ensuring real - time connectivity, tries to identify fraudulent activity using predetermined signatures or pre-established network behavior while monitoring incoming traffic. Intrusion detection systems based on signature or behavior cannot detect new attacks and fall when small deviations occur. Also, current anomaly detection approaches suffer often from high false alarms. As a solution to these problems, machine learning techniques are a new and promising tool for the identification of attacks. In this paper, the authors present a hybrid approach, combining artificial neural networks and a hybrid clustering algorithm based on k-means and genetic algorithm called GenClust++. The final framework leads to a fast, highly scalable and precise packets classification system. We tested our work on the newly published dataset CICIDS 2017. The overall process was fast, showing high accuracy classification results.

The analysis of massive data is becoming more and more critical. One of the systems that process real-time data are computer networks. The data flowing through these networks is enormous and requires technicality to manage it better, and the most central characteristics of these systems is to ensure security. To ensure this task, administrators use intrusion detection systems (IDSs). The major problems with these systems are the false positive and the speed of the system to process data and analyze it. For this, we present an optimization of the existing methods based on artificial neural networks, through combining two machine learning procedures; unsupervised clustering followed by a supervised classification framework as a Fast, highly scalable and precise packets classification system.