Integration of Adaptive Collaborative Learning Process in a Hybrid Learning Environment

Technology integration has been crucial in the practice of the learning process. The use of technology aims to find effective solutions to traditional learning problems. Despite the enormous efforts adopted, using e-learning systems was optional in many education systems. However, the COVID-19 health crisis has shown the importance of the transition to e-learning to ensure pedagogical continuity. According to several studies that have measured the impact of COVID-19 on education systems and the adopted solutions, blended learning represents an effective solution for combining the advantages of face-to-face and distance learning. But the implementation strategies regarding this mode of learning are still limited. For this purpose, we propose a hybrid learning model based on collaborative work through an intelligent assignment of learner roles. This approach aims to support adaptive learning via a hybrid learning environment. The proposed solution is based mainly on collaborative work as an active learning method, using the Naïve Bayes algorithm and Belbin theory. The usefulness of collaborative work is to keep the learning rhythm between face-to-face and distance learning and to encourage learners' engagement and motivation through this mode of learning. According to Belbin's theory, the results of this work propose an adequate role for each learner. This intelligent assignment leads the learner to live the learning situation and not undergo it. © IJASEIT is licensed under a Creative Commons Attribution-Share Alike 4.0 International License.

CNN-Based Deep Features with Ensemble Learning for COVID-19 Classification

In this paper, we propose a hybrid system that can automatically detect coronavirus disease and speed up medical image analysis processes by using artificial intelligence technique. Our system consists of two parts: First, to perform feature extraction, we used a deep convolutional network that is based on the transfer learning technique, in this step, we include eight well-known convolutional neural networks for comparison purposes. In the second part, a voting classifier is considered, combining three classifiers, including random forest (RF), support vector machine (SVM), and K-nearest neighbor (KNN), to classify radiological images into three classes: COVID-19, normal, and pneumonia, collected from two public medical repositories. The results show that deep learning and radiological images are able to retrieve relevant COVID-19 features with an accuracy of 96.87%. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Adaptive Collaborative Learning Process in a Hybrid Model

Technological progress has led to the integration of technology into the practices of the learning process. The aim of this integration is to overcome the deficiencies of traditional methods to ensure greater efficiency. Despite the technology revolution, the adoption of e-learning has always been a choice in the educational process. The COVID 19 crisis has shown the need for a total transition to e-learning during the period of lockdown. According to several studies that have assessed the impact of COVID 19 on their education systems and the solutions adopted, hybrid learning represents an adequate solution to benefit from the advantages of both modes: face-to-face and distance learning. For this purpose, we propose in this paper a hybrid learning model based on an adaptive collaborative work through an intelligent assignment of the group member's roles by using Naïve Bayes algorithm and Belbin theory. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

An improved approach to resolve a combinatorial optimization problem based CoronaVirus Optimization Algorithm

Combinatorial optimization problems refer to intractable problems that can't be performed using exact methods. The resolution of combinatorial problems geared towards the application of heuristics, metaheuristics also matheuristics, in order to provide good enough approximations. As exact methods provide resolution corresponding to small problem scale, the approximation methods target large scale of complex problems. Metaheuristics are used to deploy intelligent methods to solve complex problems in a reasonable amount of time. The performance of a metaheuristic is improved by means of parameters adjustment as well as, hybridization within heuristics, iterative improvement methods or various metaheuristics. The cooperation of several optimization algorithms leads to improve resolution, also to overcome the limitations reported in resolving NP-hard problems. The resolution of complex problems, is thus constrained by stagnation on local optimums, as the optimization process is possibly stagnant on a specific search space region. In fact, traveling salesman problem is a combinatorial problem, that arises problematics related to the efficiency of its resolution methods. The aim of this work is to investigate on the improvement of a new bio-inspired method so-called coronavirus optimization algorithm in order to provide improved resolutions to traveling salesman problem. Various intelligent approaches are investigated and hybridized within coronavirus optimization algorithm, namely random replicate operator, elitist selective replicate operator, iterated local search, stochastic hill-climbing also improved self-organizing map. The numerical results are obtained using symmetric TSPLIB benchmarks. © The Authors.

Adaptive Collaborative Learning Process in a Hybrid Model

Technological progress has led to the integration of technology into the practices of the learning process. The aim of this integration is to overcome the deficiencies of traditional methods to ensure greater efficiency. Despite the technology revolution, the adoption of e-learning has always been a choice in the educational process. The COVID 19 crisis has shown the need for a total transition to e-learning during the period of lockdown. According to several studies that have assessed the impact of COVID 19 on their education systems and the solutions adopted, hybrid learning represents an adequate solution to benefit from the advantages of both modes: face-to-face and distance learning. For this purpose, we propose in this paper a hybrid learning model based on an adaptive collaborative work through an intelligent assignment of the group member's roles by using Naïve Bayes algorithm and Belbin theory. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

DenTcov: Deep Transfer Learning-Based Automatic Detection of Coronavirus Disease (COVID-19) Using Chest X-ray Images

On 31 December 2019, COVID-19, a novel coronavirus, appeared for the first time in the Chinese city of Wuhan, to act as a preliminary warning and affected a wider human being in the world. This virus, declared a pandemic by the auspices of the World Health Organization (WHO), given its high rate of transmissibility. The protocol most often used to detect the virus is PCR. It is a time-consuming and less sensitive procedure with high false-negative results. These problems are solved through radiographic imaging techniques to detect radioactive symptoms related to COVID-19. Furthermore, significant time is required to complete the analytical task, and mistakes can occur, meaning that automation is necessary. The use of advanced Artificial intelligence tools can significantly accelerate both the time and quality of the analysis. We suggest DenTcov, a computer-aided approach to detect COVID-19 infection via chest X-ray images. Our model is a two-phase process: Phase (1) Pre-Processing and data augmentation, Phase (2) COVID-19 detection based DensNet121, a pre-trained model, then trained with the dataset prepared by us. During the experimental phase of DenTcov, we measure the performances of the architecture by calculating a set of common metrics, both 2-class and 3-class classification. The experimental assessment confirms the DenTcov model offers a 96.52 and 99% higher classification accuracy for three and two classes, respectively, compared to other proposed methodologies. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

A New Optimized Approach to Resolve a Combinatorial Problem: CoronaVirus Optimization Algorithm and Self-organizing Maps

The optimization provides resolutions to complex combinatorial problems that generally deal within large data size and expensive operating processes. Metaheuristics target the promoting of new applied algorithms to resolve NP-Hard problems in order to improve resolutions requiring enhanced search strategies. Travelling Salesman Problem (TSP) is a common combinatorial problem, applied on several benchmark networks, namely the transportation networks and the routing vehicle problem in order to establish new intelligent computing methods as well as, to prove studies on their performances and efficiencies. Collective intelligence has proven satisfactory resolutions wherewith metaheuristics, although their algorithms complexity. The aim of this work is to solve the Euclidean TSP, classified as a NP-hard problem by means of Self-Organizing Maps (SOM) which is a Kohonen-type network. The resolution is also computed corresponding to a new bio-inspired evolutionary strategy so-called coronavirus optimization algorithm combined with SOM algorithm. The present approach is combining an unsupervised learning strategy within the new coronavirus optimization algorithm to replicate iteratively new infected individuals and to generate diversification on the search space. This new hybrid method presents a good approximative resolutions, proved by applying tests for TSPLIB instances wherein the exact optimum is defined corresponding to each TSP data. However, the present resolutions are complex specifically for large scale by means of increasing the size of input data or size parameters. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Coronavirus pandemic in Morocco: Measuring the impact of containment and improving the learning process in higher education

The higher education in Morocco knows a real challenge due to the consequences of covid19. This challenge was effect by the transformation of the teaching mode from face to face (learning at school) into a distance learning (home based learning). This paper reports comparative studies of technologies that used in Moroccan Higher education and the constraints encountered the E-learning mode. The objective of this article is to describe how to success the higher education in this period of confinement via a case study and recommend a proposed solution. The experiments and results that presented in this article are based on data which collected from a private professional training institution and the collaboration of the learners in the field of study. © 2020 by the authors.