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During the COVID-19 pandemic, the usage of e-learning systems became the main challenge for many universities. E-learning has risen as cutting-edge method for promoting learning delivery. To ensure productive use, it is important to continue using e-learning. Numerous studies have shown that continued usage by the user is the indicator of success in e-learning, and in recent years, research on continued use of e-learning is being explored at a higher level than before. However, to date, there have been no attempts to systematically analyse these studies in order to provide researchers and practitioners with a picture of the current state of continued usage of e-learning. The aim of this research is to provide an in-depth look at the theory of continued use information systems in e-learning context. In this study, we used a systematic review approach to collect, evaluate, and synthesize data on the accuracy and value of previous articles published in digital databases between 2009 and 2019 that were based on this research area. To include all relevant research papers that were written during this period time, we used a Systematic Literature Review (SLR) approach to collect and review studies by following a predefined review process that included both automated and manual search strategies. We listed 87 primary studies from the review study that presented research on the continued use of e-learning. These studies were analysed using a comprehensive mapping method that collected relevant information to address a series of research questions. We summarized and analysed the published articles, which covered a wide range of research subjects, including the majority of factors that affect e-learning use. While research on the continued use of e-learning is growing and providing a promising new field of research, the systematic review found that a clearer understanding of the environment and path is not well reported. This research will contribute to a better understanding of the factors that affect e-learning use over time

Android malware is one of the most dangerous threats on the Internet.  It has been on the rise for several years.  As a result, it has impacted many applications such as healthcare, banking, transportation, government, e-commerce, etc.  One of the most growing attacks is on Android systems due to its use in many devices worldwide.  De-spite significant efforts in detecting and classifying Android malware, there is still a long way to improve the detection process and the classification performance.  There is a necessity to provide a basic understanding of the behavior displayed by the most common Android malware categories and families.  Hence, understand the distinct ob-jective of malware after identifying their family and category.  This paper proposes an effective systematic and functional parallel machine-learning model for the dynamic detection of Android malware categories and families.  Standard machine learning classifiers are implemented to analyze a massive malware dataset with 14 major mal-ware categories and 180 prominent malware families of the CCCS-CIC-AndMal2020 on dynamic layers to detect Android malware categories and families.  The paper ex-periments with many machine learning algorithms and compares the proposed model with the most recent related work.  The results indicate more than 96 % accuracy for Android Malware Category detection and more than 99% for Android Malware family detection overperforming the current related methods.  The proposed model offers a highly accurate method for dynamic analysis of Android malware that cuts down the time required to analyze smartphone malware.

The Moroccan education system has undergone several reforms in recent years, thus concluded by the strategic vision, especially project 9 which has valued the continuing training of educational actors and framework law 51-17 in articles 34 and 35 which valued the continuous development of skills of teachers in Morocco, In this perspective the Ministry of National Education has reserved significant budgets to operationalize the continuing training of teachers on the one hand and it is committed to a Moroccan-American partnership within the framework of the Millennium project Challenge Account-Morocco. By committing to the establishment-challenge project, the ministry has set up a set of priorities, in particular the operationalization of the digital strategy for schools declared by decree 2.20.474, thus benefiting from the contribution of digital and while trying to overcome some constraints related to face-to-face training, a new regime that is continuous online training is implemented, it is the e-takwine platform. In addition, given the interest that evaluation represents for maintaining and improving the quality of a digital training device, we opted in this research, by evaluating the technopedagogical modality of e-takwine reserved at the start of the continuing training of novice teachers in Morocco and then developed for the training of all second cycle teachers from the start of the 2021-2022 school year. The sampling of our research is represented by 362 professors from the region of Fés –Meknes belonging to two different teaching cycles, and who followed their training for three months in six training modules in a more or less assiduous manner, Similarly, to conduct our survey, we administered two online questionnaires to participating teachers, in order to judge the functionality and ergonomics of the platform being researched. Finally, confirming to us that despite the satisfaction of the majority of participants with respect to the functional quality and the contribution of the device, some difficulties were noted at the levels: technical (compatibility, accessibility), functional or even with regard to technological knowledge.

This paper presents the design, simulation and realization of a patch antenna for IOT applications. The patch antenna consists of a radiating element printed on one face of a dielectric substrate, when the ground plane is placed on the other face. In this work, two techniques are used to design a miniaturized patch antenna: the set-up of slots on the radiating element and the use of defective ground plane. Also, the slot’s radius and Length of inset point effects on the performances of the antenna is illustrated. All the simulated results are performed with FEKO, a solver based on a Moments Method and measurement is made using Vector Network Analyzer Anritsu MS2026C. The propose antenna resonates in three frequency bands 3.91, 4.86 and 5.16GHz for different characteristics such as radiation pattern, gain, return loss, which makes it suitable for many wireless communication applications such as IoT applications.

Limited energy capacity, physical distance between two nodes and the stochastic link quality are the major parameters in the selection of routing path in the internet of things network. To alleviate the problem of stochastic link quality as channel gain reinforcement based Q-learning energy balanced routing is presented in this paper. Using above mentioned parameter an optimization problem has been formulated termed as reward or utility of network. Further, formulated optimization problem converted into Markov decision problem (MDP) and their state, value, action and reward function are described. Finally, a QRL algorithm is presented and their time complexity is analyses. To show the effectiveness of proposed QRL algorithm extensive simulation is performed in terms of convergence property, energy consumption, residual energy and reward with respect to state-of-art-algorithms.

Security is one of the most important aspects of data transmission. To provide controlled access to the network, user authorization and authentication are often used with the help of an AAA server. RADIUS servers provide users with access to data, user authentication, and configuration information. When designing networks with such access control method implementation, it is necessary to understand how the characteristics of the communication channel affect the switching time of IP-TV channels, and therefore the overall quality of IP-TV services. The principles of the main protocols for IP-TV using a RADIUS server are described. The main parameters of the communication channel were identified. The mathematical model and the graphs demonstrate how IP-TV service access time depends on telecommunication channel parameters. The results of a practical experiment are presented to prove the formed mathematical model. The results of a practical experiment and theoretical calculation are compared.

People are used to exploring grayscale images in their family albums but it is difficult to grasp the reality without colours. Luckily, with advancements in Machine Learning it has been possible to solve problems previously thought impossible. The authors aim to automatically colourize grayscale images using a subset of Machine Learning called Deep Learning. The system will be trained on an image dataset and given an input grayscale image the model will be able to assign aesthetically believable colours. A grayscale photograph has been provided; our approach solves the problem of visualizing a reasonable colour version of the grayscale picture. This issue is undoubtedly under controlled; therefore earlier methods to this problem have either counted majorly on user interaction or it leads to in unsaturated colourizations. The authors put forward a completely automatic approach that will try to produce realistic and vibrant colourizations as much as possible. The proposed system has been applied as a feed-forward in a Convolutional Neural Network and has been trained on over twenty thousand colour images currently.

Falls are a serious concern among the elderly people, causing severe physical pain to them and placing a strain on medical infrastructure. The global elderly population is expected to grow significantly in the coming years, as advances in healthcare allow lifespans to increase globally. This will bring more chances for falls to occur. With this in mind, there is a need for new research to be conducted on finding ways to reduce this problem. One area which shows promise is the use of Machine Learning to perform fall detection. Machine Learning is a rapidly growing field, and it has many applications in various fields such as finance, technology and medicine. When it comes to fall detection, Machine Learning systems are often able to detect falls much better and efficiently than a human can, given the same input data. The goal of this paper is to conduct a survey study on the main and most common machine learning algorithms implemented in the field of early fall detection for elderly people and the characteristics. The paper will discuss the different types of fall detection systems, algorithms, tools, datasets, applications, and challenges.  By conducting this research, a better understanding of the context, progress and trends in the field will be possible so that future research will have a guide to build upon.

To ensure the continuity of the training activity within the framework of the specific precau-tions for the Covid 19 health crisis , the regional centers for training and education profes-sions, such as universities and schools, have radically reversed their training strategy, from face-to-face training to alternating training mode known as `` hybrid training mode '', the training of which is provided remotely one week and face-to-face during the following week and vice versa. In this sense, digital platforms of social networks and videoconferencing soft-ware have been mobilized to succeed in the mission of trainers.  The objective of this article is to verify the contribution of videoconferencing software '' on training practices on the one hand and on the development of trainees' skills on the other hand, in order to trace a training methodology based on technological choices aimed at guar-anteeing maximum socio-professional performance. Finally, we confirm that despite the satisfaction of the majority of trainees 87% with respect to the advantages of videoconferencing tools in the process of assessing skills, technical, spa-tiotemporal and organizational disparities were noted among trainers compared to the use of digital tools in the evaluation (only 53% agree), or also in relation to the transition from one technological choice to another. 

The real-time video surveillance system has become an integral part of our life and Highways play a very crucial role in transportation. For a transportation system to work, the management of highways are necessary. It also prevents accident and other challenging issues on highways. Various machine learning and artificial intelligence based techniques are evolving with numerous advancement in this domain. These algorithms are efficient and very less time consuming. So the use of machine learning and artificial intelligence in transportation systems and highways could be very beneficial. In this paper, various approaches related to moving vehicle detection for the transportation system especially for highways are considered. The literature also reveals for existing research for the machine learning and AI based methodologies to resolve more complex real-time problems. The proposed work is also compared with the existing peer methods and demonstrated better performance achieved experimentally.

The aim of this study is to evaluate the performance of the pre-trained models and compare them with the probability percentage of prediction in terms of execution time. This study uses the COCO dataset to evaluate both pre-trained image recognition and object detection, models. The results revealed that Tiny-YoloV3 is considered the best method for real-time applications as it takes less time. Whereas ResNet 50 is required for those applications which require a high probability percentage of prediction, such as medical image classification. In general, the rate of probability varies from 75% to 90% for the large objects in ResNet 50. Whereas in Tiny-YoloV3, the rate varies from 35% to 80% for large objects, besides it extracts more objects, so the rise of execution time is sensible. Whereas small size and high percentage probability makes SqueezeNet suitable for portable applications, while reusing features makes DenseNet suitable for applications for object identification.

This paper surveys recent publications on maritime logistics with a focus on blockchain capabilities. Maritime logistics plays an important role in global trade and an important part of the global economy depends on it. The growth of world trade has led to the creation of various systems that are prone to error and fraud, the current supply chain needs to connect and trust many intermediaries which not only increase abuse and errors but also it will increase cost and time in the chain. Hence, transparency and stability are highly demanded. On the other hand, with the onset of the fourth industrial revolution, a new approach called “blockchain” was introduced which can have very important applications in maritime logistics. As this novel technology is in its early stages of development, its applications in maritime logistics have not been specifically investigated. So, this paper takes a deep dive into blockchain technology capabilities for maritime logistics by reviewing relevant scientific journal papers since 2014. The paper also discusses and challenges the current operational paradigms and by categorizing efforts, provides a taxonomy of research topics in maritime logistics. Finally, the paper in addition to presenting challenges, identifies new avenues for academic research based on current trends and development.

This study compared the performance of the partial least squares-structural equation modelling (PLS-SEM) and the robust partial least squares -structural equation modelling (RPLS-SEM) methods through Winsorisation approach The inputs and the outputs used in this model were based on the electricity generation data, derived from the Al-Zawiya Steam Power Plant, Libya. Furthermore, the researchers compared the novel RPLS-SEM approach with the traditional PLS-SEM approach and noted that the novel RPLS-SEM method was more efficient compared to PLS-SEM.