درخت حوزه‌های تخصصی

This research deals with integrating RE and CBC in the product design process. RE improves the product value based on evaluate the competition products, whereas CBC aims to use a process approach to define activities and prelates those activities to products or customers using the product’s Characteristics. Integrating RE and CBC together leads to the improvement of product value, an increase of customer satisfaction, and support of competitive advantage. In this study first, a conceptual model of integration of these two approaches is provided and then the implementation procedures in product design cycle are explained, and finally, the results got from implementing it in an Electronic Industries company are discussed.

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.

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.

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.

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.

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 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.