ABSTRACT
This article reviews some governments' strategies in the field of digital education especially during the recent pandemic period in the European Union. It includes some early lessons from the COVID-19 crisis. Besides, the authors cover various essential remote teaching tools for organizing effective virtual courses by evaluating and analyzing e-learning and distance education in terms of threats, challenges, opportunities, strengths, and weaknesses. Finally, this study presents a methodology with two hypotheses using SPSS statistics v26 to perform a survey on the effect of digital transformation. During the COVID-19 pandemic in Budapest, the participants' perceptions of the disease and the efficiency of the enforced restrictions to curb its spread were studied. The study methodology includes statistical data and analytical outcomes.
ABSTRACT
The COVID-19 pandemic has affected mobility and caused a significant impact on the transportation sector. The research aims to assess the effects of the pandemic by conducting a survey questionnaire in two capitals Budapest - Hungary and Amman - Jordan. The study will assess the impact of the digital transformation on work, learning, and services as well as the degree of passenger satisfaction with public transportation (PT) and how participants evaluate the digital transformation associated with the pandemic. The data was analyzed using AMOS with SPSS software v.26, the Structural Equation Models SEM test and emphasis the hypotheses, it has been found that the impact of the pandemic on mobility exceeds the traditional limits and applicable restrictions. Such studies can benefit risk management researchers, decision-makers, and planners. © 2022 IEEE.
ABSTRACT
Covid-19 is a global health emergency and a major concern in the industrial and residential sectors. It has the ability to spread leading to health problems or death. Wearing a mask in public locations and busy areas is the most effective COVID-19 prevention measure. Face recognition provides an accurate method that overcomes uncertainties such as false prediction, high cost, and time consumption, as it is understood that the primary identification for every human being is his face. As a result, masked face identification is required to solve the issue of recognizing individuals with masks in several applications such as door access systems and smart attendance systems. This paper offers an important and intelligent method to solve this issue. We propose deep transfer learning approach for masked face human identification. We created a dataset of masked-face images and examined six convolutional neural network (CNN) models on this dataset. All models show great performance in terms of very high face recognition accuracy and short training time © 2022. International Journal of Advanced Computer Science and Applications.All Rights Reserved
ABSTRACT
The purpose of this research is to explain fuzzy logic, specifically Fuzzy Transportation Problem FTP which is considered more appropriate for planning and decision making in transportation, since the primary strength of a fuzzy approach is that it is applicable to human knowledge and the deductive process fuzzy is perfect method in engineering public transportation research. Humans can manage complicated tasks under significant ambiguousness, this ambiguous information is represented through human linguistic terms and is used for decision making concerning urban planning, mobility and passengers satisfaction. The study also refers to the methods under fuzzy technology with safety technique for fuzzy modelling mobility during COVID-19 pandemic and presents a description of the public transportation system as part of the literature review to detect and improve the performance of mobility. Results show how the applied fuzzy approach can work as a powerful tool for the appraisal of the transport service.
ABSTRACT
The Coronavirus COVID-19 has been considered a pandemic due to its rapid spread increasing the number of affected cases and causing severe health issues and deaths all over the world. Meanwhile no particular treatment or vaccination has been identified for this disease, and therefore, the initial and early identification is crucial to control and break down the chain of COVID-19. In this research, a smart fuzzy inference system is proposed for initial identification of COVID-19 based on the patient symptoms and travel and contact history. The symptoms include cold, cough, fever, flu, breathing difficulties, throat pain and headache. Based on a particular patient data, the proposed system predicts the severity level of the disease that he/she has. © 2020 IEEE.
ABSTRACT
The coronavirus (COVID-19) pandemic has caused severe adverse effects on the human life and the global economy affecting all communities and individuals due to its rapid spreading, increase in the number of affected cases and creating severe health issues and death cases worldwide. Since no particular treatment has been acknowledged so far for this disease, prompt detection of COVID-19 is essential to control and halt its chain. In this paper, we introduce an intelligent fuzzy inference system for the primary diagnosis of COVID-19. The system infers the likelihood level of COVID-19 infection based on the symptoms that appear on the patient. This proposed inference system can assist physicians in identifying the disease and help individuals to perform self-diagnosis on their own cases. © 2021