ABSTRACT
The first contact of freshmen students with computer thinking and programming languages is not an easy task. There are several strategies that can be used before, during and after face-to-face classes. Flipped classes are a way to save time in activities that can be better done in the form of previous work - guided by the teacher. A n d the time gained in person can be used in tasks that are much more productive than traditional lectures. This study is based on an introductory programming semester of post lockdown COVID-19, initially with 101 students, with the strategy of providing study materials for students to work on before class (as in flipped classes). We use attendance in classes, acce ss-work MOODLE, the two grades obtained during the semester, as well as some information about the student (age, course, gender, previous knowledge of programming languages), and the level that the students think it was their presence in classes and on MOODLE in the middle and at the end of the semester to measure the success of the experiment. It seems that this type of strategy can be excellent for students who attend classes weekly and do their homework, but it can be a cause of dropout if taken to the extreme. © 2022 IEEE.
ABSTRACT
Food industry has a significant impact on human life. However, this industry has faced many challenges in Jordan in the last decade. These challenges might be globally or locally and effecting its supply chain. Nowadays, the term e-food retailing services have become viral. These services can organize the procedure of the food sector and its supply chain. Also, it will save time and effort for consumers and provides privacy, security, reliability, and service availability. In the study, we investigated the factors that might encourage or concern people to use e-food retailing services instead of traditional food retailing services to help the conventional food retailing service operator pay attention to these factors when transferring to online service. Therefore, we developed a survey to discover these factors. Based on 78 responses, we found that delivery time and cost, food quality, food freshness, service availability, and reliability are the top factors that concern people using e-food retailing services. However, adding some to the e-food applications such as quickly reordering their previous orders, easily reviewing their previous invoices, getting a competitive price, providing special offers, providing loyalty points, privacy, security, friendliness of the user interface, and quickly finding information about the food, can encourage people to use e-food retailing applications. In addition, most participants emphasized that COVID-19 encouraged them to use e-food retailing services. © 2022 IEEE.
ABSTRACT
Travel time plays a vital role in people's daily lives. It can help them not merely avoid traffic congestion but save time as well. When people need to drive to different cities by taking highways, travel time become more and more important now that they can check it to arrange better routes. Moreover, because COVID-19 are epidemic across Taiwan, people prefer to drive rather than taking public transportation. Therefore, accurate predictions of travel time is of great significance. In order to obtain precise predictions and correspond to situations in real life, we divide data into long and short sequences and create three types of dataset, including the whole year, only national holidays, and non-holidays. Additionally, on account of the interactive influence of time in different segments of the freeway, we exploit data to predict next-hour travel time instead of next 5 minutes. We introduce a deep learning model which hybrids tendency from XGBoost and recency embeddings from a fully-connected neural network, respectively. It can capture crucial features of both long and short sequences and observe implicit correlations between XGBoost and a fully-connected neural network. Extensive experiments on the dataset illustrate that our model achieves eminent performances and outperforms other state-of-the-art models. © 2021 IEEE.
ABSTRACT
According to the figures obtained by the Department of Health (DOH), COVID-19, the worldwide pandemic, has had an enormous global impact, infecting a growing population and causing over three million fatalities.Because of the global epidemic, governments all over the world were obliged to institute lockdowns in order to prevent virus transmission. The use of face masks and safe social distance, according to sources, are two of the best safety precautions to be observed by the public to avoid the transmission of the virus. Modern deep neural network models are combined with geometrical approaches to develop a strong model that incorporates three components of the system's detection, monitoring, and testing. As a consequence, the technique presented saves time while reducing corona virus transmission.It might be used efficiently in the current circumstances, where lockdown is being loosened in order to inspect people at public meetings, retail malls, and other locations. Automated inspection saves time and money by reducing the number of people needed to examine the public, and it can be used everywhere to ensure safety. © 2021 IEEE.
ABSTRACT
The spread of COVID-19 disease rapidly worldwide and the increase in deaths are a threat to humanity. This threat prompted researchers in deep learning (DL) to find ways to diagnose COVID-19 from computed tomography (CT) or x-rays. Working deep learning identifies the infection accurately through medical imaging, and the practising radiologist can diagnose the illness. This survey will discuss the reason behind deep learning and the technology used in medical image processing. Exposure to the most common research in the recent period uses deep learning techniques in the medical field. We will then collect research related to diagnosing COVID-19 by using medical images, studying them, discussing the better future suggestion and methods proposed by other researchers. We focus on initial available research that detects COVID-19 by deep learning and sees how they can save time and effort in this field. © 2021 IEEE
ABSTRACT
Amidst the deadly COVID-19 pandemic situation, the increasing number of cases is a major concern especially at places where tests are not available easily, are inconvenient and results take a long time to be declared. We present a solution by which tests can be performed easily by individuals with the aid of a mask. It involves no hazard and saves time for immediate treatment of positive patients. To enhance the efficiency of the product, we have also incorporated a predictive model using machine learning which produces outcome based on real life scenarios. © 2021 IEEE.
ABSTRACT
This paper presents the design and development of an efficient and intelligent chatbot. The proposed idea is to create a health companion which can be used for a daily well-being. An agile approach was used for its development and deployed on a platform such that all types of users can communicate with it. This virtual assistant makes our life easier and saves time via automated user query replies. Responding to user queries regarding home remedies as well as the recent pandemic COVID-19, the bot is well-equipped and reliable. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.