COVID-19 Crisis: Forecasting the Arab World Economy Performance Using the ARIMA Model

This paper predicts Coronavirus Disease (COVID-19)'s potential influence on the Arab country's economy by using the Autoregressive Integrated Moving Average (ARIMA) model. The world bank offers data of the Arab countries' Gross Domestic Product (GDP) over the period 1960-2019. As we show up at the pinnacle of the COVID-19 pandemic, quite possibly the most critical inquiry going up against us is: what is the potential impact of the progressing crisis on the Arab countries' economic improvement rate? The results have shown that the GDP growth is approximately -3.8% to 1.5% for 2021 and 2022, respectively. The referenced outcomes show that pandemic status significantly affects the Arab world economy special after the energy demand decline, which prompts a fall in oil price. In spite of the fact that the Arab world's financial development is growing again, it is not most likely going to re-visitation of business as usual for quite a while to come. © 2022 Newswood Limited. All rights reserved.

Forecasting of the american digital economy using ARIMA model

This paper forecasts the digital economy trends during a COVID-19 pandemic in the world. Considered the USA one of the world's largest economies and has recently been shifting almost completely to digital economies. Therefore, this paper used the auto-regressive integrated moving average (ARIMA) model and the gross domestic product (GDP) for the USA over the period 1960-2019. As we arrive at the peak of the COVID-19 pandemic, one of the most squeezing questions confronting us is: what is the likely effect of the ongoing emergency on the digital economy development rate? The results have been shown first that the GDP growth for both years 2020 and 2021 is approximately 6% for the USA. Second, we conclude that the COVID-19 pandemic cannot influence the countries that depend on technology and the digital economy. Thus, technology is playing a very significant role in our daily life and nations' economies. © 2021 IEEE.

Handwritten chemical formulas classification model using deep transfer convolutional neural networks

With the spread of the COVID19 pandemic, blended learning has become one of the most used methods in educational organizations such as universities, community colleges, and schools. In blended learning, the students' practical activities are done in more than one way, including simulation software and the place of study. For chemical experiment programs, the classification of handwritten chemical formulas plays an important role in determining the simulation software's efficiency. Accordingly, in this study, we propose a model for handwritten chemical formula classification. First, this paper describes a handwritten chemical formulas dataset that contains eight classes (HCFD8). Second, convolutional neural networks (CNNs) with pre-trained weights are used as a deep feature extractor to extract features from the images. Third, due to limited training images per class, the proposed model uses data augmentation techniques to expand the training images. Then, an enhanced multilayer perceptron (EMLP) strategy is used to classify the image. Finally, we provide a performance analysis of typical deep learning approaches on HCFD8, which shows that the proposed model performs good accuracy results. © 2021 IEEE.