Intelligent ResNet-18 based Approach for Recognizing and Assessing Arabic Children's Handwriting

Studies tackling handwriting recognition and its applications using deep learning have been promoted by developing advanced machine learning techniques. Yet, a shortage in research that serves the Arabic language and helps develop teaching and learning processes still exists. Moreover, COVID-19 pandemic affected the education system considerably in many countries and yielded an immediate shift to distance learning and extensive use of e-learning tools. An intelligent system was proposed and used in this paper to recognize isolated Arabic handwritten characters. Particularly, pre-trained CNN models were exploited and fine-tuned to meet the requirements of the considered application. Specifically, the designed system automatically supports teaching Arabic letters and evaluating children's writing skills. The Arabic Handwritten Character Dataset (AHCD) was used to train the models built upon ResNet-18 and assess the overall system performance. Furthermore, several models were investigated using various hyper-parameter settings in order to determine the most accurate one. The best model with the highest accuracy rate of 99% was used and integrated into the proposed system to recognize the Arabic alphabets. © 2023 IEEE.

Estimation of the Daily Recovery Cases in Egypt for COVID-19 Using Power Odd Generalized Exponential Lomax Distribution

Covid-19 has become an important topic this days, because of its bad effect in many fields such as Economics, industrial and commerce. In this paper, Covid-19 will be studied statistically point of view depending on the recovery cases in the Arab Republic of Egypt in the interval of (20 March to 20 August 2020). A power odd generalized exponential Lomax distribution has been considered. Some mathematical properties of the distribution are studied. The method of maximum likelihood and maximum product of spacings are used for estimating the model parameters. Also 95% asymptotic confidence intervals for the estimates of the parameters are derived. A simulation study was conducted to evaluate the numerical behavior of the estimates. The proposed methods are utilized to find estimates of the parameters of power odd generalized exponential Lomax distribution for the recovery cases of corona virus in Egypt. © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.