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"Arabisch"
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Arabic handwriting recognition system using convolutional neural network
Automatic handwriting recognition is an important component for many applications in various fields. It is a challenging problem that has received a lot of attention in the past three decades. Research has focused on the recognition of Latin languages’ handwriting. Fewer studies have been done for the Arabic language. In this paper, we present a new dataset of Arabic letters written exclusively by children aged 7–12 which we call Hijja. Our dataset contains 47,434 characters written by 591 participants. In addition, we propose an automatic handwriting recognition model based on convolutional neural networks (CNN). We train our model on Hijja, as well as the Arabic Handwritten Character Dataset (AHCD) dataset. Results show that our model’s performance is promising, achieving accuracies of 97% and 88% on the AHCD dataset and the Hijja dataset, respectively, outperforming other models in the literature.
Journal Article
Automatic recognition of handwritten Arabic characters: a comprehensive review
The paper is a comprehensive review of the current research trends in the area of Arabic language especially state-of-the-art approaches to highlight the current status of diverse research aspects of that area to facilitate the adaption and extension of previous systems into new applications and systems. The Arabic language has deep, widespread and unexplored scope to research although the tremendous effort and researches that had been done previously. Modern state-of-the-art methods and approaches with fewer errors are required according to the high speed of hardware and technology development. The focus of this article will be on the offline Arabic handwritten text recognition as it is one of the most important topics in the Arabic scope. The main objective of this paper is critically analyzing the current researches to identify the problem areas and challenges faced by the previous researchers. This identification is intended to provide many recommendations for future advances in the area. It also compares and contrasts technical challenges, methods and the performances of handwritten text recognition previous researches works. It summarizes the critical problems and enumerates issues that should be considered when addressing these tasks. It also shows some of the Arabic datasets that can be used as inputs and benchmarks for training, testing and comparisons. Finally, it provides a fundamental comparison and discussion of some of the remaining open problems and trends in that field.
Journal Article
Deep learning CNN–LSTM framework for Arabic sentiment analysis using textual information shared in social networks
Recently, the world has witnessed an exponential growth of social networks which have opened a venue for online users to express and share their opinions in different life aspects. Sentiment analysis has become a hot-trend research topic in the field of natural language processing due to its significant roles in analyzing the public’s opinion and deriving effective opinion-based decisions. Arabic is one of the widely used languages across social networks. However, its morphological complexities and varieties of dialects make it a challenging language for sentiment analysis. Therefore, inspired by the success of deep learning algorithms, in this paper, we propose a novel deep learning model for Arabic language sentiment analysis based on one layer CNN architecture for local feature extraction, and two layers LSTM to maintain long-term dependencies. The feature maps learned by CNN and LSTM are passed to SVM classifier to generate the final classification. This model is supported by FastText words embedding model. Extensive experiments carried out on a multi-domain corpus demonstrate the outstanding classification performance of this model with an accuracy of 90.75%. Furthermore, the proposed model is validated using different embedding models and classifiers. The results show that FastText skip-gram model and SVM classifier are more valuable alternatives for the Arabic sentiment analysis. The proposed model outperforms several well-established state-of-the-art approaches on relevant corpora with up to
+
20.71
%
accuracy improvement.
Journal Article
Effects of home quarantine during COVID-19 lockdown on physical activity and dietary habits of adults in Saudi Arabia
Public health endorsements during the present COVID-19 pandemic has led the governments of largely affected countries to imply policies that restrict social mobility to slow COVID-19 spread. The study aimed to explore the effects of COVID-19 home quarantine on lifestyle and health behavior of Saudi residents. An online survey in Saudi Arabia was launched from May 11 to June 6, 2020. The survey was designed by multidisciplinary scientists and academics uploaded and shared through the Google platform in Arabic and English languages. Questions presented related to responses “before” and “during” COVID-19 home quarantine. A total of 1965 respondents participated and were included in the analysis [921 (47.0%) males and 1044 (53.0%) females]. Non-Saudis were more likely to increase their physical activity during quarantine [odds ratio (95% confidence interval 1.41 (1.11–1.79);
p
< 0.005]. Prevalence of participants walking daily for more than 4 times per week significantly decreased during pandemic (before vs during, 30.5% vs 29.1%) which was in parallel to the significant increase in the prevalence of participants who did not perform daily walking during the quarantine (21% vs 22.9%;
p
< 0.001). The prevalence of participants who often consume snacks between meals increased during quarantine (27.4% vs 29.4%,
p
< 0.001), while the prevalence of participants who never consumed fresh fruits and vegetables significantly increased during home quarantine (2.4% vs 3.7%;
p
= 0.019). The lockdown imposed in Saudi Arabia modestly but significantly impacted physical activity and dietary behaviors of several citizens and residents in an unhealthy way. Interventions to alleviate these acute adverse lifestyle behaviors during pandemic should be formulated.
Journal Article
Two-step closure of the Miocene Indian Ocean Gateway to the Mediterranean
The Tethys Ocean was compartmentalized into the Mediterranean Sea and Indian Ocean during the early Miocene, yet the exact nature and timing of this disconnection are not well understood. Here we present two new neodymium isotope records from isolated carbonate platforms on both sides of the closing seaway, Malta (outcrop sampling) and the Maldives (IODP Site U1468), to constrain the evolution of past water mass exchange between the present day Mediterranean Sea and Indian Ocean via the Mesopotamian Seaway. Combining these data with box modeling results indicates that water mass exchange was reduced by ~90% in a first step at ca. 20 Ma. The terminal closure of the seaway then coincided with the sea level drop caused by the onset of permanent glaciation of Antarctica at ca. 13.8 Ma. The termination of meridional water mass exchange through the Tethyan Seaway resulted in a global reorganization of currents, paved the way to the development of upwelling in the Arabian Sea and possibly led to a strengthening of South Asian Monsoon.
Journal Article