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"Arabisch."
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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
The Economic Incentives of Cultural Transmission: Spatial Evidence from Naming Patterns Across France
This paper studies how economic incentives influence cultural transmission, using a crucial expression of cultural identity: Child naming decisions. Our focus is on Arabic versus Non-Arabic names given in France over the 2003-2007 period. Our model of cultural transmission features three determinants: (i) vertical (parental) cultural transmission culture; (ii) horizontal (neighborhood) influence; (iii) information on the economic penalty associated with Arabic names. We find that economic incentives largely influence naming choices: Would the parental expectation on the economic penalty be zero, the annual number of babies born with an Arabic name would be more than 50 percent larger.
Paper
Observed rainfall changes in the past century (1901-2019) over the wettest place on Earth
Changes in rainfall affect drinking water, river and surface runoff, soil moisture, groundwater reserve, electricity generation, agriculture production and ultimately the economy of a country. Trends in rainfall, therefore, are important for examining the impact of climate change on water resources for its planning and management. Here, as analysed from 119 years of rainfall measurements at 16 different rain gauge stations across northeast India, a significant change in the rainfall pattern is evident after the year 1973, with a decreasing trend in rainfall of about 0.42 ± 0.024 mm dec−1. The wettest place of the world has shifted from Cherrapunji (CHE) to Mawsynram (MAW) (separated by 15 km) in recent decades, consistent with long-term rainfall changes in the region. The annual mean accumulated rainfall was about 12 550 mm at MAW and 11 963 mm at CHE for the period 1989-2010, as deduced from the available measurements at MAW. The changes in the Indian Ocean temperature have a profound effect on the rainfall in the region, and the contribution from the Arabian Sea temperature and moisture is remarkable in this respect, as analysed with a multivariate regression procedure for the period 1973-2019. The changes in land cover are another important aspect of this shift in rainfall pattern, as we find a noticeable reduction in vegetation area in northeast India in the past two decades, implying the human influence on recent climate change.
Journal Article