In March 2020, the World Health Organization (WHO) declared the outbreak of Coronavirus disease 2019 (COVID-19) as a pandemic, which affected all countries worldwide. During the outbreak, public sentiment analyses contributed valuable information toward making appropriate public health responses. This study aims to develop a model that predicts an individual's awareness of the precautionary procedures in five main regions in Saudi Arabia. In this study, a dataset of Arabic COVID-19 related tweets was collected, which fell in the period of the curfew. The dataset was processed, based on several machine learning predictive models: Support Vector Machine (SVM), K-nearest neighbors (KNN), and Naive Bayes (NB), along with the N-gram feature extraction technique. The results show that applying the SVM classifier along with bigram in Term Frequency-Inverse Document Frequency (TF-IDF) outperformed other models with an accuracy of 85%. The results of awareness prediction showed that the south region observed the highest level of awareness towards COVID-19 containment measures, whereas the middle region was the least. The proposed model can support the medical sectors and decision-makers to decide the appropriate procedures for each region based on their attitudes towards the pandemic.

A Sentiment Analysis Approach to Predict an Individual's Awareness of the Precautionary Procedures to Prevent COVID-19 Outbreaks in Saudi Arabia.

Hand Gesture Recognition using Image Processing and Feature Extraction Techniques

At this time, the song is also one of the learning methods. This method is usually used for children because, as we know, children are very interested in listening to exciting lessons, especially preschool children. We can also teach children -Children of Arabic through this song method. This study aims to show whether preschool children of Motherland II can learn Arabic using songs. The method used in this study is experimental. While the subjects are children of early childhood education, the results of this study indicate that teachers about learning by using songs can be easily captured by early childhood children but have shortcomings where most children - Early child education Pertiwi II children can memorize quickly, but some children do not know the meaning. Based on this research, it can be concluded that Early Child Education Pertiwi II children can learn Arabic by using songs but still cannot know the meaning perfectly because Children can memorize notes easily but not by understanding the lyrics of each song.

Fun Arabic Teaching With Media Song For Early Child Education School

People with hearing impairments are found worldwide; therefore, the development of effective local level sign language recognition (SLR) tools is essential. We conducted a comprehensive review of automated sign language recognition based on machine/deep learning methods and techniques published between 2014 and 2021 and concluded that the current methods require conceptual classification to interpret all available data correctly. Thus, we turned our attention to elements that are common to almost all sign language recognition methodologies. This paper discusses their relative strengths and weaknesses, and we propose a general framework for researchers. This study also indicates that input modalities bear great significance in this field; it appears that recognition based on a combination of data sources, including vision-based and sensor-based channels, is superior to a unimodal analysis. In addition, recent advances have allowed researchers to move from simple recognition of sign language characters and words towards the capacity to translate continuous sign language communication with minimal delay. Many of the presented models are relatively effective for a range of tasks, but none currently possess the necessary generalization potential for commercial deployment. However, the pace of research is encouraging, and further progress is expected if specific difficulties are resolved.

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Deep Learning for Sign Language Recognition: Current Techniques, Benchmarks, and Open Issues

Sign language reduces the barrier for communicating with the humans having impaired of speech and hearing, on the other hand Sign language cannot be easily understood by common people. Therefore, a platform is necessary that is built using an algorithm to recognize various signs it is called as Sign Language Recognition (SLR). It is a technique that simplifies the communication between speech and hearing impaired people with normal people, the main aim of SLR is to overcome the aforementioned drawback. In this manuscript it is aimed to review various techniques that have been employed in the recent past for SLR that are employed at various stages of recognition. Adding to the above, various image based with or without the glove employed for detection, their advantages and difficulties encountered during this process. Also, segmentation, feature extraction, methods used for feature vector quantization and reduction techniques are discussed in detail. Along with these, during classification it involves training, testing that employs various training models, including Hidden Markov Model based approaches and Deep learning methods such as CNN, also techniques like k-NN, ANN, SVM and others. Then finally discussion on results and observations from several techniques are compared. The approaches that are being reviewed are so flexible that they can be employed for major sign detections with applications in various domains.

Sign Language Recognition Techniques- A Review

With the increase in the number of deaf-mute people in the Arab world and the lack of Arabic sign language (ArSL) recognition benchmark data sets, there is a pressing need for publishing a large-volume and realistic ArSL data set. This study presents such a data set, which consists of 150 isolated ArSL signs. The data set is challenging due to the great similarity among hand shapes and motions in the collected signs. Along with the data set, a sign language recognition algorithm is presented. The authors’ proposed method consists of three major stages: hand segmentation, hand shape sequence and body motion description, and sign classification. The hand shape segmentation is based on the depth and position of the hand joints. Histograms of oriented gradients and principal component analysis are applied on the segmented hand shapes to obtain the hand shape sequence descriptor. The covariance of the three-dimensional joints of the upper half of the skeleton in addition to the hand states and face properties are adopted for motion sequence description. The canonical correlation analysis and random forest classifiers are used for classification. The achieved accuracy is 55.57% over 150 ArSL signs, which is considered promising.

https://ietresearch.onlinelibrary.wiley.com/doi/pdf/10.1049/iet-cvi.2017.0598

Asmaa Shoala

Papers

Multi-modality-based Arabic sign language recognition