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Deaf and hard-of-hearing people mainly communicate using sign language, which is a set of signs made using hand gestures combined with facial expressions to make meaningful and complete sentences. The problem that faces deaf and hard-of-hearing people is the lack of automatic tools that translate sign languages into written or spoken text, which has led to a communication gap between them and their communities. Most state-of-the-art vision-based sign language recognition approaches focus on translating non-Arabic sign languages, with few targeting the Arabic Sign Language (ArSL) and even fewer targeting the Saudi Sign Language (SSL). This paper proposes a mobile application that helps deaf and hard-of-hearing people in Saudi Arabia to communicate efficiently with their communities. The prototype is an Android-based mobile application that applies deep learning techniques to translate isolated SSL to text and audio and includes unique features that are not available in other related applications targeting ArSL. The proposed approach, when evaluated on a comprehensive dataset, has demonstrated its effectiveness by outperforming several state-of-the-art approaches and producing results that are comparable to these approaches. Moreover, testing the prototype on several deaf and hard-of-hearing users, in addition to hearing users, proved its usefulness. In the future, we aim to improve the accuracy of the model and enrich the application with more features. [ABSTRACT FROM AUTHOR]

Recently, researchers have turned their focus to predicting the age of people since numerous applications depend on facial recognition approaches. In the medical field, Alzheimer's disease mainly depends on patients' ages. Multiple methods have been implemented and developed to predict age. However, these approaches lack accuracy because every image has unique features, such as shape, pose, and scale. In Saudi Arabia, Vision 2030, concerning the quality of life, is one of the twelve initiatives that were launched recently. The health sector has gained increasing attention as the government has introduced age-based policies to improve the health of its elderly residents. These residents are urgently advised to vaccinate against COVID-19 based on their age. In this paper, proposing a practical, consistent, and trustworthy method to predict age is presented. This method uses the color intensity of eyes and a Convolutional Neural Network (CNN) to predict age in real time based on the ensemble of CNN. A segmentation algorithm is engaged since the approach takes its input from a video stream or an image. This algorithm extracts data from one of the essential parts of the face: the eyes. This part is also informative. Several experiments have been conducted on MATLAB to verify and validate results and relative errors. A Kaggle website dataset is utilized for ages 4 to 59. This dataset includes over 270,000 images, and its size is roughly 2 GB. Consequently, the proposed approach produces ±8.69 years of Mean Square Error (MSE) for the predicted ages. Lastly, a comparative evaluation of relevant studies and the presented algorithm in terms of accuracy, MSE, and Mean Absolute Error (MAE) is also provided. This evaluation shows that the approach developed in the current study outperforms all considered performance metrics since its accuracy is 97.29%. This study found that the color intensity of eyes is highly effective in predicting age, given the high accuracy and acceptable MSE and MAE results. This indicates that it is helpful to utilize this methodology in real-life applications. [ABSTRACT FROM AUTHOR]

In the recent past, the COVID-19 epidemic has impeded global economic progress and, by extension, all of society. This type of pandemic has spread rapidly, posing a threat to human lives and the economy. Because of the growing scale of COVID-19 cases, employing artificial intelligence for future prediction purposes during this pandemic is crucial. Consequently, the major objective of this research paper is to compare various deep learning forecasting algorithms, including auto-regressive integrated moving average, long short-term memory, and conventional neural network techniques to forecast how COVID-19 would spread in Saudi Arabia in terms of the number of people infected, the number of deaths, and the number of recovered cases. Three different time horizons were used for COVID-19 predictions: short-term forecasting, medium-term forecasting, and long-term forecasting. Data pre-processing and feature extraction steps were performed as an integral part of the analysis work. Six performance measures were applied for comparing the efficacy of the developed models. LSTM and CNN algorithms have shown superior predictive precision with errors of less than 5% measured on available real data sets. The best model to predict the confirmed death cases is LSTM, which has better RMSE and R 2 values. Still, CNN has a similar comparative performance to LSTM. LSTM unexpectedly performed badly when predicting the recovered cases, with RMSE and R 2 values of 641.3 and 0.313, respectively. This work helps decisionmakers and health authorities reasonably evaluate the status of the pandemic in the country and act accordingly. [ABSTRACT FROM AUTHOR]

Breast cancer (BC) is the most common cause of women's deaths worldwide. The mammography technique is the most important modality for the detection of BC. To detect abnormalities in mammographic images, the Breast Imaging Reporting and Data System (BI-RADs) is used as a baseline. The correct allocation of BI-RADs categories for mammographic images is always an interesting task, even for specialists. In this work, to detect and classify the mammogram images in BI-RADs, a novel hybrid model is presented using a convolutional neural network (CNN) with the integration of a support vector machine (SVM). The dataset used in this research was collected from different hospitals in the Qassim health cluster of Saudi Arabia. The collection of all categories of BI-RADs is one of the major contributions of this paper. Another significant contribution is the development of a hybrid approach through the integration of CNN and SVM. The proposed hybrid approach uses three CNN models to obtain ensemble CNN model results. This ensemble model saves the values to integrate them with SVM. The proposed system achieved a classification accuracy, sensitivity, specificity, precision, and F1-score of 93.6%, 94.8%, 96.9%, 96.6%, and 95.7%, respectively. The proposed model achieved better performance compared to previously available methods. [ABSTRACT FROM AUTHOR]

Crowd counting in specific places has recently been considered as a significant contribution in many applications in terms of security and economic values. Recently, the Kingdom of Saudi Arabia has considered new ways and methods to diversify sources of income, where many non-traditional establishments in several fields have been initiated and put in place. However, controlling the number of visitors and participants to events and exhibitions has always been a challenge, as it has always been considered as an important success factor to any event. Smart public places approach is one of the inevitable directions of development in Saudi Arabia, where security, comfort, and safety of crowds is to be controlled and managed using machine learning techniques, more specifically, IoT-based crowd counting techniques. Such a technology will not only help in resolving security and safety problems, but also will play a significant role in reducing waiting time for visitors, by giving indicators, projections and advices on crowded places. In this paper, a mobile-based model is proposed for counting people in high and low crowded public places in Saudi Arabia under various scene conditions with no prior knowledge. The proposed model is built based on pre-trained convolutional neural network (CNN) called VGG-16 with some modifications on the last layer of the CNN to increase the efficiency of the training model. In addition to the improvement of efficiency, the proposed method accepts images of arbitrary sizes/scales as inputs. The applicability of the proposed method has been evaluated by incorporating IoT architecture, where surveillance cameras to be connected to the Internet to capture live pictures of different public places. To achieve this goal, New and special Saudi people dataset as well as some other existing dataset, have been produced and used to train the network. The result shows a significant improvement to the efficiency of the DCNN over the existing counting networks. [ABSTRACT FROM AUTHOR]

The World Health Organization (WHO) declared the outbreak of Coronavirus disease 2019 (COVID-19) a pandemic on 11 March 2020. The evolution of this pandemic has raised global health concerns, making people worry about how to protect themselves and their families. This has greatly impacted people's sentiments. There was a dire need to investigate a large amount of social data such as tweets and others that emerged during the post-pandemic era for the assessment of people's sentiments. As a result, this study aims at Arabic tweet-based sentiment analysis considering the COVID-19 pandemic in Saudi Arabia. The datasets have been collected in two different periods in three major regions in Saudi Arabia, which are: Riyadh, Dammam, and Jeddah. Tweets were annotated with three sentiments: positive, negative, and neutral after due pre-processing. Convolutional neural networks (CNN) and bi-directional long short memory (BiLSTM) deep learning algorithms were applied for classifying the sentiment of Arabic tweets. This experiment showed that the performance of CNN achieved 92.80% accuracy. The performance of BiLSTM was scored at 91.99% in terms of accuracy. Moreover, as an outcome of this study, an overwhelming upsurge in negative sentiments were observed in the dataset during COVID-19 compared to the negative sentiments of the dataset before COVID-19. The technique has been compared with the state-of-the-art techniques in the literature and it was observed that the proposed technique is promising in terms of various performance parameters. [ABSTRACT FROM AUTHOR]

Predicting dental development in individuals, especially children, is important in evaluating dental maturity and determining the factors that influence the development of teeth and growth of jaws. Dental development can be accelerated in patients with an accelerated skeletal growth rate and can be related to the skeletal growth pattern as a child. The dental age (DA) of an individual is essential to the dentist for planning treatment in relation to maxillofacial growth. A deep-learning-based regression model was developed in this study using panoramic radiograph images to predict DA. The dataset included 529 samples of panoramic radiographs collected from the dental hospital at Imam Abdulrahman Bin Faisal university in Saudi Arabia. Different deep learning methods were applied to implement the model, including Xception, VGG16, DenseNet121, and ResNet50. The results indicated that the Xception model had the best performance, with an error rate of 1.417 for the 6–11 age group. The proposed model can assist the dentist in determining the appropriate treatment for patients based on their DA rather than their chronological age. [ABSTRACT FROM AUTHOR]