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Sign language is an essential means of communication for individuals with hearing disabilities. However, there is a significant shortage of sign language interpreters in some languages, especially in Saudi Arabia. This shortage results in a large proportion of the hearing-impaired population being deprived of services, especially in public places. This paper aims to address this gap in accessibility by leveraging technology to develop systems capable of recognizing Arabic Sign Language (ArSL) using deep learning techniques. In this paper, we propose a hybrid model to capture the spatio-temporal aspects of sign language (i.e., letters and words). The hybrid model consists of a Convolutional Neural Network (CNN) classifier to extract spatial features from sign language data and a Long Short-Term Memory (LSTM) classifier to extract spatial and temporal characteristics to handle sequential data (i.e., hand movements). To demonstrate the feasibility of our proposed hybrid model, we created a dataset of 20 different words, resulting in 4000 images for ArSL: 10 static gesture words and 500 videos for 10 dynamic gesture words. Our proposed hybrid model demonstrates promising performance, with the CNN and LSTM classifiers achieving accuracy rates of 94.40% and 82.70%, respectively. These results indicate that our approach can significantly enhance communication accessibility for the hearing-impaired community in Saudi Arabia. Thus, this paper represents a major step toward promoting inclusivity and improving the quality of life for the hearing impaired. [ABSTRACT FROM AUTHOR]

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]

Nowadays, air pollution is getting an extreme problem that affects the whole environment. Due to the dangerous effects of air pollution on human's health, this study proposes an air pollution prediction system. Because of the high dust pollution in Saudi Arabia, and the fact that there is no system for predicting the percentage of air pollution in it, this study applies an air pollution prediction system to the most affected area in Saudi Arabia. This paper aims to forecast the concentrations of PM10 particles due to their dangerous effects. This study aims to align with the Saudi vision 2030 by having an ideal environment and act in an efficient way in case of a warning situation. It applies a deep learning technique, which called Long Short-Term Memory (LSTM) to predict the air pollution in Saudi Arabia and achieved exceptional results due to the low error rates that have been obtained by this study. The error rate of Mean Absolute Error (MAE) is 0.98, for Root Mean Square Error (RMSE) is 8.68 and 0.999 for R-Squared. [ABSTRACT FROM AUTHOR]

One of the predominant health issues affecting Saudi Arabia and leading to many complications is Type 2 diabetes (T2D). Early detection and significant preventative measures lead to curbing and controlling the health issue. There are fewer datasets in the literature for the detection of T2D in the Saudi population. Past studies using Saudi data have favoured machine learning algorithms to classify T2D. Although the application of this data in machine learning is evident, no studies exist in the literature that compare this data, especially those related to deep learning algorithms. This study's objective is to use specific Saudi data to develop multiple deep learning models that could be used to detect T2D. The research uses a Deep Neural Network (DNN), an Autoencoder (AE), and a Convolutional Neural Network (CNN) to create predictive models and compare their performance with a traditional machine learning classifier used on the same dataset that outperformed other machine learning algorithms such as a Decision Forest (DF). Various metrics were used to evaluate the effectiveness of the models, such as accuracy, precision, recall, F1 score and area under the ROC curve (AUC) where the ROC acts as a receiver operating characteristic curve. There are two cases in this paper: (i) uses all features of the dataset and (ii) uses six of the ten features, such as DF. In case (i), the results were shown that AE outperformed other models with the highest accuracy for imbalanced and balanced data 81.12% and 79.16%, respectively. The results for case (ii) showed that AE scored the highest 81.01% accuracy with imbalanced data compared to DF and DF achieved the highest accuracy of 82.1% with balanced data. As a result, both cases explored in this study revealed that AE has a constant superior performance if imbalanced data is used. In contrast, DF demonstrated the highest accuracy when a balanced dataset was used with a feature set reduction. They help to identify the undiagnosed T2D, and they are essential for professionals in Saudi Arabia in the health sector to promote health connections, identify risks and contain or improve their diabetes management. [ABSTRACT FROM AUTHOR]

The acceleration of house technology via the use of mobile phones has made it easier to control houses, where occupants (especially older people) spend most of their time. The climate of Saudi Arabia, especially in the northern area, is too hot during summer and cold during winter. Control of the indoor environment in a smart home is a preferable choice that can reduce power consumption to operate heating, ventilation, and air-conditioning. Machine learning algorithms have been used to predict physical variables of indoor environment, such as temperature and humidity. The model can be trained, learn, and make predictions using historical data. Machine learning techniques can automate temperature monitoring and control. This paper proposes an algorithm that combines Artificial Neural Networks (ANNs) and GridSearchCV to predict physical variables in indoor environments in Saudi Arabia. GridSearchCV was utilized to tune the parameters of the machine learning algorithm. The assessment of the proposed algorithm involved its performance comparison to state-of-the-art machine learning algorithms. A real-world dataset was generated to estimate the performance of the considered algorithms. The room data were collected every 5 min for 31 days during July 2022. The dataset contains 6 columns and 8,910 records from 6 sensors (timestamps, light, temperature, humidity, pressure, and altitude). Random Forest (RF), Decision Tree (DT), and ANN methods were compared with the proposed algorithm. The RF had the highest R2 value of 0.84 and the lowest Mean Square Error (MSE) of 0.43. The DT achieved an R2 score of 0.78, while the ANN achieved R2 score of 0.61, MSE of 1.04, and Mean Absolute Error (MAE) of 0.75. The proposed algorithm achieved an R2 of 0.69, MSE of 0.87, and MAE of 0.67. [ABSTRACT FROM AUTHOR]

Despite advancements in smart grid (SG) technology, effective load forecasting utilizing big data or large-scale datasets remains a complex task for energy management, planning, and control. The Saudi SGs, in alignment with the Saudi Vision 2030, have been envisioned as future electrical grids with a bidirectional flow of power and data. To that end, data analysis and predictive models can enhance Saudi SG planning and control via artificial intelligence (AI). Recently, many AI methods including deep learning (DL) algorithms for SG applications have been published in the literature and have shown superior time series predictions compared with conventional prediction models. Current load-prediction research for the Saudi grid focuses on identifying anticipated loads and consumptions, on utilizing limited historical data and the behavior of the load's consumption, and on conducting shallow forecasting models. However, little scientific proof on complex DL models or real-life application has been conducted by researchers; few articles have studied sophisticated large-scale prediction models for Saudi grids. This paper proposes hybrid DL methods to enhance the outcomes in Saudi SG load forecasting, to improve problem-relevant features, and to accurately predict complicated power consumption, with the goal of developing reliable forecasting models and of obtaining knowledge of the relationships between the various features and attributes in the Saudi SGs. The model in this paper utilizes a real dataset from the Jeddah and Medinah grids in Saudi Arabia for a full year, 2021, with a one-hour time resolution. A benchmark strategy using different conventional DL methods including artificial neural network, recurrent neural network (RNN), conventional neural networks (CNN), long short-term memory (LSTM), gated recurrent unit (GRU), and different real datasets is used to verify the proposed models. The prediction results demonstrate the effectiveness of the proposed hybrid DL models, with CNN–GRU and CNN–RNN with NRMSE obtaining 1.4673% and 1.222% improvements, respectively, in load forecasting accuracy. [ABSTRACT FROM AUTHOR]

Problem: Sentiment Analysis (SA) automates the classification of the sentiment of people's attitudes, feelings or reviews employing natural language processing (NLP) and computational approaches. Deep learning has recently demonstrated remarkable success in the field of SA in many languages including Arabic. Arabic sentiment analysis, however, still has to be improved, due to the complexity of the Arabic language's structure, the variety of dialects, and the lack of lexicons. Moreover, in Arabic, anxiety as a psychological sentiment has not been the target of much research. Aim: This paper aims to provide solutions to one of the challenges of Arabic Sentiment Analysis (ASA) using a deep learning model focused on predicting the anxiety level during COVID-19 in Saudi Arabia. Methods: A psychological scale to determine the level of anxiety was built and validated. It was then used to create the Arabic Psychological Lexicon (AraPh) containing 138 different dialectical Arabic words that express anxiety, which was used to annotate our corpus (Aranxiety). Aranxiety comprises 955 Arabic tweets representing the level of user anxiety during COVID-19. Bi-GRU model with word embedding was then applied to analyze the sentiment of the tweets and to determine the anxiety level. Results: For SA, the applied model achieved 88% on accuracy, 89% on precision, 88% on recall, and 87% for F1. A majority of 77% of tweets presented no anxiety, whereas 17% represented mild anxiety and a mere 6% represented high anxiety. Conclusion: The proposed model can be used by the Saudi Ministry of Health and members of the research community to formulate solutions to increase psychological resiliency among the Saudi population. [ABSTRACT FROM AUTHOR]

Wind turbines and solar photovoltaic (PV) systems are intermittent and uncertain energy sources that disturb grid planning operations. In this paper, we establish a multi-objective distributionally robust optimization model for optimal locations of wind turbines and solar photovoltaics (PV) that minimize the variance of renewable energy sources and maximize power production. Moreover, this paper evaluates the accuracy of the Autoregressive Moving Average (ARMA), Deep Learning Gated Recurrent unit (GRU), and Deep Learning Long Short-Term Memory (LSTM) as forecasting models for wind speed and solar irradiation and compares their root mean square errors (RMSE). Using the forecasting error information, we characterize the uncertain variables in the ambiguity set, incorporating the bounds, means, and covariance values. Furthermore, we propose a modified multi-objective non-dominated sorting genetic algorithm (NSGA-II) approach to achieve a tractable Pareto front solution. To verify the effectiveness of the model, we use the actual candidate sites for wind turbines and solar photovoltaic (PV) systems in Saudi Arabia. The results demonstrate that our proposed model is an attractive and less conservative solution than a multi-objective robust optimization model when considering forecasting uncertainties. [ABSTRACT FROM AUTHOR]

Classifying the quality of dates after harvesting the crop plays a significant role in reducing waste from date fruit production. About one million tons of date fruit is produced annually in Saudi Arabia. Part of this production goes to local factories to be produced and packaged to be ready for use. Classifying and sorting edible dates from inedible dates is one of the first and most important stages in the production process in the date fruit industry. As this process is still performed manually in date production factories in Saudi Arabia, this may cause an increase in the waste of date fruit and reduce the efficiency of production. Therefore, in our paper, we propose a system to automate the classification of dates fruit production. The proposed system focuses on classifying the quality of date fruit at the postharvesting stage. By automating the process of classifying date fruit at this stage, we can increase the production efficiency, raise the classification accuracy, control the product quality, and perform data analysis within the industry. As a result, this increases the market competitiveness, reduces production costs, and increases the productivity. The system was developed based on convolutional neural network models. For the purpose of training the models, we constructed a new image dataset that contains two main classes that have images of date fruit with excellent surface quality and another class for date fruit with poor surface quality. The results show that the used model can classify date fruit based on their surface quality with an accuracy of 97%. [ABSTRACT FROM AUTHOR]

The advancement of the Internet of Things applications (technologies and enabling platforms), consisting of software and hardware (e.g., sensors, actuators, etc.), allows healthcare providers and users to analyze and measure physical environments at home or hospital. The measured physical environment parameters contribute to improving healthcare in real time. Researchers in this domain require existing representative datasets to develop machine-learning techniques to learn physical variables from the surrounding environments. The available environmental datasets are rare and need too much effort to be generated. To our knowledge, it has been noticed that no datasets are available for some countries, including Saudi Arabia. Therefore, this paper presents one of the first environmental data generated in Saudi Arabia's environment. The advantage of this dataset is to encourage researchers to investigate the effectiveness of machine learning in such an environment. The collected data will also help utilize the machine learning and deep learning algorithms in smart home and health care applications based on the Saudi Arabia environment. Saudi Arabia has a special environment in each session, especially in the northern area where we work, where it is too hot in the summer and cold in the winter. Therefore, environmental data measurements in both sessions are important for the research community, especially those working in smart and healthcare environments. The dataset is generated based on the indoor environment from six sensors (timestamps, light, temperature, humidity, pressure, and altitude sensors). The room data were collected for 31 days in July 2022, acquiring 8910 records. The datasets include six columns of different data types that represent sensor values. During the experiment, the sensors captured the data every 5 min, storing them in a comma-separated value file. The data are already validated and publicly available at PLOMS Press and can be applied for training, testing, and validating machine learning algorithms. This is the first dataset developed by the authors for the research community for such an environment, and other datasets will follow it in different environments and places. [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]

Automatic handwriting recognition has received considerable attention over the past three decades. Handwriting recognition systems are useful for a wide range of applications. Much research has been conducted to address the problem in Latin languages. However, less research has focused on the Arabic language, especially concerning recognizing children's Arabic handwriting. This task is essential as the demand for educational applications to practice writing and spelling Arabic letters is increasing. Thus, the development of Arabic handwriting recognition systems and applications for children is important. In this paper, we propose two deep learning-based models for the recognition of children's Arabic handwriting. The proposed models, a convolutional neural network (CNN) and a pre-trained CNN (VGG-16) were trained using Hijja, a recent dataset of Arabic children's handwriting collected in Saudi Arabia. We also train and test our proposed models using the Arabic Handwritten Character Dataset (AHCD). We compare the performance of the proposed models with similar models from the literature. The results indicate that our proposed CNN outperforms the pre-trained CNN (VGG-16) and the other compared models from the literature. Moreover, we developed Mutqin, a prototype to help children practice Arabic handwriting. The prototype was evaluated by target users, and the results are reported. [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]

Corporate social responsibility (CSR) has gained a great deal of interest in recent years due to the need for information that can help many stakeholders (e.g., governments, investors, professional organizations, researchers, etc.) understand companies' contributions to the environment and society. CSR disclosure (CSRD) is now the key source of such information when analyzing, for example, an institution's future performance. In the current body of CSRD literature, the majority of quantitative CSRD studies have relied on traditional statistical approaches for the correlation analysis of CSRD influencing factors. In this paper, we intend to quantitatively analyze firms' characteristics related to CSRD in Saudi Arabia, understand CSRD and its influencing factors, and predict CSRD patterns. This study lays the groundwork to help companies make informed decisions. It also helps many other stakeholders better understand CSRD's impacts. To achieve this, we propose a deep learning framework based on long short-term memory (LSTM) for identifying and predicting CSRD patterns. Moreover, a correlation-based technique is also used to visualize the relationships between variables and identify the significant features. The dataset used in this study was collected from annual reports, CSR reports, and firms' websites between 2015 and 2018. It contains a variety of variables to explain the CSR behaviour of 117 companies. The proposed framework is evaluated with several approaches, including logistic regression (LR), K-nearest neighbours (KNN), support vector machines (SVM), random forests (RF), and decision trees (DT). Compared to other machine learning models, experiment results show that LSTM achieved acceptable results with the highest accuracy of 88 % . [ABSTRACT FROM AUTHOR]

Customer satisfaction and loyalty are essential for every business. Feedback prediction and social media classification are crucial and play a key role in accurately identifying customer satisfaction. This paper presents sentiment analysis-based customer feedback prediction based on Twitter Arabic datasets of telecommunications companies in Saudi Arabia. The human brain, which contains billions of neurons, provides feedback based on the current and past experience provided by the services and other related stakeholders. Artificial Intelligent (AI) based methods, parallel to human brain processing methods such as Deep Learning (DL) algorithms, are famous for classifying and analyzing such datasets. Comparing the Arabic Dataset to English, it is pretty challenging for typical methods to outperform in the classification or prediction tasks. Therefore, the Arabic Bidirectional Encoder Representations from Transformers (AraBERT) model was used and analyzed with various parameters such as activation functions and topologies and simulated customer satisfaction prediction takes using Arabic Twitter datasets. The prediction results were compared with two famous DL algorithms: Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN). Results show that these methods have been successfully applied and obtained highly accurate classification results. AraBERT achieved the best prediction accuracy among the three ML methods, especially with Mobily and STC datasets. [ABSTRACT FROM AUTHOR]

Visual pollution (VP) is the deterioration or disruption of natural and man-made landscapes that ruins the aesthetic appeal of an area. It also refers to physical elements that limit the movability of people on public roads, such as excavation barriers, potholes, and dilapidated sidewalks. In this paper, an end-to-end visual pollution prediction (VPP) framework based on a deep active learning (DAL) approach is proposed to simultaneously detect and classify visual pollutants from whole public road images. The proposed framework is architected around the following steps: real VP dataset collection, pre-processing, a DAL approach for automatic data annotation, data splitting as well as augmentation, and simultaneous VP detection and classification. This framework is designed to predict VP localization and classify it into three categories: excavation barriers, potholes, and dilapidated sidewalks. A real dataset with 34,460 VP images was collected from various regions across the Kingdom of Saudi Arabia (KSA) via the Ministry of Municipal and Rural Affairs and Housing (MOMRAH), and this was used to develop and fine-tune the proposed artificial intelligence (AI) framework via the use of five AI predictors: MobileNetSSDv2, EfficientDet, Faster RCNN, Detectron2, and YOLO. The proposed VPP-based YOLO framework outperforms competitor AI predictors with superior prediction performance at 89% precision, 88% recall, 89% F1-score, and 93% mAP. The DAL approach plays a crucial role in automatically annotating the VP images and supporting the VPP framework to improve prediction performance by 18% precision, 27% recall, and 25% mAP. The proposed VPP framework is able to simultaneously detect and classify distinct visual pollutants from annotated images via the DAL strategy. This technique is applicable for real-time monitoring applications. [ABSTRACT FROM AUTHOR]

Amniotic Fluid (AF) refers to a protective liquid surrounding the fetus inside the amniotic sac, serving multiple purposes, and hence is a key indicator of fetal health. Determining the AF levels at an early stage helps to ascertain the maturation of lungs and gastrointestinal development, etc. Low AF entails the risk of premature birth, perinatal mortality, and thereby admission to intensive care unit (ICU). Moreover, AF level is also a critical factor in determining early deliveries. Hence, AF detection is a vital measurement required during early ultrasound (US), and its automation is essential. The detection of AF is usually a time-consuming process as it is patient specific. Furthermore, its measurement and accuracy are prone to errors as it heavily depends on the sonographer's experience. However, automating this process by developing robust, precise, and effective methods for detection will be beneficial to the healthcare community. Therefore, in this paper, we utilized transfer learning models in order to classify the AF levels as normal or abnormal using the US images. The dataset used consisted of 166 US images of pregnant women, and initially the dataset was preprocessed before training the model. Five transfer learning models, namely, Xception, Densenet, InceptionResNet, MobileNet, and ResNet, were applied. The results showed that MobileNet achieved an overall accuracy of 0.94. Overall, the proposed study produces an effective result in successfully classifying the AF levels, thereby building automated, effective models reliant on transfer learning in order to aid sonographers in evaluating fetal health. [ABSTRACT FROM AUTHOR]

Saudi Telecom Company (STC) is among the most popular companies in Saudi Arabia, with many customers. Yet, there is still a big room for improvement in users' satisfaction. Social media is the most robust platform to gauge users' satisfaction and determine their sentiments and critics. Twitter is among the most popular social media platform in this regard. STC customers prefer to use Twitter to write their feedback because it's a fast way to get responses due to the STC customer services account. One way to achieve customer demands and improve customer service is using the Sentiment Analysis tool. Sentiment Analysis on Twitter is highly used because of the significant number of tweets and the different opinions. Likewise, Deep learning is the best existing Sentiment Analysis method, and it has diverse models. Bidirectional Encoder Representations from Transformers (BERT) model is one of the deep learning models which have achieved excellent results in Sentiment Analysis for Natural Language Processing (NLP). NLP is mainly investigated in the English language. However, for Arabic, there is a significant gap to be filled. This study trained the proposed model using MARBERT and measured the performance using f1-score, precision, and recall metrics. We trained the model with an Arabic dataset of 24,513 tweets, including 1,437 positive, 13,828 negative, 5,694 neutral, 1,221 sarcasm, and 2,297 indeterminate tweets. The main goal is to analyze the tweets and get the sentiment to improve STC customer service. The proposed scheme is promising in terms of accuracy in contrast to existing techniques in the literature. [ABSTRACT FROM AUTHOR]

The forecasting of stock prices is an important area of research because of the benefits it provides for individuals, corporations, and governments. The purpose of this study is to investigate the application of a key of study to the prediction of the adjusted closing price of a particular firm. Estimating a stock's volatility is one of the more difficult tasks that traders must undertake. Investors are able to mitigate the risks associated with their portfolios and investments to a greater extent when stock prices can be accurately predicted. Prices of stocks do not move in a linear fashion. We propose artificial intelligence (AI) for multilayer perceptron (MLP) and long short-term memory (LSTM) models to predict fluctuations on the Saudi Stock Exchange (Tadawul). This paper focuses on the future forecasting of the stock exchange in the communication, energy, financial, and industrial sectors. The historical records from Tadawul were used as a basis for data collection for these sectors, in time periods from 2018 to 2020. For the purpose of predicting the future values of various stock market sectors, the AI algorithms were applied over a period of 60 days. They demonstrated highly effective performance when simulated using input data, which was carried out to validate the proposed model. In addition, the correlation coefficient (R) of the LSTM and MLP models for predicting the stock market in four sectors in the Saudi Stock Exchange (Tadawul) was >0.9950, which indicates that the outcomes were in good agreement with the predicted values. The outcomes of the forecasts were provided for each method based on four different measures. Among all the algorithms utilized in this work, LSTM demonstrated the most accurate findings and had the best capacity for model fitting. [ABSTRACT FROM AUTHOR]

Olive trees grow all over the world in reasonably moderate and dry climates, making them fortunate and medicinal. Pesticides are required to improve crop quality and productivity. Olive trees have had important cultural and economic significance since the early pre-Roman era. In 2019, Al-Jouf region in a Kingdom of Saudi Arabia's north achieved global prominence by breaking a Guinness World Record for having more number of olive trees in a world. Unmanned aerial systems (UAS) were increasingly being used in aerial sensing activities. However, sensing data must be processed further before it can be used. This processing necessitates a huge amount of computational power as well as the time until transmission. Accurately measuring the biovolume of trees is an initial step in monitoring their effectiveness in olive output and health. To overcome these issues, we initially formed a large scale of olive database for deep learning technology and applications. The collection comprises 250 RGB photos captured throughout Al-Jouf, KSA. This paper employs among the greatest efficient deep learning occurrence segmentation techniques (Mask Regional-CNN) with photos from unmanned aerial vehicles (UAVs) to calculate the biovolume of single olive trees. Then, using satellite imagery, we present an actual deep learning method (SwinTU-net) for identifying and counting of olive trees. SwinTU-net is a U-net-like network that includes encoding, decoding, and skipping links. SwinTU-net's essential unit for learning locally and globally semantic features is the Swin Transformer blocks. Then, we tested the method on photos with several wavelength channels (red, greenish, blues, and infrared region) and vegetation indexes (NDVI and GNDVI). The effectiveness of RGB images is evaluated at the two spatial rulings: 3 cm /pixel and 13 cm /pixel, whereas NDVI and GNDV images have only been evaluated at 13 cm/pixel. As a result of integrating all datasets of GNDVI and NDVI, all generated mask regional-CNN-based systems performed well in segmenting tree crowns (F1-measure from 95.0 to 98.0 percent). Based on ground truth readings in a group of trees, a calculated biovolume was 82 percent accurate. These findings support all usage of NDVI and GNDVI spectrum indices in UAV pictures to accurately estimate the biovolume of distributed trees including olive trees. [ABSTRACT FROM AUTHOR]

Automatic License Plate Recognition (ALPR) has remained an active research topic for years due to various applications, especially in Intelligent Transportation Systems (ITS). This paper presents an efficient ALPR technique based on deep learning, which accurately performs license plate (LP) recognition tasks in an unconstrained environment, even when trained on a limited dataset. We capture real traffic videos in the city and label the LPs and the alphanumeric characters in the LPs within different frames to generate training and testing datasets. Data augmentation techniques are applied to increase the number of training and testing samples. We apply the transfer learning approach to train the recently released YOLOv5 object detecting framework to detect the LPs and the alphanumerics. Next, we train a convolutional neural network (CNN) to recognize the detected alphanumerics. The proposed technique achieved a recognition rate of 92.8% on a challenging proprietary dataset collected in several jurisdictions of Saudi Arabia. This accuracy is higher than what was achieved on the same dataset by commercially available Sighthound (86%), PlateRecognizer (67%), OpenALPR (77%), and a state-of-the-art recent CNN model (82%). The proposed system also outperformed the existing ALPR solutions on several benchmark datasets. [ABSTRACT FROM AUTHOR]

Car manufacturers around the globe are in a race to design and build driverless cars. The concept of driverless is also being applied to any moving vehicle such as wheelchairs, golf cars, tourism carts in recreational parks, etc. To achieve this ambition, vehicles must be able to drive safely on streets stay within required lanes, sense moving objects, sense obstacles, and be able to read traffic signs that are permanent and even temporary signs. It will be a completely integrated system of the Internet of Things (IoT), Global Positioning System (GPS), Machine Learning (ML)/Deep Learning (DL), and Smart Technologies. A lot of work has been done on traffic sign recognition in the English language, but little has been done for Arabic traffic sign recognition. The concepts used for traffic sign recognition can also be applied to indoor signage, smart cities, supermarket labels, and others. In this paper, we propose two optimized Residual Network (ResNet) models (ResNet V1 and ResNet V2) for automatic traffic sign recognition using the Arabic Traffic Signs (ArTS) dataset. Additionally, the authors developed a new dataset specifically for Arabic Traffic Sign recognition consisting of 2,718 images taken from random places in the Eastern province of Saudi Arabia. The optimized proposed ResNet V1 model achieved the highest training and validation accuracies of 99.18% and 96.14%, respectively. It should be noted here that the authors accounted for both overfitting and underfitting in the proposed models. It is also important to note that the results achieved using the proposed models outperform similar methods proposed in the extant literature for the same dataset or similar-size dataset. [ABSTRACT FROM AUTHOR]

Effective learning by students is judged according to specific outcomes. The development of curriculum documents typically includes a description of specific skills, knowledge and dispositions that students are expected to achieve through engagement with classroom practices. By focusing on the cognitive elements in curriculum documents it is possible to determine whether these are sufficient to encourage development across simple to more complex processes including critical thinking. The main goal of this study was to analyse two sets of student learning outcomes in the Saudi Arabian Home Economics national curriculum developed in 1978 and 2013. This was done by determining the extent to which student learning outcomes reflected the cognitive domain of the revised Bloom's Taxonomy, and then determining the similarities and differences. Findings revealed greater emphasis on student learning outcomes in the Understanding and Applying levels within both curriculum and higher levels being evident in 2013. The Taxonomy is predicated on the idea that if students are to become critical thinkers, educators would provide learning activities at the higher levels of thinking. The paper has implications for future curriculum development in Home Economics in Saudi Arabia and may provide a framework for curriculum analysis in other jurisdictions. [ABSTRACT FROM AUTHOR]

In the last decade, technical advancements and faster Internet speeds have also led to an increasing number of mobile devices and users. Thus, all contributors to society, whether young or old members, can use these mobile apps. The use of these apps eases our daily lives, and all customers who need any type of service can access it easily, comfortably, and efficiently through mobile apps. Particularly, Saudi Arabia greatly depends on digital services to assist people and visitors. Such mobile devices are used in organizing daily work schedules and services, particularly during two large occasions, Umrah and Hajj. However, pilgrims encounter mobile app issues such as slowness, conflict, unreliability, or user-unfriendliness. Pilgrims comment on these issues onmobile app platforms through reviews of their experiences with these digital services. Scholars have made several attempts to solve such mobile issues by reporting bugs or non-functional requirements by utilizing user comments. However, solving such issues is a great challenge, and the issues still exist. Therefore, this study aims to propose a hybrid deep learning model to classify and predict mobile app software issues encountered by millions of pilgrims during the Hajj and Umrah periods from the user perspective. Firstly, a dataset was constructed using user-generated comments from relevant mobile apps using natural language processing methods, including information extraction, the annotation process, and pre-processing steps, considering a multi-class classification problem. Then, several experiments were conducted using common machine learning classifiers, Artificial Neural Networks (ANN), Long Short-Term Memory (LSTM), and Convolutional Neural Network Long Short-Term Memory (CNN-LSTM) architectures, to examine the performance of the proposed model. Results show 96% in F1-score and accuracy, and the proposed model outperformed the mentioned models. [ABSTRACT FROM AUTHOR]

The novel coronavirus disease (COVID-19) is regarded as one of the most imminent disease outbreaks which threaten public health on various levels worldwide. Because of the unpredictable outbreak nature and the virus's pandemic intensity, people are experiencing depression, anxiety, and other strain reactions. The response to prevent and control the new coronavirus pneumonia has reached a crucial point. Therefore, it is essential—for safety and prevention purposes—to promptly predict and forecast the virus outbreak in the course of this troublesome time to have control over its mortality. Recently, deep learning models are playing essential roles in handling time-series data in different applications. This paper presents a comparative study of two deep learning methods to forecast the confirmed cases and death cases of COVID-19. Long short-term memory (LSTM) and gated recurrent unit (GRU) have been applied on time-series data in three countries: Egypt, Saudi Arabia, and Kuwait, from 1/5/2020 to 6/12/2020. The results show that LSTM has achieved the best performance in confirmed cases in the three countries, and GRU has achieved the best performance in death cases in Egypt and Kuwait. [ABSTRACT FROM AUTHOR]

In a different area of a field of the real life, problem of accurate forecasting has acquired great importance that present the interesting serve which led to the best ways to achieve a goal. So, in this paper, we aimed to compare the accuracy of some statistical models such as Time Series and Deep Learning models, to forecasting the fertility rate in the Kingdom of Saudi Arabia, the data source is the World Health Organization over the period of 1960 to 2019. The performances of models were evaluated by errors measures mean absolute percentage error. [ABSTRACT FROM AUTHOR]

The enhancement of artificial intelligence (AI) and related machine learning represents one of many recent technological developments, causing educators to consider the potential of AI for teaching and learning. In this case study, opinions were gathered from six mature students of English as a foreign language (EFL) in a university in Saudi Arabia. The researcher especially explored the use of a voice recognition device (Amazon's virtual assistant, Alexa) based on natural language processing (NLP) for deep learning. The participants were instructed to interact with Alexa individually for 30 minutes each, over the course of one week, including a game of 'hide-and-seek'. Structured observations were performed of this activity, and semistructured interviews were subsequently conducted to gather the participants' opinions of the device. Main and sub-themes emerged from the results, related to deep learning through NLP. This theoretical perspective was adopted because Alexa was found to develop its voice recognition across different language structures and styles. Moreover, Alexa was very responsive to the participants, sometimes asking them to reword or modify their questions, so that it could find answers. The game also proved helpful for presenting the language, reflecting a real game of hide-and-seek. The participants mentioned that such technology could be useful in language labs, as it was fun, entertaining, convenient, and easy to use. It is therefore recommended to invest in these devices for learning activities in education. Furthermore, statistical studies are recommended to test the impact of voice recognition devices on teaching and learning for generalisable results. [ABSTRACT FROM AUTHOR]

Road detection technology plays an essential role in a variety of applications, such as urban planning, map updating, traffic monitoring and automatic vehicle navigation. Recently, there has been much development in detecting roads in high-resolution (HR) satellite images based on semantic segmentation. However, the objects being segmented in such images are of small size, and not all the information in the images is equally important when making a decision. This paper proposes a novel approach to road detection based on semantic segmentation and edge detection. Our approach aims to combine these two techniques to improve road detection, and it produces sharp-pixel segmentation maps, using the segmented masks to generate road edges. In addition, some well-known architectures, such as SegNet, used multi-scale features without refinement; thus, using attention blocks in the encoder to predict fine segmentation masks resulted in finer edges. A combination of weighted cross-entropy loss and the focal Tversky loss as the loss function is also used to deal with the highly imbalanced dataset. We conducted various experiments on two datasets describing real-world datasets covering the three largest regions in Saudi Arabia and Massachusetts. The results demonstrated that the proposed method of encoding HR feature maps effectively predicts sharp segmentation masks to facilitate accurate edge detection, even against a harsh and complicated background. [ABSTRACT FROM AUTHOR]

Social networks like Twitter are extremely popular and widely used, which has increased interest in studying the information posted there. One such analytical application is extracting location information of users for real-time monitoring of the objects and events of interest, such as political and social events, disease surveillance, natural calamities, and crime prevention. Identifying geographic location is a nontrivial task, as user profiles contain outdated and inaccurate location information. Furthermore, extracting geographical information from Arabic tweets is challenging since they contain many nonstandard data (dialects), complex structures, abbreviations, grammatical and spelling mistakes, etc. This study focuses on the localization of Saudi Arabian users who tweet in Arabic. This study proposes a convolutional neural network-based deep learning model to predict a Twitter user's region-level location using user profiles, text texts, place attachments, and historical tweets. The model was evaluated empirically on a dataset of 95,739 tweets written in Arabic and produced by 4,331 users from Saudi Arabia cities. Regarding classification accuracy, the proposed CNN model outperformed machine learning classifiers such as NB, LR, and SVM with a 60% accuracy on the test set. This study is the first of its kind, aimed at localizing Saudi users based on their tweets. [ABSTRACT FROM AUTHOR]

In this paper, we propose an original deep learning framework for the automated counting and geolocation of palm trees from aerial images using convolutional neural networks. For this purpose, we collected aerial images from two different regions in Saudi Arabia, using two DJI drones, and we built a dataset of around 11,000 instances of palm trees. Then, we applied several recent convolutional neural network models (Faster R-CNN, YOLOv3, YOLOv4, and EfficientDet) to detect palms and other trees, and we conducted a complete comparative evaluation in terms of average precision and inference speed. YOLOv4 and EfficientDet-D5 yielded the best trade-off between accuracy and speed (up to 99% mean average precision and 7.4 FPS). Furthermore, using the geotagged metadata of aerial images, we used photogrammetry concepts and distance corrections to automatically detect the geographical location of detected palm trees. This geolocation technique was tested on two different types of drones (DJI Mavic Pro and Phantom 4 pro) and was assessed to provide an average geolocation accuracy that attains 1.6 m. This GPS tagging allows us to uniquely identify palm trees and count their number from a series of drone images, while correctly dealing with the issue of image overlapping. Moreover, this innovative combination between deep learning object detection and geolocalization can be generalized to any other objects in UAV images. [ABSTRACT FROM AUTHOR]

Satellite images have drawn increasing interest from a wide variety of users, including business and government, ever since their increased usage in important fields ranging from weather, forestry and agriculture to surface changes and biodiversity monitoring. Recent updates in the field have also introduced various deep learning (DL) architectures to satellite imagery as a means of extracting useful information. However, this new approach comes with its own issues, including the fact that many users utilize ready-made cloud services (both public and private) in order to take advantage of built-in DL algorithms and thus avoid the complexity of developing their own DL architectures. However, this presents new challenges to protecting data against unauthorized access, mining and usage of sensitive information extracted from that data. Therefore, new privacy concerns regarding sensitive data in satellite images have arisen. This research proposes an efficient approach that takes advantage of privacy-preserving deep learning (PPDL)-based techniques to address privacy concerns regarding data from satellite images when applying public DL models. In this paper, we proposed a partially homomorphic encryption scheme (a Paillier scheme), which enables processing of confidential information without exposure of the underlying data. Our method achieves robust results when applied to a custom convolutional neural network (CNN) as well as to existing transfer learning methods. The proposed encryption scheme also allows for training CNN models on encrypted data directly, which requires lower computational overhead. Our experiments have been performed on a real-world dataset covering several regions across Saudi Arabia. The results demonstrate that our CNN-based models were able to retain data utility while maintaining data privacy. Security parameters such as correlation coefficient (−0.004), entropy (7.95), energy (0.01), contrast (10.57), number of pixel change rate (4.86), unified average change intensity (33.66), and more are in favor of our proposed encryption scheme. To the best of our knowledge, this research is also one of the first studies that applies PPDL-based techniques to satellite image data in any capacity. [ABSTRACT FROM AUTHOR]

Time-series (TS) predictions use historical data to forecast future values. Various industries, including stock market trading, power load forecasting, medical monitoring, and intrusion detection, frequently rely on this method. The prediction of stock-market prices is significantly influenced by multiple variables, such as the performance of other markets and the economic situation of a country. This study focuses on predicting the indices of the stock market of the Kingdom of Saudi Arabia (KSA) using various variables, including opening, lowest, highest, and closing prices. Successfully achieving investment goals depends on selecting the right stocks to buy, sell, or hold. The output of this project is the projected closing prices over the next seven days, which aids investors in making informed decisions. Exponential smoothing (ES) was employed in this study to eliminate noise from the input data. This study utilized exponential smoothing (ES) to eliminate noise from data obtained from the Saudi Stock Exchange, also known as Tadawul. Subsequently, a sliding-window method with five steps was applied to transform the task of time series forecasting into a supervised learning problem. Finally, a multivariate long short-term memory (LSTM) deep-learning (DL) algorithm was employed to predict stock market prices. The proposed multivariate LSTMDL model achieved prediction rates of 97.49% and 92.19% for the univariate model, demonstrating its effectiveness in stock market price forecasting. These results also highlight the accuracy of DL and the utilization of multiple information sources in stock-market prediction. [ABSTRACT FROM AUTHOR]

Healthcare organizations rely on patients’ feedback and experiences to evaluate their performance and services, thereby allowing such organizations to improve inadequate services and address any shortcomings. According to the literature, social networks and particularly Twitter are effective platforms for gathering public opinions. Moreover, recent studies have used natural language processing to measure sentiments in text segments collected from Twitter to capture public opinions about various sectors, including healthcare. The present study aimed to analyze Arabic Twitter-based patient experience sentiments and to introduce an Arabic patient experience corpus. The authors collected 12,400 tweets from Arabic patients discussing patient experiences related to healthcare organizations in Saudi Arabia from 1 January 2008 to 29 January 2022. The tweets were labeled according to sentiment (positive or negative) and sector (public or private), and thereby the Hospital Patient Experiences in Saudi Arabia (HoPE-SA) dataset was produced. A simple statistical analysis was conducted to examine differences in patient views of healthcare sectors. The authors trained five models to distinguish sentiments in tweets automatically with the following schemes: a transformer-based model fine-tuned with deep learning architecture and a transformer-based model fine-tuned with simple architecture, using two different transformer-based embeddings based on Bidirectional Encoder Representations from Transformers (BERT), Multi-dialect Arabic BERT (MARBERT), and multilingual BERT (mBERT), as well as a pre-trained word2vec model with a support vector machine classifier. This is the first study to investigate the use of a bidirectional long short-term memory layer followed by a feedforward neural network for the fine-tuning of MARBERT. The deep-learning fine-tuned MARBERT-based model—the authors’ bestperforming model—achieved accuracy, micro-F1, and macro-F1 scores of 98.71%, 98.73%, and 98.63%, respectively [ABSTRACT FROM AUTHOR]

Global warming and climate change are responsible for many disasters. Floods pose a serious risk and require immediate management and strategies for optimal response times. Technology can respond in place of humans in emergencies by providing information. As one of these emerging artificial intelligence (AI) technologies, drones are controlled in their amended systems by unmanned aerial vehicles (UAVs). In this study, we propose a secure method of flood detection in Saudi Arabia using a Flood Detection Secure System (FDSS) based on deep active learning (DeepAL) based classification model in federated learning to minimize communication costs and maximize global learning accuracy. We use blockchain-based federated learning and partially homomorphic encryption (PHE) for privacy protection and stochastic gradient descent (SGD) to share optimal solutions. InterPlanetary File System (IPFS) addresses issues with limited block storage and issues posed by high gradients of information transmitted in blockchains. In addition to enhancing security, FDSS can prevent malicious users from compromising or altering data. Utilizing images and IoT data, FDSS can train local models that detect and monitor floods. A homomorphic encryption technique is used to encrypt each locally trained model and gradient to achieve ciphertext-level model aggregation and model filtering, which ensures that the local models can be verified while maintaining privacy. The proposed FDSS enabled us to estimate the flooded areas and track the rapid changes in dam water levels to gauge the flood threat. The proposed methodology is straightforward, easily adaptable, and offers recommendations for Saudi Arabian decision-makers and local administrators to address the growing danger of flooding. This study concludes with a discussion of the proposed method and its challenges in managing floods in remote regions using artificial intelligence and blockchain technology. [ABSTRACT FROM AUTHOR]

Text-To-Speech (TTS) is a speech processing tool that is highly helpful for visually-challenged people. The TTS tool is applied to transform the texts into human-like sounds. However, it is highly challenging to accomplish the TTS outcomes for the non-diacritized text of the Arabic language since it has multiple unique features and rules. Some special characters like gemination and diacritic signs that correspondingly indicate consonant doubling and short vowels greatly impact the precise pronunciation of the Arabic language. But, such signs are not frequently used in the texts written in the Arabic language since its speakers and readers can guess them from the context itself. In this background, the current research article introduces an Optimal Deep Learning-driven Arab Text-to-Speech Synthesizer (ODLD-ATSS) model to help the visually-challenged people in the Kingdom of Saudi Arabia. The prime aim of the presented ODLD-ATSS model is to convert the text into speech signals for visually-challenged people. To attain this, the presented ODLD-ATSS model initially designs a Gated Recurrent Unit (GRU)-based prediction model for diacritic and gemination signs. Besides, the Buckwalter code is utilized to capture, store and display the Arabic texts. To improve the TSS performance of the GRU method, the Aquila Optimization Algorithm (AOA) is used, which shows the novelty of the work. To illustrate the enhanced performance of the proposed ODLD-ATSS model, further experimental analyses were conducted. The proposed model achieved a maximum accuracy of 96.35%, and the experimental outcomes infer the improved performance of the proposed ODLD-ATSS model over other DL-based TSS models. [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]

Diabetic retinopathy (DR) is a leading cause of blindness in middle-aged diabetic patients. Regular screening for DR using fundus imaging aids in detecting complications and delays the progression of the disease. Because manual screening takes time and is subjective, deep learning has been used to help graders. Pre-trained or brute force CNN models are used in existing DR grading CNN-based approaches that are not suited to fundus image complexity. To solve this problem, we present a method for automatically customizing CNN models based on fundus image lesions. It uses k-medoid clustering, principal component analysis (PCA), and inter-class and intra-class variations to determine the CNN model's depth and width. The designed models are lightweight, adapted to the internal structures of fundus images, and encode the discriminative patterns of DR lesions. The technique is validated on a local dataset from King Saud University Medical City, Saudi Arabia, and two challenging Kaggle datasets: EyePACS and APTOS2019. The auto-designed models outperform well-known pre-trained CNN models such as ResNet152, DenseNet121, and ResNeSt50, as well as Google's AutoML and Auto-Keras models based on neural architecture search (NAS). The proposed method outperforms current CNN-based DR screening methods. The proposed method can be used in various clinical settings to screen for DR and refer patients to ophthalmologists for further evaluation and treatment. [ABSTRACT FROM AUTHOR]

The Kingdom of Saudi Arabia is considered to be one of the world leaders in olive production accounting for about 6% of the global olive production. Given the fact that 94% of the olive groves are mainly rain-fed using traditional methods of production, the annual olive production is witnessing a noticeable fluctuation which is worse due to infectious diseases and climate change. Thus, early and effective detection of plant diseases is both required and urgent. Most farmers use traditional methods, for example, visual inspection or laboratory examination, to identify plant diseases. Currently, deep learning (DL) techniques have been shown to be useful methods for diagnosing olive leaf diseases and many other fields. In this work, we use a deep feature concatenation (DFC) mechanism to combine features extracted from input images using the two modern pretrained CNN models, i.e., ResNet50 and MobileNet. Hence, we propose MobiRes-Net: A neural network that is a concatenation of the ResNet50 and MobileNet models for overall improvement of prediction capability. To build the dataset used in the study, 5400 olive leaf images were collected from an olive grove using a remote-controlled agricultural unmanned aerial vehicle (UAV) equipped with a camera. The overall performance of the MobiRes-Net model achieved a classification accuracy of 97.08% which showed its superiority over ResNet50 and MobileNet that achieved classification accuracies of 94.86% and 95.63%, respectively. [ABSTRACT FROM AUTHOR]

Automotive industry experiences multiple injuries in our everyday life. The increasing road accident rate is due to driver drowsiness, such as fatigue and insomnia. This research is intended primarily to diagnose exhaustion and drowsiness, utilizing the deep learning models like convolution neural network. A research project has been initiated in the Kingdom of Saudi Arabia to address the problem of identifying the driver's drowsiness state. In this article, we present a real-time driver disturbance monitoring method using Convolutional Neural Network (CNN). Detection is done using state-of-the-art CNN models such as InceptionV3, VGG16, and ResNet50. We have analyzed that InceptionV3 has 90.70% with 0.6931 loss, the VGG16 has 39.87% with 0.6931 loss, and the ResNet50 has 93.69% accuracy with 0.6931 loss. So, the ResNet50 model has the best accuracy as compared to all other deep learning models. Using sleepy and active human face image collection, we present the output review of the CNN models. We expanded device deployment and performance review of the best-performing CNN model among the evaluated models, utilizing our custom dataset to enhance real-time efficiency. This unique data collection is similar to the driver end scenarios because it includes not just the side view, but also the driver's front view, greatly enhancing the performance. Extending this work into a commodity and its large-scale production will also render a significant contribution to the Kingdom's economy. [ABSTRACT FROM AUTHOR]

Analysing big data job posts in Saudi cyberspace to describe the future market need for sustainable skills, this study used the power of artificial intelligence, deep learning, and big data technologies. The study targeted three main stakeholders: students, universities, and job providers. It provides analytical insights to improve student satisfaction, retention, and employability, investigating recent trends in the essential skills pinpointed as enhancing the social effect of learning, and identifying and developing the competencies and talents required for the Kingdom of Saudi Arabia's (KSA's) digital transformation into a regional and global leader in technology-driven innovation. The methodological framework comprises smart data processing, word embedding, and case-based reasoning to identify the skills required for job positions. The study's outcomes may promote the alignment of KSA's business and industry to academia, highlighting where to build competencies and skills. They may facilitate the parameterisation of the learning process, boost universities' ability to promote learning efficiency, and foster the labour market's sustainable evolution towards technology-driven innovation. We believe that this study is crucial to Vision 2030's realisation through a long-term, inclusive approach to KSA's transformation of knowledge and research into new employment, innovation, and capacity. [ABSTRACT FROM AUTHOR]

Despite being an important economic component of Taif region and the Kingdom of Saudi Arabia (KSA) as a whole, Taif rose experiences challenges because of uncontrolled conditions. In this study, we developed a phenotyping prediction model using deep learning (DL) that used simple and accurate methods to obtain and analyze data collected from ten rose farms. To maintain broad applicability and minimize computational complexity, our model utilizes a complementary learning approach in which both spatial and temporal instances of each dataset are processed simultaneously using three state-of-the-art deep neural networks: (1) convolutional neural network (CNN) to treat the image, (2) long short-term memory (LSTM) to treat the timeseries and (3) fully connected multilayer perceptions (MLPs)to obtain the phenotypes. As a result, this approach not only consolidates the knowledge gained from processing the same data from different perspectives, but it also leverages on the predictability of the model under incomplete or noisy datasets. An extensive evaluation of the validity of the proposed model has been conducted by comparing its outcomes with comprehensive phenotyping measurements taken from real farms. This evaluation demonstrates the ability of the proposed model to achieve zero mean absolute percentage error ( MAPE ) and mean square percentage error (MSPE) within a small number of epochs and under different training to testing schemes. [ABSTRACT FROM AUTHOR]

COVID-19 has affected every individual physically or physiologically, leading to substantial impacts on how they perceive and respond to the pandemic's danger. Due to the lack of vaccines or effective medicines to cure the infection, an urgent control measure is required to prevent the continued spread of COVID-19. This can be achieved using advanced computing, such as artificial intelligence (AI), machine learning (ML), deep learning (DL), cloud computing, and edge computing. To control the exponential spread of the novel virus, it is crucial for countries to contain and mitigate interventions. To prevent exponential growth, several control measures have been applied in the Kingdom of Saudi Arabia to mitigate the COVID-19 epidemic. As the pandemic has been spreading globally for more than a year, an ample amount of data is available for researchers to predict and forecast the effect of the pandemic in the near future. This article interprets the effects of COVID-19 using the Susceptible-Infected-Recovered (SIR-F) while F-stands for 'Fatal with confirmation,' age-structured SEIR (Susceptible Exposed Infectious Removed) and machine learning for smart health care and the well-being of citizens of Saudi Arabia. Additionally, it examines the different control measure scenarios produced by the modified SEIR model. The evolution of the simulation results shows that the interventions are vital to flatten the virus spread curve, which can delay the peak and decrease the fatality rate. [ABSTRACT FROM AUTHOR]

During the COVID-19 epidemic, Twitter has become a vital platform for people to express their impressions and feelings towards the COVID-19 epidemic. There is an unavoidable need to examine various patterns on social media platforms in order to reduce public anxiety and misconceptions. Based on this study, various public service messages can be disseminated, and necessary steps can be taken to manage the scourge. There has already been a lot of work conducted in several languages, but little has been conducted on Arabic tweets. The primary goal of this study is to analyze Arabic tweets about COVID-19 and extract people's impressions of different locations. This analysis will provide some insights into understanding public mood variation on Twitter, which could be useful for governments to identify the effect of COVID-19 over space and make decisions based on that understanding. To achieve that, two strategies are used to analyze people's impressions from Twitter: machine learning approach and the deep learning approach. To conduct this study, we scraped Arabic tweets up with 12,000 tweets that were manually labeled and classify them as positive, neutral or negative feelings. Specialising in Saudi Arabia, the collected dataset consists of 2174 positive tweets and 2879 negative tweets. First, TF-IDF feature vectors are used for feature representation. Then, several models are implemented to identify people's impression over time using Twitter Geo-tag information. Finally, Geographic Information Systems (GIS) are used to map the spatial distribution of people's emotions and impressions. Experimental results show that SVC outperforms other methods in terms of performance and accuracy. [ABSTRACT FROM AUTHOR]

Since the Pre-Roman era, olive trees have a significant economic and cultural value. In 2019, the Al-Jouf region, in the north of the Kingdom of Saudi Arabia, gained a global presence by entering the Guinness World Records, with the largest number of olive trees in the world. Olive tree detecting and counting from a given satellite image are a significant and difficult computer vision problem. Because olive farms are spread out over a large area, manually counting the trees is impossible. Moreover, accurate automatic detection and counting of olive trees in satellite images have many challenges such as scale variations, weather changes, perspective distortions, and orientation changes. Another problem is the lack of a standard database of olive trees available for deep learning applications. To address these problems, we first build a large-scale olive dataset dedicated to deep learning research and applications. The dataset consists of 230 RGB images collected over the territory of Al-Jouf, KSA. We then propose an efficient deep learning model (SwinTUnet) for detecting and counting olive trees from satellite imagery. The proposed SwinTUnet is a Unet-like network which consists of an encoder, a decoder, and skip connections. Swin Transformer block is the fundamental unit of SwinTUnet to learn local and global semantic information. The results of an experimental study on the proposed dataset show that the SwinTUnet model outperforms the related studies in terms of overall detection with a 0.94% estimation error. [ABSTRACT FROM AUTHOR]

There is a growing interest in the flipped design model in K-12 mathematics classrooms, as it has been shown to have a positive influence on learning. This study aims to help improve students' learning of irrational numbers using the flipped design model. This study outlines four design elements of the flipped environment that are shown to increase student engagement. Two eighth-grade classes (n = 60) were examined using a quasi-experimental research design in terms of students' mathematical achievements and engagement levels. Each class was tested using a different instructional approach: traditional learning or flipped learning. The results indicated that the eighth-grade students in the flipped learning model group demonstrated an overall medium to a slightly high level of cognitive, behavioral, emotional, and social engagement, as well as a slightly high performance with irrational numbers. Also, when comparing the flipped and traditional learning environments, the results revealed that neither group was significantly different in terms of mathematics performance or engagement. Several recommendations and implications are discussed for teaching complex mathematics concepts via a flipped learning environment, including enriching the learning environment with student collaboration, social and emotional support, and problem solving. [ABSTRACT FROM AUTHOR]

The Covid-19 epidemic poses a serious public health threat to the world, where people with little or no pre-existing human immunity can be more vulnerable to its effects. Thus, developing surveillance systems for predicting the Covid-19 pandemic at an early stage could save millions of lives. In this study, a deep learning algorithm and a Holt-trend model are proposed to predict the coronavirus. The Long-Short Term Memory (LSTM) and Holt-trend algorithms were applied to predict conirmed numbers and death cases. The real time data used has been collected from the World Health Organization (WHO). In the proposed research, we have considered three countries to test the proposed model, namely Saudi Arabia, Spain and Italy. The results suggest that the LSTM models show better performance in predicting the cases of coronavirus patients. Standard measure performance Mean squared Error (MSE), Root Mean Squared Error (RMSE), Mean error and correlation are employed to estimate the results of the proposed models. The empirical results of the LSTM, using the correlation metrics, are 99.94%, 99.94% and 99.91% in predicting the number of confirmed cases in the three countries. As far as the results of the LSTM model in predicting the number of death of Covid-19, they are 99.86%, 98.876% and 99.16% with respect to Saudi Arabia, Italy and Spain respectively. Similarly, the experiment's results of the Holt-Trend model in predicting the number of confirmed cases of Covid-19, using the correlation metrics, are 99.06%, 99.96%and 99.94%,whereas the results of the Holt-Trend model in predicting the number of death cases are 99.80%, 99.96%and 99.94% with respect to the Saudi Arabia, Italy and Spain respectively. The empirical results indicate the efficient performance of the presented model in predicting the number of confirmed and death cases of Covid-19 in these countries. Such findings provide better insights regarding the future of Covid-19 this pandemic in general. The results were obtained by applying time series models, which need to be considered for the sake of saving the lives of many people. [ABSTRACT FROM AUTHOR]

Photovoltaic (PV) systems have become one of the most promising alternative energy sources, as they transform the sun's energy into electricity. This can frequently be achieved without causing any potential harm to the environment. Although their usage in residential places and building sectors has notably increased, PV systems are regarded as unpredictable, changeable, and irregular power sources. This is because, in line with the system's geographic region, the power output depends to a certain extent on the atmospheric environment, which can vary drastically. Therefore, artificial intelligence (AI)-based approaches are extensively employed to examine the effects of climate change on solar power. Then, the most optimal AI algorithm is used to predict the generated power. In this study, we used machine learning (ML)-based algorithms to predict the generated power of a PV system for residential buildings. Using a PV system, Pyranometers, and weather station data amassed from a station at King Khalid University, Abha (Saudi Arabia) with a residential setting, we conducted several experiments to evaluate the predictability of various well-known ML algorithms from the generated power. A backward feature-elimination technique was applied to find the most relevant set of features. Among all the ML prediction models used in the work, the deep-learning-based model provided the minimum errors with the minimum set of features (approximately seven features). When the feature set is greater than ten features, the polynomial regression model shows the best prediction, with minimal errors. Comparing all the prediction models, the highest errors were associated with the linear regression model. In general, it was observed that with a small number of features, the prediction models could minimize the generated power prediction's mean squared error value to approximately 0.15. [ABSTRACT FROM AUTHOR]