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This study explores X’s (formerly Twitter’s) capacity to serve as a real-time barometer of public sentiment, contextualized within the transformative reforms of Saudi Arabia during 2016–2017. The objective was to decipher the populace’s response to these significant national changes by analyzing approximately 200 million tweets in native Arabic dialects, thereby aiming for an authentic portrayal of local sentiment. Our methodology entailed a dual-phase analysis: initial tweet examination to discern prevalent social behaviors, followed by stance detection to classify tweets according to their support, neutrality, or opposition to the divisive issues at hand. For sentiment extraction, we employed a sophisticated feature vector, integrating the k most frequent words and stems. A comprehensive evaluation of various classifiers was conducted, including Support Vector Machine and several variants of K-nearest neighbors (K-NN), with a particular emphasis on their applicability to our dataset. Notably, the 9-NN classifier, and more specifically, the weighted K-NN approach, demonstrated remarkable performance, achieving an F-score of 72.45%. These insights not only shed light on the public’s reception to the Saudi reforms but also position Twitter as a viable, real-time alternative to traditional survey methods for capturing the nuances of public opinion, thereby offering valuable perspectives for policy formulation.

This study harnesses the linguistic diversity of Arabic dialects to create two expansive corpora from X (formerly Twitter). The Gulf Arabic Corpus (GAC-6) includes around 1.7 million tweets from six Gulf countries—Saudi Arabia, UAE, Qatar, Oman, Kuwait, and Bahrain—capturing a wide range of linguistic variations. The Saudi Dialect Corpus (SDC-5) comprises 790,000 tweets, offering in-depth insights into five major regional dialects of Saudi Arabia: Hijazi, Najdi, Southern, Northern, and Eastern, reflecting the complex linguistic landscape of the region. Both corpora are thoroughly annotated with dialect-specific seed words and geolocation data, achieving high levels of accuracy, as indicated by Cohen’s Kappa scores of 0.78 for GAC-6 and 0.90 for SDC-5. The annotation process leverages AI-driven techniques, including machine learning algorithms for automated dialect recognition and feature extraction, to enhance the granularity and precision of the data. These resources significantly contribute to the field of Arabic dialectology and facilitate the development of AI algorithms for linguistic data analysis, enhancing AI system design and efficiency. The data provided by this research are crucial for advancing AI methodologies, supporting diverse applications in the realm of next-generation AI technologies.

Abstract In this article, we introduce two new types of generalized contraction mappings in double controlled metric type spaces: Θ-double controlled contraction mapping and Ćirić-Reich-Rus-type-Θ-double controlled contraction mapping. For each contraction mapping, we establish the existence and uniqueness of the fixed point theorems on the complete double controlled metric type space and provide examples. We present an application of our results and demonstrate how our results generalize several existing fixed point theorems in the literature.

Fixed point theory can be generalized to cover multidisciplinary areas such as computer science; it can also be used for image authentication to ensure secure communication and detect any malicious modifications. In this article, we introduce the notion of complex-valued controlled rectangular metric-type spaces, where we prove fixed point theorems for self-mappings in such spaces. Furthermore, we present several examples and give two applications of our main results: solving linear systems of equations and finding a unique solution for an equation of the form $ f(x) = 0 $.

In the domain of automated story generation, the intricacies of the Arabic language pose distinct challenges. This study introduces a novel methodology that moves away from conventional event-driven narrative frameworks, emphasizing the restructuring of narrative constructs through sophisticated language models. Utilizing mBERT, our approach begins by extracting key story entities. Subsequently, XLM-RoBERTa and a BERT-based linguistic evaluation model are employed to direct beam search algorithms in the replacement of these entities. Further refinement is achieved through Low-Rank Adaptation (LoRA), which fine-tunes the extensive 3 billion-parameter BLOOMZ model specifically for generating Arabic narratives. Our methodology underwent thorough testing and validation, involving individual assessments of each submodel. The ROCStories dataset provided the training ground for our story entity extractor and new entity generator, and was also used in the fine-tuning of the BLOOMZ model. Additionally, the Arabic ComVE dataset was employed to train our commonsense evaluation model. Our extensive analyses yield crucial insights into the efficacy of our approach. The story entity extractor demonstrated robust performance with an F-score of 96.62%. Our commonsense evaluator reported an accuracy of 84.3%, surpassing the previous best by 3.1%. The innovative beam search strategy effectively produced entities that were linguistically and semantically superior to those generated using baseline models. Further subjective evaluations affirm our methodology’s capability to generate high-quality Arabic stories characterized by linguistic fluency and logical coherence.

Since the early 1980s, the legal domain has shown a growing interest in Artificial Intelligence approaches to tackle the increasing number of cases worldwide. TaSbeeb is a deep learning (DL)-based judicial decision support system (JDSS) designed for legal professionals in Saudi courts by retrieving judicial reasoning, Qur’anic verses, and hadiths from a knowledge base. The proposed system consists of three phases: annotation, classification, and information retrieval. To annotate judicial text, we developed Ann-Judicial, a semi-automatic method. To handle the imbalanced corpus for classification, we devised homogeneous and heterogeneous stacking DL models. For information retrieval, we proposed Jud_RoBERTa, a judicial language model. TaSbeeb achieved high accuracy and F-scores in both the classification and information retrieval blocks, showing good accuracy despite complexities in the judicial field and interference between cases. Specifically, the classification phase achieved an accuracy and F-score of 95.8%, while the information retrieval phase achieved an accuracy of 79.8% and F-score of 79.3%. The proposed JDSS has potential for extension to other courts and can be used in judicial inspection. TaSbeeb represents a significant stride towards a more efficient and accurate judicial decision-making process in the Arabic legal system, which has been hindered by a lack of research on Arabic JDSS.

The process of text simplification (TS) is crucial for enhancing the comprehension of written material, especially for people with low literacy levels and those who struggle to understand written content. In this study, we introduce the first automated approach to TS that combines word-level and sentence-level simplification techniques for Arabic text. We employ three models: a neural machine translation model, an Arabic-BERT-based lexical model, and a hybrid model that combines both methods to simplify the text. To evaluate the models, we created and utilized two Arabic datasets, namely EW-SEW and WikiLarge, comprising 82,585 and 249 sentence pairs, respectively. As resources were scarce, we made these datasets available to other researchers. The EW-SEW dataset is a commonly used English TS corpus that aligns each sentence in the original English Wikipedia (EW) with a simpler reference sentence from Simple English Wikipedia (SEW). In contrast, the WikiLarge dataset has eight simplified reference sentences for each of the 249 test sentences. The hybrid model outperformed the other models, achieving a BLEU score of 55.68, a SARI score of 37.15, and an FBERT score of 86.7% on the WikiLarge dataset, demonstrating the effectiveness of the combined approach.

Abstract Background One of the worst pandemics of recent memory, COVID-19, severely impacted the public. In particular, students were physically and mentally affected by the lockdown and the shift from physical person-to-person classrooms to virtual learning (online classes). This increased the prevalence of psychological stress, anxiety, and depression among university students. In this study, we investigated the depression levels in Saudi Arabian university students who were learning virtually because of the COVID-19 pandemic and examined its impact on their educational proficiency. Methods The study focused on two points: first, examining the depression levels among undergraduate students in Saudi Arabia, by adapting the Zung (Self-Rating Depression Scale) questionnaire. Second, whether there is an association between the levels of depression and various distress factors associated with virtual (online) learning resulting from the COVID-19 pandemic and its impact on students’ educational behaviors. The questionnaire was prepared using a monkey survey and shared online, via email, and on WhatsApp groups, with participants in two universities, a public and private university in the largest city of Saudi Arabia. A total of 157 complete responses were received. Data were analyzed using SPSS-24, the chi-square test, descriptive statistics, and multilinear regression. Results The results indicated that three-fourths of the university students suffered from different depressive symptoms, half of which had moderate to extreme levels of depression. Our study confirmed that a boring virtual (online) learning method, stress, fear of examinations, and decreased productivity were significantly associated with increased depression. In addition, 75% and 79% of the students suffered from stress and fear of examinations, respectively. About half of the students were associated with increased depression. The outcome also indicated that female students experienced extreme depression, stress, and fear of examinations more than males. Conclusion These findings can inform government agencies and representatives of the importance of making swift, effective decisions to address students’ depression levels. It is essential to provide training for students to change their educational experience mindset, which might help decrease "depression and stress-related growth." There is also a need to search for a better virtual teaching delivery method to lessen students' stress and fear of examinations.

The concept of an F-contraction was introduced by Wardowski, while Samet et al. introduced the class of α-admissible mappings and the concept of (α-ψ)-contractive mapping on complete metric spaces. In this paper, we study and extend two types of contraction mappings: (α-ψ)-contraction mapping and (α-F)-contraction mapping, and establish new fixed point results on double controlled metric type spaces. Moreover, we demonstrate some examples and present an application of our result on the existence and uniqueness of the solution for an integral equation.

The end-case analysis of Arabic sentences is one of the keys to their meaning. This process is called i‘raab, a daunting task for the students. The outcome of the analysis is twofold: (a) placing a proper diacritical marking on the end-cases of individual words, and (b) providing a logical justification. Our objective is to generate a full i‘raabof the sentences, which could be incorporated in a computer-assisted Arabic grammar learning environment, teaching and helping the students with the i‘raabprocess. Our system comprises four components that work together to generate a proper i‘raabfor the sentence. We devise an enhanced context-free grammar (eCFG) that covers all the cases and rules taught in Saudi schools’ grammar textbooks (grades 7–12). Moreover, our eCFGgrammar eliminates the need for specialized grammars, e.g., head-driven phrase structure grammar and link grammar, to resolve complex cases involving dependencies. Furthermore, we utilize ontology to determine the correct semantics. The system was tested on 300 sentences varying in complexity from the Arabic grammar textbooks used in the schools. Our system achieved an overall accuracy of 88.33%.

In recent years, the concept of cloud computing has been gaining traction to provide dynamically increasing access to shared computing resources (software and hardware) via the internet. It’s not secret that cloud computing’s ability to supply mission-critical services has made job scheduling a hot subject in the industry right now. Cloud resources may be wasted, or in-service performance may suffer because of under-utilization or over-utilization, respectively, due to poor scheduling. Various strategies from the literature are examined in this research in order to give procedures for the planning and performance of Job Scheduling techniques (JST) in cloud computing. To begin, we look at and tabulate the existing JST that is linked to cloud and grid computing. The present successes are then thoroughly reviewed, difficulties and flows are recognized, and intelligent solutions are devised to take advantage of the proposed taxonomy. To bridge the gaps between present investigations, this paper also seeks to provide readers with a conceptual framework, where we proposed an effective job scheduling technique in cloud computing. These findings are intended to provide academics and policymakers with information about the advantages of a more efficient cloud computing setup. In cloud computing, fair job scheduling is most important. We proposed a priority-based scheduling technique to ensure fair job scheduling. Finally, the open research questions raised in this article will create a path for the implementation of an effective job scheduling strategy.

In this article, we delve into the study of fuzzy triple controlled metric spaces, investigating their properties and presenting a range of illustrative examples. We emphasize the broader applicability of this concept in comparison to fuzzy rectangular metric spaces and fuzzy rectangular b-metric spaces. By introducing the novel concept of (α-ψ)-fuzzy contractive mappings, we derive fixed point results specifically designed for complete fuzzy triple controlled metric spaces. Our theorems extend and enrich previous findings in this field. Additionally, we demonstrate the practical significance of our study by applying our findings to the solution of an integral equation and providing an example of its application. Furthermore, we propose potential avenues for future research endeavors.

Machine learning models have been very popular nowadays for providing rigorous solutions to complicated real-life problems. There are three main domains named supervised, unsupervised, and reinforcement. Supervised learning mainly deals with regression and classification. There exist several types of classification algorithms, and these are based on various bases. The classification performance varies based on the dataset velocity and the algorithm selection. In this article, we have focused on developing a model of angular nature that performs supervised classification. Here, we have used two shifting vectors named Support Direction Vector (SDV) and Support Origin Vector (SOV) to form a linear function. These vectors form a linear function to measure cosine-angle with both the target class data and the non-target class data. Considering target data points, the linear function takes such a position that minimizes its angle with target class data and maximizes its angle with non-target class data. The positional error of the linear function has been modelled as a loss function which is iteratively optimized using the gradient descent algorithm. In order to justify the acceptability of this method, we have implemented this model on three different standard datasets. The model showed comparable accuracy with the existing standard supervised classification algorithm. Doi: 10.28991/esj-2021-01306 Full Text: PDF

Fixed point theory is a versatile mathematical theory that finds applications in a wide range of disciplines, including computer science, engineering, fractals, and even behavioral sciences. In this study, we propose triple controlled metric-like spaces as a generalization of controlled rectangular metric-like spaces. By examining the Θ-contraction mapping within these spaces, we extend and enhance the existing literature to establish significant fixed point results. Utilizing these findings, we demonstrate the existence of solutions to a Fredholm integral equation and provide an example of a numerical iteration method applicable to a specific case of this Fredholm integral equation.

In this study, we establish unique and common fixed point results in the context of a complete complex-valued b-metric space using rational-type inequalities. The presented work generalizes some well-known results from the existing literature. Furthermore, to ensure the validity of the findings, we have included some examples and a section on the existence of solutions for the systems of Volterra–Hammerstein integral equations and Urysohn integral equations, respectively.

Background and AimsUniversity students are commonly identified as susceptible, suffering from higher anxiety, stress, and depression than the overall population. During the Corona Virus Disease pandemic (COVID), education was shifted to the virtual learning environment. Students' ambiguity regarding academic accomplishment, imminent careers, changes in social life, and other concerns all these factors played a role in amplifying their stress levels, anxiety, and depression worldwide. This study investigates university students' self-esteem and depressions after they have been online learning for over 1 year due to the COVID-19 pandemic in Saudi Arabia.MethodsFor this research, an adapted questionnaire of Rosenberg (Self-Esteem Scale) and Zung (Self-Rating Depression Scale) was used to get the responses of the participants of public and private universities in Saudi Arabia during March-April 2021. We received a total of 151 valid responses from respondents. For data analysis, we used descriptive statistics, ANOVA, multiple regression and binary logistic regression.FindingsThe results showed that 75% of the students experienced different levels of depressions, with half (37.5%) having moderate to extreme levels of depression. A total of 41% of students experienced low self-esteem (38% females and 45% males). The regression results indicated depressive symptoms for low self-esteem. Furthermore, results of logistic regression showed that high self-esteem reduces the chances of getting depressive symptoms by 17%. The depressive symptoms were higher in female students than their male counterparts; furthermore, males experienced depressive symptoms less than females by 38%.ConclusionsBased on the current research results, it is concluded that the presence of the COVID-19 pandemic has dramatically increased the depressive symptoms in students, especially in female students. The findings suggested instant consideration and support for students. It is also suggested to the quest for potential managing policies that have been known and effective during the pandemic. Moreover, training should be provided for students to shift their educational experience mindset to an adaptive mindset, which can help them adapt to the new ways of learning and education.

We introduce the novel concept of (α-ψ)-fuzzy contractive mappings on fuzzy double-controlled metric spaces and demonstrate some fixed-point results. The theorems presented generalize some intriguing findings in the literature. Thus, we prove the fixed-point theorem in the settings of fuzzy double-controlled metric spaces. Furthermore, we provide several examples and an application of our result on the existence of the solution to an integral equation.

The concept of Cognitive Radio (CR) has emerged as a practical solution to solve the issue of the fixed spectrum and bandwidth scarcity in wireless communication. However, the nature of dynamic Mobile Cognitive Radio Networks (MCRNs) drives to the emergence of new challenges, especially concerning the routing protocol operations. Applying a cross-layer design is considered a sufficient remedy to overcome routing protocol challenges such (e.g. channel diversity, integration route discovery with spectrum decision, mobility, etc.). Consequently, the cross-layer design has a magic solution to overwhelm routing challenges in MCRNs due to the ability to be free from the strict boundary and share the information and services with other layers in a manner that contributes to enhancing routing performance. Thus, the scope of this survey is to review and taxonomy numerous routing protocols in MCRNs according to methods of design to highlight the strength and weakness points. Also, machine learning has acquired much interest in this literature. A cross-layer framework for smart routing protocol in MCRNs has been proposed by exploiting machine learning mechanisms. Finally, the open research issues of routing protocol in MCRNs are summed up.

Document classification is a classical problem in information retrieval, and plays an important role in a variety of applications. Automatic document classification can be defined as content-based assignment of one or more predefined categories to documents. Many algorithms have been proposed and implemented to solve this problem in general, however, classifying Arabic documents is lagging behind similar works in other languages. In this paper, we present seven deep learning-based algorithms to classify the Arabic documents. These are: Convolutional Neural Network (CNN), CNN-LSTM (LSTM = Long Short-Term Memory), CNN-GRU (GRU = Gated Recurrent Units), BiLSTM (Bidirectional LSTM), BiGRU, Att-LSTM (Attention-based LSTM), and Att-GRU. And for word representation, we applied the word embedding technique (Word2Vec). We tested our approach on two large datasets-with six and eight categories-using ten-fold cross-validation. Our objective was to study how the classification is affected by the stemming strategies and word embedding. First, we looked into the effects of different stemming algorithms on the document classification with different deep learning models. We experimented with eleven different stemming algorithms, broadly falling into: root-based and stem-based, and no stemming. We performed ANOVA test on the classification results using the different stemmers, which helps assure if the results are significant. The results of our study indicate that stem-based algorithms perform slightly better compared to root-based algorithms. Among the deep learning models, the Attention mechanism and the Bidirectional learning gave outstanding performance with Arabic text categorization. Our best performance is F-score = 97.96%, achieved using the Att-GRU model with stem-based algorithm. Next, we looked into different controlling parameters for word embedding. For Word2Vec, both skip-gram and bag-of-words (CBOW) perform well with either stemming strategies. However, when using a stem-based algorithm, skip-gram achieves good results with a vector of smaller dimension, while CBOW requires a larger dimension vector to achieve a similar performance.

In this paper, we introduce grand weighted Herz–Morrey spaces with a variable exponent and prove the boundedness of fractional integrals on these spaces.

In this study, we utilize the notion of triple controlled metric type space that preserves the symmetry property, which is a generalization of b-metric-type spaces, to prove new fixed-point results. We introduce (α-F)-contractive mappings and Θ-contractive mappings on triple controlled metric type space settings. Then, we establish the existence and uniqueness of fixed-point results on complete triple controlled metric type space. Moreover, some examples and applications to boundary-value problems of the fourth-order differential equation are presented to display the usage of the obtained result.

The existing automatic fingerprint verification methods are designed to work under the assumption that the same sensor is installed for enrollment and authentication (regular matching). There is a remarkable decrease in efficiency when one type of contact-based sensor is employed for enrolment and another type of contact-based sensor is used for authentication (cross-matching or fingerprint sensor interoperability problem). The ridge orientation patterns in a fingerprint are invariant to the sensor type. Based on this observation, we propose a robust fingerprint descriptor called the co-occurrence of ridge orientations (Co-Ror), which encodes the spatial distribution of ridge orientations. Employing this descriptor, we introduce an efficient automatic fingerprint verification method for cross-matching problem. Furthermore, to enhance the robustness of the method, we incorporate scale-based ridge orientation information through the Gabor-HoG descriptor. The two descriptors are fused with the canonical correlation analysis (CCA), and the matching score between two fingerprints is calculated using city-block distance. The proposed method is alignment-free and can handle the matching process without the need for a registration step. The intensive experiments on two benchmark databases (FingerPass and MOLF) show the effectiveness of the method and reveal its significant enhancement over the state-of-the-art methods, such as VeriFinger (a commercial SDK), minutia cylinder-code (MCC), MCC with scale, and the thin-plate spline (TPS) model. The proposed research will help security agencies, service providers, and law-enforcement departments to overcome the interoperability problem of contact sensors of different technologies and interaction types.

We are living in the age of big data, a majority of which is stream data. The real-time processing of this data requires careful consideration from different perspectives. Concept drift is a change in the data’s underlying distribution, a significant issue, especially when learning from data streams. It requires learners to be adaptive to dynamic changes. Random forest is an ensemble approach that is widely used in classical non-streaming settings of machine learning applications. At the same time, the Adaptive Random Forest (ARF) is a stream learning algorithm that showed promising results in terms of its accuracy and ability to deal with various types of drift. The incoming instances’ continuity allows for their binomial distribution to be approximated to a Poisson(1) distribution. In this study, we propose a mechanism to increase such streaming algorithms’ efficiency by focusing on resampling. Our measure, resampling effectiveness (ρ), fuses the two most essential aspects in online learning; accuracy and execution time. We use six different synthetic data sets, each having a different type of drift, to empirically select the parameter λ of the Poisson distribution that yields the best value for ρ. By comparing the standard ARF with its tuned variations, we show that ARF performance can be enhanced by tackling this important aspect. Finally, we present three case studies from different contexts to test our proposed enhancement method and demonstrate its effectiveness in processing large data sets: (a) Amazon customer reviews (written in English), (b) hotel reviews (in Arabic), and (c) real-time aspect-based sentiment analysis of COVID-19-related tweets in the United States during April 2020. Results indicate that our proposed method of enhancement exhibited considerable improvement in most of the situations.

Traditional information retrieval systems return a ranked list of results to a user’s query. This list is often long, and the user cannot explore all the results retrieved. It is also ineffective for a highly ambiguous language such as Arabic. The modern writing style of Arabic excludes the diacritical marking, without which Arabic words become ambiguous. For a search query, the user has to skim over the document to infer if the word has the same meaning they are after, which is a time-consuming task. It is hoped that clustering the retrieved documents will collate documents into clear and meaningful groups. In this paper, we use an enhanced k-means clustering algorithm, which yields a faster clustering time than the regular k-means. The algorithm uses the distance calculated from previous iterations to minimize the number of distance calculations. We propose a system to cluster Arabic search results using the enhanced k-means algorithm, labeling each cluster with the most frequent word in the cluster. This system will help Arabic web users identify each cluster’s topic and go directly to the required cluster. Experimentally, the enhanced k-means algorithm reduced the execution time by 60% for the stemmed dataset and 47% for the non-stemmed dataset when compared to the regular k-means, while slightly improving the purity.

The release of carbon dioxide in the environment is increasing yearly due to human activities and it will affect greenhouse gas. To overcome this issue, adsorption technology found to be the best candidate due to its performance to capture high CO2 with lower capital cost. Much attention has focused on metal-organic framework (MOF) due to high potential of CO2 capture compared with conventional adsorbents. More research has been done on MOF-74 due to presence of the open-metal site that favors CO2 binding. The presence of metal in MOF-74 able to give higher surface area and porosity of the molecules thus result in higher adsorption of CO2. However, there is limited research related to metal in MOF-74 where most focused on the Mg-MOF-74 due to its ability to adsorb twice of CO2 compared with zeolites. Yet, Mg-MOF-74 found to lose stability in presence of water where it's only able to recover 15% from initial adsorption. Synthesizing MOF-74 requires high cost and providing not a promising result for each synthesizes. Thus, this paper introduces to screen MOF-74 for different metal centers using modeling approach by Material Studio. As result, Ni-MOF-74 shows the highest adsorption of CO2 with 12.35mmol/g compared to other metals.

A steel beam compressive strain sensor using single-mode-multimode-single-mode (SMS) fiber structure is demonstrated. Parallel measurements are made using an electrical resistance (ER) strain gauge along with the SMS to sense the compressive strain on the steel beam. The ER strain gauge result shows that the steel beam has an elastic limit at a compressive load of 42 kN, as predicted via calculation. On the other hand, the SMS sensor is capable to detect the elastic limit by its significant increase in the slope of the peak center wavelength from compressive strain of 0.000261-0.001986 mm/mm. Moreover, the SMS sensor exhibits compressive strain sensitivity of -1411.2 nm/(mm/mm) from the initial load until it reaches the beam elastic limit.