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Abstract Dry eye syndrome (DES) is a tear film disorder caused by increased tear evaporation or decreased production. The heavy workload on the eye and the increased usage of digital screens may decrease blink frequency, leading to an increased evaporation rate and an upsurge in the incidence and severity of DES. This study aims to assess the severity of DES symptoms and the risk factors among university students. A cross-sectional study was conducted at Umm AlQura University to evaluate the severity of DES among students and explore its potential association with digital screen use. Validated questionnaires were used to assess the severity of DES and digital screen usage. The study included 457 participants, of which 13% had symptoms suggestive of severe DES. Furthermore, multiple risk factors had a significant association with the severity of DES, including gender, use of monitor filters, monitor and room brightness, and smoking habits. DES symptoms were prevalent among university students, particularly female students. Although there was no significant association with the duration of screen usage and collage distribution. Other factors however, such as the usage of screen monitors and the brightness of both the monitor and the room, were significantly associated with the severity of DES symptoms. more...

Web 3.0 marks the beginning of a new era for the internet, characterized by distributed technology that prioritizes data ownership and value expression. Web 3.0 aims to empower users by providing them with ownership and control of their data and digital assets rather than leaving them in the hands of large corporations. Web 3.0 relies on decentralization, which uses blockchain technology to ensure secure user communication. However, Web 3.0 still faces many security challenges that might affect its deployment and expose users’ data and digital assets to cybercriminals. This survey investigates the current evolution of Web 3.0, outlining its background, foundation, and application. This review presents an overview of cybersecurity risks that face a mature Web 3.0 application domain (i.e., decentralized finance (DeFi)) and classifies them into seven categories. Moreover, state-of-the-art methods for addressing these threats are investigated and categorized based on the associated security risks. Insights into the potential future directions of Web 3.0 security are also provided. more...

The Ordovician Ra’an Shale Member of the Qassim Formation in Saudi Arabia has potential as an unconventional hydrocarbon play. This shale unit, however, has not been studied in detail for its geochemical characteristics, thermal maturity, and reservoir quality. This study aims to investigate this shale member from a well-exposed section at Khashm Ra’an in the Qassim region, central Saudi Arabia. Detailed fieldwork followed by comprehensive geochemical and petrographic analyses were conducted to evaluate the hydrocarbon potential and interpret the depositional setting of this shale member. Integration of field sedimentological description, sample description and thin section petrography facilitated the identification of partially burrowed laminated fish-bearing shale, bioturbated siltstone, graptolite-bearing shale, shale/siltstone and sandstone/shale interbeds in the investigated outcrop. The intervals were sandwiched between the bioturbated sandstone of the underlying Kahfah Sandstone and the overlying Quwarah Formation. These lithofacies are interpreted to have been deposited in a shallow marine depositional setting. The preservation of graptolite in some intervals indicates low bottom dissolved oxygen conditions. The mineralogical analysis indicated that clay minerals, quartz, and feldspars are the major mineralogical components in the study samples. Based on mineralogical compositions, the studied samples were classified into three main lithofacies types; silica-rich argillaceous mudstone, clay-rich siliceous mudstone, and mixed siliceous mudstone lithofacies. Elemental analysis proxies indicated that most of the tested samples were deposited in anoxic environment. The calculated values of the chemical index of weathering (CIW) indicated that samples were exposed to high rates of weathering, which negatively impacted organic matter preservation.Microscopic investigation identified the pore systems of the Ra’an Member as interparticle, interparticle and fracture pore systems, and observed the diagenetic features in the form of cementation, compaction, and dissolution. Organic geochemical analysis indicates that the samples are mainly of kerogen type III and II/III with poor to fair potential for hydrocarbon generation. This study provides an improved understanding of the depositional setting, organic geochemical, and thermal evolution of the Ra'an Member as a potential unconventional hydrocarbon reservoir. more...

When people use social networks, they often fall prey to a clickbait scam. The scammer attempts to create a striking headline that attracts the majority of users and attaches a link. The user follows the link and can be redirected to a fraudulent resource where the user easily loses personal data. To solve this problem, a Blockchain-enabled deep recurrent neural network (BDRNN) is proposed to detect the nature safe and malicious clickbait from the contents. The proposed BDRNN consists of three phases: analysis of clickbait and source rating, clickbait search process and multi-layered clickbait detection. The analysis of clickbait and source rating phase helps to analyze different sources to detect the clickbait and also rating the content-sources. To achieve the clickbait analysis and source rating, the detection of blocklisted/allowlisted source and source rating check algorithms are introduced. The clickbait search process is accomplished by incorporating the binary search features for a faster and more efficient search process for malicious content-detection. The multi-layered clickbait detection is main phase of the proposed BDRNN that consists of three models: content-to-vector model (layer-1), deep neural network model(layer-2), and Blockchain-enabled malicious content detection model (layer-3). These models collectively detect the malicious and safe clickbait from the contents. The extensive experiments are conducted to determine the effectiveness of the proposed BDRNN model and compared with the existing state-of-the-art neural network models designed for clickbait detection, and the result demonstrates that the proposed BDRNN model outperforms the counterparts from the, accuracy, link detection, memory usage, analogous perspectives, and attacker’s successful content capturing rate. more...

Speech recognition systems play an important role in human–machine interactions. Many systems exist for Arabic speech, however, there are limited systems for dialectal Arabic speech. The Arabic language comprises many properties, some of which are ideal for building automatic speech recognition systems such as syntax and phonology, while other properties are unsuitable for developing speech systems. Importantly, most data are in non-diacritized form, vary in dialect, and contain morphological complexity. Moreover, the Arabic dialects lack a standard structure. In this paper, we highlighted the works and frameworks that have been developed in the last fourteen years of dialectal Arabic speech recognition systems. The paper also presented an analysis and evaluation for several studies using different approaches and techniques. The main goal of this paper is to compare and discuss different studies in dialectal Arabic speech systems including several criteria such as techniques, datasets, evaluation metrics, and dialect types. The study also includes a description of some techniques used in all steps of the dialectal Arabic speech system such as hidden Markov models (HMM), convolutional neural network (CNN), and deep neural network (DNN). In addition, we introduced the challenges and problems of dialectal Arabic speech recognition systems. Overall, more studies are required to obtain a more accurate speech system for dialectal Arabic. more...

One of the main challenges facing the current approaches of speech emotion recognition is the lack of a dataset large enough to train the currently available deep learning models properly. Therefore, this paper proposes a new data augmentation algorithm to enrich the speech emotions dataset with more sam Department, College of Computing and ples through a careful addition of noise fractions. In addition, the hyperparameters of the currently available deep learning models are either handcrafted or adjusted during the training process. However, this approach does not guarantee finding the best settings for these parameters. Therefore, we propose an optimized deep learning model in which the hyperparameters are optimized to find their best settings and thus achieve more recognition results. This deep learning model consists of a convolutional neural network (CNN) composed of four local feature-learning blocks and a long short-term memory (LSTM) layer for learning local and long-term correlations in the log Mel-spectrogram of the input speech samples. To improve the performance of this deep network, the learning rate and label smoothing regularization factor are optimized using the recently emerged stochastic fractal search (SFS)-guided whale optimization algorithm (WOA). The strength of this algorithm is the ability to balance between the exploration and exploitation of the search agents’ positions to guarantee to reach the optimal global solution. To prove the effectiveness of the proposed approach, four speech emotion datasets, namely, IEMOCAP, Emo-DB, RAVDESS, and SAVEE, are incorporated in the conducted experiments. Experimental results confirmed the superiority of the proposed approach when compared with state-of-the-art approaches. Based on the four datasets, the achieved recognition accuracies are 98.13%, 99.76%, 99.47%, and 99.50%, respectively. Moreover, a statistical analysis of the achieved results is provided to emphasize the stability of the proposed approach. more...

Background Patients seeking emergency department (ED) care for non-acute conditions are a major contributor to ED overcrowding, which results in longer wait times. Method This was a cross-sectional study, conducted using an online survey among the Saudi population to assess their awareness about primary healthcare clinics (PHCCs) and urgent care clinics (UCCs), their role, and their scope of practice. Results A total of 565 participants were included in this study. Most of the respondents (81.1%) reported lengthy waiting times in the ED. Moreover, most (81.6%) stated that they had never visited a family doctor, yet they (92.6%) favoured having one for follow-up care. Close to half of the participants (50.3%) reported attending PHCCs without an appointment, and the majority of them (69.2%) said that PHCCs were overcrowded. Finally, most participants (92.4%) had not heard aboutUCCs. Conclusion ED overcrowding and prolonged waiting times remain a public concern. PHCCs and UCCs are underutilised, and this is attributed to the lack of awareness about their scope and their services. more...

The Industrial Internet of Things (IIoT) paradigm is a key research area derived from the Internet of Things (IoT). The emergence of IIoT has enabled a revolution in manufacturing and production, through the employment of various embedded sensing devices connected by an IoT network, along with a collection of enabling technologies, such as artificial intelligence (AI) and edge/fog computing. One of the unrivaled characteristics of IIoT is the inter-connectivity provided to industries; however, this characteristic might open the door for cyber-criminals to launch various attacks. In fact, one of the major challenges hindering the prevalent adoption of the IIoT paradigm is IoT security. Inevitably, there has been an inevitable increase in research proposals over the last decade to overcome these security concerns. To obtain an overview of this research area, conducting a literature survey of the published research is necessary, eliciting the various security requirements and their considerations. This paper provides a literature survey of IIoT security, focused on the period from 2017 to 2023. We identify IIoT security threats and classify them into three categories, based on the IIoT layer they exploit to launch these attacks. Additionally, we characterize the security requirements that these attacks violate. Finally, we highlight how emerging technologies, such as AI and edge/fog computing, can be adopted to address security concerns and enhance IIoT security. more...

Mobile technologies are an essential part of people’s everyday lives since they are utilized for a variety of purposes, such as communication, entertainment, commerce, and education. However, when these gadgets are misused, the human body is exposed to continuous radiation from the electromagnetic field created by them. The communication services available are improving as mobile technologies advance; however, the problem is becoming more severe as the frequency range of mobile devices expands. To solve this complex case, it is necessary to propose a comprehensive approach that combines and processes data obtained from different types of research and sources of information, such as thermal imaging, electroencephalograms, computer models, and surveys. In the present article, a complex model for the processing and analysis of heterogeneous data is proposed based on mathematical and statistical methods in order to study the problem of electromagnetic radiation from mobile devices in-depth. Data science selection/preprocessing is one of the most important aspects of data and knowledge processing aiming at successful and effective analysis and data fusion from many sources. Special types of logic-based binding and pointing constraints are considered for data/knowledge selection applications. The proposed logic-based statistical modeling method provides both algorithmic as well as data-driven realizations that can be evolutionary. As a result, non-anticipated and collateral data/features can be processed if their role in the selected/constrained area is significant. In this research, the data-driven part does not use artificial neural networks; however, this combination was successfully applied in the past. It is an independent subsystem maintaining control of both the statistical and machine-learning parts. The proposed modeling applies to a wide range of reasoning/smart systems. more...

Porous carbons are highly attractive and demanding materials which could be prepared using biomass waste; thus, they are promising for enhanced electrochemical capacitive performance in capacitors and cycling efficiency in Li-ion batteries. Herein, biomass (rice husk)-derived activated carbon was synthesized via a facile chemical route and used as anode materials for Li-ion batteries. Various characterization techniques were used to study the structural and morphological properties of the prepared activated carbon. The prepared activated carbon possessed a carbon structure with a certain degree of amorphousness. The morphology of the activated carbon was of spherical shape with a particle size of ~40–90 nm. Raman studies revealed the characteristic peaks of carbon present in the prepared activated carbon. The electrochemical studies evaluated for the fabricated coin cell with the activated carbon anode showed that the cell delivered a discharge capacity of ~321 mAhg−1 at a current density of 100 mAg−1 for the first cycle, and maintained a capacity of ~253 mAhg−1 for 400 cycles. The capacity retention was found to be higher (~81%) with 92.3% coulombic efficiency even after 400 cycles, which showed excellent cyclic reversibility and stability compared to commercial activated carbon. These results allow the waste biomass-derived anode to overcome the problem of cyclic stability and capacity performance. This study provides an insight for the fabrication of anodes from the rice husk which can be redirected into creating valuable renewable energy storage devices in the future, and the product could be a socially and ethically acceptable product. more...

Context-aware computing has become a certainty due to the widespread use of smartphone devices equipped with sensors. A wide range of services, such as vehicular traffic monitoring and smart parking, can be accomplished with the help of awareness of user mobility. Transportation mode detection (TMD) using machine learning algorithms and the data captured from smartphone embedded sensors have attracted research community attention. In this research, ensemble learning is utilized to differentiate between transportation modes, including walking, standing, riding a train, driving a car, and riding a bus. The ensemble learning consists of three classifiers; each classifier votes independently on the instances, and the majority vote is applied for robust generalization. The proposed method was validated using three datasets; the samples included in these datasets were gathered by smartphone sensors (belonging to heterogeneous users), such as rotation vector sensors, accelerometers, uncalibrated gyroscopes, linear acceleration, orientation, speed, game rotation vector, sound, and gyroscopes. The proposed ensemble learning method achieves an accuracy of 89%, 93%, and 95% on the first, second, and third datasets, respectively, when 10% and 90% of the data are used for testing and training, respectively. On the other set of experiments, in which 30% and 70% of the data are used for testing and training, respectively, the proposed method yields accuracies of 86.8%, 92.1%, and 94.9% on the first, second, and third datasets, respectively. The proposed method shows promising results compared to existing human activity recognition (HAR) methods. more...

Time series data are significant, and are derived from temporal data, which involve real numbers representing values collected regularly over time. Time series have a great impact on many types of data. However, time series have anomalies. We introduce an anomaly detection paradigm called novel matrix profile (NMP) to solve the all-pairs similarity search problem for time series data in the healthcare. The proposed paradigm inherits the features from two state-of-the-art algorithms: Scalable Time series Anytime Matrix Profile (STAMP) and Scalable Time-series Ordered-search Matrix Profile (STOMP). The proposed NMP caches the output in an easy-to-access fashion for single- and multidimensional data. The proposed NMP can be used on large multivariate data sets and generates approximate solutions of high quality in a reasonable time. It is implemented on a Python platform. To determine its effectiveness, it is compared with the state-of-the-art matrix profile algorithms, i.e., STAMP and STOMP. The results confirm that the proposed NMP provides higher accuracy than the compared algorithms. more...

Effective security has become a vital part of our daily routine, for example securing cash, jewelry, documents, and other valuables. One way is to protect these items by keeping them in a safe box. However, current safe boxes are vulnerable to a number of security attacks such as physical, repudiation, anonymously modifying, etc. In this regard, this paper presents a robust smart safe box using the blockchain solution to ensure each transaction and secure it from unauthorized access. Also, it traces back the local safe items by reading back to blockchain data transactions and protecting them from thieves by tracking the burglar with a real-time tracking location Global Positioning System (GPS) tracker and capturing a picture and video recording through the external camera. Similarly, with more security enhancement using the TensorFlow framework to analyze optical detection and identify things inside the box using an internal camera. At the same time, each transaction is stored by using Write Once and Read Many (WORM) patterns saved in local private blockchain data block logs. Finally, it uses three-way authentication mechanisms to unlock the safe box that authenticates the users, (1) face recognition, (2) fingerprint and (3) the keypad password. The performance of the proposed smart safe box is tested by a number of hardware and blockchain tests. The results reveal that the proposed smart safe box ensures safe and authenticated access to the box and records each transaction using blockchain technology. Further, the performance evaluation of the private blockchain significantly improved the integrity and safeness of the proposed smart safe box. more...

Blood cancer, or leukemia, has a negative impact on the blood and/or bone marrow of children and adults. Acute lymphocytic leukemia (ALL) and acute myeloid leukemia (AML) are two sub-types of acute leukemia. The Internet of Medical Things (IoMT) and artificial intelligence have allowed for the development of advanced technologies to assist in recently introduced medical procedures. Hence, in this paper, we propose a new intelligent IoMT framework for the automated classification of acute leukemias using microscopic blood images. The workflow of our proposed framework includes three main stages, as follows. First, blood samples are collected by wireless digital microscopy and sent to a cloud server. Second, the cloud server carries out automatic identification of the blood conditions—either leukemias or healthy—utilizing our developed generative adversarial network (GAN) classifier. Finally, the classification results are sent to a hematologist for medical approval. The developed GAN classifier was successfully evaluated on two public data sets: ALL-IDB and ASH image bank. It achieved the best accuracy scores of 98.67% for binary classification (ALL or healthy) and 95.5% for multi-class classification (ALL, AML, and normal blood cells), when compared with existing state-of-the-art methods. The results of this study demonstrate the feasibility of our proposed IoMT framework for automated diagnosis of acute leukemia tests. Clinical realization of this blood diagnosis system is our future work. more...

People who use social networks often fall prey to clickbait, which is commonly exploited by scammers. The scammer attempts to create a striking headline that attracts the majority of users to click an attached link. Users who follow the link can be redirected to a fraudulent resource, where their personal data are easily extracted. To solve this problem, a novel browser extension named ClickBaitSecurity is proposed, which helps to evaluate the security of a link. The novel extension is based on the legitimate and illegitimate list search (LILS) algorithm and the domain rating check (DRC) algorithm. Both of these algorithms incorporate binary search features to detect malicious content more quickly and more efficiently. Furthermore, ClickBaitSecurity leverages the features of a deep recurrent neural network (RNN). The proposed ClickBaitSecurity solution has greater accuracy in detecting malicious and safe links compared to existing solutions. more...

Cyber-physical systems (CPSs) have greatly contributed to many applications. A CPS is capable of integrating physical and computational capabilities to interact with individuals through various new modalities. However, there is a need for such a paradigm to focus on the human central nervous system to provide faster data access. This paper introduces the CPS paradigm that consists of CPS enabled human brain monitoring (CPS-HBM) and efficient data-balancing for CPS (EDB-CPS). The CPS-HBM provides architectural support to make an efficient and secure transfer and storage of the sensed data over fog cloud computing. The CPS-HBM consists of four components: physical domain and data processing (PDDP), brain sensor network (BSN), Service-oriented architecture (SOA), and data management domain (DMD). The EDB-CPS module aims to balance data flow for obtaining better throughput and lower hop-to-hop delay. The EDB-CPS accomplishes the goal by employing three processes: A node advertisement (NA), A node selection and recruitment (NSR), and optimal distance determination with mid-point (ODDMP). The processes of the EDB-CPS are performed on the PDDP of the CPS-HBM module. Thus, to determine the validity of EDB-CPS, the paradigm was programmed with C++ and implemented on a network simulator-3 (NS3). Finally, the performance of the proposed EDB-CPS was compared with state-of-the-art methods in terms of hop-to-hop delay and throughput. The proposed EDB-CPS produced better throughput between 443.2–445.2 KB/s and 0.05–0.078 ms hop-to-hop delay. more...

Modern information technology (IT) is well developed, and almost everyone uses the features of IT and services within the Internet. However, people are being affected due to cybersecurity threats. People can adhere to the recommended cybersecurity guidelines, rules, adopted standards, and cybercrime preventive measures to largely mitigate these threats. The ignorance of or lack of cybersecurity knowledge also causes a critical problem regarding confidentiality and privacy. It is not possible to fully avoid cybercrimes that often lead to sufficient business losses and spread forbidden themes (disgust, extremism, child porn, etc.). Therefore, to reduce the risk of cybercrimes, a web-based Blockchain-enabled cybersecurity awareness program (WBCA) process is introduced in this paper. The proposed WBCA trains users to improve their security skills. The proposed program helps with understanding the common behaviors of cybercriminals and improves user knowledge of cybersecurity hygiene, best cybersecurity practices, modern cybersecurity vulnerabilities, and trends. Furthermore, the proposed WBCA uses Blockchain technology to protect the program from potential threats. The proposed program is validated and tested using real-world cybersecurity topics with real users and cybersecurity experts. We anticipate that the proposed program can be extended to other domains, such as national or corporate courses, to increase the cybersecurity awareness level of users. A CentOS-based virtual private server is deployed for testing the proposed WBCA to determine its effectiveness. Finally, WBCA is also compared with other state-of-the-art web-based programs designed for cybersecurity awareness. more...

Remote sensing technologies have been widely used in the contexts of land cover and land use. The image classification algorithms used in remote sensing are of paramount importance since the reliability of the result from remote sensing depends heavily on the classification accuracy. Parametric classifiers based on traditional statistics have successfully been used in remote sensing classification, but the accuracy is greatly impacted and rather constrained by the statistical distribution of the sensing data. To eliminate those constraints, new variants of support vector machine (SVM) are introduced. In this paper, we propose and implement land use classification based on improved SVM-enabled radial basis function (RBF) and SVM-Linear for image sensing. The proposed variants are applied for the cross-validation to determine how the optimization of parameters can affect the accuracy. The accuracy assessment includes both training and test sets, addressing the problems of overfitting and underfitting. Furthermore, it is not trivial to determine the generalization problem merely based on a training dataset. Thus, the improved SVM-RBF and SVM-Linear also demonstrate the outstanding generalization performance. The proposed SVM-RBF and SVM-Linear variants have been compared with the traditional algorithms (Maximum Likelihood Classifier (MLC) and Minimum Distance Classifier (MDC)), which are highly compatible with remote sensing images. Furthermore, the MLC and MDC are mathematically modeled and characterized with new features. Also, we compared the proposed improved SVM-RBF and SVM-Linear with the current state-of-the-art algorithms. Based on the results, it is confirmed that proposed variants have higher overall accuracy, reliability, and fault-tolerance than traditional as well as latest state-of-the-art algorithms. more...

Herein, for the first time, the growth of ZnO nanorods directly on aluminum (Al) substrate via a low temperature (80 °C) wet chemical method, and used as binder-free electrode for supercapacitors were reported. XRD pattern and HRTEM images showed that high crystalline nanorods grown on Al substrate with c-axis orientation. Morphological studies revealed that the nanorods possessed well defined hexagon phase with length and diameter of ~2 µm and 100–180 nm, respectively. Raman spectrum of ZnO nanorods showed that the characteristic E2H mode corresponds to the vibration associated with the oxygen atoms of ZnO. The optical properties of ZnO nanorods studied using Room-temperature PL spectra revealed a near-band-edge (NBE) peak at ~388 nm emission and deep level (DLE) at ~507 nm. Electrochemical measurements showed that ZnO nanorods on Al substrate exhibited remarkably enhanced performance as electrode for supercapacitors with a value of specific capacitance of 394 F g−1 measured with scan rate of 20 mV s−1. This unique nanorods structures also exhibited excellent stability of >98% capacitance retention for 1000 cycles that were measured at 1A g−1. The presented easy and cost-effective method might open up the possibility for the mass production of binder-free electrodes for efficient electrochemical energy storage devices. more...

We report on the direct conversion of carbon dioxide (CO2) in a photoelectrochemical cell consisting of germanium doped gallium nitride nanowire anode and copper (Cu) cathode. Various products including methane (CH4), carbon monoxide (CO), and formic acid (HCOOH) were observed under light illumination. A Faradaic efficiency of ∼10% was measured for HCOOH. Furthermore, this photoelectrochemical system showed enhanced stability for 6 h CO2 reduction reaction on low cost, large area Si substrates. more...

Media access control (MAC) addresses in wireless networks can be trivially spoofed using off-the-shelf devices. The aim of this research is to detect MAC address spoofing in wireless networks using a hard-to-spoof measurement that is correlated to the location of the wireless device, namely the received signal strength (RSS). We developed a passive solution that does not require modification for standards or protocols. The solution was tested in a live test-bed (i.e., a wireless local area network with the aid of two air monitors acting as sensors) and achieved 99.77%, 93.16% and 88.38% accuracy when the attacker is 8–13 m, 4–8 m and less than 4 m away from the victim device, respectively. We implemented three previous methods on the same test-bed and found that our solution outperforms existing solutions. Our solution is based on an ensemble method known as random forests. more...