Prince Sultan University

The COVID-19 pandemic and the lockdown has taken the world by storm. This study examines its impact on the anxiety level of university students in Malaysia during the peak of the crisis and the pertinent characteristics affecting their anxiety. A cross-sectional online survey, using Zung’s self-rating anxiety questionnaire was conducted during the COVID-19 pandemic and lockdown. Out of the 983 respondents, 20.4%, 6.6%, and 2.8% experienced minimal to moderate, marked to severe, and most extreme levels of anxiety. Female gender (OR = 21.456, 95% CI = 1.061, 1.998, p = 0.020), age below 18 years (OR = 4.147, 95% CI = 1.331, 12.918, p = 0.014), age 19 to 25 (OR = 3.398, 95% CI = 1.431, 8.066, p = 0.006), pre-university level of education (OR = 2.882, 95% CI = 1.212, 6.854, p = 0.017), management studies (OR = 2.278, 95% CI = 1.526, 3.399, p < 0.001), and staying alone (OR = 2.208, 95% CI = 1.127, 4.325, p = 0.021) were significantly associated with higher levels of anxiety. The main stressors include financial constraints, remote online teaching and uncertainty about the future with regard to academics and career. Stressors are predominantly financial constraints, remote online learning, and uncertainty related to their academic performance, and future career prospects.

In today’s world, social media is playing an indispensable role on the learning behavior of university students to achieve sustainable education. The impact of social media on sustainable education is becoming an essential and impelling factor. The world has become a global village and technology use has made it a smaller world through social media and how it is changing instruction. This original study is amongst the few to perform a focalized investigation on revealing the relationship between positive and negative characteristics of social media and the learning attitude of university students for sustainable education. However, this study aims to examine the constructive and adverse factors that impact on students’ minds and how these helped students to share positive and negative aspects with others. It is increasingly noticeable that social networking sites and their applications present enormous benefits for as well as risks to university students and their implications on students’ psychological adjustment or learning behaviors are not well understood. This study adapted the cluster sampling method, and respondents participated from five selected regions. Researchers distributed 1013 questionnaires among the targeted sample of university students with an age range of 16 to 35 years, and they collected 831 complete/valid responses. This study applied the social gratification theory to examine students’ behavior practicing social media usage. This study specifically identified 18 adversarial and constructive factors of social media from the previous literature. The findings revealed that the usage of social media in Pakistan has a negative influence on a student’s behavior as compared to positive aspects. Results may not be generalized to the entire student community as findings are specific to the specific respondents only. This study presents a relationship between antithetical and creative characteristics of social media and exhibits avenues for future studies by facilitating a better understanding of web-based social network use.

The Internet of Medical Things, Smart Devices, Information Systems, and Cloud Services have led to a digital transformation of the healthcare industry. Digital healthcare services have paved the way for easier and more accessible treatment, thus making our lives far more comfortable. However, the present day healthcare industry has also become the main victim of external as well as internal attacks. Data breaches are not just a concern and complication for security experts; they also affect clients, stakeholders, organizations, and businesses. Though the data breaches are of different types, their impact is almost always the same. This study provides insights into the various categories of data breaches faced by different organizations. The main objective is to do an in-depth analysis of healthcare data breaches and draw inferences from them, thereby using the findings to improve healthcare data confidentiality. The study found that hacking/IT incidents are the most prevalent forms of attack behind healthcare data breaches, followed by unauthorized internal disclosures. The frequency of healthcare data breaches, magnitude of exposed records, and financial losses due to breached records are increasing rapidly. Data from the healthcare industry is regarded as being highly valuable. This has become a major lure for the misappropriation and pilferage of healthcare data. Addressing this anomaly, the present study employs the simple moving average method and the simple exponential soothing method of time series analysis to examine the trend of healthcare data breaches and their cost. Of the two methods, the simple moving average method provided more reliable forecasting results.

This manuscript is based on the standard fractional calculus iteration procedure on conformable derivatives. We introduce new fractional integration and differentiation operators. We define spaces and present some theorems related to these operators.

3D printing has revolutionized various industries by enabling the production of complex designs and shapes. Recently, the potential of new materials in 3D printing has led to an exponential increase in the technology's applications. However, despite these advancements, the technology still faces significant challenges, including high costs, low printing speeds, limited part sizes, and strength. This paper critically reviews the recent trends in 3D printing technology, with a particular focus on the materials and their applications in the manufacturing industry. The paper highlights the need for further development of 3D printing technology to overcome its limitations. It also summarizes the research conducted by experts in this field, including their focuses, techniques, and limitations. By providing a comprehensive overview of the recent trends in 3D printing, this review aims to provide valuable insights into the technology's prospects.

Manual identification of brain tumors is an error-prone and tedious process for radiologists; therefore, it is crucial to adopt an automated system. The binary classification process, such as malignant or benign is relatively trivial; whereas, the multimodal brain tumors classification (T1, T2, T1CE, and Flair) is a challenging task for radiologists. Here, we present an automated multimodal classification method using deep learning for brain tumor type classification. The proposed method consists of five core steps. In the first step, the linear contrast stretching is employed using edge-based histogram equalization and discrete cosine transform (DCT). In the second step, deep learning feature extraction is performed. By utilizing transfer learning, two pre-trained convolutional neural network (CNN) models, namely VGG16 and VGG19, were used for feature extraction. In the third step, a correntropy-based joint learning approach was implemented along with the extreme learning machine (ELM) for the selection of best features. In the fourth step, the partial least square (PLS)-based robust covariant features were fused in one matrix. The combined matrix was fed to ELM for final classification. The proposed method was validated on the BraTS datasets and an accuracy of 97.8%, 96.9%, 92.5% for BraTs2015, BraTs2017, and BraTs2018, respectively, was achieved.

To enhance the photovoltaic (PV) power-generation conversion, maximum power point tracking (MPPT) is the foremost constituent. This article introduces an adaptive neuro-fuzzy inference system-particle swarm optimization (ANFIS-PSO)-based hybrid MPPT method to acquire rapid and maximal PV power with zero oscillation tracking. The inverter control strategy is implemented by a space vector modulation hysteresis current controller to get quality inverter current by tracking accurate reference sine-shaped current. The ANFIS-PSO-based MPPT method has no extra sensor requirement for measurement of irradiance and temperature variables. The employed methodology delivers remarkable driving control to enhance PV potential extraction. An ANFIS-PSO-controlled Zeta converter is also modeled as an impedance matching interface with zero output harmonic agreement and kept between PV modules and load regulator power circuit to perform MPPT action. The attainment of recommended hybrid ANFIS-PSO design is equated with perturb and observe, PSO, ant colony optimization, and artificial bee colony MPPT methods for the PV system. The practical validation of the proposed grid-integrated PV system is done through MATLAB interfaced dSPACE interface and the obtained responses accurately justify the proper design of control algorithms employed with superior performance.

Wireless Sensor Networks (WSN) have become increasingly one of the hottest research areas in computer science due to their wide range of applications including critical military and civilian applications. Such applications have created various security threats, especially in unattended environments. To ensure the security and dependability of WSN services, an Intrusion Detection System (IDS) should be in place. This IDS has to be compatible with the characteristics of WSNs and capable of detecting the largest possible number of security threats. In this paper a specialized dataset for WSN is developed to help better detect and classify four types of Denial of Service (DoS) attacks: Blackhole, Grayhole, Flooding, and Scheduling attacks. This paper considers the use of LEACH protocol which is one of the most popular hierarchical routing protocols in WSNs. A scheme has been defined to collect data from Network Simulator 2 (NS-2) and then processed to produce 23 features. The collected dataset is called WSN-DS. Artificial Neural Network (ANN) has been trained on the dataset to detect and classify different DoS attacks. The results show that WSN-DS improved the ability of IDS to achieve higher classification accuracy rate. WEKA toolbox was used with holdout and 10-Fold Cross Validation methods. The best results were achieved with 10-Fold Cross Validation with one hidden layer. The classification accuracies of attacks were 92.8%, 99.4%, 92.2%, 75.6%, and 99.8% for Blackhole, Flooding, Scheduling, and Grayhole attacks, in addition to the normal case (without attacks), respectively.

In the recent era of the Internet of Things, the dominant role of sensors and the Internet provides a solution to a wide variety of real-life problems. Such applications include smart city, smart healthcare systems, smart building, smart transport and smart environment. However, the real-time IoT sensor data include several challenges, such as a deluge of unclean sensor data and a high resource-consumption cost. As such, this paper addresses how to process IoT sensor data, fusion with other data sources, and analyses to produce knowledgeable insight into hidden data patterns for rapid decision-making. This paper addresses the data processing techniques such as data denoising, data outlier detection, missing data imputation and data aggregation. Further, it elaborates on the necessity of data fusion and various data fusion methods such as direct fusion, associated feature extraction, and identity declaration data fusion. This paper also aims to address data analysis integration with emerging technologies, such as cloud computing, fog computing and edge computing, towards various challenges in IoT sensor network and sensor data analysis. In summary, this paper is the first of its kind to present a complete overview of IoT sensor data processing, fusion and analysis techniques.

In this article, we study generalized fractional derivatives that contain kernels depending on a function on the space of absolute continuous functions. We generalize the Laplace transform in order to be applicable for the generalized fractional integrals and derivatives and apply this transform to solve some ordinary differential equations in the frame of the fractional derivatives under discussion.

This study examines the role of ChatGPT as a writing assistant in academia through a systematic literature review of the 30 most relevant articles. Since its release in November 2022, ChatGPT has become the most debated topic among scholars and is also being used by many users from different fields. Many articles, reviews, blogs, and opinion essays have been published in which the potential role of ChatGPT as a writing assistant is discussed. For this systematic review, 550 articles published six months after ChatGPT’s release (December 2022 to May 2023) were collected based on specific keywords, and the final 30 most relevant articles were finalized through PRISMA flowchart. The analyzed literature identifies different opinions and scenarios associated with using ChatGPT as a writing assistant and how to interact with it. Findings show that artificial intelligence (AI) in education is a part of the ongoing development process, and its latest chatbot, ChatGPT is a part of it. Therefore, the education process, particularly academic writing, has both opportunities and challenges in adopting ChatGPT as a writing assistant. The need is to understand its role as an aid and facilitator for both the learners and instructors, as chatbots are relatively beneficial devices to facilitate, create ease and support the academic process. However, academia should revisit and update students’ and teachers’ training, policies, and assessment ways in writing courses for academic integrity and originality, like plagiarism issues, AI-generated assignments, online/home-based exams, and auto-correction challenges.

Purpose Through social media technologies, small and medium-sized enterprises (SMEs) can communicate information and respond to competitors with minimal cost. The ability to share and access information can affect SMEs’ performance, but there is little research on the link between SMEs’ social media adoption and their performance. The purpose of this paper is to present a quantitative survey to explore factors that influenced social media adoption by SMEs in the United Arab Emirates (UAE), and its impact on performance. Design/methodology/approach The study used a multi-perspective framework combining technological, organizational and environmental elements affecting SMEs. Survey questionnaires were used to collect data from a random sample of SMEs operating in the UAE. Using partial least squares and structural equation modeling techniques, 144 responses were analyzed. Findings Social media adoption had no effect on SMEs’ performance. These findings could help managers and decision makers in the SME sector to try to keep pace with research on social media innovations, and enable them to benefit from social commerce as it becomes more ubiquitous. Research limitations/implications This has implications for social media experts and anyone wishing to encourage social media use by SMEs. Originality/value The study developed a suitable multi-perspective framework covering various factors that may affect social media use. It also tested the framework empirically on a sample of SMEs from the UAE.

Abstract Policymakers face a daunting task when it comes to achieving sustainable environmental development and avoiding additional environmental degradation. This study examines the significance of green technology innovation and green financing in creating a more sustainable environment. The impact of green technology innovation and green investment on carbon dioxide (CO 2 ) emissions has yet to be empirically and theoretically examined in the literature, especially in conjunction with a moderating component, particularly social globalisation. Accordingly, this research examines the role of green technological innovation and green financing in reducing CO 2 emissions in the G7 countries. Our study uses empirical research data from a panel of the G7 countries covering the period 1995 to 2019. We employ advanced panel approaches to address panel data analysis concerns, such as cross‐sectional dependence, structural break, and slope heterogeneity (the Banerjee and Carrion‐i‐Silvestre unit root and cointegration test and cross‐sectional augmented ARDL). This study shows that green technology innovation (GINV) as well as green financing (GFIN) have a negative but significant impact on CO 2 emissions. Whilst economic growth has shown a positive and significant impact on CO 2 emissions in the G7 countries, social globalisation positively moderates the relationship between CO 2 emissions and GDP, but negatively and significantly causes GFIN and GINV with CO 2 emissions amongst the G7 countries. According to our study, countries would be able to meet the United Nations' SDG‐7 and SDG‐13 targets if they implemented green financing and green technology policies.

A significant number of educational stakeholders are concerned about the issue of digitalization in higher educational institutions (HEIs). Digital skills are becoming more pertinent throughout every context, particularly in the workplace. As a result, one of the key purposes for universities has shifted to preparing future managers to address issues and look for solutions, including information literacy as a vital set of skills. The research of educational technology advances in higher education is now being discussed and debated, with various laws, projects, and tactics being offered. Digital technology has been a part of the lives of today’s children from the moment they are born. There are still many different types of digital divisions that exist in our society, and they affect the younger generation and their digital futures. Today’s students do not have the same level of preparation for the technology-rich society they will have. Universities and teaching should go through a significant digital transformation to fulfill the demands of today’s generation and the fully digitized world they will be living in. The COVID-19 pandemic has quickly and unexpectedly compelled HEIs and the educational system to engage in such a shift. In this study, we investigate the digital transformation brought about by COVID-19 in the fundamental education of the younger generation. Additionally, the study investigates the various digital divides that have emerged and been reinforced, as well as the potential roadblocks that have been reported along the way. In this paper, the study suggests that research into information management must better address students, their increasingly digitalized everyday lives, and basic education as key focus areas.

The digital revolution has taken business sectors to a new height through the advancement of technology. The healthcare sector also embraced digital technology to facilitate technological change from mechanical and analogue electronic devices to the digital technology that is available today. The common use of digital technology in the healthcare sector includes searching medical knowledge resources, monitoring quality patient care and improving clinical support. The article presents the impact of technology in healthcare along with the privacy and security concerns related to technology use in healthcare.

Medical images have made a great impact on medicine, diagnosis, and treatment. The most important part of image processing is image segmentation. Many image segmentation methods for medical image analysis have been presented in this paper. In this paper, we have described the latest segmentation methods applied in medical image analysis. The advantages and disadvantages of each method are described besides examination of each algorithm with its application in Magnetic Resonance Imaging and Computed Tomography image analysis. Each algorithm is explained separately with its ability and features for the analysis of grey-level images. In order to evaluate the segmentation results, some popular benchmark measurements are presented in the final section.

Acute Leukemia is a life-threatening disease common both in children and adults that can lead to death if left untreated. Acute Lymphoblastic Leukemia (ALL) spreads out in children's bodies rapidly and takes the life within a few weeks. To diagnose ALL, the hematologists perform blood and bone marrow examination. Manual blood testing techniques that have been used since long time are often slow and come out with the less accurate diagnosis. This work improves the diagnosis of ALL with a computer-aided system, which yields accurate result by using image processing and deep learning techniques. This research proposed a method for the classification of ALL into its subtypes and reactive bone marrow (normal) in stained bone marrow images. A robust segmentation and deep learning techniques with the convolutional neural network are used to train the model on the bone marrow images to achieve accurate classification results. Experimental results thus obtained and compared with the results of other classifiers Naïve Bayesian, KNN, and SVM. Experimental results reveal that the proposed method achieved 97.78% accuracy. The obtained results exhibit that the proposed approach could be used as a tool to diagnose Acute Lymphoblastic Leukemia and its sub-types that will definitely assist pathologists.

This research aims to evaluate the level of postsecondary student satisfaction with online learning platforms and learning experiences during the novel coronavirus COVID-19 pandemic in the Kingdom of Saudi Arabia (KSA). This paper is based on the premise of transformative learning theories [1], which describe the learners’ authority and investment over their learning. Quantitative research was carried out using a survey that was sent out to 283 students enrolled at one higher education institution in KSA. These data were analyzed using SPSS. Average Mean Score (AMS) was used for data analysis, where the results are validated using the Standard Deviation (SD), Skewness and Kurtosis test, and Cronbach Alpha test. The research findings revealed that the students are satisfied with the university staff and faculty members who agreed on specific online platforms to use, grading system, assessment options, training workshops, online technical support, and more. The research findings also showed that participants were highly satisfied with Google Hangouts the most for lecture delivery, followed by Google Classroom and LMS (Moodle) for course management and assessments. With only respect to the students’ online learning experiences, the COVID-19 situation within this study context was handled adequately. This study calls for further research into the integration of professional development workshops and practical training courses for online learning and teaching to endorse innovative teaching techniques and alternative assessment plans for instructors, learners, administrators, and policymakers.

Higher education institutions are going through major changes in their education and operations. Several influences are driving these major changes. Digital transformation, online courses, digital-navy students, operational costs, and micro and nano degrees are just some examples of these influences. Digital technologies show a range of tools selected to include formalized learning environments in teaching in higher education, and students utilize these tools to promote their learning. The Industrial Revolution 4.0’s technological growth has penetrated higher education institutions (HEIs), forcing them to deal with the digital transformation (DT) in all of its dimensions. As they enable us to characterize the various interrelationships among stakeholders in a digitally enabled context of teaching and learning, applying digital transformation techniques to the education sector is an emerging field that has attracted attention recently. The aim of this study is to provide an overview of the distinguishing features of the digital transformation implementation process that has occurred at higher education institutions. In addition, how digital learning can be seen as part of the ecosystem of modern higher education. Further study is necessary to determine how higher education institutions can comprehend digital transformation and meet the demands imposed by the fourth Industrial Revolution.

Brain tumor is one of the most dreadful natures of cancer and caused a huge number of deaths among kids and adults from the past few years. According to WHO standard, the 700,000 humans are being with a brain tumor and around 86,000 are diagnosed since 2019. While the total number of deaths due to brain tumors is 16,830 since 2019 and the average survival rate is 35%. Therefore, automated techniques are needed to grade brain tumors precisely from MRI scans. In this work, a new deep learning-based method is proposed for microscopic brain tumor detection and tumor type classification. A 3D convolutional neural network (CNN) architecture is designed at the first step to extract brain tumor and extracted tumors are passed to a pretrained CNN model for feature extraction. The extracted features are transferred to the correlation-based selection method and as the output, the best features are selected. These selected features are validated through feed-forward neural network for final classification. Three BraTS datasets 2015, 2017, and 2018 are utilized for experiments, validation, and accomplished an accuracy of 98.32, 96.97, and 92.67%, respectively. A comparison with existing techniques shows the proposed design yields comparable accuracy.