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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.

In the present times, digital transformation has gained momentum. Contemporary higher education institutions have been embracing new technologies and transforming their practices, business models and process. Digital transformation in the higher education institutions is about the development of new more advanced and effective methods and practices in pursuit of the higher education’s mission. The present paper links digital transformation and higher education institutions. The paper discusses existing models for the incorporation of digital transformation in higher education institutions. The paper also delineates the challenges faced by higher education institutions in pursuit of digital transformation.

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 software engineering landscape is undergoing a significant transformation with the advent of artificial intelligence (AI). AI technologies are poised to redefine traditional software development practices, offering innovative solutions to long-standing challenges. This paper explores the integration of AI into software engineering processes, aiming to identify its impacts, benefits, and the challenges that accompany this paradigm shift. A comprehensive analysis of current AI applications in software engineering is conducted, supported by case studies and theoretical models. The study examines various phases of software development to assess where AI contributes most effectively. The integration of AI enhances productivity, improves code quality, and accelerates development cycles. Key areas of impact include automated code generation, intelligent debugging, predictive maintenance, and enhanced decision-making processes. AI is revolutionizing software engineering by introducing automation and intelligence into the development lifecycle. Embracing AI-driven tools and methodologies is essential for staying competitive in the evolving technological landscape.

The evolution of software is necessary for the success of software systems. Studying the evolution of software and understanding it is a vocal topic of study in software engineering. One of the primary concepts of software evolution is that the internal quality of a software system declines when it evolves. In this paper, the method of evolution of the internal quality of object-oriented open-source software systems has been examined by applying a software metric approach. More specifically, we analyze how software systems evolve over versions regarding size and the relationship between size and different internal quality metrics. The results and observations of this research include: (i) there is a significant difference between different systems concerning the LOC variable (ii) there is a significant correlation between all pairwise comparisons of internal quality metrics, and (iii) the effect of complexity and inheritance on the LOC was positive and significant, while the effect of Coupling and Cohesion was not significant.

The pursuit to spot abnormal behaviors in and out of a network system is what led to a system known as intrusion detection systems for soft computing besides many researchers have applied machine learning around this area. Obviously, a single classifier alone in the classifications seems impossible to control network intruders. This limitation is what led us to perform dimensionality reduction by means of correlation-based feature selection approach (CFS approach) in addition to a refined ensemble model. The paper aims to improve the Intrusion Detection System (IDS) by proposing a CFS + Ensemble Classifiers (Bagging and Adaboost) which has high accuracy, high packet detection rate, and low false alarm rate. Machine Learning Ensemble Models with base classifiers (J48, Random Forest, and Reptree) were built. Binary classification, as well as Multiclass classification for KDD99 and NSLKDD datasets, was done while all the attacks were named as an anomaly and normal traffic. Class labels consisted of five major attacks, namely Denial of Service (DoS), Probe, User-to-Root (U2R), Root to Local attacks (R2L), and Normal class attacks. Results from the experiment showed that our proposed model produces 0 false alarm rate (FAR) and 99.90% detection rate (DR) for the KDD99 dataset, and 0.5% FAR and 98.60% DR for NSLKDD dataset when working with 6 and 13 selected features.

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.

Coronavirus-19 (COVID-19) started from Wuhan, China, in late December 2019. It swept most of the world’s countries with confirmed cases and deaths. The World Health Organization (WHO) declared the virus a pandemic on 11 March 2020 due to its widespread transmission. A public health crisis was declared in specific regions and nation-wide by governments all around the world. Citizens have gone through a wide range of emotions, such as fear of shortage of food, anger at the performance of governments and health authorities in facing the virus, sadness over the deaths of friends or relatives, etc. We present a monitoring system of citizens’ concerns using emotion detection in Twitter data. We also track public emotions and link these emotions with COVID-19 symptoms. We aim to show the effect of emotion monitoring on improving people’s daily health behavior and reduce the spread of negative emotions that affect the mental health of citizens. We collected and annotated 5.5 million tweets in the period from January to August 2020. A hybrid approach combined rule-based and neural network techniques to annotate the collected tweets. The rule-based technique was used to classify 300,000 tweets relying on Arabic emotion and COVID-19 symptom lexicons while the neural network was used to expand the sample tweets that were annotated using the rule-based technique. We used long short-term memory (LSTM) deep learning to classify all of the tweets into six emotion classes and two types (symptom and non-symptom tweets). The monitoring system shows that most of the tweets were posted in March 2020. The anger and fear emotions have the highest number of tweets and user interactions after the joy emotion. The results of user interaction monitoring show that people use likes and replies to interact with non-symptom tweets while they use re-tweets to propagate tweets that mention any of COVID-19 symptoms. Our study should help governments and decision-makers to dispel people’s fears and discover new symptoms associated with the symptoms that were declared by the WHO. It can also help in the understanding of people’s mental and emotional issues to address them before the impact of disease anxiety becomes harmful in itself.

As the process of urbanization continues to accelerate, the demand for sustainable cities has become more critical than ever before. The incorporation of electric vehicles (EVs) is a key component in creating sustainable cities. However, the development of smart cities for EVs entails more than just the installation of charging stations. Software engineering plays a crucial role in realizing smart cities for electric vehicles. This paper examines the role of software engineering in the creation of smart cities for electric vehicles, the techniques utilized in electric vehicle charging infrastructure, the obstacles faced by software engineers, and the future of software engineering in sustainable cities. Specifically, the paper explores the significance of software engineering in integrating EVs into the transportation system, including the design of smart charging and energy management systems, and the establishment of intelligent transportation systems. Additionally, the paper offers case studies to demonstrate successful software engineering implementations for smart cities. Finally, the paper concludes with a discussion of the challenges that software engineers encounter in implementing intelligent transportation systems for EVs and provides future directions for software engineering in sustainable cities.

Data integrity continues to be a persistent problem in the current healthcare sector. It ensures that the data is correct and has not even in any manner been improperly changed. Incorrect data might become significant health threats for patients and a big responsibility for clinicians, resulting in problems such as scam, misconduct, inadequate treatment and data theft. This sort of endangering scenario causes tremendous difficulty in handling healthcare data. This research intends to describe the threat plot of data integrity in healthcare through numerous attack statistics from around the world and Saudi Arabia and identify the criticality in Saudi Arabia in particular. A literature review by descriptive analysis, unit analysis and rating analysis to achieve the planned systematic literature review goal is outlined. The outcome of ranking analysis using a fuzzy analytical hierarchy process methodology offers a route for Saudi Arabian researchers to promote medical records or data security in Arabic healthcare. It is suggested that blockchain is the most prioritized method for regular use and adaptation across Saudi Arabia in all data integrity management techniques. To address the challenges of data integrity and future path, the authors critically examine the challenges posed by data integrity in the healthcare sector.