In recent years, technology has advanced to the fourth industrial revolution (Industry 4.0), where the Internet of things (IoTs), fog computing, computer security, and cyberattacks have evolved exponentially on a large scale. The rapid development of IoT devices and networks in various forms generate enormous amounts of data which in turn demand careful authentication and security. Artificial intelligence (AI) is considered one of the most promising methods for addressing cybersecurity threats and providing security. In this study, we present a systematic literature review (SLR) that categorize, map and survey the existing literature on AI methods used to detect cybersecurity attacks in the IoT environment. The scope of this SLR includes an in-depth investigation on most AI trending techniques in cybersecurity and state-of-art solutions. A systematic search was performed on various electronic databases (SCOPUS, Science Direct, IEEE Xplore, Web of Science, ACM, and MDPI). Out of the identified records, 80 studies published between 2016 and 2021 were selected, surveyed and carefully assessed. This review has explored deep learning (DL) and machine learning (ML) techniques used in IoT security, and their effectiveness in detecting attacks. However, several studies have proposed smart intrusion detection systems (IDS) with intelligent architectural frameworks using AI to overcome the existing security and privacy challenges. It is found that support vector machines (SVM) and random forest (RF) are among the most used methods, due to high accuracy detection another reason may be efficient memory. In addition, other methods also provide better performance such as extreme gradient boosting (XGBoost), neural networks (NN) and recurrent neural networks (RNN). This analysis also provides an insight into the AI roadmap to detect threats based on attack categories. Finally, we present recommendations for potential future investigations.

The rapid development of smart technologies and data analytics empowers most industries to evolve their systems and introduce innovative applications. Consequently, smart metering technology, an internet of things-based application service, is diffusing rapidly in the energy sector. Regardless of its associated benefits, smart meters continue to struggle from consumers’ acceptance. To promote smart meters’ successful deployment, research is needed to better understand consumers’ acceptance of smart metering. Motivated by these concerns, a smart meter acceptance model is developed to evaluate the moderation role of experience and personal innovativeness factors among residential consumers. A cross-sectional research design was used in this study. Data were collected using a self-administrated questionnaire from 318 smart meters consumers who have had experience in using it. Hypothetical relationships were assessed and validated using partial least squares structural equation modelling. The empirical findings exert the moderating role of experience and personal innovativeness of smart meter acceptance that achieved an acceptable fit with the data, and specifically, five out of nine hypotheses were supported.

Today's increasing demand for electricity requires solutions that better align with energy demand and supply. Innovative technological solutions such as smart metering applications are gaining popularity among electricity providers. Despite numerous benefits, smart meters, a part of the technology on the Internet of Things (IoT), continue to struggle for widespread consumer acceptance due to limited knowledge on electricity savings and environmental awareness. These factors were examined in isolation and have not been theoretically incorporated or examined. Hence, this study investigates the factors that influence residential consumers' acceptance of smart meters by integrating electricity-saving knowledge and environmental awareness with the second generation of ''unified theory of acceptance and use of technology'' (UTAUT2). The literature revealed an important link between users' behavioural intention and users' use behaviour. Well-established theories of acceptance like ''technology acceptance model'' (TAM) and UTAUT, incorporate the behavioural intention variable in the nomological network of technology adoption determinants. This study highlighted the impact of users' behavioural intention on users' use behaviour, which was not examined previously by any of the smart meter acceptance models. The data were collected from 318 consumers of residential smart meters in Putrajaya and Malacca, the cities in Malaysia, and were statistically tested using SME-PLS The study confirms that adding electricity-saving knowledge and environmental awareness to the UTAUT2 leads to a significant increase in the explained variance in consumer acceptance of smart meter.

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Mobile health (mHealth) apps have great potential to improve health outcomes. Given that mHealth apps have become ubiquitous, there is limited focus on their abandonment. Data concerning crucial metrics, including reasons for adoption and discontinued use, are limited. This study aims to gain broad insights into utilization of mHealth and game-like features promoting user engagement. We conducted a cross-sectional survey of 209 mHealth users worldwide. The 17-item survey assessed sociodemographics, as well as the key motivators for mHealth uptake and discontinued use. Our findings show that sports and fitness activity tracking were the most common categories of health apps, with most users engaging with them at least several times a week. Interestingly, the most downloaded mHealth apps among younger adults include MyFitnessPal, Fitbit, Nike Run Club, and Samsung Health. Critical drivers of abandonment of mHealth apps were amotivation, loss of interest, and experimenting with different apps to identify the most suitable tool. Additionally, the financial cost of mHealth apps is crucial, with most participants advocating for free or more affordable apps. The study findings suggest that while many individuals utilize mHealth, several factors drive their abandonment. Moreover, data indicate that mHealth developers need to consider gamification strategies to sustain user commitment, as well as psychological variables, such as intrinsic motivation.

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Student performance is related to complex and correlated factors. The implementation of a new advancement of technologies in educational displacement has unlimited potentials. One of these advances is the use of analytics and data mining to predict student academic accomplishment and performance. Given the existing literature, machine learning (ML) approaches such as Artificial Neural Networks (ANNs) can continuously be improved. This work examines and surveys the current literature regarding the ANN methods used in predicting students’ academic performance. This study also attempts to capture a pattern of the most used ANN techniques and algorithms. Of note, the articles reviewed mainly focused on higher education. The results indicated that ANN is always used in combination with data analysis and data mining methodologies, allowing studies to assess the effectiveness of their findings in evaluating academic achievement. No pattern was detected regarding selecting the input variables as they are mainly based on the context of the study and the availability of data. Moreover, the very limited tangible findings referred to the use of techniques in the actual context and target objective of improving student outcomes, performance, and achievement. An important recommendation of this work is to overcome the identified gap related to the only theoretical and limited application of the ANN in a real-life situation to help achieve the educational goals.

Purpose The reasons behind the project management failure of higher education institutions (HEIs) have been researched for the past few years. One of the reasons is the lack of tools to integrate their knowledge process capabilities (KPC) with their project management (PM) to measure maturity by assessing these capabilities. Various project management maturity (PMM) models exist. Yet, there is a limited number of empirical studies that support the four integrations of KPC and PMM. Therefore, this study aims to propose a new heretical model, namely, KPC-knowledge management (KM) and evaluates a research model that includes the four KPC as an antecedent to PMM. Design/methodology/approach The suggested research model is assessed by using partial least squares structural equation modeling. Furthermore, the study's hypotheses were examined based on a sample of 352 respondents from the project management departments in 10 public universities in Yemen. Findings Analysis revealed that the derived PMM status could be benchmarked with the project management maturity model. Also, the study found that integrating the KPC into PM enables the institutions to perform critical tasks and value chain activities and enhance the PM maturity level as well. In contrast, if one of the capabilities does not positively impact PMM, it affects the maturity level of the entire project. Research limitations/implications The findings are obtained concerning data collected from public universities and represent the Yemeni context, limiting the generalization on a different geographical area. Also, this proposed model can be evaluated in a practical way like conducting a focus group, a set of interviews with specialists, a case study or action research. The qualitative research will help academics to validate our proposal for future research purposes. Practical implications The proposed approach may be adapted to the characteristics of organizations involved in projects as external performers (project-based organizations) and not just the HEIs projects. This study provides managers and policymakers with insights into assessing PMM and improving their organizational effectiveness when deciding which KPCs to focus on in the future. Social implications This study contributes to the current PM awareness in Yemen and facilitates its success using the knowledge processes capabilities in Yemen's HEIs. It encourages organizations to take this opportunity to revive the projects and achieve a maximum level of maturity. Originality/value This study provides new insights into two domains through the link between knowledge management and PM. To the best of the authors' knowledge, this paper is among the first to empirically study the impact of the four KPC toward PMM. It enriches the theoretical perspective of PM. Also, it contributes to the literature on the success factor of KPC, which can be considered to improve organizational performance.