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 field of low-resource sentiment analysis has seen significant developments in recent years. This research review SLR evaluates the approaches and data sources utilized in low-resource sentiment analysis by deep learning. The primary aim is to discover suitable approaches for future sentiment analysis in low-resource. Our studies explore various languages, models, and data sources expressing a desire to create effective approaches. Our emphasis lies in the critical evaluation of the approaches and the datasets utilized, to identify areas where further research is needed. Our analysis study adds to the existing body of literature reviews, encompassing multilingual low-resource sentiment analysis research spanning from 2018 to 2023. The findings indicate that the transfer learning approach is the most frequently used, followed by word embedding learning and machine translation systems. Additionally, the study shows that social media is the most used platform for data collection, followed by product reviews, movies, and hotels. There has been a significant surge in the adoption of pre-trained transformers, indicating a growing interest in exploring the potential of these models for low-resource languages within the natural language processing (NLP) community. This trend is largely attributed to the novel nature of these models and their feature of being non-labour intensive. However, the scarcity of annotated datasets for such languages remains a major hurdle. finally, these research findings are relevant and informative for any researcher working in the field of low-resource multilingual sentiment analysis. The study introduces a conceptual framework for performing sentiment analysis in low-resource. The study provides a valuable resource for future researchers.

The demand for cyber-physical systems (CPSs) has recently increased in various domains, such as smart grids, intelligent transportation, and critical infrastructure. The massive data networks and communication layers generated make CPSs vulnerable to threats and cyberattacks. To mitigate these threats, artificial intelligence (AI) approaches are employed. However, AI models struggle to keep up with the constantly changing attack landscape. This study investigates the application of extreme gradient boosting (XGBoost) and long-short-term memory (LSTM) AI models for cyberattack detection in a CPS. Accuracy, precision, recall, and the F1-score validate the approach as evaluation metrics. The methods were tested on a gas pipeline industrial control system dataset and other benchmark datasets, such as NetML-2020 and IoT-23, which contain various cyberattacks. The performance of the two methods was found to be better than other models such as support vector machine (SVM) and artificial neural networks (ANN) on several evaluation metrics. Finally, we present recommendations for future research.

The current oil industry is moving towards digitalization, which is a good opportunity that will bring value to all its stakeholders. The digitalization of oil and gas discovery, which are production-based industries, is driven by enabling technologies which include machine learning (ML) and big data analytics. However, the existing Metocean system generates data manually using sensors such as the wave buoy, anemometer, and acoustic doppler current profiler (ADCP). Additionally, these data which appear in ASCII format to the Metocean system are also manual and silos. This slows down provisioning, while the monitoring element of the Metocean data path is partial. In this paper, we demonstrate the capabilities of ML for the development of Metocean data integration interoperability based on intelligent operations and automation. A comprehensive review of several research studies, which explore the needs of ML in oil and gas industries by investigating the in-depth integration of Metocean data interoperability for intelligent operations and automation using an ML-based approach, is presented. A new model integrated with the existing Metocean data system using ML algorithms to monitor and interoperate with maximum performance is proposed. The study reveals that ML is one of the crucial and key enabling tools that the oil and gas industries are now focused on for implementing digital transformation, which allows the industry to automate, enhance production, and have less human capacity. Lastly, user recommendations for potential future investigations are offered.

Machine Learning (ML) plays a key role in time-series applications because it analyzes observed data and predicts future values. The effectiveness of ML models in time-series forecasting is reduced by the occurrence of Concept Drift (CD). CD refers to continuous changes in the statistical properties of datasets. This affects the predictive performance of ML models. To address this issue, researchers have developed several CD detection and adaptation techniques. However, certain limitations exist, and most studies on CD detection and adaptation in time-series data have focused on classification learning, with minimal attention paid to regression learning. This study conducted a Systematic Literature Review (SLR) that classified, identified, and recommended an optimal method for the detection and adaptation of CD in regression and classification tasks involving time-series data. A systematic search was performed using the SCOPUS, ScienceDirect, IEEE Xplore, Web of Science, MDPI, and ACM databases. Based on the identified records, 60 studies published between 2013 and 2024 were thoroughly surveyed and evaluated using PRISMA guidelines. The findings show that Support Vector Machines (SVM) is the most effective learning algorithms for the detection and adaptation of CD in regression and classification tasks using time-series data. This is possible because of their high detection accuracy and effective memory. Moreover, this SLR presents a roadmap for detecting CDs using Artificial Intelligence (AI)-based learners, along with a comparative analysis of well-known baseline methods. Future work should focus on developing adaptive learning models capable of adapting to CD without explicit re-training, thereby, ensuring optimal performance.