This literature review explores the pivotal role of brain–computer interface (BCI) technology, coupled with electroencephalogram (EEG) technology, in advancing rehabilitation for individuals with damaged muscles and motor systems. This study provides a comprehensive overview of recent developments in BCI and motor control for rehabilitation, emphasizing the integration of user-friendly technological support and robotic prosthetics powered by brain activity. This review critically examines the latest strides in BCI technology and its application in motor skill recovery. Special attention is given to prevalent EEG devices adaptable for BCI-driven rehabilitation. The study surveys significant contributions in the realm of machine learning-based and deep learning-based rehabilitation evaluation. The integration of BCI with EEG technology demonstrates promising outcomes for enhancing motor skills in rehabilitation. The study identifies key EEG devices suitable for BCI applications, discusses advancements in machine learning approaches for rehabilitation assessment, and highlights the emergence of novel robotic prosthetics powered by brain activity. Furthermore, it showcases successful case studies illustrating the practical implementation of BCI-driven rehabilitation techniques and their positive impact on diverse patient populations. This review serves as a cornerstone for informed decision-making in the field of BCI technology for rehabilitation. The results highlight BCI’s diverse advantages, enhancing motor control and robotic integration. The findings highlight the potential of BCI in reshaping rehabilitation practices and offer insights and recommendations for future research directions. This study contributes significantly to the ongoing transformation of BCI technology, particularly through the utilization of EEG equipment, providing a roadmap for researchers in this dynamic domain.

The current advancement of robotics, especially in Cyber-Physical Systems (CPS), leads to a prominent combination between the mining industry and connected-embedded technologies. This progress has arisen in the form of state-of-the-art automated giant vehicles with Autonomous Haulage Systems (AHS) that can transport ore without human intervention. Like CPS, AHS enable autonomous and/or remote control of physical systems (e.g., mining trucks). Thus, similar to CPS, AHS are also susceptible to cyber attacks such as Wi-Fi De-Auth and GPS attacks. With the use of the AHS, several mining activities have been strengthened due to increasing the efficiency of operations. Such activities require ensuring accurate data collection from which precise information about the state of the mine should be generated in a timely and consistent manner. Consequently, the presence of secure and reliable communications is crucial in making AHS mines safer, productive, and sustainable. This paper aims to identify and discuss the relation between safety of AHS in the mining environment and both cybersecurity and communication as well as highlighting their challenges and open issues. We survey the literature that addressed this aim and discuss its pros and cons and then highlight some open issues. We conclude that addressing cybersecurity issues of AHS can ensure the safety of operations in the mining environment as well as providing reliable communication, which will lead to better safety. Additionally, it was found that new communication technologies, such 5G and LTE, could be adopted in AHS-based systems for mining, but further research is needed to considered related cybersecurity issues and attacks.

Nowadays, ocean observation technology continues to progress, resulting in a huge increase in marine data volume and dimensionality. This volume of data provides a golden opportunity to train predictive models, as the more the data is, the better the predictive model is. Predicting marine data such as sea surface temperature (SST) and Significant Wave Height (SWH) is a vital task in a variety of disciplines, including marine activities, deep‐sea, and marine biodiversity monitoring. The literature has efforts to forecast such marine data; these efforts can be classified into three classes: machine learning, deep learning, and statistical predictive models. To the best of the authors’ knowledge, no study compared the performance of these three approaches on a real dataset. This paper focuses on the prediction of two critical marine features: the SST and SWH. In this work, we proposed implementing statistical, deep learning, and machine learning models for predicting the SST and SWH on a real dataset obtained from the Korea Hydrographic and Oceanographic Agency. Then, we proposed comparing these three predictive approaches on four different evaluation metrics. Experimental results have revealed that the deep learning model slightly outperformed the machine learning models for overall performance, and both of these approaches greatly outperformed the statistical predictive model.

Insurance fraud is a prevalent issue that insurance companies must face, particularly in the realm of automobile insurance. This type of fraud has significant cost implications for insurance firms and can have a long-term impact on pricing strategies and insurance rates. As a result, accurately predicting and detecting insurance fraud has become a crucial challenge for insurers. The fraud datasets are usually imbalanced, as the number of fraudulent instances is much less than the ligament instances and contains missing values. Prior research has employed machine learning methods to address this class imbalance dataset problem, but there is limited effort handling the class imbalance dataset present in insurance fraud datasets. Moreover, we could not find an overfitting analysis for the relevant predictive models. This paper addresses these two limitations by employing two car insurance company datasets, namely, an Egyptian real-life dataset and a standard dataset. We proposed addressing the missing data and the class imbalance problems with different methods. Then, the predictive models were trained on processed datasets to predict insurance fraud as a classification problem. The classifiers are evaluated on several evaluation metrics. Moreover, we proposed the first overfitting analysis for insurance fraud classifiers, to our knowledge. The obtained results outline that addressing the class imbalance in the insurance fraud detection dataset has a significant positive effect on the performance of the predictive model, while addressing the problem of missing values has a slight effect. Moreover, the proposed methods outperform all of the existing methods on the accuracy metric.

The use of underwater wireless sensor networks (UWSNs) for collaborative monitoring and marine data collection tasks is rapidly increasing. One of the major challenges associated with building these networks is handover prediction; this is because the mobility model of the sensor nodes is different from that of ground-based wireless sensor network (WSN) devices. Therefore, handover prediction is the focus of the present work. There have been limited efforts in addressing the handover prediction problem in UWSNs and in the use of ensemble learning in handover prediction for UWSNs. Hence, we propose the simulation of the sensor node mobility using real marine data collected by the Korea Hydrographic and Oceanographic Agency. These data include the water current speed and direction between data. The proposed simulation consists of a large number of sensor nodes and base stations in a UWSN. Next, we collected the handover events from the simulation, which were utilized as a dataset for the handover prediction task. Finally, we utilized four machine learning prediction algorithms (i.e., gradient boosting, decision tree (DT), Gaussian naive Bayes (GNB), and K-nearest neighbor (KNN)) to predict handover events based on historically collected handover events. The obtained prediction accuracy rates were above 95%. The best prediction accuracy rate achieved by the state-of-the-art method was 56% for any UWSN. Moreover, when the proposed models were evaluated on performance metrics, the measured evolution scores emphasized the high quality of the proposed prediction models. While the ensemble learning model outperformed the GNB and KNN models, the performance of ensemble learning and decision tree models was almost identical.

Insolvency is a crucial problem for several insurance companies that suffer from it. This problem has direct or indirect effects on both the people working in the financial business and normal citizens. Thus, in insurance companies, the ability to predict insolvency is in great demand. There are several efforts proposed to predict insurance company insolvency using computer science methods (e.g., support vector machine and fuzzy systems). Each country has its own data patterns due to interior matters. Thus, insurance companies from different countries may have different data patterns. Consequently, the utilized predictive model should adapt to the dataset at hand. To our best knowledge, despite there are several efforts to build an insolvency predictive model, none of these efforts explored the Egyptian market. In addition, even the existing efforts did not utilize the ensemble learning methods in the insolvency prediction problem. In this context, we have two main contributions to this work. First, we proposed the first public access dataset of Egyptian insurance companies. The collected dataset was gathered from 11 Egyptian insurance companies during the years 1999 to 2019. The dataset consists of a set of 22 ratios (21 input features and one output feature), e.g., retention and investment yield alongside the solvency ration (i.e., the target feature). In the second contribution, we proposed exploring the performance of the ensemble learning methods to address the insolvency prediction problem. Thus, we proposed building several insolvency predictive models using ensemble learning and classic machine learning models. Next, the proposed models are evaluated on different accuracy metrics, e.g., Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). The experimental results revealed that the ensemble learning-based models outperformed the classic machine learning-based models. Moreover, the correlation analysis between the utilized 22 financial ratios revealed that the most significant ratios, for the task of predicting the solvency ratio, are the technical provisions to shareholders’ funds, insurance companies’ debit balances to shareholders, and earnings after taxes to shareholders’ funds.