Artificial Intelligent (AI) applications in e-health have evolved considerably in the last 25 years. To track the current research progress in this field, there is a need to analyze the most recent trend of adopting AI applications in e-health. This bibliometric analysis study covers AI applications in e-health. It differs from the existing literature review as the journal articles are obtained from the Scopus database from its beginning to late 2021 (25 years), which depicts the most recent trend of AI in e-health. The bibliometric analysis is employed to find the statistical and quantitative analysis of available literature of a specific field of study for a particular period. An extensive global literature review is performed to identify the significant research area, authors, or their relationship through published articles. It also provides the researchers with an overview of the work evolution of specific research fields. The study's main contribution highlights the essential authors, journals, institutes, keywords, and states in developing the AI field in e-health.

The stability of the economy and political system of any country highly depends on the policy of anti-money laundering (AML). If government policies are incapable of handling money laundering activities in an appropriate way, the control of the economy can be transferred to criminals. The current literature provides various technical solutions, such as clustering-based anomaly detection techniques, rule-based systems, and a decision tree algorithm, to control such activities that can aid in identifying suspicious customers or transactions. However, the literature provides no effective and appropriate solutions that could aid in identifying relationships between suspicious customers or transactions. The current challenge in the field is to identify associated links between suspicious customers who are involved in money laundering. To consider this challenge, this paper discusses the challenges associated with identifying relationships such as business and family relationships and proposes a model to identify links between suspicious customers using social network analysis (SNA). The proposed model aims to identify various mafias and groups involved in money laundering activities, thereby aiding in preventing money laundering activities and potential terrorist financing. The proposed model is based on relational data of customer profiles and social networking functions metrics to identify suspicious customers and transactions. A series of experiments are conducted with financial data, and the results of these experiments show promising results for financial institutions who can gain real benefits from the proposed model.

Smart grids and smart homes are getting people’s attention in the modern era of smart cities. The advancements of smart technologies and smart grids have created challenges related to energy efficiency and production according to the future demand of clients. Machine learning, specifically neural network-based methods, remained successful in energy consumption prediction, but still, there are gaps due to uncertainty in the data and limitations of the algorithms. Research published in the literature has used small datasets and profiles of primarily single users; therefore, models have difficulties when applied to large datasets with profiles of different customers. Thus, a smart grid environment requires a model that handles consumption data from thousands of customers. The proposed model enhances the newly introduced method of Neural Basis Expansion Analysis for interpretable Time Series (N-BEATS) with a big dataset of energy consumption of 169 customers. Further, to validate the results of the proposed model, a performance comparison has been carried out with the Long Short Term Memory (LSTM), Blocked LSTM, Gated Recurrent Units (GRU), Blocked GRU and Temporal Convolutional Network (TCN). The proposed interpretable model improves the prediction accuracy on the big dataset containing energy consumption profiles of multiple customers. Incorporating covariates into the model improved accuracy by learning past and future energy consumption patterns. Based on a large dataset, the proposed model performed better for daily, weekly, and monthly energy consumption predictions. The forecasting accuracy of the N-BEATS interpretable model for 1-day-ahead energy consumption with “day as covariates” remained better than the 1, 2, 3, and 4-week scenarios.

Purpose One of the most important Information Security (IS) concerns nowadays is data theft or data leakage. To mitigate this type of risk, organisations use a solid infrastructure and deploy multiple layers of security protection technology and protocols such as firewalls, VPNs and IPsec VPN. However, these technologies do not guarantee data protection, and especially from insiders. Insider threat is a critical risk that can cause harm to the organisation through data theft. The main purpose of this study was to investigate and identify the threats related to data theft caused by insiders in organisations and explore the efforts made by them to control data leakage. Design/methodology/approach The study proposed a conceptual model to protect organisations’ data by preventing data theft by malicious insiders. The researchers conducted a comprehensive literature review to achieve the objectives of this study. The collection of the data for this study is based on earlier studies conducted by several researchers from January 2011 to December 2020. All the selected literature is from journal articles, conference articles and conference proceedings using various databases. Findings The study revealed three main findings: first, the main risks inherent in data theft are financial fraud, intellectual property theft, and sabotage of IT infrastructure. Second, there are still some organisations that are not considering data theft by insiders as being a severe risk that should be well controlled. Lastly, the main factors motivating the insiders to perform data leakage activities are financial gain, lack of fairness and justice in the workplace, the psychology or characteristics of the insiders, new technologies, lack of education and awareness and lack of management tools for understanding insider threats. Originality/value The study provides a holistic view of data theft by insiders, focusing on the problem from an organisational point of view. Organisations can therefore take into consideration our recommendations to reduce the risks of data leakage by their employees.

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