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The emergence of machine learning has opened a new way for researchers. It allows them to supplement the traditional manual methods for conducting a literature review and turning it into smart literature. This study aims to present a framework for incorporating machine learning into financial statement fraud (FSF) literature analysis. This framework facilitates the analysis of a large amount of literature to show the trend of the field and identify the most productive authors, journals and potential areas for future research.

In this study, a framework was introduced that merges bibliometric analysis techniques such as word frequency, co-word analysis and coauthorship analysis with the Latent Dirichlet Allocation topic modeling approach. This framework was used to uncover subtopics from 20 years of financial fraud research articles. Furthermore, the hierarchical clustering method was used on selected subtopics to demonstrate the primary contexts in the literature on FSF.

This study has contributed to the literature in two ways. First, this study has determined the top journals, articles, countries and keywords based on various bibliometric metrics. Second, using topic modeling and then hierarchy clustering, this study demonstrates the four primary contexts in FSF detection.

In this study, the authors tried to comprehensively view the studies related to financial fraud conducted over two decades. However, this research has limitations that can be an opportunity for future researchers. The first limitation is due to language bias. This study has focused on English language articles, so it is suggested that other researchers consider other languages as well. The second limitation is caused by citation bias. In this study, the authors tried to show the top articles based on the citation criteria. However, judging based on citation alone can be misleading. Therefore, this study suggests that the researchers consider other measures to check the citation quality and assess the studies’ precision by applying meta-analysis.

Despite the popularity of bibliometric analysis and topic modeling, there have been limited efforts to use machine learning for literature review. This novel approach of using hierarchical clustering on topic modeling results enable us to uncover four primary contexts. Furthermore, this method allowed us to show the keywords of each context and highlight significant articles within each context.

A wide band perfect THz absorber is presented in this work. The structure includes two layers of graphene disks on the silicon dioxide dielectric layer while a golden plate is placed at the bottom to act as a fully reflecting mirror against THz waves. According to the simulations, the device is robust enough to show independent operation versus layers thicknesses variations, chemical potentials mismatches and changing of electron relaxation time. The designed THz absorber in this work is an appropriate basic block for several applications in THz optical systems such as sensors, detectors and modulators.

The layers in the proposed device are modeled via passive circuit elements and consequently, the equivalent circuit of the device is calculated. Leveraging the developed equivalent circuit model (ECM) and impedance matching concept, the proposed device is designed to perfect absorption with 4.7 THz bandwidth that possesses over 90% absorption. Ample simulations are performed using MATLAB (ECM) and CST (finite element method) to verify the superior performance of the device. According to the simulations, the device is robust enough to show independent operation versus layers thicknesses variations, chemical potentials mismatches and changing of electron relaxation time.

This work reports a wideband THz absorber, composed of two graphene layers. This paper considers the circuit model representation for two different layers of the device. For a unique structure, a highly tunable response versus chemical potential is obtained. The circuit model approach and impedance matching theory are exploited to reduce computational time regarding conventional approaches.

A wide band absorber in THz band is presented. Leveraging circuit model approach and impedance matching theory, the design procedure is simplified regarding CPU time and memory requirements compared to conventional methods. Detailed calculations and ample simulations verify the performance excellency of the device to absorb THz incident waves in 2–6.5 THz frequencies. Also, the robustness of the device is investigated versus parameters mismatches like layers thicknesses and chemical potentials values. According to the simulations and absorption response, the proposed device is an appropriate block to be used in THz optical systems such as detectors, imaging systems and optical modulators.