Deep learning presents excellent learning ability in constructing learning model and greatly promotes the development of artificial intelligence, but its conventional models cannot handle uncertain or imprecise circumstances. Fuzzy systems, can not only depict uncertain and vague concepts widely existing in the real world, but also improve the prediction accuracy in deep learning models. Thus, it is important and necessary to go through the recent contributions about the fusion of deep learning and fuzzy systems. At first, we introduce the deep learning into fuzzy community from two perspectives: statistical results of relevant publications and conventional deep learning algorithms. Then, the fusing framework and graphic form of deep learning and fuzzy systems are constructed. Followed by, are the current situations of several types of fuzzy techniques used in deep learning, some reasons why use fuzzy techniques in deep learning, and the application fields of the fusion, respectively. Finally, some discussions and future challenges are provided regarding the fusion technology of deep learning and fuzzy systems, the application scenarios of fusing deep learning and fuzzy systems, and some limitations of the current fusion, respectively. After summarizing the recent contributions, we have found that this field is an emerging research direction and it is increasingly paying much more attention. Especially, fuzzy systems make great effects on deep learning models in the aspect of classification, prediction, natural language processing, auto-control, etc., and the fusion is applied into different fields, like but not limited to computer science, natural language, medical system, smart energy management systems and manufacturing industry.

The relationship between foreign direct investment and economic growth (FDI-EG) has been subject to extensive research. The first document related to FDI-EG research in Web of Science was published in 1992 so that the research time is close to 30 years. This article aims to make a bibliometric study to measure the FDI-EG research from different points of view. 1,075 documents with respect to FDI-EG research were collected, and a strong bibliometric analysis was carried out by Bibliometrix software and review of approaches. The Bibliometrix software has revealed the leading researchers, conceptual structure, and thematic evolution of the FDI-EG research. At the same time, reviewing the literature helps to make content analysis, the most influential document analysis, co-authorship analysis, and citation and co-citation analysis. Amongst the findings, C. Chen, Journal of International Economics, and the USA are the most influential researcher, journal, and country, respectively. The FDI-EG research are focussed on the technology and firm performance, research modelling, and theoretical inquiry. More discussions are made in terms of current research status, avenues for future research, advantages and drawbacks of the methods. This article helps in understanding the evolution of the FDI-EG research from the perspectives of the bibliometric and review.