**8. Conclusions**

This paper firstly defined the concept of HLNNs by integrating a HFS with a LNN. Then, the normalized generalized distance and similarity measures of HLNNs were presented based on the LCMC method. Next, a novel MADM method based on the proposed similarity measure was presented under the HLNN environment. Finally, a MADM example of an investment problem was illustrated to demonstrate that the developed method is feasible and applicable. Since the HLNN combines the merits of the HFS and LNN, containing more information than the LNN, the MADM method of HLNNs based on the LCMC method is more objective and more suitable for the practical applications with HLNN information.

However, some advantages of the proposed HLNNs and MADM method based on the LCMC method are listed as follows:


Future research on HLNNs will focus on the development of new aggregation operators and correlation coefficients of HLNNs, and their applications in fault diagnosis, medical diagnosis, decision-making, and so on in the HLNN setting.

**Author Contributions:** J.Y. originally proposed HLNNs and their operations, and W.C. presented the MADM method of HLNNs and the calculation and comparative analysis of an actual example. Both coauthors wrote the paper together.

**Funding:** This paper was supported by the National Natural Science Foundation of China (No. 61703280).

**Conflicts of Interest:** The authors declare no conflict of interest.
