Neural Processing Letters

2.9k papers and 37.1k indexed citations i.

About

The 2.9k papers published in Neural Processing Letters in the last decades have received a total of 37.1k indexed citations. Papers published in Neural Processing Letters usually cover Artificial Intelligence (1.6k papers), Computer Vision and Pattern Recognition (1.1k papers) and Computer Networks and Communications (608 papers) specifically the topics of Neural Networks and Applications (824 papers), Neural Networks Stability and Synchronization (519 papers) and Face and Expression Recognition (366 papers). The most active scholars publishing in Neural Processing Letters are Johan A. K. Suykens, Joos Vandewalle, Bernd Fritzke, Jinde Cao, Min-Ling Zhang, Songcan Chen, Daoqiang Zhang, Liqun Zhou, Jouni Lampinen and Joni‐Kristian Kämäräinen.

In The Last Decade

Fields of papers published in Neural Processing Letters

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers published in Neural Processing Letters. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers published in Neural Processing Letters.

Countries where authors publish in Neural Processing Letters

Since Specialization
Citations

This map shows the geographic impact of research published in Neural Processing Letters. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by papers published in Neural Processing Letters with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Neural Processing Letters more than expected).

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar’s output or impact.

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