Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence
- Journal
- Medical Entomology and Zoology
In The Last Decade
doi.org/w79104489 →Countries where authors are citing Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence
This map shows the geographic impact of Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence. 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 Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence more than expected).
Fields of papers citing Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence
This network shows the impact of Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence.
About Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence
This paper, published in 1996, received 3.4k indexed citations . Written by Jyh‐Shing Roger Jang and Chuen–Tsai Sun covering the research area of Artificial Intelligence. It is primarily cited by scholars working on Artificial Intelligence (1.5k citations), Control and Systems Engineering (717 citations) and Electrical and Electronic Engineering (532 citations). Published in Medical Entomology and Zoology.
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.
This paper is also available at doi.org/w79104489.