Applied Soft Computing

11.1k papers and 327.0k indexed citations i.

About

The 11.1k papers published in Applied Soft Computing in the last decades have received a total of 327.0k indexed citations. Papers published in Applied Soft Computing usually cover Artificial Intelligence (4.8k papers), Computer Vision and Pattern Recognition (1.8k papers) and Computational Theory and Mathematics (1.8k papers) specifically the topics of Metaheuristic Optimization Algorithms Research (1.7k papers), Advanced Multi-Objective Optimization Algorithms (1.1k papers) and Multi-Criteria Decision Making (980 papers). The most active scholars publishing in Applied Soft Computing are Derviş Karaboğa, Bahriye Akay, Zeshui Xu, Ponnuthurai Nagaratnam Suganthan, Witold Pedrycz, Oscar Castillo, Ramin Rajabioun, Seyedali Mirjalili, Patricia Melín and Ali Rıza Yıldız.

In The Last Decade

Fields of papers published in Applied Soft Computing

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers published in Applied Soft Computing. 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 Applied Soft Computing.

Countries where authors publish in Applied Soft Computing

Since Specialization
Citations

This map shows the geographic impact of research published in Applied Soft Computing. 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 Applied Soft Computing with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Applied Soft Computing 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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