Progress in Artificial Intelligence

314 papers and 5.2k indexed citations i.

About

The 314 papers published in Progress in Artificial Intelligence in the last decades have received a total of 5.2k indexed citations. Papers published in Progress in Artificial Intelligence usually cover Artificial Intelligence (193 papers), Computer Vision and Pattern Recognition (48 papers) and Information Systems (43 papers) specifically the topics of Imbalanced Data Classification Techniques (30 papers), Machine Learning and Data Classification (28 papers) and Metaheuristic Optimization Algorithms Research (25 papers). The most active scholars publishing in Progress in Artificial Intelligence are Bartosz Krawczyk, Anamika Dhillon, Gyanendra K. Verma, João Gama, Hadi Fanaee‐T, Nitesh V. Chawla, T. Ryan Hoens, Robi Polikar, Amparo Alonso‐Betanzos and Verónica Bolón‐Canedo.

In The Last Decade

Fields of papers published in Progress in Artificial Intelligence

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers published in Progress in Artificial Intelligence. 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 Progress in Artificial Intelligence.

Countries where authors publish in Progress in Artificial Intelligence

Since Specialization
Citations

This map shows the geographic impact of research published in Progress in Artificial 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 papers published in Progress in Artificial Intelligence with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Progress in Artificial Intelligence 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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