Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
Ensemble approaches for regression
2012485 citationsJoão Mendes‐Moreira, Carlos Soares et al.profile →
YAKE! Keyword extraction from single documents using multiple local features
2019339 citationsRicardo Campos, Alípio Jorge et al.profile →
The CirCor DigiScope Dataset: From Murmur Detection to Murmur Classification
2021119 citationsJorge Oliveira, Francesco Renna et al.IEEE Journal of Biomedical and Health Informaticsprofile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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This map shows the geographic impact of Alípio Jorge's research. 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 Alípio Jorge with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Alípio Jorge more than expected).
This network shows the impact of papers produced by Alípio Jorge. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Alípio Jorge. The network helps show where Alípio Jorge may publish in the future.
Co-authorship network of co-authors of Alípio Jorge
This figure shows the co-authorship network connecting the top 25 collaborators of Alípio Jorge.
A scholar is included among the top collaborators of Alípio Jorge based on the total number of
citations received by their joint publications. Widths of edges
represent the number of papers authors have co-authored together.
Node borders
signify the number of papers an author published with Alípio Jorge. Alípio Jorge is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Oliveira, Jorge, Francesco Renna, Paulo Dias Costa, et al.. (2021). The CirCor DigiScope Dataset: From Murmur Detection to Murmur Classification. IEEE Journal of Biomedical and Health Informatics. 26(6). 2524–2535.119 indexed citations breakdown →
Sá, Cláudio Rebelo de, Paulo J. Azevedo, Carlos Soares, Alípio Jorge, & Arno Knobbe. (2017). Preference Rules for Label Ranking: Mining Patterns in Multi-Target Relations. Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT).3 indexed citations
Sá, Cláudio Rebelo de, Carlos Soares, Alípio Jorge, Paulo J. Azevedo, & Joaquim Pinto da Costa. (2011). Mining association rules for label ranking. RepositóriUM (Universidade do Minho).11 indexed citations
Jorge, Alípio. (1999). Iterative induction of logic programs: An approach to logic program synthesis from incomplete specifications. AI Communications. 12(3). 173–174.1 indexed citations
19.
Jorge, Alípio, et al.. (1995). Transient Detection Using Wavelets.. Mathematical Systems Theory. 2.2 indexed citations
20.
Brazdil, Pavel & Alípio Jorge. (1994). Learning by Refining Algorithm Sketches. European Conference on Artificial Intelligence. 443–447.3 indexed citations
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.