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.
Identification of autism spectrum disorder using deep learning and the ABIDE dataset
Countries citing papers authored by Felipe Meneguzzi
Since
Specialization
Citations
This map shows the geographic impact of Felipe Meneguzzi'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 Felipe Meneguzzi with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Felipe Meneguzzi more than expected).
Fields of papers citing papers by Felipe Meneguzzi
This network shows the impact of papers produced by Felipe Meneguzzi. 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 Felipe Meneguzzi. The network helps show where Felipe Meneguzzi may publish in the future.
Co-authorship network of co-authors of Felipe Meneguzzi
This figure shows the co-authorship network connecting the top 25 collaborators of Felipe Meneguzzi.
A scholar is included among the top collaborators of Felipe Meneguzzi 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 Felipe Meneguzzi. Felipe Meneguzzi is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Couto, Júlia, et al.. (2019). mir-pucrs/smartix-rl v1.0. Zenodo (CERN European Organization for Nuclear Research).1 indexed citations
3.
Vered, Mor, et al.. (2018). Online Goal Recognition as Reasoning over Landmarks. National Conference on Artificial Intelligence. 638–645.2 indexed citations
4.
Vered, Mor, et al.. (2018). Towards online goal recognition combining goal mirroring and landmarks: extended abstract. Adaptive Agents and Multi-Agents Systems. 2112–2114.1 indexed citations
5.
Meneguzzi, Felipe, Nir Oren, & Ramon Fraga Pereira. (2017). Monitoring Plan Optimality using Landmarks and Domain-Independent Heuristics. National Conference on Artificial Intelligence.4 indexed citations
6.
Granada, Roger, et al.. (2017). Hybrid Activity and Plan Recognition for Video Streams. Research Explorer (The University of Manchester).15 indexed citations
7.
Oren, Nir, et al.. (2017). Bounded-Monitor Placement in Normative Environments. Aberdeen University Research Archive (Aberdeen University). 28–37.1 indexed citations
8.
Meneguzzi, Felipe, et al.. (2017). A Deep Neural Architecture for Kitchen Activity Recognition. The Florida AI Research Society. 56–61.8 indexed citations
9.
Pereira, Ramon Fraga, et al.. (2016). DOVETAIL - An abstraction for Classical Planning using a Visual Metaphor. Research Explorer (The University of Manchester). 74–79.3 indexed citations
10.
Schmidt, Daniela, et al.. (2016). An Ontology-based Mobile Application for Task Managing in Collaborative Groups. The Florida AI Research Society. 522–526.6 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.