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.
Countries citing papers authored by Massimo Piccardi
Since
Specialization
Citations
This map shows the geographic impact of Massimo Piccardi'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 Massimo Piccardi with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Massimo Piccardi more than expected).
Fields of papers citing papers by Massimo Piccardi
This network shows the impact of papers produced by Massimo Piccardi. 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 Massimo Piccardi. The network helps show where Massimo Piccardi may publish in the future.
Co-authorship network of co-authors of Massimo Piccardi
This figure shows the co-authorship network connecting the top 25 collaborators of Massimo Piccardi.
A scholar is included among the top collaborators of Massimo Piccardi 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 Massimo Piccardi. Massimo Piccardi is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Pham, Son Bao, et al.. (2021). Learning Neural Textual Representations for Citation Recommendation. UTS ePRESS (University of Technology Sydney).4 indexed citations
6.
Parnell, Jacob, et al.. (2021). BERTTune: Fine-Tuning Neural Machine Translation with BERTScore. UTS ePRESS (University of Technology Sydney).11 indexed citations
7.
Seifollahi, Sattar, Massimo Piccardi, & Alireza Jolfaei. (2021). An Embedding-Based Topic Model for Document Classification. ACM Transactions on Asian and Low-Resource Language Information Processing. 20(3). 1–13.13 indexed citations
Borzeshi, Ehsan Zare, et al.. (2018). English-Basque Statistical and Neural Machine Translation. Language Resources and Evaluation.4 indexed citations
13.
Borzeshi, Ehsan Zare, et al.. (2018). BiLSTM-CRF for Persian Named-Entity Recognition ArmanPersoNERCorpus: the First Entity-Annotated Persian Dataset.. Language Resources and Evaluation.15 indexed citations
Cucchiara, Rita & Massimo Piccardi. (1999). Vehicle Detection under Day and Night Illumination.. IRIS UNIMORE (University of Modena and Reggio Emilia).72 indexed citations
19.
Cucchiara, Rita, et al.. (1998). A RULE-BASED VEHICULAR TRAFFIC TRACKING SYSTEM. 4. 334–337.3 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.