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.
On-Line Building Energy Optimization Using Deep Reinforcement Learning
2018449 citationsElena Mocanu, Decebal Constantin Mocanu et al.IEEE Transactions on Smart Gridprofile →
A systematic review on affective computing: emotion models, databases, and recent advances
2022298 citationsYan Wang, Wei Song et al.Information Fusionprofile →
Enhancement of Underwater Images With Statistical Model of Background Light and Optimization of Transmission Map
2020249 citationsWei Song, Yan Wang et al.IEEE Transactions on Broadcastingprofile →
A systematic review and analysis of deep learning-based underwater object detection
2023161 citationsMinghua Zhang, Wei Song et al.profile →
Computer-aided diagnosis for breast cancer classification using deep neural networks and transfer learning
2022139 citationsHanan Aljuaid, Lucia Cavallaro et al.profile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
hero ref
Countries citing papers authored by Antonio Liotta
Since
Specialization
Citations
This map shows the geographic impact of Antonio Liotta'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 Antonio Liotta with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Antonio Liotta more than expected).
This network shows the impact of papers produced by Antonio Liotta. 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 Antonio Liotta. The network helps show where Antonio Liotta may publish in the future.
Co-authorship network of co-authors of Antonio Liotta
This figure shows the co-authorship network connecting the top 25 collaborators of Antonio Liotta.
A scholar is included among the top collaborators of Antonio Liotta 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 Antonio Liotta. Antonio Liotta is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Cavallaro, Lucia, Stefania Costantini, Pasquale De Meo, Antonio Liotta, & Giovanni Stilo. (2022). Network Connectivity Under a Probabilistic Node Failure Model. IEEE Transactions on Network Science and Engineering. 9(4). 2463–2480.5 indexed citations
Song, Wei, Yan Wang, Dongmei Huang, Antonio Liotta, & Cristian Perra. (2020). Enhancement of Underwater Images With Statistical Model of Background Light and Optimization of Transmission Map. IEEE Transactions on Broadcasting. 66(1). 153–169.249 indexed citations breakdown →
Mocanu, Elena, Decebal Constantin Mocanu, Phuong H. Nguyen, et al.. (2018). On-Line Building Energy Optimization Using Deep Reinforcement Learning. IEEE Transactions on Smart Grid. 10(4). 3698–3708.449 indexed citations breakdown →
16.
Galzarano, Stefano, Giancarlo Fortino, & Antonio Liotta. (2011). A task-based architecture for autonomic body sensor networks. Data Archiving and Networked Services (DANS). 7. 140–151.1 indexed citations
17.
Liotta, Antonio, et al.. (2008). QoE analysis of a peer-to-peer television system. TU/e Research Portal.10 indexed citations
18.
Liotta, Antonio, et al.. (2002). Supporting adaptation-aware services through the virtual home environment. TU/e Research Portal (Eindhoven University of Technology).1 indexed citations
19.
Liotta, Antonio, et al.. (1999). On the performance and scalability of decentralised monitoring using mobile Agents. 3–18.6 indexed citations
20.
Liotta, Antonio, et al.. (1998). Decomposition Patterns for Mobile-Code-based Management. UCL Discovery (University College London).1 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.