Decebal Constantin Mocanu

2.2k total citations · 1 hit paper
50 papers, 1.2k citations indexed

About

Decebal Constantin Mocanu is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Computer Networks and Communications. According to data from OpenAlex, Decebal Constantin Mocanu has authored 50 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 27 papers in Computer Vision and Pattern Recognition, 21 papers in Artificial Intelligence and 9 papers in Computer Networks and Communications. Recurrent topics in Decebal Constantin Mocanu's work include Image and Video Quality Assessment (17 papers), Advanced Image Processing Techniques (9 papers) and Video Coding and Compression Technologies (8 papers). Decebal Constantin Mocanu is often cited by papers focused on Image and Video Quality Assessment (17 papers), Advanced Image Processing Techniques (9 papers) and Video Coding and Compression Technologies (8 papers). Decebal Constantin Mocanu collaborates with scholars based in Netherlands, Italy and United States. Decebal Constantin Mocanu's co-authors include Antonio Liotta, Elena Mocanu, Madeleine Gibescu, Phuong H. Nguyen, Michael E. Webber, J.G. Slootweg, Maria Torres Vega, Mykola Pechenizkiy, Georgios Exarchakos and Haitham Bou Ammar and has published in prestigious journals such as Scientific Reports, IEEE Transactions on Smart Grid and Pattern Recognition.

In The Last Decade

Decebal Constantin Mocanu

48 papers receiving 1.1k citations

Hit Papers

On-Line Building Energy Optimization Using Deep Reinforce... 2018 2026 2020 2023 2018 100 200 300 400
What are hit papers?

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.

  1. On-Line Building Energy Optimization Using Deep Reinforcement Learning
    2018 449 citations Elena Mocanu, Decebal Constantin Mocanu et al. IEEE Transactions on Smart Grid profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Decebal Constantin Mocanu Netherlands 17 454 340 306 243 149 50 1.2k
Rajgopal Kannan United States 17 500 1.1× 364 1.1× 493 1.6× 80 0.3× 529 3.6× 145 1.3k
Zhongwei Si China 18 522 1.1× 301 0.9× 377 1.2× 65 0.3× 379 2.5× 61 1.2k
Vincent Havyarimana China 18 243 0.5× 145 0.4× 200 0.7× 97 0.4× 150 1.0× 37 920
Dezhi Hong United States 15 178 0.4× 253 0.7× 311 1.0× 59 0.2× 159 1.1× 60 937
Héctor Quintián Spain 19 176 0.4× 85 0.3× 300 1.0× 113 0.5× 135 0.9× 75 809
D. Binu India 12 348 0.8× 185 0.5× 411 1.3× 123 0.5× 171 1.1× 33 1.1k
Štěpán Hubálovský Czechia 16 308 0.7× 195 0.6× 490 1.6× 123 0.5× 159 1.1× 52 1.3k
Martin Fleury United Kingdom 20 508 1.1× 577 1.7× 283 0.9× 167 0.7× 445 3.0× 184 1.5k
Yu Cui China 14 230 0.5× 486 1.4× 330 1.1× 58 0.2× 378 2.5× 54 1.4k

Countries citing papers authored by Decebal Constantin Mocanu

Since Specialization
Citations

This map shows the geographic impact of Decebal Constantin Mocanu'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 Decebal Constantin Mocanu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Decebal Constantin Mocanu more than expected).

Fields of papers citing papers by Decebal Constantin Mocanu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Decebal Constantin Mocanu. 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 Decebal Constantin Mocanu. The network helps show where Decebal Constantin Mocanu may publish in the future.

Co-authorship network of co-authors of Decebal Constantin Mocanu

This figure shows the co-authorship network connecting the top 25 collaborators of Decebal Constantin Mocanu. A scholar is included among the top collaborators of Decebal Constantin Mocanu 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 Decebal Constantin Mocanu. Decebal Constantin Mocanu is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Ma, Pingchuan, Lu Yin, Stavros Petridis, et al.. (2024). Dynamic Data Pruning for Automatic Speech Recognition. TU/e Research Portal. 4488–4492.
2.
Mocanu, Decebal Constantin, et al.. (2023). Hu-bot: promoting the cooperation between humans and mobile robots. Neural Computing and Applications. 35(23). 16841–16852.
3.
Menkovski, Vlado, et al.. (2021). Efficient and effective training of sparse recurrent neural networks. Neural Computing and Applications. 33(15). 9625–9636. 22 indexed citations
4.
Liu, Shiwei, et al.. (2020). Topological Insights in Sparse Neural Networks.. arXiv (Cornell University). 2 indexed citations
5.
Liu, Shiwei, et al.. (2020). Sparse evolutionary deep learning with over one million artificial neurons on commodity hardware. Neural Computing and Applications. 33(7). 2589–2604. 25 indexed citations
6.
Mocanu, Decebal Constantin, et al.. (2018). Limited evaluation cooperative co-evolutionary differential evolution for large-scale neuroevolution. Proceedings of the Genetic and Evolutionary Computation Conference. 569–576. 12 indexed citations
7.
Cauteruccio, Francesco, Giancarlo Fortino, Antonio Guerrieri, et al.. (2018). Short-long term anomaly detection in wireless sensor networks based on machine learning and multi-parameterized edit distance. Information Fusion. 52. 13–30. 76 indexed citations
8.
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 →
9.
Mocanu, Decebal Constantin, Georgios Exarchakos, & Antonio Liotta. (2018). Decentralized dynamic understanding of hidden relations in complex networks. Scientific Reports. 8(1). 1571–1571. 13 indexed citations
10.
Mocanu, Decebal Constantin, Elena Mocanu, Peter Stone, et al.. (2017). Evolutionary training of sparse artificial neural networks : a network science perspective. Munich Personal RePEc Archive (Ludwig Maximilian University of Munich). 72(1). 1–61. 5 indexed citations
11.
Mocanu, Decebal Constantin. (2017). Network computations in artificial intelligence. Data Archiving and Networked Services (DANS). 4 indexed citations
12.
Mocanu, Decebal Constantin. (2016). On the synergy of network science and artificial intelligence. TU/e Research Portal. 4020–4021. 2 indexed citations
13.
Vega, Maria Torres, et al.. (2016). An experimental survey of no-reference video quality assessment methods. International Journal of Pervasive Computing and Communications. 12(1). 66–86. 16 indexed citations
14.
Vega, Maria Torres, Decebal Constantin Mocanu, & Antonio Liotta. (2016). A Regression Method for real-time video quality evaluation. TU/e Research Portal. 57. 217–224. 3 indexed citations
15.
Mocanu, Decebal Constantin, et al.. (2016). Predictive Power Control in Wireless Sensor Networks. TU/e Research Portal. 309–312. 7 indexed citations
16.
Mocanu, Decebal Constantin, Elena Mocanu, Phuong H. Nguyen, Madeleine Gibescu, & Antonio Liotta. (2016). A topological insight into restricted Boltzmann machines (extented abstract). TU/e Research Portal. 1 indexed citations
17.
Vega, Maria Torres, et al.. (2015). Accuracy of No-Reference Quality Metrics in Network-impaired Video Streams. TU/e Research Portal. 326–333. 4 indexed citations
18.
Ammar, Haitham Bou, Eric Eaton, Matthew E. Taylor, et al.. (2014). An automated measure of MDP similarity for transfer in reinforcement learning. National Conference on Artificial Intelligence. 31–37. 29 indexed citations
19.
Mocanu, Decebal Constantin, Georgios Exarchakos, & Antonio Liotta. (2014). Node centrality awareness via swarming effects. TU/e Research Portal. 19–24. 11 indexed citations
20.
Liotta, Antonio, et al.. (2013). Instantaneous Video Quality Assessment for lightweight devices. TU/e Research Portal. 525–531. 17 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.

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