A. Lamas

482 total citations
10 papers, 308 citations indexed

About

A. Lamas is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Aerospace Engineering. According to data from OpenAlex, A. Lamas has authored 10 papers receiving a total of 308 indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Artificial Intelligence, 3 papers in Computer Vision and Pattern Recognition and 2 papers in Aerospace Engineering. Recurrent topics in A. Lamas's work include Advanced Neural Network Applications (3 papers), Evolutionary Algorithms and Applications (2 papers) and Video Surveillance and Tracking Methods (2 papers). A. Lamas is often cited by papers focused on Advanced Neural Network Applications (3 papers), Evolutionary Algorithms and Applications (2 papers) and Video Surveillance and Tracking Methods (2 papers). A. Lamas collaborates with scholars based in Spain, Saudi Arabia and Mexico. A. Lamas's co-authors include Siham Tabik, Francisco Herrera, Roberto Olmos, Francisco Pérez-Hernández, Hamido Fujita, Rosana Montes, Natalia Díaz-Rodríguez, Ivan Donadello, David Filliat and Gianni Franchi and has published in prestigious journals such as Neurocomputing, Knowledge-Based Systems and Information Fusion.

In The Last Decade

A. Lamas

10 papers receiving 290 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
A. Lamas Spain	6	154	152	27	18	18	10	308
Lu Leng China	8	263 1.7×	56 0.4×	13 0.5×	24 1.3×	13 0.7×	17	351
Yanting Pei China	6	220 1.4×	59 0.4×	6 0.2×	13 0.7×	13 0.7×	9	308
Liqi Yan China	8	394 2.6×	111 0.7×	11 0.4×	62 3.4×	29 1.6×	18	482
Zaiyang Yu China	8	144 0.9×	66 0.4×	9 0.3×	22 1.2×	15 0.8×	20	313
Miguel A. Molina‐Cabello Spain	12	173 1.1×	136 0.9×	12 0.4×	19 1.1×	22 1.2×	45	370
Mani Ranjbar Canada	6	244 1.6×	123 0.8×	13 0.5×	11 0.6×	5 0.3×	8	321
Zeming Li China	6	375 2.4×	158 1.0×	17 0.6×	46 2.6×	12 0.7×	8	450
Fan Ma China	10	363 2.4×	184 1.2×	21 0.8×	39 2.2×	10 0.6×	22	476
Jianbiao He China	8	155 1.0×	69 0.5×	12 0.4×	9 0.5×	12 0.7×	28	256

Countries citing papers authored by A. Lamas

Since Specialization

Citations

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

Fields of papers citing papers by A. Lamas

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by A. Lamas. 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 A. Lamas. The network helps show where A. Lamas may publish in the future.

Co-authorship network of co-authors of A. Lamas

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

All Works

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10 of 10 papers shown

1.

Lamas, A., et al.. (2022). Human pose estimation for mitigating false negatives in weapon detection in video-surveillance. Neurocomputing. 489. 488–503. 39 indexed citations

2.

Díaz-Rodríguez, Natalia, A. Lamas, Gianni Franchi, et al.. (2022). EXplainable Neural-Symbolic Learning (X-NeSyL) methodology to fuse deep learning representations with expert knowledge graphs: The MonuMAI cultural heritage use case. arXiv (Cornell University). 62 indexed citations

3.

Lamas, A., et al.. (2020). MonuMAI: Dataset, deep learning pipeline and citizen science based app for monumental heritage taxonomy and classification. Neurocomputing. 420. 266–280. 29 indexed citations

4.

Pérez-Hernández, Francisco, Siham Tabik, A. Lamas, et al.. (2020). Object Detection Binary Classifiers methodology based on deep learning to identify small objects handled similarly: Application in video surveillance. Knowledge-Based Systems. 194. 105590–105590. 132 indexed citations

5.

Olmos, Roberto, Siham Tabik, A. Lamas, Francisco Pérez-Hernández, & Francisco Herrera. (2018). A binocular image fusion approach for minimizing false positives in handgun detection with deep learning. Information Fusion. 49. 271–280. 34 indexed citations

6.

Bellas, Francisco, A. Lamas, & Richard J. Duro. (2006). Multilevel Darwinist Brain and Autonomously Learning to Walk. 2 indexed citations

7.

Duro, Richard J., José Sántos, Francisco Bellas, & A. Lamas. (2006). On Line Darwinist Cognitive Mechanism for an Artificial Organism. 3 indexed citations

8.

Casás, Vicente Díaz, Fernando López Peña, Richard J. Duro, & A. Lamas. (2005). Automatic Aerodynamic Design of a Wind Turbine through Evolutionary Techniques. 454–459. 5 indexed citations

9.

Lamas, A. & Richard J. Duro. (2005). Automatic 3D Morphological Design through Evolution. 564–569. 1 indexed citations

10.

Lamas, A., et al.. (2004). Evolutionary design of a multi-ANN control structure for a submersible catamaran. 2510. 32–37. 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.

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