Mark Elshaw

632 total citations
23 papers, 350 citations indexed

About

Mark Elshaw is a scholar working on Social Psychology, Control and Systems Engineering and Computer Vision and Pattern Recognition. According to data from OpenAlex, Mark Elshaw has authored 23 papers receiving a total of 350 indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Social Psychology, 10 papers in Control and Systems Engineering and 7 papers in Computer Vision and Pattern Recognition. Recurrent topics in Mark Elshaw's work include Robot Manipulation and Learning (9 papers), Action Observation and Synchronization (9 papers) and Face recognition and analysis (4 papers). Mark Elshaw is often cited by papers focused on Robot Manipulation and Learning (9 papers), Action Observation and Synchronization (9 papers) and Face recognition and analysis (4 papers). Mark Elshaw collaborates with scholars based in United Kingdom and Germany. Mark Elshaw's co-authors include Stefan Wermter, Vasile Palade, Günther Palm, Cornelius Weber, Stratis Kanarachos, Sujan Rajbhandari, M. Nazmul Huda, Chitta Saha, P. Corcoran and Christo Panchev and has published in prestigious journals such as Neural Networks, Knowledge-Based Systems and Sensors and Actuators A Physical.

In The Last Decade

Mark Elshaw

23 papers receiving 335 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Mark Elshaw United Kingdom	11	141	102	87	82	81	23	350
Markus Hofbauer Germany	10	91 0.6×	109 1.1×	54 0.6×	129 1.6×	55 0.7×	31	323
Eric Chown United States	8	65 0.5×	42 0.4×	49 0.6×	77 0.9×	119 1.5×	21	404
Mahwish Ilyas Pakistan	5	112 0.8×	171 1.7×	70 0.8×	36 0.4×	123 1.5×	9	365
Md. Mokammel Haque Bangladesh	8	65 0.5×	97 1.0×	44 0.5×	33 0.4×	69 0.9×	27	332
Matthias Kerzel Germany	12	101 0.7×	36 0.4×	68 0.8×	88 1.1×	149 1.8×	43	380
Wen-Bing Horng Taiwan	8	263 1.9×	119 1.2×	51 0.6×	40 0.5×	33 0.4×	22	528
BoYu Gao China	11	122 0.9×	36 0.4×	35 0.4×	103 1.3×	61 0.8×	44	380
P. Arockia Jansi Rani India	10	129 0.9×	70 0.7×	59 0.7×	85 1.0×	70 0.9×	49	365
Sven Magg Germany	11	178 1.3×	124 1.2×	78 0.9×	73 0.9×	204 2.5×	22	468
Samer Al Kork Kuwait	13	94 0.7×	66 0.6×	63 0.7×	146 1.8×	57 0.7×	45	445

Countries citing papers authored by Mark Elshaw

Since Specialization

Citations

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

Fields of papers citing papers by Mark Elshaw

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Mark Elshaw

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

All Works

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20 of 20 papers shown

Huda, M. Nazmul, et al.. (2019). Pedestrian and Cyclist Detection and Intent Estimation for Autonomous Vehicles: A Survey. Applied Sciences. 9(11). 2335–2335. 60 indexed citations

Palade, Vasile, et al.. (2018). Deep Learning for Illumination Invariant Facial Expression Recognition. Pure (Coventry University). 15. 1–6. 7 indexed citations

Elshaw, Mark, et al.. (2018). A hybrid deep learning neural approach for emotion recognition from facial expressions for socially assistive robots. Neural Computing and Applications. 29(7). 359–373. 60 indexed citations

Elshaw, Mark, et al.. (2017). Stacked deep convolutional auto-encoders for emotion recognition from facial expressions. Pure (Coventry University). 1586–1593. 39 indexed citations

Raghu, S., et al.. (2013). Emotional recognition from the speech signal for a virtual education agent. Journal of Physics Conference Series. 450. 12053–12053. 16 indexed citations

Malone, James, et al.. (2006). Spatio-temporal neural data mining architecture in learning robots. Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005.. 5. 2802–2807. 2 indexed citations

Weber, Cornelius, et al.. (2006). A camera-direction dependent visual-motor coordinate transformation for a visually guided neural robot. Knowledge-Based Systems. 19(5). 348–355. 7 indexed citations

Wermter, Stefan, Günther Palm, & Mark Elshaw. (2005). Biomimetic Neural Learning for Intelligent Robots: Intelligent Systems, Cognitive Robotics, and Neuroscience (Lecture Notes in Computer Science / Lecture Notes in Artificial Intelligence). Springer eBooks. 4 indexed citations

Weber, Cornelius, Stefan Wermter, & Mark Elshaw. (2005). A hybrid generative and predictive model of the motor cortex. Neural Networks. 19(4). 339–353. 13 indexed citations

10.

Wermter, Stefan, Cornelius Weber, & Mark Elshaw. (2005). ASSOCIATIVE NEURAL MODELS FOR BIOMIMETIC MULTI-MODAL LEARNING IN A MIRROR NEURON-BASED ROBOT. 31–46. 4 indexed citations

11.

Elshaw, Mark, et al.. (2005). An associator network approach to robot learning by imitation through vision, motor control and language. 49. 591–596. 6 indexed citations

12.

Wermter, Stefan, Günther Palm, & Mark Elshaw. (2005). Biomimetic Neural Learning for Intelligent Robots: Intelligent Systems, Cognitive Robotics, and Neuroscience. Medical Entomology and Zoology. 16 indexed citations

13.

Wermter, Stefan, Günther Palm, & Mark Elshaw. (2005). Biomimetic Neural Learning for Intelligent Robots. Lecture notes in computer science. 31 indexed citations

14.

Wermter, Stefan, et al.. (2004). Towards multimodal neural robot learning. Robotics and Autonomous Systems. 47(2-3). 171–175. 28 indexed citations

15.

Elshaw, Mark, Stefan Wermter, & Peter Watt. (2004). Self-organisation of language instruction for robot action control. 1. 22–27. 1 indexed citations

16.

Weber, Cornelius, et al.. (2004). A Multimodal Hierarchial Approach to Robot Learning by Imitation. CogPrints (University of Southampton). 1 indexed citations

17.

Wermter, Stefan & Mark Elshaw. (2003). Learning robot actions based on self-organising language memory. Neural Networks. 16(5-6). 691–699. 13 indexed citations

18.

Wermter, Stefan, et al.. (2003). Towards Integrating Learning by Demonstration and Learning by Instruction in a Multimodal Robotics. 6 indexed citations

19.

Elshaw, Mark & Stefan Wermter. (2003). A neurocognitive approach to self-organisation of verb actions. 12. 29–34. 2 indexed citations

20.

Corcoran, P., et al.. (1999). The application of genetic algorithms to sensor parameter selection for multisensor array configuration. Sensors and Actuators A Physical. 76(1-3). 57–66. 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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