Melvin Robinson

547 total citations
16 papers, 400 citations indexed

About

Melvin Robinson is a scholar working on Artificial Intelligence, Electrical and Electronic Engineering and Computer Vision and Pattern Recognition. According to data from OpenAlex, Melvin Robinson has authored 16 papers receiving a total of 400 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Artificial Intelligence, 5 papers in Electrical and Electronic Engineering and 4 papers in Computer Vision and Pattern Recognition. Recurrent topics in Melvin Robinson's work include Neural Networks and Applications (3 papers), Radiomics and Machine Learning in Medical Imaging (2 papers) and Digital Imaging for Blood Diseases (2 papers). Melvin Robinson is often cited by papers focused on Neural Networks and Applications (3 papers), Radiomics and Machine Learning in Medical Imaging (2 papers) and Digital Imaging for Blood Diseases (2 papers). Melvin Robinson collaborates with scholars based in United States and Colombia. Melvin Robinson's co-authors include M. A. Rafe Biswas, Nelson Fumo, Ponnada A. Narayana, Sheeba J. Sujit, Refaat E. Gabr, Sushmita Datta, Ivan Coronado, Xiaojun Sun, Jerry S. Wolinsky and Fred Lublin and has published in prestigious journals such as Energy, Multiple Sclerosis Journal and Neural Processing Letters.

In The Last Decade

Melvin Robinson

15 papers receiving 395 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Melvin Robinson United States	8	194	167	76	64	62	16	400
Yuvaraj Natarajan India	13	61 0.3×	60 0.4×	61 0.8×	114 1.8×	18 0.3×	39	423
Ching-Liang Chen Taiwan	7	66 0.3×	89 0.5×	19 0.3×	137 2.1×	34 0.5×	8	378
Peter Wei United States	10	89 0.5×	67 0.4×	21 0.3×	30 0.5×	24 0.4×	36	372
Yisu Ge China	11	119 0.6×	39 0.2×	26 0.3×	68 1.1×	29 0.5×	33	339
Hussein Alahmer Jordan	16	72 0.4×	28 0.2×	21 0.3×	92 1.4×	121 2.0×	21	512
Dan D. Micu Romania	13	410 2.1×	32 0.2×	18 0.2×	39 0.6×	33 0.5×	136	630
Lukas Mauch Germany	8	201 1.0×	80 0.5×	5 0.1×	78 1.2×	66 1.1×	18	411
Zhi Yuan China	9	317 1.6×	26 0.2×	17 0.2×	65 1.0×	58 0.9×	22	481
Zhichen Wang China	13	50 0.3×	112 0.7×	9 0.1×	49 0.8×	35 0.6×	41	420

Countries citing papers authored by Melvin Robinson

Since Specialization

Citations

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

Fields of papers citing papers by Melvin Robinson

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Melvin Robinson

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

All Works

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16 of 16 papers shown

Arteaga-Arteaga, Harold Brayan, Alejandro Mora-Rubio, Simón Orozco-Arias, et al.. (2021). Machine learning applications to predict two-phase flow patterns. PeerJ Computer Science. 7. e798–e798. 24 indexed citations

Ebalunode, Jerry O., et al.. (2020). Deep learning to classify single-cell RNA sequencing in primary glioblastoma. 1–4. 1 indexed citations

Gabr, Refaat E., Ivan Coronado, Melvin Robinson, et al.. (2019). Brain and lesion segmentation in multiple sclerosis using fully convolutional neural networks: A large-scale study. Multiple Sclerosis Journal. 26(10). 1217–1226. 61 indexed citations

Sujit, Sheeba J., Refaat E. Gabr, Ivan Coronado, et al.. (2018). Automated Image Quality Evaluation of Structural Brain Magnetic Resonance Images using Deep Convolutional Neural Networks. 33–36. 8 indexed citations

Robinson, Melvin, et al.. (2018). Convolutional Neural Networks for Breast Cancer Histopathological Image Classification. 1–6. 6 indexed citations

Narayana, Ponnada A., Ivan Coronado, Melvin Robinson, et al.. (2018). Multimodal MRI Segmentation of Brain Tissue and T2-Hyperintense White Matter Lesions in Multiple Sclerosis using Deep Convolutional Neural Networks and a Large Multi-center Image Database. 13–16. 6 indexed citations

Biswas, M. A. Rafe & Melvin Robinson. (2017). Prediction of Direct Methanol Fuel Cell Stack Performance Using Artificial Neural Network. Journal of Electrochemical Energy Conversion and Storage. 14(3). 13 indexed citations

Robinson, Melvin, et al.. (2016). Deep Brain Stimulation Signal Classification Using Deep Belief Networks. 12. 155–158. 6 indexed citations

El-Kishky, H., et al.. (2016). Novel GSSA modeling and control of high power inverters for modern aircraft electric power systems. 13. 494–498. 1 indexed citations

10.

Biswas, M. A. Rafe, Melvin Robinson, & Nelson Fumo. (2016). Prediction of residential building energy consumption: A neural network approach. Energy. 117. 84–92. 240 indexed citations

11.

Robinson, Melvin, et al.. (2016). Properties of a Batch Training Algorithm for Feedforward Networks. Neural Processing Letters. 45(3). 841–854. 2 indexed citations

12.

El-Kishky, H., et al.. (2016). Impact of pulsed power loads on advanced aircraft electric power systems with hybrid APU. 434–437. 12 indexed citations

13.

Biswas, M. A. Rafe & Melvin Robinson. (2015). Performance Estimation of Direct Methanol Fuel Cell Using Artificial Neural Network. 4 indexed citations

14.

Robinson, Melvin & M.T. Manry. (2013). Two-stage second order training in feedforward neural networks. The Florida AI Research Society. 8 indexed citations

15.

Robinson, Melvin & M.T. Manry. (2013). Partially affine invariant training using dense transform matrices. ii. 1–6. 1 indexed citations

16.

Robinson, Melvin, et al.. (2009). Spherical coded imagers: improving lens speed, depth-of-field, and manufacturing yield through enhanced spherical aberration and compensating image processing. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 7429. 74290M–74290M. 7 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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