Michael R. Smith

838 total citations
25 papers, 495 citations indexed

About

Michael R. Smith is a scholar working on Artificial Intelligence, Electrical and Electronic Engineering and Computational Theory and Mathematics. According to data from OpenAlex, Michael R. Smith has authored 25 papers receiving a total of 495 indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Artificial Intelligence, 10 papers in Electrical and Electronic Engineering and 4 papers in Computational Theory and Mathematics. Recurrent topics in Michael R. Smith's work include Machine Learning and Data Classification (10 papers), Imbalanced Data Classification Techniques (7 papers) and Machine Learning and Algorithms (6 papers). Michael R. Smith is often cited by papers focused on Machine Learning and Data Classification (10 papers), Imbalanced Data Classification Techniques (7 papers) and Machine Learning and Algorithms (6 papers). Michael R. Smith collaborates with scholars based in United States, Canada and Iran. Michael R. Smith's co-authors include Tony Martinez, Christophe Giraud-Carrier, Fadhel M. Ghannouchi, Andrew Kwan, Paul Bain, Andrew L. Beam, Mohamed Helaoui, Benjamin Kompa, Andrew Bate and Oualid Hammi and has published in prestigious journals such as Machine Learning, Artificial Intelligence Review and Drug Safety.

In The Last Decade

Michael R. Smith

25 papers receiving 475 citations

Peers

Michael R. Smith

EEE

CVPR

CNC

David A. Cieslak United States

Zhaozhao Xu China

Prabhjot Kaur India

Hongxing He Australia

Feng Tan China

Nele Verbiest Belgium

Mahdi Pakdaman Naeini United States

Yuanting Yan China

Weimin Chen China

Pepa Ramírez‐Cobo Spain

David A. Cieslak United States

Michael R. Smith

503 ×1.7

184 ×1.8

EEE

89 ×1.6

53 ×1.3

CVPR

73 ×2.4

CNC

Citations per year, relative to Michael R. Smith Michael R. Smith (= 1×) peers David A. Cieslak

Countries citing papers authored by Michael R. Smith

Since Specialization

Citations

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

Fields of papers citing papers by Michael R. Smith

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Michael R. Smith. 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 Michael R. Smith. The network helps show where Michael R. Smith may publish in the future.

Co-authorship network of co-authors of Michael R. Smith

This figure shows the co-authorship network connecting the top 25 collaborators of Michael R. Smith. A scholar is included among the top collaborators of Michael R. Smith 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 Michael R. Smith. Michael R. Smith 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

Kompa, Benjamin, Joe B. Hakim, Anil Palepu, et al.. (2022). Artificial Intelligence Based on Machine Learning in Pharmacovigilance: A Scoping Review. Drug Safety. 45(5). 477–491. 41 indexed citations

Smith, Michael R., et al.. (2017). 2 to 18 GHz high-power and high-efficiency amplifiers. 126–129. 11 indexed citations

Smith, Michael R., Kristofor D. Carlson, Craig M. Vineyard, et al.. (2017). A novel digital neuromorphic architecture efficiently facilitating complex synaptic response functions applied to liquid state machines. OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information). 2421–2428. 6 indexed citations

Smith, Michael R. & Tony Martinez. (2016). The robustness of majority voting compared to filtering misclassified instances in supervised classification tasks. Artificial Intelligence Review. 49(1). 105–130. 15 indexed citations

Smith, Michael R. & Tony Martinez. (2015). Using Classifier diversity to handle label noise. 1–8. 2 indexed citations

Smith, Michael R., Tony Martinez, & Christophe Giraud-Carrier. (2015). The potential benefits of data set filtering and learning algorithm hyperparameter optimization. 3–14. 1 indexed citations

Smith, Michael R. & Tony Martinez. (2014). A Comparative Evaluation of Curriculum Learning with Filtering and Boosting in Supervised Classification Problems. Computational Intelligence. 32(2). 167–195. 12 indexed citations

Smith, Michael R., Andrew Dickson White, Christophe Giraud-Carrier, & Tony Martinez. (2014). An easy to use repository for comparing and improving machine learning algorithm usage. 41–48. 1 indexed citations

Smith, Michael R., Tony Martinez, & Christophe Giraud-Carrier. (2013). An instance level analysis of data complexity. Machine Learning. 95(2). 225–256. 252 indexed citations

10.

Kwan, Andrew, Fadhel M. Ghannouchi, Oualid Hammi, Mohamed Helaoui, & Michael R. Smith. (2012). Look-up table-based digital predistorter implementation for field programmable gate arrays using long-term evolution signals with 60 MHz bandwidth. IET Science Measurement & Technology. 6(3). 181–188. 12 indexed citations

11.

Mohammadi, Siamak, et al.. (2011). Mutant Fault Injection in Functional Properties of a Model to Improve Coverage Metrics. 422–425. 1 indexed citations

12.

Hammi, Oualid, Mayada Younes, Andrew Kwan, Michael R. Smith, & Fadhel M. Ghannouchi. (2010). PERFORMANCE-DRIVEN DIMENSION ESTIMATION OF MEMORY POLYNOMIAL BEHAVIORAL MODELS FOR WIRELESS TRANSMITTERS AND POWER AMPLIFIERS. Progress In Electromagnetics Research C. 12. 173–189. 9 indexed citations

13.

Smith, Michael R.. (2009). An Empirical Study of Instance Hardness. ScholarsArchive (Brigham Young University). 4 indexed citations

14.

Jiang, Yu, et al.. (2009). A Scalable Testing Framework for Location-Based Services. Journal of Computer Science and Technology. 24(2). 386–404. 3 indexed citations

15.

Smith, Michael R., et al.. (2009). A More Agile Approach to Embedded System Development. IEEE Software. 26(3). 50–57. 4 indexed citations

16.

Kwan, Andrew, Mohamed Helaoui, Slim Boumaiza, Michael R. Smith, & Fadhel M. Ghannouchi. (2008). Wireless Communications Transmitter Performance Enhancement Using Advanced Signal Processing Algorithms Running in a Hybrid DSP/FPGA Platform. Journal of Signal Processing Systems. 56(2-3). 187–198. 6 indexed citations

17.

Kwan, Andrew, Slim Boumaiza, Michael R. Smith, & Fadhel M. Ghannouchi. (2006). Automating the Verification of SDR Base band Signal Processing Algorithms Developed on DSP/FPGA Platform. 5–9. 2 indexed citations

18.

Succi, Giancarlo, Raymond Wong, Eric Liu, & Michael R. Smith. (2000). Supporting dynamic composition of components. Archivio istituzionale della ricerca (Alma Mater Studiorum Università di Bologna). 787–787. 3 indexed citations

19.

Góngora-Rubio, Mário Ricardo, Jorge J. Santiago‐Avilés, Luis Sola-Laguna, & Michael R. Smith. (1999). Integrated LTCC Coils for Multiple Applications in Meso-Electro-Mechanical Systems. Micro-Electro-Mechanical Systems (MEMS). 189–194. 3 indexed citations

20.

Salzman, E W, Mark Weinstein, Ronald M. Weintraub, et al.. (1986). Treatment with Desmopressin Acetate to Reduce Blood Loss After Cardiac Surgery. Survey of Anesthesiology. 30(6). 371–371. 23 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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