Juana Canul-Reich

556 total citations
29 papers, 336 citations indexed

About

Juana Canul-Reich is a scholar working on Artificial Intelligence, Molecular Biology and Neurology. According to data from OpenAlex, Juana Canul-Reich has authored 29 papers receiving a total of 336 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Artificial Intelligence, 6 papers in Molecular Biology and 6 papers in Neurology. Recurrent topics in Juana Canul-Reich's work include Evolutionary Algorithms and Applications (7 papers), Peripheral Neuropathies and Disorders (6 papers) and Metaheuristic Optimization Algorithms Research (4 papers). Juana Canul-Reich is often cited by papers focused on Evolutionary Algorithms and Applications (7 papers), Peripheral Neuropathies and Disorders (6 papers) and Metaheuristic Optimization Algorithms Research (4 papers). Juana Canul-Reich collaborates with scholars based in Mexico, United States and France. Juana Canul-Reich's co-authors include Pilar Pozos-Parra, Efrén Mezura‐Montes, Marco Antonio Cruz‐Chávez, Lawrence Hall, Dmitry B. Goldgof, Steven A. Eschrich, Juan Frausto–Solís, Carlos A. Ortega, Julio Ponce and Erick de la Cruz‐Hernandez and has published in prestigious journals such as SHILAP Revista de lepidopterología, IEEE Access and Pattern Recognition Letters.

In The Last Decade

Juana Canul-Reich

27 papers receiving 321 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Juana Canul-Reich Mexico 10 142 49 38 37 36 29 336
Mahsa Torkamanian-Afshar Türkiye 9 99 0.7× 26 0.5× 24 0.6× 26 0.7× 37 1.0× 14 288
Moolchand Sharma India 12 115 0.8× 29 0.6× 37 1.0× 19 0.5× 17 0.5× 37 417
Mehmet R. Tolun Türkiye 7 145 1.0× 62 1.3× 42 1.1× 38 1.0× 11 0.3× 26 289
Ko-Wei Huang Taiwan 9 122 0.9× 24 0.5× 41 1.1× 31 0.8× 23 0.6× 29 269
Harun Bingöl Türkiye 10 204 1.4× 26 0.5× 63 1.7× 68 1.8× 12 0.3× 24 367
Nannan Ji China 11 139 1.0× 13 0.3× 84 2.2× 13 0.4× 54 1.5× 19 335
Miho Ohsaki Japan 9 199 1.4× 90 1.8× 114 3.0× 51 1.4× 14 0.4× 54 430
Behrooz Masoumi Iran 11 159 1.1× 51 1.0× 85 2.2× 17 0.5× 10 0.3× 44 372
Liyu Gong China 8 126 0.9× 26 0.5× 110 2.9× 12 0.3× 27 0.8× 19 316

Countries citing papers authored by Juana Canul-Reich

Since Specialization
Citations

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

Fields of papers citing papers by Juana Canul-Reich

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Juana Canul-Reich. 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 Juana Canul-Reich. The network helps show where Juana Canul-Reich may publish in the future.

Co-authorship network of co-authors of Juana Canul-Reich

This figure shows the co-authorship network connecting the top 25 collaborators of Juana Canul-Reich. A scholar is included among the top collaborators of Juana Canul-Reich 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 Juana Canul-Reich. Juana Canul-Reich 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.
Canul-Reich, Juana, et al.. (2023). Impact of data balancing a multiclass dataset before the creation of association rules to study bacterial vaginosis. SHILAP Revista de lepidopterología. 4(3). 188–199. 5 indexed citations
2.
Canul-Reich, Juana, et al.. (2023). Evolutionary Selection of a Set of Association Rules Considering Biological Constraints Describing the Prevalent Elements in Bacterial Vaginosis. Mathematical and Computational Applications. 28(3). 75–75.
3.
Canul-Reich, Juana, et al.. (2021). An Enhanced Method for Diagnosis of Bacterial Vaginosis based on Support Vector Machines with Linear Kernel. 12(3). 109–121. 2 indexed citations
4.
Canul-Reich, Juana, et al.. (2021). Depth Sliding Windows Application on Geophysical Well Log Data. 1–8. 2 indexed citations
5.
Canul-Reich, Juana, et al.. (2021). Induction of decision trees as classification models through metaheuristics. Swarm and Evolutionary Computation. 69. 101006–101006. 43 indexed citations
6.
Canul-Reich, Juana, et al.. (2020). Predictor Selection for Bacterial Vaginosis Diagnosis Using Decision Tree and Relief Algorithms. Applied Sciences. 10(9). 3291–3291. 1 indexed citations
7.
Canul-Reich, Juana, et al.. (2019). Augmented Reality Labels for Security Signs based on Color Segmentation with PSO for Assisting Colorblind People. 10(3). 7–20. 1 indexed citations
8.
Canul-Reich, Juana, et al.. (2019). Bacterial foraging optimization algorithm with mutation to solve constrained problems. Acta Universitaria. 29. 1–16. 2 indexed citations
9.
Canul-Reich, Juana, et al.. (2018). Mining: Students Comments about Teacher Performance Assessment using Machine Learning Algorithms. 9(3). 26–40. 17 indexed citations
10.
Canul-Reich, Juana, et al.. (2018). Bacterial Foraging Optimization Algorithm for Menu Planning. IEEE Access. 6. 8619–8629. 96 indexed citations
11.
Canul-Reich, Juana, et al.. (2018). A Predictive Model for Guillain–Barré Syndrome Based on Ensemble Methods. Computational Intelligence and Neuroscience. 2018. 1–10. 4 indexed citations
12.
Canul-Reich, Juana, et al.. (2016). Proposal of a Sentiment Analysis Model in Tweets for Improvement of the Teaching -Learning Process in the Classroom Using a Corpus of Subjectivity. 7(2). 22–34. 15 indexed citations
13.
14.
Canul-Reich, Juana, et al.. (2015). Finding relevant features for identifying subtypes of Guillain-Barré Syndrome using Quenching Simulated Annealing and Partitions Around Medoids. Redalyc (Universidad Autónoma del Estado de México). 6(2). 11–27. 2 indexed citations
15.
Canul-Reich, Juana, et al.. (2015). Towards a predictive model for Guillain-Barré syndrome. PubMed. 2015. 7234–7237. 5 indexed citations
16.
Canul-Reich, Juana, et al.. (2014). Feature Selection for Better Identification of Subtypes of Guillain-Barré Syndrome. Computational and Mathematical Methods in Medicine. 2014. 1–9. 10 indexed citations
17.
Canul-Reich, Juana, et al.. (2013). Predictores de falla respiratoria y de la necesidad de ventilación mecánica en el síndrome de Guillain-Barré: una revisión de la literatura. 14(5). 272–280. 2 indexed citations
18.
Canul-Reich, Juana, et al.. (2012). ITERATIVE FEATURE PERTURBATION AS A GENE SELECTOR FOR MICROARRAY DATA. International Journal of Pattern Recognition and Artificial Intelligence. 26(5). 1260003–1260003. 22 indexed citations
19.
Canul-Reich, Juana. (2010). An iterative feature perturbation method for gene selection from microarray data. Digital Commons - University of South Florida (University of South Florida). 1 indexed citations
20.
Canul-Reich, Juana, Lawrence Hall, Dmitry B. Goldgof, & Steven A. Eschrich. (2008). Feature selection for microarray data by AUC analysis. Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics. 768–773. 10 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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