Application of pseudoderivative for the identification of inflection points in datasets
DOI:
https://doi.org/10.61117/ipsumtec.v8i3.386Keywords:
Big Data, Data Mining, Pattern RecognitionAbstract
In the field of pattern recognition, there are techniques to identify the desired characteristics of a data set, in other words, to classify the information into predefined categories. The pseudoderivative is a tool that can be used in image recognition, voice recognition, handwriting recognition, etc. This work presents the application of the pseudoderivative for the identification of characteristics in data sets, based on an algorithm for data processing based on linear differential equations, which allows processing a data at a specific time from the analysis of n immediately preceding and following data, in such a way that the number of elements for manipulation can be increased or decreased. The pseudoderivative not only allows identifying minimums and maximums in a first application, but, in addition to this information, when applied a second time to the same data set, it allows obtaining the inflection points of the information on which it operates.
Downloads
Metrics
References
Adrian, Cecilia, et al. “Theoretical Aspect In Formulating Assesment Model Of Big Data Analytics Environment.” Acta Mechanica Malaysia, vol. 1, no. 1, 2018, pp. 16–17., doi:10.26480/amm.01.2018.16.17. DOI: https://doi.org/10.26480/amm.01.2018.16.17
Zeng, Wenrong, et al. “Access Control for Big Data Using Data Content.” 2013 IEEE International Conference on Big Data, 2013, doi:10.1109/bigdata.2013.6691798. DOI: https://doi.org/10.1109/BigData.2013.6691798
Fernández, Alicia, et al. “Pattern Recognition in Latin America in the ‘Big Data’ Era.” Pattern Recognition, vol. 48, no. 4, 2015, pp. 1185–1196., doi:10.1016/j.patcog.2014.04.012. DOI: https://doi.org/10.1016/j.patcog.2014.04.012
Ianni, M., Masciari, E. & Sperlí, G. A survey of Big Data dimensions vs Social Networks analysis. J Intell Inf Syst 57, 73–100 (2021). https://doi.org/10.1007/s10844-020-00629-2 DOI: https://doi.org/10.1007/s10844-020-00629-2
Zheng, C., Zhang, Q., Long, G., Zhang, C., Young, S.D., Wang, W. (2020). Measuring time-sensitive and topic-specific influence in social networks with lstm and self-attention. IEEE Access, 8, 82481–82492. DOI: https://doi.org/10.1109/ACCESS.2020.2991683
Tian, S., Mo, S., Wang, L., Peng, Z. (2020). Deep reinforcement learning-based approach to tackle topic-aware influence maximization. Data Science and Engineering, 1–11. DOI: https://doi.org/10.1007/s41019-020-00117-1
Shao, H., Sun, D., Su, L., Wang, Z., Liu, D., Liu, S., Kaplan, L., Abdelzaher, T. (2020). Truth discovery with multi-modal data in social sensing. IEEE Transactions on Computers, 1–1.
Franklinos, Lydia, et al. “Key Opportunities and Challenges for the Use of Big Data Inmigration Research and Policy.” 2020, doi:10.14324/111.444/000042.v1. DOI: https://doi.org/10.14324/111.444/000042.v1
Gaidarski, Ivan, and Pavlin Kutinchev. “Using Big Data for Data Leak Prevention.” 2019 Big Data, Knowledge and Control Systems Engineering (BdKCSE), 2019, doi:10.1109/bdkcse48644.2019.9010596. DOI: https://doi.org/10.1109/BdKCSE48644.2019.9010596
Zhang, Y., Wang, J., & Liu, Y. (2023). Pseudoderivada-based feature extraction for image classification. Pattern Recognition, 132, 108925.
Li, X., Zhang, H., & Sun, S. (2022). Speaker identification using pseudoderivatives. Speech Communication, 143, 109-118.
Wang, Y., Zhang, X., & Li, X. (2021). Handwritten digit recognition using pseudoderivatives. International Journal of Pattern Recognition and Artificial Intelligence, 35(04), 2150017.
Lohr, Sharon. “Big Data and Crime Statistics.” Measuring Crime, 2019, pp. 125–136., doi:10.1201/9780429201189-10. DOI: https://doi.org/10.1201/9780429201189-10
Paz Grebe, M de la, Centeno, Angel M, Galazi, M Lucía, & Campos, M Soledad. (2021). Turning points: puntos de inflexión en la vida de los estudiantes de medicina. FEM: Revista de la Fundación Educación Médica, 24(6), 291-293. Epub 17 de enero de 2022.https://dx.doi.org/10.33588/fem.246.1157. DOI: https://doi.org/10.33588/fem.246.1157
Datos Abiertos Dirección General de Epidemiología, Influenza, COVID-19 y otros virus respiratorios [base de datos en línea]. Disponible en: https://www.gob.mx/salud/documentos/datos-abiertos-152127
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Misael López Nava , Juan Reyes Reyes , Gloria Lilia Osorio Gordillo , Carlos Manuel Astorga Zaragoza, Luis José Muñiz Rascado

This work is licensed under a Creative Commons Attribution 4.0 International License.
