Application of pseudoderivative for the identification of inflection points in datasets

Authors

DOI:

https://doi.org/10.61117/ipsumtec.v8i3.386

Keywords:

Big Data, Data Mining, Pattern Recognition

Abstract

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.

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Author Biographies

Misael López Nava , National Technological Institute of Mexico

Computer Systems Engineer (1997-2002) and Master of Engineering Sciences (2027- 2019) from the Zacatepec Institute of Technology. Specialist in Leadership and Institutional Management (2014-2016) from the Interdisciplinary Centre for Research and Teaching in Technical Education (CIIDET). He is currently pursuing a Doctorate in Electronic Engineering Sciences at CENIDET. He has 20 years of teaching experience in the field of computing.

Juan Reyes Reyes , National Technological Institute of Mexico

Industrial Engineer in Electronics (1990-1994) from the Saltillo Institute of Technology. He completed a Master of Science degree with a specialisation in Electrical Engineering (1995-1997) and a Doctorate in Science in the Department of Automatic Control (1998-2001) at the IPN Centre for Research and Advanced Studies. Level 1 in the National System of Researchers. Since 2012, he has been a research professor at the National Centre for Research and Technological Development (TecNM/CENIDET).

Gloria Lilia Osorio Gordillo , National Technological Institute of Mexico

Electronics Engineer from the National Technological Institute of Mexico, Oaxaca campus. She holds a Master's and Doctorate in Electronic Engineering from the National Centre for Research and Development (CENIDET), as well as a Doctorate in Automation, Signal Processing and Computer Science from the University of Lorraine in Longwy, France. She is currently a research professor at CENIDET in the Engineering Sciences and Electronic Engineering Sciences programmes.

Carlos Manuel Astorga Zaragoza, National Technological Institute of Mexico

Electrical Engineer in Instrumentation from the Technological Institute of Minatitlán. Master of Science in Electrical Engineering from the National Centre for Research and Technological Development (CENIDET). Doctorate in Process Engineering from Claude Bernard University, Lyon, France. He completed a postdoctoral degree in Automatic Control at Henri Poincaré University, Nancy, France. Level 2 of the National System of Researchers. Professor-Researcher at CENIDET.

Luis José Muñiz Rascado , National Technological Institute of Mexico

He studied Computer Systems Engineering (1997-2002) and obtained a Master's Degree in Information Technology (2004-2007) at TecNM/Instituto Tecnológico de Zacatepec (ITZ). He worked at the Centre for Genomic Sciences at UNAM (2004 to 2013). Since 2013, he has been a professor in the Department of Systems and Computing at ITZ. He is currently pursuing a PhD in Computer Science at TecNM/CENIDET.

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Published

2025-10-08

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How to Cite

López Nava , M., Reyes Reyes , J., Osorio Gordillo , G. L., Astorga Zaragoza, C. M., & Muñiz Rascado , L. J. (2025). Application of pseudoderivative for the identification of inflection points in datasets. REVISTA IPSUMTEC, 8(3), 88–95. https://doi.org/10.61117/ipsumtec.v8i3.386

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