My articles and publications --(full text, click here. You may be asked to sign up --it is free) --Mis publicaciones (texto completo: http://ipn.academia.edu/AdolfoGuzman Quizá le pida suscribirse --es gratis) Mi página Web -- (click here) -- My Web page (http://alum.mit.edu/www/aguzman). ALGUNOS VIDEOS SOBRE LO QUE HAGO. Conferencia 'Ciudad inteligente, con conectividad y tecnología' (oct. 2010), parte 1 (15min), parte 2 (8min), parte 3 (9min), parte 4 (2min). Entrevista por redCudiMéxico, 2012: aquí (11 min). Avances en Inteligencia Artificial, entrevista en la Univ. IBERO, Puebla, 2013. Pulse aquí (53min). Video in the series "Personalities in the history of ESIME" (for the 100 years anniversary of ESIME-IPN, in Spanish) about Adolfo Guzman": 2014, click here. (1h)
Entrevista "La visión de los egresados del IPN, a 80 años de la creación del IPN y 100 años de la creación de la ESIME, 2014: ver en youtube (1h). Seminario sobre "Big Data" (la Ciencia de Datos). 2014. Pulse aquí (56min). Seminar on "Big Data", in English, 2014. Click here (56min). Algunos trabajos sobre Minería de Datos y sus Aplicaciones (CIC-IPN, 2016): pulse aquí (5min). El auge y el ocaso de las máquinas de Lisp (Plática en la Reunión Anual 2016 de la Academia Mexicana de Computación): pulse aquí (56min). Entrevista sobre la funcionalidad y competitividad de Hotware 10: 2016, aquí (6 min). Adolfo Guzmán Arenas, Ingeniero Electrónico e investigador del Centro de Investigación en Computación del IPN, conversó sobre su trayectoria y la importancia de las ciencias aplicadas para el desarrollo del país. 2017, Canal 11, Noticias TV (30min). Cómo se construyó la primera computadora en el mundo de procesamiento paralelo con Lisp. Marzo 2018. https://www.youtube.com/watch?v=dzyZGDhxwrU (12 min). Charla "Historias de éxito en la computación mexicana", ciclo Códice IA. Entrevista a A. Guzmán, "Entre la vida y la academia": https://bit.ly/3sIOQBc (45 min). El CIC cumple 25 años. Pulse aquí (51min. Habla Adolfo: "Pasado y futuro del CIC": minutos 13.57 a 22.70 ).
Perfil en ResearchGate -- Adolfo Guzman-Arenas My URL in Google Scholar: http://scholar.google.com/citations?user=Nw5lSdEAAAAJ My ORCID number 0000-0002-8236-0469. Scopus Author ID 6602302516.

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Climate patterns of political division units obtained using automatic classification trees

Article published in ATMÓSFERA. Authors: Sergio R. Coria, Carlos Gay-García, Lourdes Villers-Ruiz, Adolfo Guzmán-Arenas, Óscar Sánchez-Meneses, Oswaldo R. Ávila-Barrón, Mónica Pérez-Meza, Xóchitl Cruz-Núñez, Gilberto Lorenzo Martínez-Luna. Full text: click here.

|Abstract. This article proposes a methodology to discover patterns in observed climatologic data, particularly temperatures and rainfall, in subnational political division units using an automatic classification algorithm (a decision tree produced by the C4.5 algorithm). Thus, the patterns represent classification trees, assuming that: (1) every political division unit contains at least one climatological station, and (2) the recording periods of the stations are relatively similar in duration and in their initial and ending years. A series of classification models are produced by using different subsets from an experimental dataset. This dataset contains information from 3606 climatological stations in Mexico with recording periods whose durations, initial and ending years are diverse. The target (dependent) variable in all these models is the name of the political unit (i.e., the state). The predictors are 36 monthly features per each climatological station: 12 features corresponding to a minimum temperature, 12 to a maximum temperature, and 12 to cumulative rainfall. The altitude feature is also used as one of the predictors, in addition to the other 36; however, it is used only to quantify its additional contribution to the modelling. The results show that classification trees are effective models for describing and representing non-trivial patterns to characterize the political division units based on their monthly temperatures and rainfalls. One of the remarkable findings is that the cumulative rainfall of May is the feature with highest discrimination capability to the characterization task, which is consistent with the theoretical background on Mexican climatology. In addition, classification trees offer higher expressivity to non-experts in machine learning.

Atmósfera, vol. 29, issue 4, October-December 2016, is a quarterly journal published by the Universidad Nacional Autónoma de México.

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