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المؤلفون: Hernández, Guillermo, González-Sánchez, Carlos, González-Arrieta, Angélica, Sánchez-Brizuela, Guillermo, Fraile, Juan-Carlos

المصدر: Sensors (14248220); May2024, Vol. 24 Issue 10, p3157, 11p

مصطلحات موضوعية: CATTLE, HUMAN behavior, TIME series analysis, FORECASTING, CAREGIVERS, HUMAN activity recognition

مستخلص: Livestock monitoring is a task traditionally carried out through direct observation by experienced caretakers. By analyzing its behavior, it is possible to predict to a certain degree events that require human action, such as calving. However, this continuous monitoring is in many cases not feasible. In this work, we propose, develop and evaluate the accuracy of intelligent algorithms that operate on data obtained by low-cost sensors to determine the state of the animal in the terms used by the caregivers (grazing, ruminating, walking, etc.). The best results have been obtained using aggregations and averages of the time series with support vector classifiers and tree-based ensembles, reaching accuracies of 57% for the general behavior problem (4 classes) and 85% for the standing behavior problem (2 classes). This is a preliminary step to the realization of event-specific predictions. [ABSTRACT FROM AUTHOR]

: Copyright of Sensors (14248220) is the property of MDPI and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

المؤلفون: López-Sánchez, Daniel, González Arrieta, Angélica, Corchado, Juan M.

المصدر: In Neurocomputing 20 July 2020 398:411-421

المؤلفون: López-Sánchez, Daniel, Corchado, Juan Manuel, González Arrieta, Angélica

المصدر: In Information Sciences April 2018 436-437:214-226

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المؤلفون: Galdámez, Pedro Luis, Raveane, William, González Arrieta, Angélica

المصدر: Journal of Applied Logic; Nov2017 Part A, Vol. 24, p62-70, 9p

مصطلحات موضوعية: PATTERN recognition systems, ARTIFICIAL neural networks, DEEP learning, BIOMETRY, SOFTWARE measurement

مستخلص: The process of precisely recognize people by ears has been getting major attention in recent years. It represents an important step in the biometric research, especially as a complement to face recognition systems which have difficult in real conditions. This is due to the great variation in shapes, variable lighting conditions, and the changing profile shape which is a planar representation of a complex object. An ear recognition system involving a convolutional neural networks (CNN) is proposed to identify a person given an input image. The proposed method matches the performance of other traditional approaches when analyzed against clean photographs. However, the F1 metric of the results shows improvements in specificity of the recognition. We also present a technique for improving the speed of a CNN applied to large input images through the optimization of the sliding window approach. [ABSTRACT FROM AUTHOR]

: Copyright of Journal of Applied Logic is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)