Tecnologico de Estudios Superiores de Jocotitlan

Jocotitlán, Mexico

Tecnologico de Estudios Superiores de Jocotitlan

Jocotitlán, Mexico

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MoraRamirez T.D.L.,Tecnologico de Estudios Superiores de Jocotitlan | Donu Ruiz M.A.,Polytechnic University of the Valley of Mexico | Lopez Perrusquia N.,Polytechnic University of the Valley of Mexico | Sanchez-Huerta D.,Metropolitan Autonomous University | Cortez Juarez V.J.,Metropolitan Autonomous University
Defect and Diffusion Forum | Year: 2016

One of the most used in the field of medicine for the treatment of tibial shaft fractures internal fixation methods is by osteosynthesis plates, the most common plate limited contact dynamic compression (DCP-LC) [1]. This paper presents the results of the fracture site grade 1, where the contact plate and the bone callus area on plates made of bone (LVM stainless steel, titanium alloy different biomedical materials and cobalt alloy), in recovery conditions 1% (one week of recovery), 50% (three weeks of recovery), 75% (six weeks of recovery) and 100%. The fractured tibia bone was modeled with an ideal geometry in CAD [2], modeling of commercial DCP-LC plate was obtained by parameterization of the part using a coordinate machine equipment for the exact geometry. The finite element method for the analysis of each case under the same loads and boundary conditions is used, the results were used to determine stress concentrations in the displacement plate and the fracture callus in the load direction, to have a starting point in the optimization of the geometry from a commercial plate minimizing mass while determining that the material has faster and better biocompatibility with the human body recovery. The results obtained show that the plate under the conditions of three different types of biomaterials has a greater stress concentration in the part located in the fracture zone from stage with 1% recovery between the surfaces of the bone callus upper and lower, keeping this a significant effect on the recovery of the fracture. The compression and tension strength that occur in the intact part of the bone and the tibial fracture interface at different stages of osseous healing have been investigated, The results were compared and presented, showing that the stress distribution in the callus to 1% recovery in the stainless steel plate indicate considerable compression in the area of the callus with this causing deterioration in the area fracture because the callus is not strengthened by contact between fractured bone by increasing the recovery time, the results also indicate that the titanium plate is the one with the lower shielding effect [3] according to the distribution of contact stresses according to the recovery period in the part of callus, making it the material of which the best adaptability to the bone is obtained. © 2016 Trans Tech Publications, Switzerland.


Alejo R.,Tecnologico de Estudios Superiores de Jocotitlan | Monroy-de-Jesus J.,National Autonomous University of Mexico | Ambriz-Polo J.C.,Tecnologico de Estudios Superiores de Jocotitlan | Pacheco-Sanchez J.H.,Toluca Institute of Technology
Neural Computing and Applications | Year: 2017

In this paper, we present an improved dynamic sampling approach (I-SDSA) for facing the multi-class imbalance problem. I-SDSA is a modification of the back-propagation algorithm, which is focused to make a better use of the training samples for improving the classification performance of the multilayer perceptron (MLP). I-SDSA uses the mean square error and a Gaussian function to identify the best samples to train the neural network. Results shown in this article stand out that I-SDSA makes better exploitation of the training dataset and improves the MLP classification performance. In others words, I-SDSA is a successful technique for dealing with the multi-class imbalance problem. In addition, results presented in this work indicate that the proposed method is very competitive in terms of classification performance with respect to classical over-sampling methods (also, combined with well-known features selection methods) and other dynamic sampling approaches, even in training time and size it is better than the over-sampling methods. © 2017 The Natural Computing Applications Forum


Alejo R.,Tecnologico de Estudios Superiores de Jocotitlan | Sotoca J.M.,Jaume I University | Garcia V.,Jaume I University | Valdovinos R.M.,Valle de México University
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2011

The class imbalance problem has been considered a critical factor for designing and constructing the supervised classifiers. In the case of artificial neural networks, this complexity negatively affects the generalization process on under-represented classes. However, it has also been observed that the decrease in the performance attainable of standard learners is not directly caused by the class imbalance, but is also related with other difficulties, such as overlapping. In this work, a new empirical study for handling class overlap and class imbalance on multi-class problem is described. In order to solve this problem, we propose the joint use of editing techniques and a modified MSE cost function for MLP. This analysis was made on a remote sensing data . The experimental results demonstrate the consistency and validity of the combined strategy here proposed. © 2011 Springer-Verlag.


Alejo R.,Tecnologico de Estudios Superiores de Jocotitlan | Garcia V.,Jaume I University | Marques A.I.,Jaume I University | Sanchez J.S.,Jaume I University | Antonio-Velazquez J.A.,Tecnologico de Estudios Superiores de Jocotitlan
Advances in Intelligent Systems and Computing | Year: 2013

In practical applications to credit risk evaluation, most prediction models often make inaccurate decisions because of the lack of sufficient default data. The challenging issue of highly skewed class distribution between defaulter and nondefaulters is here faced by means of an algorithmic solution based on cost-sensitive learning. The present study is conducted on the popular Multilayer Perceptron neural network using three misclassification cost functions, which are incorporated into the training process. The experimental results on real-life credit data sets show that the proposed cost functions to train such a neural network are quite effective to improve the prediction of examples belonging to the defaulter (minority) class. © Springer International Publishing Switzerland 2013.


Alejo R.,Tecnologico de Estudios Superiores de Jocotitlan | Valdovinos R.M.,Valle de México University | Garcia V.,Jaume I University | Pacheco-Sanchez J.H.,Toluca Institute of Technology
Pattern Recognition Letters | Year: 2013

Class imbalance and class overlap are two of the major problems in data mining and machine learning. Several studies have shown that these data complexities may affect the performance or behavior of artificial neural networks. Strategies proposed to face with both challenges have been separately applied. In this paper, we introduce a hybrid method for handling both class imbalance and class overlap simultaneously in multi-class learning problems. Experimental results on five remote sensing data show that the combined approach is a promising method. © 2012 Elsevier B.V. All rights reserved.


Alejo R.,Tecnologico de Estudios Superiores de Jocotitlan | Garcia V.,Autonomous University of Ciudad Juárez | Pacheco-Sanchez J.H.,Toluca Institute of Technology
Neural Processing Letters | Year: 2015

In this paper a new dynamic over-sampling method is proposed, it is a hybrid method that combines a well known over-sampling technique (SMOTE) with the sequential back-propagation algorithm. The method is based on the back-propagation mean square error (MSE) for automatically identifying the over-sampling rate, i.e., it allows only the use of necessary training samples for dealing with the class imbalance problem and avoiding to increase excessively the (neural networks) NN training time. The main aim of the proposed method is to obtain a trade-off between NN classification performance and NN training time on scenarios where the training data set represents a multi-class classification problem, it is high imbalanced and it might request a large NN training time. Experimental results on fifteen multi-class imbalanced data sets show that the proposed method is promising. © 2014, Springer Science+Business Media New York.


Fuente-Arriaga J.A.D.L.,Tecnologico de Estudios Superiores de Jocotitlan | Felipe-Riveron E.M.,National Polytechnic Institute of Mexico | Garduno-Calderon E.,Centro Oftalmologico Of Atlacomulco
Computers in Biology and Medicine | Year: 2014

This paper presents a methodology for glaucoma detection based on measuring displacements of blood vessels within the optic disc (vascular bundle) in human retinal images. The method consists of segmenting the region of the vascular bundle in an optic disc to set a reference point in the temporal side of the cup, determining the position of the centroids of the superior, inferior, and nasal vascular bundle segmented zones located within the segmented region, and calculating the displacement from normal position using the chessboard distance metric. The method was successful in 62 images out of 67, achieving 93.02% sensitivity, 91.66% specificity, and 91.34% global accuracy in pre-diagnosis. © 2014 Elsevier Ltd.


De La Fuente-Arriaga J.A.,Tecnologico de Estudios Superiores de Jocotitlan | Felipe-Riveron E.M.,National Polytechnic Institute of Mexico | Garduno-Calderon E.,Centro Oftalmologico Of Atlacomulco
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2013

This work presents a methodology for detecting human retina images suspect of glaucoma based on the measurement of displacement of the vascular bundle caused by the growth of the excavation or cup. The results achieved are due to the relative increase in size of the cup or excavation that causes a displacement of the blood vessel bundle to the superior, inferior and nasal optic disc areas. The method consists of the segmentation of the optic disc contour and the vascular bundle located within it, and calculation of its displacement from its normal position using the chessboard metric. The method was successful in 62 images of a total of 67, achieving an accuracy of 93.02% of sensitivity and 91.66% of specificity in the pre-diagnosis. © Springer-Verlag 2013.


Alejo R.,Tecnologico de Estudios Superiores de Jocotitlan | Antonio J.A.,Tecnologico de Estudios Superiores de Jocotitlan | Valdovinos R.M.,Valle de México University | Pacheco-Sanchez J.H.,Toluca Institute of Technology
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2013

In this paper we study some of the most common global measures employed to measure the classifier performance on the multi-class imbalanced problems. The aim of this work consists of showing the relationship between global classifier performance (measure by global measures) and partial classifier performance, i.e., to determine if the results of global metrics match with the improved classifier performance over the minority classes. We have used five strategies to deal with the class imbalance problem over five real multi-class datasets on neural networks context. © 2013 Springer-Verlag Berlin Heidelberg.


PubMed | National Polytechnic Institute of Mexico, Centro Oftalmologico Of Atlacomulco and Tecnologico de Estudios Superiores de Jocotitlan
Type: | Journal: Computers in biology and medicine | Year: 2014

This paper presents a methodology for glaucoma detection based on measuring displacements of blood vessels within the optic disc (vascular bundle) in human retinal images. The method consists of segmenting the region of the vascular bundle in an optic disc to set a reference point in the temporal side of the cup, determining the position of the centroids of the superior, inferior, and nasal vascular bundle segmented zones located within the segmented region, and calculating the displacement from normal position using the chessboard distance metric. The method was successful in 62 images out of 67, achieving 93.02% sensitivity, 91.66% specificity, and 91.34% global accuracy in pre-diagnosis.

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