M. G. Cortina-Januchs

Feature selection using Sequential Forward Selection and classification applying Artificial Metaplasticity Neural Network

The feature selection has been widely used to reduce the data dimensionality. Data reduction improve the classification performance, the approximation function, and pattern recognition systems in terms of speed, accuracy and simplicity. A strategy to reduce the number of features in local search are the sequential search algorithms. In this work is presented a feature selection method based on Sequential Forward Selection (SFS) and Feed Forward Neural Network (FFNN) to estimate the prediction error as a selection criterion. Three well-known database have been used to test the SFS-FFNN with Artificial Metaplasticity on Perceptron Multilayer (AMMLP). The AMMLP is a new method applied for classification of patterns. The results obtained by SFS-FFNN with AMMLP in classification accuracy are superior than obtained by conventional BP algorithm and other recent feature selection algorithms applied to the same database. By these reasons the proposed method SFS-FFNN with AMMLP is an interesting alternative to reduce the data dimensionality and provide a high accuracy.

Edge detection using ant colony search algorithm and multiscale contrast enhancement

In this paper, Ant Colony System (ACS) algorithm is applied for edge detection in grayscale images. The novelty of the proposed method is to extract a set of images from the original grayscale image using Multiscale Adaptive Gain for image contrast enhancement and then apply the ACS algorithm to detect the edges on each of the extracted images. The resulting set of images represents the pheromone trails matrices which are summed to produce the output image. The image contrast enhancement makes ACS algorithm more effective when accumulating pheromone trails on the true edge pixels. The results of the experiments are presented to confirm the effectiveness of the proposed method.

Air Pollutant Level Estimation Applying a Self-organizing Neural Network

This paper presents a novel Neural Network application in order to estimate Air Pollutant Levels. The application considers both Pollutant concentrations and Meteorological variables. In order to compute the Air Pollutant Level the method considers three important stages. In first stage, A process to validate data information and built a threedimensional Information Feature Vector with Pollutant concentrations and both wind speed and wind direction meteorological variables is developed. The information Feature Vector is orderly like a time series to estimate the Air Pollutant Level. In second stage, considering the behavior space knowledge a priori about pollutant and meteorological variables distribution a threedimensional Representative Vector is built in order to reduces the computational cost in Neural Network training process. In last stage, a Neural Network is designed and trained with the Threedimensional Representative Vector, then using the Threedimensional Information Feature Vector the Air Pollutant Level is estimated. This paper considers a real time series from an Automatic Environmental Monitoring Network from Salamanca, Guanajuato, Mexico, and therefore in this proposal a real Air Pollutant Level is also estimated.

Improvement for detection of microcalcifications through clustering algorithms and artificial neural networks

A new method for detecting microcalcifications in regions of interest (ROIs) extracted from digitized mammograms is proposed. The top-hat transform is a technique based on mathematical morphology operations and, in this paper, is used to perform contrast enhancement of the mi-crocalcifications. To improve microcalcification detection, a novel image sub-segmentation approach based on the possibilistic fuzzy c-means algorithm is used. From the original ROIs, window-based features, such as the mean and standard deviation, were extracted; these features were used as an input vector in a classifier. The classifier is based on an artificial neural network to identify patterns belonging to microcalcifications and healthy tissue. Our results show that the proposed method is a good alternative for automatically detecting microcalcifications, because this stage is an important part of early breast cancer detection.

Air pollution analysis with a PFCM clustering algorithm applied in a real database of Salamanca (Mexico)

Over the last ten years, Salamanca has been considered among the most polluted cities in México. Nowadays, there is an Automatic Environmental Monitoring Network (AEMN) which measures air pollutants (Sulphur Dioxide (SO2), Particular Matter (PM10), Ozone (O3), etc.), as well as environmental variables (wind speed, wind direction, temperature, and relative humidity), and it takes a sample of the variables every minute. The AEM Network is mainly based on three monitoring stations located at Cruz Roja, DIF, and Nativitas. In this work, we use the PFCM (Possibilistic Fuzzy c Means) clustering algorithm as a mean to get a combined measure, from the three stations, looking to provide a tool for better management of contingencies in the city, such that local or general action can be taken in the city according to the pollution level given by each station and the combined measure. Besides, we also performed an analysis of correlation between pollution and environmental variables. The results show a significative correlation between pollutant concentrations and some environmental variables. So, the combined measure and the correlations can be used for the establishment of general contingency thresholds.

Combination of nonlinear filters and ANN for detection of microcalcifications in digitized mammography

Breast cancer is one of the leading causes to women mortality in the world. Cluster of Microcalcifications (MCCs) in mammograms can be an important early sign of breast cancer, the detection is important to prevent and treat the disease. In this paper, we present a novel method for the detection of MCCs in mammograms which consists of image enhancement by histogram adaptive equalization technique, MCCs edge detection by coordinate logic filters (CLF), generation, clustering and labelling of suboptimal features vectors by self organizing map (SOM) neural network. The experiment results show that the proposed method can locate MCCs in an efficient way.

Data fusion and neural network combination method for air pollution level monitoring

Over the last ten years, Salamanca has been considered among the most polluted cities in México, with the most important air pollutants being SO2 and PM10. Currently, in Salamanca, an Environmental Monitoring Network (EMN) is installed in which time series of criteria pollutants and meteorological variables are obtained. Unfortunately air pollution level is computed in each monitoring station without taking into account those meteorological variables. In this paper, we propose a novel methodology to compute air pollution levels taking the meteorological variables as a decision factor by means of data fusion and neural networks. First, in preprocessing stage two Feature Vectors (FVSO2 and FVPM10 ) are built for each monitoring station. Next, in data fusion stage, a Representative Feature Vector by pollutant (RFVSO2 and RFVPM10 ) is built with the maximum value of the three FVs. Finally, an Artificial Neural Network (ANN) is trained with the RFV in order to classify future environmental situations. Self-Organizing Map (SOM) is the ANN applied. In this paper, time series of pollutant concentrations and meteorological variables are obtained from the EMN. EMN is composed for the three monitoring stations in Salamanca. Data used in this study have approved according to Proaire environmental authority standards.

Pollution Alarm System in Mexico

Air pollution is one of the most important environmental problems. The prediction of air pollutant concentrations would allow taking preventive measures such as reducing the pollutant emission to the atmosphere. This paper presents a pollution alarm system used to predict the air pollution concentrations in Salamanca, Mexico. The work focuses on the daily maximum concentration of PM 10 . A Feed Forward Neural Network has been used to make the prediction. A database used to train the Neural Network corresponds to historical time series of meteorological variables (wind speed, wind direction, temperature and relative humidity) and air pollutant concentrations of PM 10 along a year. Our experiments with the proposed system show the importance of this set of meteorological variables on the prediction of PM 10 pollutant concentrations and the neural network efficiency. The performance estimation is determined using the Root Mean Square Error (RMSE) and Mean Absolute Error (MAE).

Sustainable agriculture using an intelligent mechatronic system

The goal of the Project group created by U.P.M. in collaboration with foreign universities, research institutions and companies is the development of an intelligent mechatronic system for the use of precision and sustainable agriculture. The project as a whole includes the following components: photographing and decoding of the soil surface; fertility determination and formation of the fertility map; generation of the controlling signal for mechatronic dosing device; intelligent dosing of fertilizers; simulation, prototype and testing; human-machine interaction and training preparation.

Detection of Microcalcifications Using Coordinate Logic Filters and Artificial Neural Networks

Breast cancer is one of the leading causes to women mortality in the world. Cluster of Microcalcifications (MCC) in mammograms can be an important early sign of breast cancer, the detection is important to prevent and treat the disease. In this paper, we present a novel method for the detection of MCC in mammograms which consists of image enhancement by histogram adaptive equalization technique, MCC edge detection by Coordinate Logic Filters (CLF), generation, clustering and labelling of suboptimal features vectors by means of Self Organizing Map (SOM) Neural Network. Like comparison we applied an unsupervised clustering K-means in the stage of labelling of our method. In the labelling stage, we obtain better results with the proposed SOM Neural Network compared with the k-means algorithm. Then, we show that the proposed method can locate MCCs in an efficient way.