Remote sensing imaging datasets for classification generally present high levels of imbalance between classes of interest. This work presented a study of a set of performance evaluation metrics for an imbalance dataset. In this work, a support vector machine (SVM) was used to perform the classification of seven classes of interest in a popular dataset called Salinas-A. The performance evaluation of the classifier was performed using two types of metrics: 1) Metrics for multi-class classification, and 2) Metrics based on the binary confusion matrix. In the results, a comparison of the scores of each metric is developed, some being more optimistic than others due to the bias that they present given the imbalance. In addition, our case study helps to conclude that the Matthews correlation coefficient (MCC) presents the lowest bias in imbalanced cases and is regarded to be robust metric. These results can be extended to any imbalanced dataset taking into account the equations developed by Luque.

The increase in the number of COVID-19 cases in Mexico has caused an important effort in the analysis of the virus, due to the above, a statistical study of laboratory data of 109 patients from the State Center for Critical Care of Salamanca (CECCS) with or without COVID-19, whose purpose is to analyze in more detail the disease and the reactions in patients. First, a classification was made in men and women, then positive, negative, healthy and deceased patients. It is important to mention that a data cleaning was carried out, in addition to information filling using the mean of the laboratory values, then the clinical data were normalized from 0 to 1, to obtain the BMI of the patients, box plots and graphs days to days of hospitalization with a sample of patients.

The evolution of the SARS-CoV-2 Virus in recent months in the world and especially in Mexico has also created changes in the patient’s treatment and their laboratory studies, as is the case of patients admitted to the CECCS in Salamanca, Guanajuato, in which after months of caring for COVID-19 patients, new relevant laboratory studies were added (Ferritin and D-Dimer), therefore updates were made in statistical analyzes, and day-to-day behavior. The study is based on updating and cleaning the database, taking a sample of more patients with COVID-19, statistical analysis including admission tables, Body Mass Index, Variance, Normalization, relationship between the first day and the last day of patients who died and lived. The main idea of the work is to learn with more detail those laboratories that contain information to study in the future if there is a relationship between any medication supplied with the improvement or aggravation of the patient.

Suicide is today one of the leading causes of death in the world. People at risk of suicide commonly leave messages around them, and that often includes social media. Therefore, early detection of suicidal risk is a fundamental prevention factor. At present, the search for suicide risk based on text analysis does not consider fundamental factors such as the analysis based on suicide notes. This research proposes a new text analysis for the detection of suicidal risk based on the study of suicide notes.

This paper shows the obtained results of applying image enhancement techniques in regions of interest extracted of mamographies. The objective is to improve the detection of breast lesions, such as the microcalcifications and thus contribute to the early detection of breast cancer. Usually, the quality of the region of interest images is not better, and they have some deficiencies that difficult the microcalcifications detection. Due to this, the approach in this paper is to increase the quality of the region of interest image by means of edge and contour enhancement, modifying the contrast and noise reduction and its possible suppression of the background of the image. The techniques that were implemented are the modification of the contrast by lineal and non-lineal transformations, the histogram equalization technique, spatial filtering technique and a technique based on mathematical morphology operations. Finally, a contrast improvement index was applied in order to evaluate in a quantitative way each of the contrast enhancement technique implemented.

This paper describes the design and development of a Graphical User Interface through the virtual instrumentation software NI LabVIEW using the VISA function, to graphically visualize and storage the data of the climatological variables of temperature and relative humidity. The graphical interface offers the option to export the date, time and data of the two variables to text documents with extension “.txt”, which acquires the information of the electronic board wireless monitoring and control, which uses a main device PIC16F877A microcontroller. AMT1001 Precision Analog Sensor was used to sense temperature and relative humidity. The PIC16F877A was programmed using a C programming language in the CCS Compiler compiler, to the data acquisition, and send it via RS232 communication to the computer, using the PL2303 module USB to TTL converter. To check the GUI operation, the electronic wireless monitoring and control card was connected to the computer equipment by wire, however, the monitoring of the climate variables can be done wirelessly by XBEE technology. Future work aims to monitor the climate of a horticultural greenhouse with XBBE technology, so that the data is sent wirelessly to a computer that has the GUI, and is also connected to Ethernet or WIFI, which will have the LabVIEW graphical interface explained in this article, and the data will be displayed / analyzed through the internet.