Data sciences and teaching methods—learning

Data Science (DS) is an interdisciplinary field responsible for extracting knowledge from the data. This discipline is particularly complex in the face of Big Data: large volumes of data make it difficult to store, process and analyze with standard computer science technologies. The new revolution in Data Science is already changing the way we do business, healthcare, politics, education and innovation. This article describes three different teaching and learning models for Data Science, inspired by the experiential learning paradigm

Storage, processing and distribution of information and communication: Cloud computing in Latin-American

Remote computing operations including storage, processing and distribution of information and communication began to emerge in the last decade of the last century in business and corporate environments. However, its use has spread as a fundamental service in the latest web applications as usual practice of any current Internet user. Tightly, interwoven with network communication experiences they are in turn transforming the use of computer technology, now based on parameters such as maximum mobility, greater lightness on computers, communication and concurrency in creating mini ad hoc applications, among others. This study describes some of the most important communicative features of cloud computing as well as their social uses. Among them, breaking the traditional concepts of space, time and identity constructio

Recognition of diseases and use of herbal in Latin America indigenous areas: Case Colombian

The disease is something that accompanies the human being from its existence which has sought to explain those symptomatology beyond their logic, thus, start the explanations from the plane of magic. Religious which are configured in a series of practices in which various plants and roots which are transformed into teas or foods that promote specific relief conditions are involved. Thus, various plants and roots have medicinal acquired the adjective as in Colombian popular culture have established themselves as healing. In this sense, we try through this research to identify the most common diseases in the Colombian indigenous areas. To do this, several researchers including stand will be used: Navarrete, Sanabria, Lozoya, among others methodologically the research was documentary-bibliographical as several texts were analyzed in relation to theme. They were obtained as results within the Colombian indigenous cultures have used a series of magical methods-religious and personal interaction in which the recommendation consumption of plants and roots, it is almost vital to achieving the goals of healing. The traditional practice of home gardens is one of the most widespread aspects within communities. This point is taken into account as a central objective for the management and use of plant species that can probably overcome some health problems requiring immediate ambulatory care

Sales segmentation for a mobile phone service through logistic regression algorithm

The research aims to describe the CRISP-DM method to identify optimal customer groups that are likely to migrate from a prepaid to postpaid plan in order to formulate an improvement plan in call management by sorting the database. The logistic regression model was applied to analyze the characteristics generated by the purchase of different services. In this sense, groups differentiated by their probability of sales success (migrating from a prepaid to postpaid plan) were found, as segments that reflect needs and characteristics that allow to design marketing actions focused on the objective of increasing the effectiveness, contactability, and sales

Sales segmentation for a mobile phone service throug logistic regression algorithm

The research aims to describe the CRISP-DM method to identify optimal customer groups that are likely to migrate from a prepaid to postpaid plan in order to formulate an improvement plan in call management by sorting the database. The logistic regression model was applied to analyze the characteristics generated by the purchase of different services. In this sense, groups differentiated by their probability of sales success (migrating from a prepaid to postpaid plan) were found, as segments that reflect needs and characteristics that allow to design marketing actions focused on the objective of increasing the effectiveness, contactability, and sales

Neural network configuration for pollen analysis

Palynology is a botanical discipline devoted to the study of pollen and spores [1], focusing mainly on the analysis of the external morphology that presents structural patterns different from those of the variations in the exine, which is the external wall of the pollen grains. The study and microscopic analysis of its symmetry, wall opening, contour, shape, size, etc., have a taxonomic value and allows distinguishing different taxa at different levels: family, genera, species. The study of pollen grains is a difficult task, in its different phases, from small microscopic samples. The analysis of these is an important source of information for many scientific and industrial applications, making palynology a valuable tool for various areas of knowledge [1]. In palynology, neural networks have been successfully applied for the classification of pollen grains. For this purpose, RPROP was selected as a neural network training algorithm for the classification of a previously reported dataset

Recognition of handwritten digits by image processing methods and classification models

OCR (Optical Character Recognition) is a line of research within image processing for which many techniques and methodologies have been developed. Set of pixels recognized based on the digitalized image and this study presents an iterative process that consists of five phases of the OCR. For this purpose, several image processing methods are applied, as well as two variable selection methods, and several supervised automated learning methods are explored. Among the classification models, those of deep learning stand out for their novelty and enormous potential

Energy balance in a greenhouse: temperature and humidity monitoring

Currently, the world is in a necessary stage of energy transition due to the high rates of pollutants emitted into the environment.The agricultural sector contributes only 7% of these to the environment, a figure that is not alarming but certainly intervenes in the generation of pollution [1]. Environment offers some properties that can be considered such as the radiation provided by the sun, which is used today in greenhouses ranging from small and rustic to others of large dimensions and sophisticated systems of control and monitoring. This study consists of the supervision with a data acquisition system which (temperature and relative humidity sensors), thus allowing to know which physical magnitude varies faster through time

Solving the problem of optimizing wind farm design using genetic algorithms

Renewable energies have become a topic of great interest in recent years because the natural sources used for the generation of these energies are inexhaustible and non-polluting. In fact, environmental sustainability requires a considerable reduction in the use of fossil fuels, which are highly polluting and unsustainable [1]. In addition, serious environmental pollution is threatening human health, and many public concerns have been raised [2]. As a result, many countries have proposed ambitious plans for the production of green energy, including wind power, and consequently, the market for wind energy is expanding rapidly worldwide [3]. In this research, an evolutionary metaheuristic is implemented, specifically genetic algorithms

Texture analysis in skull magnetic resonance imaging

raumatic brain injury (TBI) represents a serious public health problem worldwide. It is the most common cause of death and disability in the young population (aged 15–45 years), with major family, social and economic implications [1]. In medical terms, the human body can be studied as an object. The reconstruction of bone structures after physical damage generated by such an unfortunate event as disease or trauma can range from the implementation of prostheses to the engineering of artificial bone implants [2]. To make a virtual or physical model of any human anatomy, it must first be captured in three dimensions in a way that can be used by computational processes. Most hospital scanners capture data from the entire body both internally and externally. These machines are typically medical imaging devices capable of scanning the entire human body, among which, the most common is the magnetic resonance imaging (MRI) equipment [3]. The goal of the research is to analyze the texture in magnetic resonance imaging and its relationship to bone mineral content (BMC) using simple linear regression