Multispectral image analysis for the detection of diseases in coffee production

Coffee is produced in Latin America, Africa and Asia, and is one of the most traded agricultural products in international markets. The coffee agribusiness has been diversified all over the world and constitutes an important source of employment, income and foreign exchange in many producing countries. In recent years, its global supply has been affected by adverse weather factors and pests such as rust, which has been reflected in a highly volatile international market for this product [1]. This paper shows a method for the detection of coffee crops and the presence of pests and diseases in the production of these crops, using multispectral images from the Landsat 8 satellite

CTR prediction of internet ads using artificial organic networks

For advertising networks to increase their revenues, priority must be given to the most profitable ads. The most important factor in the profitability of an ad is the click-through-rate (CTR) which is the probability that a user will click on the ad on a Web page. To predict the CTR, a number of supervised rating models have been trained and their performance is compared to artificial organic networks (AON). The conclusion is that these networks are a good solution to predict the CTR of an ad

Vanet heterogeneous networks with wireless technology variation according to the capacity of users in urban areas

As of late, traffic blockage, street mishaps, and ecological contamination brought about by traffic, alongside the need to associate and utilize constant applications, have become issues of worldwide intrigue. Different on-screen characters, for example, vehicle producers, the scholarly community, and government offices have begun to invest a ton of energy together towards the acknowledgment of the idea of huge scope vehicular interchanges. One of the primary methodologies in this kind of system is the advancement of remote advances and their assorted organizations, concentrating on the association with the Internet through WiFi systems, cell systems, or specially appointed vehicular systems. VANETs are essentially intended to give data trade through Vehicle to Vehicle (V2V) and Vehicle to foundation (V2I) interchanges, permitting ceaseless network and being exceptionally utilized for short-range correspondence, with high transmission speed through which it is proposed that clients keep up an association and distinguish occasions about clog or street conditions. This exploration presents a vehicular situation that tries to acquire a sufficient presentation while executing a heterogeneous network in a few segments of the city of Bogotá, Colombia

Comparative analysis between different automatic learning environments for sentiment analysis

Sentiment Analysis is a branch of Natural Language Processing in which an emotion is identified through a sentence, phrase or written expression on the Internet, allowing the monitoring of opinions on different topics discussed on the Web. The study discussed in this paper analyzed phrases or sentences written in Spanish and English expressing opinions about the service of Restaurants and opinions written in the English language about Laptops. Experiments were carried out using 3 automatic classifiers: Support Vector Machine (SVM), Naïve Bayes and Multinomial Naïve Bayes, each one being tested with the three data sets in the Weka automatic learning software and in Python, in order to make a comparison of results between these two tool

Web platform for the identification and analysis of events on twitter

Due to the great popularity of social networks among people, businesses, public figures, etc., there is a need for automatic methods to facilitate the search, retrieval, and analysis of large amounts of information. Given this situation, the Online Reputation Analyst (ORA) faces the challenge of identifying relevant issues around an event, product and/or public figure, from which it can propose different strategies to strengthen and/or reverse trends. Therefore, this paper proposes and describes a web tool whose main objective is to support the tasks performed by an ORA. The proposed visualization techniques make it possible to immediately identify the relevance and scope of the opinions generated about an event that took place on Twitter

Bayesian networks applied to climate conditions inside a naturally ventilated greenhouse

The prediction of gradients in a naturally ventilated greenhouse is difficult to achieve, due to the inherently stochastic nature of the airflow. Bayesian networks are numerical uncertainty techniques that can be used to study this problem. A set of experimental data was obtained: air temperature, air humidity, wind speed, and CO2 concentration at one and three meters above the ground in the growing space. The data set was discretized and used to develop a Bayesian Network model that describes the relationships between the studied variables. The model shows the differences that allow to identify the degree of dependence of the variables, as well as to quantify their inference

Method for the recovery of indexed images in databases from visual content

The techniques of content-based image recovery (CBIR) provide a solution to a problem of information retrieval that may arise as follows: from an image of interest to recover or obtain similar images from among those present in a large collection, using only features or features extracted from said images Banuchitra and Kungumaraj (Int J Eng Comput Sci (IJECS) 5 (2016) [1]). Similar images are understood as those in which the same object or scene is observed with variations in perspective, lighting conditions or scale. The stored images are preprocessed and then their corresponding descriptors are indexed. The query image is also preprocessed to extract its descriptor, which is then compared to those stored by applying appropriate similarity measures, which allow the recovery of those images that are similar to the query image. In the present work, a method was developed for the recovery of indexed images in databases from their visual content, without the need to make textual annotations. Feature vectors were obtained from visual contents using artificial neural network techniques with deep learning

Deep learning of robust representations for multi-instance and multi-label image classification

In multi-instance problems (MIL), an arbitrary number of instances is associated with a class label. Therefore, the labeling of training data becomes simpler (since it is done together, instead of individually) with the disadvantage that a weakly supervised database is produced [9]. In the PCRY, each restaurant is represented by a set of images that share the attribute label(s) of that establishment. This paper explores the use of previously learned attribute extractors, trained in 3 different databases that are similar and complementary to the PCRY databas

Study of the principal component analysis in air quality databases

Technological development has facilitated daily habits, business, the manufacture of large quantities of products, among other types of industrial activities; however, these advances have caused environmental deterioration that seriously threatens the development of society. The increase of greenhouse gases in the atmosphere affects the health of millions of people and is the main factor that has modified the climate on planet Earth. Faced with this situation, it is necessary to carry out actions that allow to quickly adapt to this change and mitigate its effects. The present study proposes the analysis of main components in the data of the pollutant measurements in the city of Bogota, Colombia with the purpose of obtaining a more compact representation of these data, to later apply grouping techniques and obtain factors that allow the emission of an alert for pre-contingency and contingency

Classification of mitochondrial network images associated with the study of breast cancer

Within various cellular processes, an increase in fission (a division of a single organelle into two or more independent structures) causes mitochondrial fragmentation and an increase in fusion (the opposite reaction of fission) produces a network of mitochondria that counteracts metabolic processes [1]. A balance between fission and fusion defines a mitochondrial morphology whose purpose is to meet metabolic demands and ensure removal of damaged organelles. These events have been associated with proliferation and redistribution of mitochondria, allowing the study of different breast cancer subtypes [2, 3]. This study presents a classification method for images of mitochondrial networks extracted from different cellular lines (MCF10A, BT549, MDAMB23, and CMF) belonging to different breast cancer subtypes