Methodology for processing time series using machine learning

There are currently countless applications that can be cited in different areas of research and industry, where the data are represented in the form of time series. In the last few years, a dramatic explosion in the amount of time series ha occurred, so their analysis plays a very important role, since it permits to understand the phenomena described. A "time series" is a set of data of a certain phenomenon or equation, sequentially recorded. An alternative that allows to know the behavior and dynamics of a set of time series has been presented in the problem of classification, however, it is necessary to mention that most of the phenomena found in real life do not have a classification and that is why the unsupervised classification has brought great interest. Classification is organizing and categorizing objects into different, unlabeled classes or groups, which must be coherent or homogeneous [1][2]. This research proposes a methodology for obtaining the unsupervised classification of a set of time series using an unsupervised approach

Inspection process for dimensioning through images and fuzzy logic

This paper presents a hybrid methodology based on a type 1 fuzzy model in singleton version using a 2k factorial design that optimizes the model of the expert system and serves to perform in-line inspection. The factorial design method provides the required database for the creation of the rule base for the fuzzy model and also generates the database to train the expert system. The proposed method was validated in the process of verifying dimensional parameters by means of images compared with the ANFIS and RBFN models which show greater margins of error in the approximation of the function represented by the system compared with the proposed model. The results obtained show that the model has an excellent performance in the prediction and quality control of the industrial process studied when compared with similar expert system techniques as ANFIS and RBFN

Segmentation process and spectral characteristics in the determination of musical genres

Over the past few years there has been a tendency to store audio tracks for later use on CD-DVDs, HDD-SSDs as well as on the internet, which makes it challenging to classify the information either online or offline. For this purpose, the audio tracks must be tagged. Tags are said to be texts based on the semantic information of the sound [1]. Thus, music analysis can be done in several ways [2] since music is identified by its genre, artist, instruments and structure, by a tagging system that can be manual or automatic. The manual tagging allows the visualization of the behavior of an audio track either in time domain or in frequency domain as in the spectrogram, making it possible to classify the songs without listening to them. However, this process is very time consuming and labor intensive, including health problems [3] which shows that "the volume, sound sensitivity, time and cost required for a manual labeling process is generally prohibitive. Three fundamental steps are required to carry out automatic labelling: pre-processing, feature extraction and classification [4]. The present study developed an algorithm for performing automatic classification of music genres using a segmentation process employing spectral characteristics such as centroid (SC), flatness (SF) and spread (SS), as well as a time spectral characteristic

Low-Cost information transfer system between vehicles on roads

The authentication process is a key component to increase security in a vehicle network. Most of the authentication protocols proposed in the literature are based on asymmetric cryptography, and specifically on the use of the RSA algorithm. In addition, the use of digital certificates and a public key infrastructure is considered. Therefore, the authentication process is often complex. In order to propose a secure solution, without the use of digital certificates and the RSA algorithm, a mutual authentication protocol based on the Diffie-Hellman algorithm is presented to establish a session key between vehicle (OBU) and road unit (RSU). From the session key, a secure communication channel can be established to transmit the identifier of each participant and the respective security parameters. To perform the authentication process, the entities perform low-cost computational operations such as hash and XOR functions. Once the mutual authentication protocol is completed, the vehicle and the road unit can exchange messages securely

Analysis of crowd behavior through pattern virtualization

The study of the concentration of individuals in public places such as squares, shopping malls, parks, gardens, etc., is an open study field in the different disciplines of science, that leads to the need of having systems that allow to forecast and to predict eventualities in uncontrolled situations, as it is the case of an earthquake. From that assumption, artificial intelligence, as a branch of computational sciences, studies the human behavior in a virtual way in order to obtain simulations based on social, psychological, neuro-scientific areas, among others, with the purpose of linking these theories to the area of artificial intelligence. This paper presents a way to generate virtual multitudes with heterogeneous behaviors, in such a way that the individuals that form the multitude present different behaviors

Classification of authors for an automatic recommendation process for criminal responsibility

One problem in classifying tasks is the handling of features that characterize classes. When the list of features is long, a noise resistant algorithm of irrelevant features can be used, or these features can be reduced. Authorship attribution is a task that assigns an anonymous text to a subject on a list of possible authors, has been widely addressed as an automatic text classification task. In it, n-grams can produce long lists of features even in small corpora. Despite this, there is a lack of research exposing the effects of using noise-resistant algorithms, reducing traits, or combining both options. This paper responds to this lack by using contributions to discussion forums related to organized crime. The results show that the classifiers evaluated, in general, benefit from feature reduction, and that, thanks to such reduction, even classical algorithms outperform state-of-the-art classifiers considered highly noise resistant

Improvements for determining the number of clusters in k-means for innovation databases in SMEs

The Automatic Clustering using Differential Evolution (ACDE) is one of the grouping methods capable of automatically determining the number of the cluster. However, ACDE continues making use of the strategy manual to determine the activation threshold of k, which affects its performance. In this study, the problem of ACDE is enhanced using the U Control Chart (UCC). The performance of the proposed method was tested using five data sets from the National Administrative Department of Statistics (DANE - Departamento Administrativo Nacional de Estadísticas) and the Ministry of Commerce, Industry, and Tourism of Colombia for the innovative capacity of Small and Medium-sized Enterprises (SMEs) and were assessed by the Davies Bouldin Index (DBI) and the Cosine Similarity (CS) measure. The results show that the proposed method yields excellent performance compared to prior researches for most datasets with optimal cluster number yet lowest DBI and CS measure. It can be concluded that the UCC method is able to determine k activation threshold in ACDE that caused effective determination of the cluster number for k-means clustering

Information security in WSN applied to smart metering networks based on cryptographic techniques

The principle assaults on a Wireless Sensor Network (WSN) essentially influence the uprightness and accessibility of the data gathered, for example, Deni-al of Service, Blackhole, Wormhole, and assault on the data being transmitted. Privacy is not an important security objective because the data caught by the sensors are typically not delicate or mystery from individuals. A remote sensor organizes applied to shrewd metering frameworks might be adequately powerful as far as robotization and adjustment of the information that is gathered, however, if the system doesn’t have satisfactory security, both the client and the organization offering the support might be influenced by assaults on the respectability and accessibility of the data transmitted. This research proposes the use of MESH encryption techniques and Star topology to find the best combination that meets the requirements of a Smart Metering System

Design and implementation of a system to determine tax evasion through de stochastic techniques

The dominant view on the study and practice of the tax service, mentions that taxpayers will always seek their own interest, for that reason taxes will be considered costs that could be avoided, unless the probability of being detected is high and the severity of the penalty is not an attractive option. This means that taxpayers would be motivated to develop tax evasion strategies that would increase their economic benefits. In this regard, the following two types of intentional tax evasion procedures are identified: one, where illegal practices are employed, such as failing to file a tax return without any legal justification to do so, and, another, where legal procedures are used to avoid filing a tax return. This paper establishes a methodology based on stochastic techniques, with the objective of creating a model that allows the identification of possible tax evaders of the value added tax (VAT) in Colombia

Factors that describe the use of digital devices in Latin American universities

Mobile digital devices are at the same time a tool for social interaction, an individual learning resource and can be a valuable contribution in the context of higher education to develop and promote new teaching and learning models. Recent studies show that both the more traditional pedagogical models of face-to-face teaching and distance teaching mediated by Virtual Learning Environments (VLE) can be enhanced by the use of these devices on and off campus. Likewise, the current context of Higher Education urges university institutions to promote a series of generic and specific competencies, where the use of these devices in a personal, academic and professional way acquires an outstanding value in the European Higher Education Area (EHEA), and represents an enrichment of university educational practice. This paper presents a study of the didactic and social use made by Hispanic American university students in 10 universities in several areas in order to establish common and divergent patterns of use so that useful conclusions can be extrapolated to improve the educational context of Higher Education in the Hispanic world