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The objective of the research was to analyze the role of machine data learning in systematic literature reviews. The Natural Language Processing technique called topic modeling was applied to a set of titles and abstracts collected from the Scopus database. Specifically, the Latent Dirichlet Assignment (LDA) technique was used, from which it was possible to discover and understand the underlying themes in the collection of documents. The results showed the usefulness of the technique used in the exploratory literature review, by allowing the results to be grouped by theme. Likewise, it was possible to identify the specific areas and activities where machine learning has been applied the most, in relation to literature reviews. It is concluded that the LDA technique is an easy-to-use strategy and whose results allow a wide collection of documents to be approached in a systematic and coherent manner, notably reducing the review time. [ABSTRACT FROM AUTHOR]

this article examines the impact, potentials, challenges, and future prospects of using Machine Learning (ML) algorithms in clinical psychology, with a focus on basic research and clinical application. ML is reshaping both research and clinical practice by analyzing complex data and uncovering patterns for understanding mental disorders and predicting diagnoses, among other noteworthy aspects. despite the challenges of interpretation, privacy, and bias, the transformative potential of ML algorithms in clinical psychology is acknowledged. interdisciplinary collaboration is essential to maximize these benefits. emphasizing the importance of addressing ethical and practical considerations, this paper envisions a future where ML algorithms become integral tools in the research, diagnosis, and treatment of mental disorders, contributing to an overall enhancement in people's quality of life. [ABSTRACT FROM AUTHOR]

The water scarcity is a concern of the agricultural industry as it uses four fifths of the of the total fresh water consumed for irrigation and two thirds of the total used for human consumption. For this reason, the development of systems that optimize the use of water in irrigation is essential. In the greenhouses of the UEB "Valle del Yabú" of the Santa Clara municipality, irrigation is carried out using a drip-based system that requires the presence of an operator for decisionmaking who does not have information about some of the hydrometeorological variables that govern the crop. This paper focused on to design a support system for decision-making in irrigation based on machine learning. As an important parameter of the system, the evapotranspiration coefficient of the crop is calculated using the Turc formula. The collected environmental data is conditioned and linear regression, regressive random forests, and gradient-boosted trees regression models are trained with them to determine future evapotranspiration values using the Apache Spark framework. The model that obtained the best results was the regressive random forest with a coefficient of determination (r2) of 0,79 and with it the volume of water lost by the crop is calculated. Finally, the system was able to provide the estimates of both variables, which favour decision-making by specialists. [ABSTRACT FROM AUTHOR]

Social media have established a new way of communicating and understanding social relationships. At the same time, there are downsides, especially, their use of algorithms that have been built and developed under their umbrella and their potential to alter public opinion. This paper tries to analyse the YouTube recommendation system from the perspectives of reverse engineering and semantic mining. The first result is that, contrary to expectations, the issues do not tend to be extreme from the point of view of polarisation in all cases. Next, and through the study of the selected themes, the results do not offer a clear answer to the proposed hypotheses, since, as has been shown in similar works, the factors that shape the recommendation system are very diverse. In fact, results show that polarising content does not behave in the same way for all the topics analysed, which may indicate the existence of moderators –or corporate actions– that alter the relationship between the variables. Another contribution is the confirmation that we are dealing with non-linear, but potentially systematic, processes. Nevertheless, the present work opens the door to further academic research on the topic to clarify the unknowns about the role of these algorithms in our societies. [ABSTRACT FROM AUTHOR]

In this paper three prime numbers are presented as high potentials to be Mersenne numbers and their application in computational primality testing is suggested. These numbers are constructed from a regression algorithm based on Support vector machines (SVM) and using a Gaussian Kernel. Data training is carried out using the Phyton programming language, In the study we address the current data of Mersenne primes and work with the Ova-angular classification group for Mersenne primes 31. [ABSTRACT FROM AUTHOR]

This paper presents a document classification service that allows multiple client (multi-tenant) document management systems to provide greater confidence and credibility regarding the document types assigned to documents uploaded by users. The research was carried out through the phases of CRISP-DM, where two document representation models were evaluated (bags of words with cumulative n-grams and BERT, which was recently proposed by Google) and five machine learning techniques (multilayer perceptron, random forests, k-nearest neighbors, decision trees, and naïve bayes). The experiments were carried out with data from two organizations, and the best results were obtained by multilayer perceptron, random forests, and k-nearest neighbors, which showed very similar results regarding general accuracy and recall by class. The results are not conclusive with respect to the ability to offer the service to multiple clients with a single model, since this also depends on their documents and document types. Therefore, a service is offered which is based on a microservices architecture that allows each organization to create its own model, monitor its performance in production, and update it when performance is not adequate. [ABSTRACT FROM AUTHOR]

Performance tests are crucial to measure the quality of software developments, since they allow identifying aspects to be improved in order to achieve customer satisfaction. The objective of this research was to identify the optimal Machine Learning technique to predict whether or not a software development meets the customer's acceptance criteria. A dataset with information obtained from web services performance tests and the F1-score quality metric were used. This paper concludes that, although the Random Forest technique obtained the best score, it is not correct to state that it is the best Machine Learning technique; the quantity and quality of the data used in the training play a very important role, as well as an adequate processing of the information. [ABSTRACT FROM AUTHOR]

Traffic control in data networks has recently become very important due to the massive use of computer networks in different areas of society. Different techniques are usually used to carry out effective traffic control, allowing, among other things, to classify, predict and monitor network traffic. These techniques have evolved and are currently supported by artificial intelligence tools, which have made it possible to improve the results obtained with conventional techniques. This paper collects the different contributions made by the field of artificial intelligence to the improvement of these techniques and network management in general. The article describes the contributions made in aspects such as security, prediction, and classification of data traffic, as well as the optimization of routing in a computer network. [ABSTRACT FROM AUTHOR]

This paper presents application results of soft computing techniques in project's evaluation. The authors analyze different project management's guides regarding decision making techniques inside its. In this paper the authors combine traditional techniques for project's evaluation with soft computing techniques. The authors propose an algorithm that combine the following techniques: traditional indicators from project management guides, fuzzy inference systems, machine learning algorithms and linguistic data summarization methods. In section number 3, the paper presents the comparisons of algorithms Anfis, Hyfis, FS.HGD, based on neural networks with algorithms GFS LS RS, GFS THRIFT based on evolutionary algorithms. In experiments, we applied cross validation techniques combined with Friendman test and Wilcoxon test Afterwards, the authors present an algorithm that build linguistic summaries to detect causes of project management failures. Finally, we present a comparison of the results and the conclusions. [ABSTRACT FROM AUTHOR]

In this paper we intend to substantiate the usefulness of Deep Learning, especially feedforward neuronal networks, in the prediction of business failure. This methodology provides very good results in terms of predictive performance when large sample sizes are available. Therefore, we have developed a business failure prediction model for European companies, based on this algorithm on a sample of 61,624 companies, of which 12,128 were declared bankrupt in 2016. As independent variables were considered ratios, and economic and financial data obtained from the financial statements for the year preceding the date of failure. Deep Learning achieves a predictive performance of 94%, where companies with larger size and lower solvency are more prone to failure. The obtained results have been tested on an independent test sample, different from that used to estimate and train the model. [ABSTRACT FROM AUTHOR]

This paper presents comparison of Standardization and Scaling methods in cost predicting. Four methods were used for pre-processing dataset; after that, data was processed through artificial neural network (RNA). The first step was to build common variables in information projects based on opinions of some project managers. Second step was to simulate dataset based on information provided by CRConsulting. Third one was process data with machine learning according to the four methods proposed, RNA algorithms were the same in four cases. Last, the comparison results were presented through adjustment models according to the applied method. [ABSTRACT FROM AUTHOR]

In recent years, predicting the behavior of the Benchmark Banking Indicator (ibr) has become relevant due to its importance in the Colombian money market. The purpose of this paper is to demonstrate the efficiency of lstm networks for generating predictions of time series --through their long and shortterm memory-- that are comparable with the arima predictive model for econometric studies. The incidence of the representative market rate (trm) and the rate of 10-year public debt bonds (tes) was analyzed and compared to the ibr, seeking to determine its correlation through the Pearson method. Finally, the model efficiency was evaluated with the mean square error (rmse), using a multivariable lstm network with three inputs (ibr, tes, and trm) and one output. [ABSTRACT FROM AUTHOR]

The main goal of ontologies in computing is related to the definition of a common vocabulary for describing basic concepts and relationships on a specific domain. Main components of ontologies are classes -- concepts--, instances, properties, relations, and axioms, among other elements. The ontology population process is intended to receive an ontology as input in order to extract and relate the instances of each ontology class from heterogenous information sources. In this paper we perform a systematic state-of-the-art review about ontology population. We select papers from specialized databases and we create a research question for driving paper search. The results of our review points out ontology population as an interesting topic for researchers. Even though we have several techniques for driving the process, fully automated tools are still missing and we also miss high levels of precision and recall. [ABSTRACT FROM AUTHOR]

This paper is an advance of the research project entitled "Development of hybrid algorithms for data mining" and presents the use of a neural network with the simulated annealing algorithm to perform the prediction of a training data set. First, it addresses the problem to be solved, which is oriented to the analysis of the techniques defined for the hybrid algorithm. Then, the applied research methodology (descriptive-exploratory scientific with experimental approach) is justified. We performed a review of the techniques selected for the hybrid neuronal networks and simulated annealing technique which is applied to a set of experimental data associated with determining in a group of patients whether their spine is normal or abnormal. Then, the tests of analysis and results are presented. [ABSTRACT FROM AUTHOR]

Early warning systems are designed to inform the largest number of users, such as a country or a region, about a risky situation. However, in specific domains such as agriculture, it is commonly required for these alerts to be more specific according to the crops location and their properties. Consequently, the web services of these systems must be adapted. On the other hand, the enterprise services bus, with its mediation capabilities (such as message transformation and routing), and complex event processing with their monitoring characteristics, can be integrated to meet the adaptation requirements of web services at runtime. This paper presents an improvement for an early warning system for coffee production that, according to the area in which a crop is located and its phenology, manages the adaptation of alerts for coffee rust, based on the integration of an enterprise services bus and a complex events processing. [ABSTRACT FROM AUTHOR]

Chagas disease, caused by the parasite Trypanosoma cruzi, affects many people in Latin America. A blood test is the preferred method to generate a diagnosis of the disease; however, it is a time-consuming process, since it requires a lot of effort by experts to analyze large quantities of samples in the search of the presence of parasites. Implementation of automatic systems that facilitate the detection of the parasite in images of blood samples captured by microscope is very useful. Therefore, in this review article, the different scientific papers that use machine learning techniques to detect and segment the parasite Trypanosoma cruzi in digital images are described. [ABSTRACT FROM AUTHOR]

In Computer Security, it does not matter which Software or Hardware equipment is installed, because always the weakest link in this security chain, is the end user. From this premise are used the different types of Social Engineering attacks, whose main objective is to obtain information almost directly from the users, with the purpose of using this information against themselves. There are several attack vectors of Social Engineering, among which stand out: fake web pages, malign messages on social networks, and malicious emails that ask for confidential information from users or even redirect users to a fake web page (Phishing). The objective of this paper is to provide end users and other researchers with a look at the types of Phishing attacks that exist, and how they can be mitigated. For this, first, a systematic review of the literature in the main scientific sources is carried out, to characterize and classify the different types of Phishing attacks, and subsequently, the means by which these attacks can be mitigated are exposed and classified, ranging from a user awareness to the use of Machine Learning (ML) and Deep Learning (DL) techniques. [ABSTRACT FROM AUTHOR]

This paper presents the design and testing of a new training model for single hidden layer feedforward network based on the same properties of Extreme Learning Machine (ELM). The model acts by compressing the information coming from the hidden layer by means of a subset of nodes from the same layer. This allows to considerably reduce the computational complexity compared to ELM. Experimental results based on simulation for different classification problems indicate that the proposed model achieves the same ELM performances in terms of generalization, exceeding it in speed [ABSTRACT FROM AUTHOR]

This paper describes a process of semi-automatic systematic reviews in scientific research. On the one hand, it is presented a software prototype based on artificial intelligence, semi-automatic systematic reviews tool. On the other hand, a study case of a phd thesis project shows the maps and information with aggregate value. In addition, the software prototype were evaluated by 40 students of master and Ph.D projects, they report the utility of the software tool by a survey. This work presents the modules and functionalities, software architecture and software deployment. Furthermore, a comparative study of a set of similar software tools has been conducted according to the following criteria: input files, analysis and mapping, and software license. Finally, it is discussed the results of the comparative study, the implementation of artificial intelligence techniques, the software scalability and the easiness of include new analysis and maps. [ABSTRACT FROM AUTHOR]

Cluster analysis of climatological data is used in various investigations as it allows interesting results to be obtained for each proposed approach. Therefore, this paper presents the performance evaluation of the K-Means clustering algorithm from the use of standardization applied to a data set with four climatological variables (temperature, precipitation, relative humidity and solar radiation) for a station located in the city of Manizales, Colombia. This in order to determine the effect of applying, or not, the normalization in the quality of the clusters and to evaluate the computational cost of the algorithm according to the established characteristics. For this purpose, six execution scenarios are defined for 2, 3 and 5 clusters with different quantity and grouping of variables using Euclidean distance as a distance measure, Davies-Bouldin as a quality evaluation method of the clusters and the application of normalization with Z-transformation and Range transformation. It is concluded that, through a comparison with k-medoides and STFT application (Fourier Transform of Reduced Time), the normalization improves the results and with Z-transformation the best grouping performances are obtained according to the Davis-Bouldin index. [ABSTRACT FROM AUTHOR]

This paper presents a new methodology based on the application of Support Vector Machine algorithms, Naïve Bayes and Genetic Algorithms in diagnostics of psychosocial evaluations for the identification and prediction of the psychosocial risk level of public-school teachers in Colombia. A comparative study of the model of machine learning for prediction was carried out: Support Vector Machines (SVM) and Naïve Bayes, in two stages, first with all the variables and second, reducing the dimensionality of the database applying genetic algorithms, The best forty variables with the best efficiency in prediction accuracy were selected. The database used consisted of 3000 epidemiological records, which corresponded to teachers from public schools in the metropolitan area of a Colombian city. The use of SVM easily detected variables of physiological type and the best prediction performance was obtained with accuracy of 96.3%. [ABSTRACT FROM AUTHOR]

This paper reports the results of a series of experiments on the machine learning technique known as SVM, for the comparison of five different kernels. First, we compare and analyze their classification performance on databases with missing values. Then, we run a similar experiment with noisy values databases. Finally, we analyze the results from adding irrelevant features to the databases. The SVM technique turned out to be very robust in the three created environments, and the polynomial kernel showed the best classification results. [ABSTRACT FROM AUTHOR]

Open source intelligence (OSINT) is a cyber-intelligence branch used to obtain and analyze information related to potential adversaries, so it can support risk assessments and help to prevent damages against critical assets. This paper presents a research about different OSINT technologies and how these can be used to perform cyber intelligence tasks of a nation. A set of transforms addressed to the Colombian context are presented, which were implemented and contributed to the community allowing to the law enforcement agencies to develop information gathering process from Colombian open sources. However, the real use of the information is given by the implementation of three machine learning models used to perform sentiment analysis over this information, in order to know the opinion of the adversary about certain topic and understand his motivation and, in this way, define proper cyber defense strategies. Finally, some challenges related to the application of OSINT techniques are identified and described regarding its use by state security agencies. [ABSTRACT FROM AUTHOR]

This paper presents a new methodology based on machine learning techniques in diagnostics of psychosocial assessments to identify the risk level in teachers of public schools in Colombia. A comparative study of three important models of machine learning for prediction was done: artificial neural networks, decision trees and naive bayes, reducing the dimensionality of the data. This was done by applying genetic algorithms, algorithm of the expected amount of information, the algorithm GainRatioAttributeEval, Pearson's relation coefficient and principal components analysis. A database was used with 5340 epidemiological records, corresponding to psychosocial evaluations of teachers from public schools in the metropolitan area of a Colombian city. The best predictive performance was obtained with the model of artificial neural networks with an accuracy 93%. [ABSTRACT FROM AUTHOR]

Nowadays, social networks have become in a communication medium widely used to disseminate any type of information. In particular, the shared information in social networks usually includes a considerable number of traffic incidents reports of specific cities. In light of this, specialized social networks have emerged for detecting and disseminating traffic incidents, differentiating from generic social networks in which a wide variety of topics are communicated. In this context, Twitter is a case in point of a generic social network in which its users often share information about traffic incidents, while Waze is a social network specialized in traffic. In this paper we present a comparative study between Waze and an intelligent approach that detects traffic incidents by analyzing publications shared in Twitter. The comparative study was carried out considering Ciudad Autónoma de Buenos Aires (CABA), Argentina, as the region of interest. The results of this work suggest that both social networks should be considered as complementary sources of information. This conclusion is based on the fact that the proportion of mutual detections, i.e. traffic incidents detected by both approaches, was considerably low since it did not exceed 6% of the cases. Moreover, the results do not show that any of the approaches tend to anticipate in time to the other one in the detection of traffic incidents. [ABSTRACT FROM AUTHOR]

Environmental noise is a big problem related to the environmental pollution in cities, which affects the quality of people life. In this paper, a methodology that uses an approach based on Volunteered Geographic Information (VGI) for the monitoring, analysis and prediction of environmental noise is proposed. It can be very useful to propose alternatives and initiatives that improve the life in a city. So, this work is composed of the following stages: data acquisition, analysis and, data processing, as well as the information visualization, considering the temporality of the same and taking into account macro and micro levels of analysis for the study surface. In addition, some details of the design and development of a geographic information system are presented, consisting of a web-mapping system, an application for mobile devices called "NoiseMonitor", geospatial analysis and machine learning methods (support vector machines and artificial neural networks) for the prediction of environmental noise; by using contextual information; that is, some data related to the city. This kind of work seeks to take advantage of the willingness of citizens to participate collaboratively to sense their environment and be considered as human sensors, which unlike traditional approaches, the cost associated with the development and implementation of this project is much lower. Likewise, a case study based on the Mexico City is presented and discussed, particularly the fourth quadrant of the Historic Center of the City, which is very representative for the variety of environmental noise that is generated in that area. The application domain of this approach is oriented towards big data from a collaborative perspective, Internet of Things and smart cities. [ABSTRACT FROM AUTHOR]

This paper addresses an aspect not yet investigated in Spanish political communication: the use of data mining systems and machine learning in the construction of political discourse. This text details the potential of Calisto software, which was designed and implemented by the Popular Party (PP), previously used in Castile and Leon and then at the national level. This software acts as a digital political advisor and is currently used by 7,500 positions and party members. The program tracks information on the internet to identify the most relevant sources. In addition, its natural language processing generates ideas and arguments that politicians use to construct their speech. This article explores the benefits of this tool and its ability to automate processes that previously consumed large doses of effort by managers and consultants, and also reflects on the highly controlled model that this type of system imposes in the public arena. [ABSTRACT FROM AUTHOR]

Paraphrase detection is commonly used in various research areas related to the natural language processing, as information retrieval, machine translation, text summarization, automatic identification of text copyright infringement and question answering. Essentially, the methods for paraphrase detection aims at deciding whether two sentences have the same meaning or not. This paper presents a supervised machine learning approach for paraphrase detection, which uses lexical and semantic information. In order to identify paraphrases are used three different machine learning classifiers: support vector machines, k-nearest neighbors and decision trees. In the empirical evaluation, we explore the discriminating power of the lexical and semantic attributes set by separating these types of attributes in two different problems respectively. Moreover, we study the behavior of the three classifiers by combining these lexical and semantic attributes in a single set. We used the Microsoft Research Paraphrase Corpus data set for the empirical evaluation and our method was compared with respect to related approaches. The experiments showed that the support vector machines classifier in combination with the set of lexical attributes reached the higher performance. [ABSTRACT FROM AUTHOR]

Prediction or forecast appears frequently to solve making decisions problems, especially when using large databases. Statistical methods to support decisions are discussed, first determining the probability of an event, second using linear regression, finally main objective in this paper we use machine learning techniques for failure prediction in a Digital Signal Distribution complex system. [ABSTRACT FROM AUTHOR]

This paper offers a comparative analysis of the effectiveness of eight popular forecasting methods: univariate, linear, discriminate and logit regression; recursivepartitioning, rough sets, artificial neural networks, and DEA. Our goals are: clarify the complexity-effectiveness balance of each methodology; identify a reduced set of independent variables that are significant predictors whatever the methodology is; and discuss and relate these findings to the financial theory, to help consolidate the foundations of a theory of financial failure. Our results indicate that, whatever the methodology is, reliable predictions can be made using four variables; these ratios convey informatión about profitability, financial structure, rotation, and operating cash flows. [ABSTRACT FROM AUTHOR]

This paper shows the development and use of a hybrid algorithm of machine learning for the missing values predict task, with this task that is carried out while the data preprocessing phase. Firstly, we go through the problem to solve, which is pointed to the study and analysis of different techniques for the missing values predict in order to suggest a hybrid technique as a product of this research for the task and link it to the OLAP (On-Line Analytical Processing) technology. Then, justifying the research methodology (scientific descriptive - probing) applied in this project. The techniques review was carried out filling out the missing values; based on the techniques proof and the study cases, k-Nearest Neighbors algorithms and artificial neural networks were selected and a hybrid technique (KMediaSom) was suggested, applied to a synthetic data set and a real one, coming from a OLAP; for the algorithms implementation was used Matlab. Right away, the analysis and results are set out in order to specify its applicability about efficacy and time complexity. Results are suitable as for the synthetic data set as for the real one; according to the test signs achieved. Finally, the conclusions, where it i's proved that the technique or hybrid algorithms generate better results than the techniques used by separately. [ABSTRACT FROM AUTHOR]

This paper conceptualizes the term big data and describes its relevance in social research and journalistic practices. We explain large-scale text analysis techniques such as automated content analysis, data mining, machine learning, topic modeling, and sentiment analysis, which may help scientific discovery in social sciences and news production in journalism. We explain the required e-infrastructure for big data analysis with the use of cloud computing and we asses the use of the main packages and libraries for information retrieval and analysis in commercial software and programming languages such as Python or R. [ABSTRACT FROM AUTHOR]

Paraphrase detection is commonly used in various research areas related to the natural language processing, as information extraction, machine translation, information retrieval and automatic identification of copyright infringement. Therefore, paraphrase detection has received a lot of attention in the last years. Methods for paraphrase detection aims at deciding whether or not two sentences have the same meaning. This paper reviews the main techniques and resources available for paraphrase detection. We focus our review on methods belonging to two different groups: 1) methods using a similarity function for paraphrase detection and 2) supervised algorithms relative to machine learning and data mining context. [ABSTRACT FROM AUTHOR]

This paper discusses the results of an assessment of eight machine learning-based methods and three methods that have historically been used to complete missing data in time series. Data used for the analysis correspond to monthly precipitation totals collected by the Colombian Institute of Hydrology, Meteorology and Environmental Studies (Ideam) at four weather stations in the Baché river basin (Palermo municipality, Huila, Colombia). In order to evaluate the methods, the existing data was reproduced as missing data, and three different error metrics were calculated based on the difference between them: Root Mean Square Error (RMSE), Nash-Sutcliffe Efficiency (NSE) and bias. Results show that machine learning methods for completing time series are reliable, seeing as similar (and in some cases, better) results can be achieved without an accurate implementation and, consequently, a greater attention to them can lead to less uncertain results. [ABSTRACT FROM AUTHOR]

This paper proposes a comprehensive evaluation of supervised classification algorithms without semantic information for opinion mining on Twitter. The evaluation will assess the potential of this approach to tackle the given problem. Also, as result of evaluating, the impact of different variants of the text preprocessing is assessed, as well as different alternatives of dimension reduction techniques in the problem solution. Finally, the results obtained in the experiments offer evidence to direct the use of supervised classification to classify opinions and its valence in a domain of short messages such as Twitter. [ABSTRACT FROM AUTHOR]

One of the most fundamental problems formulated to machine learning is the development of habits and abilities in complex systems modeling. At the present paper a system named Generator of Problems Hypermedia is described and developed. This software system allows automating the process of optimization problem construction and automatic generation of problem statements for complex problem situations using the multimedia technology tools and intelligent interfaces. The work possesses significant importance in the introduction of computers in Education and in the development of Mathematical Modeling. Their successful application increases considerably the efficiency of the teaching and learning process. By means of the rational introduction of the Multimedia Technology tools students have exercised the construction and representation process of is used with the technology .net for Windows, as welloptimization problem situations. The system C as diverse multimedia technologies. [ABSTRACT FROM AUTHOR]

In this paper we development a hybrid algorithm of machine learning for the automatic locking of web pages that incite to violence through content snuff. We describe of the current situation of the problem to be solved, and we give to know the selection process of the techniques for the development of the hybrid algorithm. Also, we show the mathematical model and results analysis. [ABSTRACT FROM AUTHOR]

The maintenance management in electrical substations over the years has evolved according to the measurement equipment and the electrical parameters of global maintenance philosophies, and in particular the high voltage breakers have not been left behind. This paper is such advances from a different vision, artificial intelligence, specifically machine learning for decision-making in planning maintenance high voltage breaker sused in a substation. It determines the rate of deterioration, the coefficients of importance of each type of the deterioration and the high voltage breaker element with the help of experts. Subsequently are obtained the membership rates with the corresponding processes for fuzzification and defuzzification; finally the rule evaluation and defuzzification for the time maintenance is performed, the method is appliance in different high voltage breakers. [ABSTRACT FROM AUTHOR]

In this paper we show the realization of a project that involves the creation of a hybrid learning algorithm for supervised computer automatically blocking websites that incite violence through snuff content. In this, there is a description of the current state of the problem to solve and provides a concept of what is mining and computational learning, on this basis, three algorithms are chosen similar to the proposed works, with the to analyze their weaknesses, strengths and applications. [ABSTRACT FROM AUTHOR]

The motion estimation is an essential block in the prediction stage of the H.264/AVC video compression standard, which is crucial for obtaining an effective coding rate. Different algorithms have been proposed in the literature to optimize the architecture of this block, since it requires a long execution time and a significant number of computational resources. Traditional strategies based on the search on each block allow ensuring high quality but are inappropriate in terms of computational efficiency; other methods based on search by regions reduce the processing load, but do not guarantee a good quality. In this paper it is implemented a multilayer perceptron neural network with supervised backpropagation learning algorithm which allows making the estimation. [ABSTRACT FROM AUTHOR]