ESTUDIO DEL PROCESO DE CONTROL DE INFORMACIÓN DE EQUIPOS DE CÓMPUTO EN LA ALCALDÍA DE TUCUPITA DEL ESTADO DEL DELTA AMACURO

La Dirección de Informática de la alcaldía municipal de Tucupita tiene la misión de promover la eficiencia y eficacia en los procesos técnicos y sistemas de control, a través de la estandarización y automatización (software y hardware) de los mismos, al poner en práctica mecanismos de planificación, coordinación y evaluación que garanticen el logro de los objetivos. Se necesita consolidar e integrar tecnológicamente como una alcaldía vanguardista, con alta capacidad de repuesta y excelente calidad del servicio prestado por la adquisición y utilización de tecnología de punta, que incida en el desarrollo y productividad del ente municipal. Este trabajo está encaminado a un estudio de la necesidad de la informatización de este lugar

Identification of Babbitt Damage and Excessive Clearance in Journal Bearings through an Intelligent Recognition Approach

Journal bearings play an important role on many rotating machines placed on industrial environments, especially in steam turbines of thermoelectric power plants. Babbitt damage (BD) and excessive clearance (C) are usual faults of steam turbine journal bearings. This paper is focused on achieving an effective identification of these faults through an intelligent recognition approach. The work was carried out through the processing of real data obtained from an industrial environment. In this work, a feature selection procedure was applied in order to choose the features more suitable to identify the faults. This feature selection procedure was performed through the computation of typical testors, which allows working with both quantitative and qualitative features. The classification tasks were carried out by using Nearest Neighbors, Voting Algorithm, Naïve Associative Classifier and Assisted Classification for Imbalance Data techniques. Several performance measures were computed and used in order to assess the classification effectiveness. The achieved results (e.g., six performance measures were above 0.998) showed the convenience of applying pattern recognition techniques to the automatic identification of BD and C

Undersampling Instance Selection for Hybrid and Incomplete Imbalanced Data

This paper proposes a novel undersampling method, for dealing with imbalanced datasets. The proposal is based on a novel instance importance measure (also introduced in this paper), and is able to balance hybrid and incomplete data. The numerical experiments carried out show the proposed undersampling algorithm outperforms others algorithms of the state of art, in well-known imbalanced datasets

Prediction of High Capabilities in the Development of Kindergarten Children

Analysis and prediction of children’s behavior in kindergarten is a current need of the Cuban educational system. Despite such an early age, the kindergarten institutions are devoted to facilitate the integral children development. However, the early detection of high capabilities in a child is not always accomplished accurately; due to teachers being mostly focused on the performance of the children that are lagging behind to achieve their age range’s stated goals. In addition, the amount of children with high capabilities is usually low, which makes the prediction an imbalanced data problem. Thus, such children tend to be misguided and overlaid, with a negative impact in their sociological development. The purpose of this research is to propose an efficient algorithm that enhances the prediction in the kindergarten children data. We obtain a useful set of instances and features, thus improving the Nearest Neighbor accuracy according to the Area under the Receiving Operating Characteristic curve measure. The obtained results are of great interest for Cuban educational system, regarding the rapidly and precise prediction of the presence or absence of high capabilities for integral personality development in kindergarten children

Customized Instance Random Undersampling to Increase Knowledge Management for Multiclass Imbalanced Data Classification

Imbalanced data constitutes a challenge for knowledge management. This problem is even more complex in the presence of hybrid (numeric and categorical data) having missing values and multiple decision classes. Unfortunately, health-related information is often multiclass, hybrid, and imbalanced. This paper introduces a novel undersampling procedure that deals with multiclass hybrid data. We explore its impact on the performance of the recently proposed customized naïve associative classifier (CNAC). The experiments made, and the statistical analysis, show that the proposed method surpasses existing classifiers, with the advantage of being able to deal with multiclass, hybrid, and incomplete data with a low computational cost. In addition, our experiments showed that the CNAC benefits from data sampling; therefore, we recommend using the proposed undersampling procedure to balance data for CNAC

Customized Instance Random Undersampling to Increase Knowledge Management for Multiclass Imbalanced Data Classification

Imbalanced data constitutes a challenge for knowledge management. This problem is even more complex in the presence of hybrid (numeric and categorical data) having missing values and multiple decision classes. Unfortunately, health-related information is often multiclass, hybrid, and imbalanced. This paper introduces a novel undersampling procedure that deals with multiclass hybrid data. We explore its impact on the performance of the recently proposed customized naïve associative classifier (CNAC). The experiments made, and the statistical analysis, show that the proposed method surpasses existing classifiers, with the advantage of being able to deal with multiclass, hybrid, and incomplete data with a low computational cost. In addition, our experiments showed that the CNAC benefits from data sampling; therefore, we recommend using the proposed undersampling procedure to balance data for CNAC

A Transfer Learning Method for Pneumonia Classification and Visualization

Pneumonia is an infectious disease that affects the lungs and is one of the principal causes of death in children under five years old. The Chest X-ray images technique is one of the most used for diagnosing pneumonia. Several Machine Learning algorithms have been successfully used in order to provide computer-aided diagnosis by automatic classification of medical images. For its remarkable results, the Convolutional Neural Networks (models based on Deep Learning) that are widely used in Computer Vision tasks, such as classification of injuries and brain abnormalities, among others, stand out. In this paper, we present a transfer learning method that automatically classifies between 3883 chest X-ray images characterized as depicting pneumonia and 1349 labeled as normal. The proposed method uses the Xception Network pre-trained weights on ImageNet as an initialization. Our model is competitive with respect to state-of-the-art proposals. To make comparisons with other models, we have used four well-known performance measures, obtaining the following results: precision (0.84), recall (0.99), F1-score (0.91) and area under the ROC curve (0.97). These positive results allow us to consider our proposal as an alternative that can be useful in countries with a lack of equipment and specialized radiologists

Novel Features and Neighborhood Complexity Measures for Multiclass Classification of Hybrid Data

The present capabilities for collecting and storing all kinds of data exceed the collective ability to analyze, summarize, and extract knowledge from this data. Knowledge management aims to automatically organize a systematic process of learning. Most meta-learning strategies are based on determining data characteristics, usually by computing data complexity measures. Such measures describe data characteristics related to size, shape, density, and other factors. However, most of the data complexity measures in the literature assume the classification problem is binary (just two decision classes), and that the data is numeric and has no missing values. The main contribution of this paper is that we extend four data complexity measures to overcome these drawbacks for characterizing multiclass, hybrid, and incomplete supervised data. We change the formulation of Feature-based measures by maintaining the essence of the original measures, and we use a maximum similarity graph-based approach for designing Neighborhood measures. We also use ordering weighting average operators to avoid biases in the proposed measures. We included the proposed measures in the EPIC software for computational availability, and we computed the measures for publicly available multiclass hybrid and incomplete datasets. In addition, the performance of the proposed measures was analyzed, and we can confirm that they solve some of the biases of previous ones and are capable of natively handling mixed, incomplete, and multiclass data without any preprocessing needed