Comparison of Support Vector Machine and Back Propagation Neural Network in Evaluating the Enterprise Financial Distress

Recently, applying the novel data mining techniques for evaluating enterprise financial distress has received much research alternation. Support Vector Machine (SVM) and back propagation neural (BPN) network has been applied successfully in many areas with excellent generalization results, such as rule extraction, classification and evaluation. In this paper, a model based on SVM with Gaussian RBF kernel is proposed here for enterprise financial distress evaluation. BPN network is considered one of the simplest and are most general methods used for supervised training of multilayered neural network. The comparative results show that through the difference between the performance measures is marginal; SVM gives higher precision and lower error rates.Comment: 13 pages, 1 figur

Motivational determinants of physical education grades and the intention to practice sport in the future

Self-Determination Theory (SDT) is amongst motivational frameworks the most popular and contemporary approach to human motivation, being applied in the last decades in several domains, including sport, exercise and physical education (PE). Additionally, Achievement Goal Theory (AGT) has presented evidence of how contextual factors may influence student's behavior in this particular context. The main purpose of this study was to analyze the motivational climate created by the teacher in the classroom, students' satisfaction of Basic Psychological Needs (BPN), and how their behavioral regulation could explain PE grades and intention to practice sports in the future.Funding: This project was supported by the National Funds through FCT – Portuguese Foundation for Science and Technology (UID/ DTP/04045/2013) – and the European Fund for Regional Development (FEDER) allocated by European Union through the COMPETE 2020 Programme (POCI-01-0145FEDER-006969) – Competitiveness and Internationalization (POCI).info:eu-repo/semantics/publishedVersio

A New Computer-Aided Diagnosis System with Modified Genetic Feature Selection for BI-RADS Classification of Breast Masses in Mammograms

Mammography remains the most prevalent imaging tool for early breast cancer screening. The language used to describe abnormalities in mammographic reports is based on the breast Imaging Reporting and Data System (BI-RADS). Assigning a correct BI-RADS category to each examined mammogram is a strenuous and challenging task for even experts. This paper proposes a new and effective computer-aided diagnosis (CAD) system to classify mammographic masses into four assessment categories in BI-RADS. The mass regions are first enhanced by means of histogram equalization and then semiautomatically segmented based on the region growing technique. A total of 130 handcrafted BI-RADS features are then extrcated from the shape, margin, and density of each mass, together with the mass size and the patient's age, as mentioned in BI-RADS mammography. Then, a modified feature selection method based on the genetic algorithm (GA) is proposed to select the most clinically significant BI-RADS features. Finally, a back-propagation neural network (BPN) is employed for classification, and its accuracy is used as the fitness in GA. A set of 500 mammogram images from the digital database of screening mammography (DDSM) is used for evaluation. Our system achieves classification accuracy, positive predictive value, negative predictive value, and Matthews correlation coefficient of 84.5%, 84.4%, 94.8%, and 79.3%, respectively. To our best knowledge, this is the best current result for BI-RADS classification of breast masses in mammography, which makes the proposed system promising to support radiologists for deciding proper patient management based on the automatically assigned BI-RADS categories

Investigating Prediction Performance of an Artificial Neural Network and a Numerical Model of the Tidal Signal at Puerto Belgrano, Bahia Blanca Estuary (Argentina)

In the present study we compare performances of the prediction of hourly tidal level variations at Puerto Belgrano, a coastal site in the Bahia Blanca Estuary (Argentina), by means of the MOHID model, which is a numerical model designed for coastal and estuarine shallow water applications, and of an artificial neural network (ANN). It was shown that the ANN model is able to predict the hourly tidal levels over long term duration with at least seven days of observations and with a better performance in respect to the numerical model. Our findings can be useful to implement ANN-based tools for future studies of the hydrodynamics of Bahía Blanca estuary.Fil: Pierini, Jorge Omar. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Bahía Blanca. Instituto Argentino de Oceanografía (i); ArgentinaFil: Lovallo, Michele. Agenzia Regionale per la Protezione dell’Ambiente; ItaliaFil: Telesca, Luciano. National Research Council, Institute of Methodologies for Environmental Analysis; ItaliaFil: Gomez, Eduardo Alberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Bahía Blanca. Instituto Argentino de Oceanografía (i); Argentin

Input Dimension Reduction in Neural Network Training-Case Study in Transient Stability Assessment of Large Systems

The problem in modeling large systems by artificial neural networks (ANN) is that the size of the input vector can become excessively large. This condition can potentially increase the likelihood of convergence problems for the training algorithm adopted. Besides, the memory requirement and the processing time also increase. This paper addresses the issue of ANN input dimension reduction. Two different methods are discussed and compared for efficiency and accuracy when applied to transient stability assessment

Comparative Analysis of AI Techniques to Correct the Inconsistency in the Analytic Hierarchy Process Matrix

The Analytic Hierarchy Process (AHP) is one of the most used techniques for decision making. The complex properties of its structure allow considering the subjectivity in the judgment of the experts but also arising a considerable degree of inconsistency when the pairwise judgments of the alternatives are computed. This research paper makes a comparison between two artificial intelligence methods for diminishing the inconsistency in the AHP pairwise comparison matrixes, the Backpropagation Neural Network (BPN) and Support Vector Machines (SVM).Eje: XV Workshop de Agentes y Sistemas InteligentesRed de Universidades con Carreras de Informática (RedUNCI

Comparative Analysis of AI Techniques to Correct the Inconsistency in the Analytic Hierarchy Process Matrix

The Analytic Hierarchy Process (AHP) is one of the most used techniques for decision making. The complex properties of its structure allow considering the subjectivity in the judgment of the experts but also arising a considerable degree of inconsistency when the pairwise judgments of the alternatives are computed. This research paper makes a comparison between two artificial intelligence methods for diminishing the inconsistency in the AHP pairwise comparison matrixes, the Backpropagation Neural Network (BPN) and Support Vector Machines (SVM).Eje: XV Workshop de Agentes y Sistemas InteligentesRed de Universidades con Carreras de Informática (RedUNCI

Random minibatch projection algorithms for convex problems with functional constraints

In this paper we consider non-smooth convex optimization problems with (possibly) infinite intersection of constraints. In contrast to the classical approach, where the constraints are usually represented as intersection of simple sets, which are easy to project onto, in this paper we consider that each constraint set is given as the level set of a convex but not necessarily differentiable function. For these settings we propose subgradient iterative algorithms with random minibatch feasibility updates. At each iteration, our algorithms take a step aimed at only minimizing the objective function and then a subsequent step minimizing the feasibility violation of the observed minibatch of constraints. The feasibility updates are performed based on either parallel or sequential random observations of several constraint components. We analyze the convergence behavior of the proposed algorithms for the case when the objective function is restricted strongly convex and with bounded subgradients, while the functional constraints are endowed with a bounded first-order black-box oracle. For a diminishing stepsize, we prove sublinear convergence rates for the expected distances of the weighted averages of the iterates from the constraint set, as well as for the expected suboptimality of the function values along the weighted averages. Our convergence rates are known to be optimal for subgradient methods on this class of problems. Moreover, the rates depend explicitly on the minibatch size and show when minibatching helps a subgradient scheme with random feasibility updates.Comment: 25 page