Neural networks in geophysical applications

Neural networks are increasingly popular in geophysics. Because they are universal approximators, these tools can approximate any continuous function with an arbitrary precision. Hence, they may yield important contributions to finding solutions to a variety of geophysical applications. However, knowledge of many methods and techniques recently developed to increase the performance and to facilitate the use of neural networks does not seem to be widespread in the geophysical community. Therefore, the power of these tools has not yet been explored to their full extent. In this paper, techniques are described for faster training, better overall performance, i.e., generalization,and the automatic estimation of network size and architecture

A hybrid nonlinear-discriminant analysis feature projection technique

Feature set dimensionality reduction via Discriminant Analysis (DA) is one of the most sought after approaches in many applications. In this paper, a novel nonlinear DA technique is presented based on a hybrid of Artificial Neural Networks (ANN) and the Uncorrelated Linear Discriminant Analysis (ULDA). Although dimensionality reduction via ULDA can present a set of statistically uncorrelated features, but similar to the existing DA's it assumes that the original data set is linearly separable, which is not the case with most real world problems. In order to overcome this problem, a one layer feed-forward ANN trained with a Differential Evolution (DE) optimization technique is combined with ULDA to implement a nonlinear feature projection technique. This combination acts as nonlinear discriminant analysis. The proposed approach is validated on a Brain Computer Interface (BCI) problem and compared with other techniques. © 2008 Springer Berlin Heidelberg

Selecting Salient Features in High Feature to Exemplar Ratio Conditions

A key tenet to the Air Force\u27s vision of Global Vigilance, Reach, and Power is the ability to project power via the use of aerial refueling. Scheduling of limited tanker resources is a major concern for Air Mobility Command (AMC). Currently the Combined Mating and Ranging Planning System (CMARPS) is used to plan aerial refueling operations, however due to the complex nature of the program and the length of time needed to run a scenario, the need for a simple tool that runs in much shorter time is desired. Ant colony algorithms are recently developed heuristics for finding solutions to difficult optimization problems based on simulation the foraging behavior of ant colonies. It is a distributive metaheuristic that combines an adaptive memory function with a local heuristic function to repeatedly construct possible solutions which can then be evaluated. Using multiple ant colony heuristics combined with a simple scheduling algorithm and modeling the Tanker Assignment Problem as a modified Multiple Depot Vehicle Routing Problem, an Excel based spreadsheet tool was developed which generates very good solutions in very short time

Neural networks for nonlinear discriminant analysis in continuous speech recognition

In this paper neural networks for Nonlinear Discriminant Analysis in continuous speech recognition are presented. Multilayer Perceptrons are used to estimate a-posteriori probabilities for Hidden-Markov Model states, which are the optimal discriminant features for the separation of the HMM states. The a-posteriori probabilities are transformed by a principal component analysis to calculate the new features for semicontinuous HMMs, which are trained by the known Maximum-Likelihood training. The nonlinear discriminant transformation is used in speaker-independent phoneme recognition experiments and compared to the standard Linear Discriminant Analysis technique

Training Process Reduction Based On Potential Weights Linear Analysis To Accelarate Back Propagation Network

Learning is the important property of Back Propagation Network (BPN) and finding the suitable weights and thresholds during training in order to improve training time as well as achieve high accuracy. Currently, data pre-processing such as dimension reduction input values and pre-training are the contributing factors in developing efficient techniques for reducing training time with high accuracy and initialization of the weights is the important issue which is random and creates paradox, and leads to low accuracy with high training time. One good data preprocessing technique for accelerating BPN classification is dimension reduction technique but it has problem of missing data. In this paper, we study current pre-training techniques and new preprocessing technique called Potential Weight Linear Analysis (PWLA) which combines normalization, dimension reduction input values and pre-training. In PWLA, the first data preprocessing is performed for generating normalized input values and then applying them by pre-training technique in order to obtain the potential weights. After these phases, dimension of input values matrix will be reduced by using real potential weights. For experiment results XOR problem and three datasets, which are SPECT Heart, SPECTF Heart and Liver disorders (BUPA) will be evaluated. Our results, however, will show that the new technique of PWLA will change BPN to new Supervised Multi Layer Feed Forward Neural Network (SMFFNN) model with high accuracy in one epoch without training cycle. Also PWLA will be able to have power of non linear supervised and unsupervised dimension reduction property for applying by other supervised multi layer feed forward neural network model in future work.Comment: 11 pages IEEE format, International Journal of Computer Science and Information Security, IJCSIS 2009, ISSN 1947 5500, Impact factor 0.42

Diagnóstico no invasivo de patologías humanas combinando análisis de aliento y modelización con redes neuronales

Tesis inédita de la Universidad Complutense de Madrid, Facultad de Ciencias Químicas, leída el 09-09-2016It is currently known that there is a direct relation between the moment a disease is detected or diagnosed and the consequences it will have on the patient, as an early detection is generally linked to a more favorable outcome. This concept is the basis of the present research, due to the fact that its main goal is the development of mathematical tools based on computational artificial intelligence to safely and non-invasively attain the detection of multiple diseases. To reach these devices, this research has focused on the breath analysis of patients with diverse diseases, using several analytical methodologies to extract the information contained in these samples, and multiple feature selection algorithms and neural networks for data analysis. In the past, it has been shown that there is a correlation between the molecular composition of breath and the clinical status of a human being, proving the existence of volatile biomarkers that can aid in disease detection depending on their presence or amount. During this research, two main types of analytical approaches have been employed to study the gaseous samples, and these were cross-reactive sensor arrays (based on organically functionalized silicon nanowire field-effect transistors (SiNW FETs) or gold nanoparticles (GNPs)) and proton transfer reaction-mass spectrometry (PTR-MS). The cross-reactive sensors analyze the bulk of the breath samples, offering global, fingerprint-like information, whereas PTR-MS quantifies the volatile molecules present in the samples. All of the analytical equipment employed leads to the generation of large amounts of data per sample, forcing the need of a meticulous mathematical analysis to adequately interpret the results. In this work, two fundamental types of mathematical tools were utilized. In first place, a set of five filter-based feature selection algorithms (χ2 (chi2) score, Fisher’s discriminant ratio, Kruskal-Wallis test, Relief-F algorithm, and information gain test) were employed to reduce the amount of independent in the large databases to the ones which contain the greatest discriminative power for a further modeling task. On the other hand, and in relation to mathematical modeling, artificial neural networks (ANNs), algorithms that are categorized as computational artificial intelligence, have been employed. These non-linear tools have been used to locate the relations between the independent variables of a system and the dependent ones to fulfill estimations or classifications. The type of ANN that has been used in this thesis coincides with the one that is more commonly employed in research, which is the supervised multilayer perceptron (MLP), due to its proven ability to create reliable models for many different applications...Actualmente es sabido que existe una relación directa entre el momento en el cual se detecta o diagnostica una enfermedad y las consecuencias que tendrá sobre el paciente, ya que una detección temprana va generalmente ligada a un desarrollo más favorable. Este concepto es el cimiento de la presente investigación, cuyo objetivo fundamental es el desarrollo de herramientas basadas en inteligencia artificial computacional que consigan, mediante medios seguros y no invasivos, la detección de diversas enfermedades. Para alcanzar dichos sistemas, los estudios han sido enfocados en el análisis de muestras de aliento de pacientes de diversas enfermedades, empleando varias técnicas para extraer información, y diversos algoritmos de selección de variables y redes neuronales para el procesamiento matemático. En el pasado, se ha comprobado que hay una correlación entre la composición molecular del aliento y el estado clínico de una persona, evidenciando la existencia de biomarcadores volátiles que pueden ayudar a detectar enfermedades, ya sea por su presencia o por su cantidad. Durante el transcurso de esta investigación, se han empleado esencialmente dos tipos de técnicas analíticas para estudiar las muestras gaseosas, y estas son conjuntos de sensores de reactividad cruzada (basados en transistores de efecto de campo con nanocables de silicio (SiNW FETs) o en nanopartículas de oro (GNPs), ambos funcionalizados con cadenas orgánicas) y equipos de reacción de transferencia de protones con espectrometría de masas (PTR-MS). Los sensores de reactividad cruzada analizan el aliento en su conjunto, extrayéndose información de la muestra global, mientras que usando PTR-MS, se cuantifican las moléculas volátiles presentes en las muestras analizadas. Todas las técnicas empleadas desembocan en la generación de grandes cantidades de datos por muestra, por lo que un análisis matemático exhaustivo es necesario para poder sacar el máximo rendimiento de los estudios. En este trabajo, se emplearon principalmente dos tipos de herramientas matemáticas. Las primeras son un grupo de cinco algoritmos de selección de variables, concretamente, filtros de variables (cálculos basados en estadística de χ2 (chi2), ratio discriminante de Fisher, análisis de Kruskal-Wallis, algoritmo relief-F y test de ganancia de información), que se han empleado en las bases de datos con grandes cantidades de variables independientes para localizar aquellas con mayor importancia o poder discriminativo para una tarea de modelización matemática posterior. Por otro lado, en cuando a dicha modelización, se ha empleado un tipo de algoritmo que se cataloga dentro del área de la inteligencia artificial computacional: las redes neuronales artificiales (ANNs). Estas herramientas matemáticas de naturaleza no lineal se han utilizado para localizar las relaciones existentes entre las variables independientes de un sistema y las variables dependientes o parámetros a estimar o clasificar. Se ha empleado el tipo de ANN supervisada más extensamente usado en investigación, que son los perceptrones multicapa (MLPs), debido a su habilidad contrastada para originar modelos fiables para numerosas aplicaciones...Fac. de Ciencias QuímicasTRUEunpu

Credit-Scoring Methods (in English)

The paper reviews the best-developed and most frequently applied methods of credit scoring employed by commercial banks when evaluating loan applications. The authors concentrate on retail loans – applied research in this segment is limited, though there has been a sharp increase in the volume of loans to retail clients in recent years. Logit analysis is identified as the most frequent credit-scoring method used by banks. However, other nonparametric methods are widespread in terms of pattern recognition. The methods reviewed have potential for application in post-transition countries.banking sector, credit scoring, discrimination analysis, pattern recognition, retail loans

EXPERIMENTS ON AUTOMATIC DRUG ACTIVITY CHARACTERIZATION USING SUPPORT VECTOR CLASSIFICATION

ABSTRACT The characterization of pharmacological properties from their chemical structure has become a challenging and promising technique in computer aided drug design. The idea consists of finding appropriate representations of candidate compounds in terms of their chemical formulae and try to apply a particular machine learning method able to appropriately characterize certain desired properties or kinds of pharmacological activity. In this particular work antibacterial activity has been considered. Several classic pattern classification methods have already been applied to this problem with promising results. In this work, the support vector machine model is considered and compared to multilayer perceptrons in this particular context. The natural and unpredictable imbalance and the fact that only relatively small samples can be used for learning make this a challenging and interesting problem

Diagnosis of Malignant Melanoma using a Neural Network

Malignant melanoma is the deadliest form of all skin cancers. Approximately 32,000 new cases of malignant melanoma were diagnosed in 1991, with approximately 80 percent of patients expected to survive five years [1], Fortunately, if detected early, even malignant melanoma may be treated successfully. Thus, in recent years, there has been a rising interest in the automated detection and diagnosis of skin cancer, particularly malignant melanoma [2]. In this thesis, a novel neural network approach for the automated distinction of melanoma from three benign categories of tumors which exhibit melanoma-like characteristics is presented. The approach is based on devising new and discriminant features which are used as inputs to an artificial neural network for classification of tumor images as malignant or benign. Promising results have been obtained using this method on real skin cancer images