DETECTION OF FINANCIAL INFORMATION MANIPULATION BY AN ENSEMBLE-BASED MECHANISM

Abstract: Complicated financial information manipulation, involving heightened offender knowledge of transactional procedures, can be damaging to the reputations of corporations and the auditors, as well as cause serious turbulence in financial markets. Unfortunately, most incidents of financial information manipulation involve higher level managers who are truly knowledgeable and comprehend the limitations of standard auditing procedures. Thus, there is an urgent need for additional detection mechanisms to prevent financial information manipulation. To address this problem, the author proposes an ensemble-based mechanism (EM) consisting of feature selection and extraction ensemble and extreme learning machine (ELM). The model not only counters the redundancy-removing problem, but also gives direction to auditors who need to allocate limited audit resources to abnormal client relationships during the auditing procedure and protect the CPA firms' reputation. The experimental results demonstrate that the model is a promising alternative for detecting financial information manipulation, and one that can ensure both the confidence of investors and the stability of financial markets

Parametric and Non-Parametric Analyses for Pedestrian Crash Severity Prediction in Great Britain

The study aims to investigate the factors that are associated with fatal and severe vehicle– pedestrian crashes in Great Britain by developing four parametric models and five non-parametric tools to predict the crash severity. Even though the models have already been applied to model the pedestrian injury severity, a comparative analysis to assess the predictive power of such modeling techniques is limited. Hence, this study contributes to the road safety literature by comparing the models by their capabilities of identifying the significant explanatory variables, and by their performances in terms of the F-measure, the G-mean, and the area under curve. The analyses were carried out using data that refer to the vehicle–pedestrian crashes that occurred in the period of 2016–2018. The parametric models confirm their advantages in offering easy-to-interpret outputs and understandable relations between the dependent and independent variables, whereas the non-parametric tools exhibited higher classification accuracies, identified more explanatory variables, and provided insights into the interdependencies among the factors. The study results suggest that the combined use of parametric and non-parametric methods may effectively overcome the limits of each group of methods, with satisfactory prediction accuracies and the interpretation of the factors contributing to fatal and serious crashes. In the conclusion, several engineering, social, and management pedestrian safety countermeasures are recommended

Clasificador de máquinas de vectores de soporte para problemas desbalanceados con selección automática de parámetros

La mayoría de los métodos de clasificación asumen que el número de muestras en las clases estudiadas son las mismas (balanceadas). Sin embargo, realizar esta asunción puede llevar a desempeños sesgados, ya que, la mayoría de aplicaciones y bases de datos reales no son balanceadas, llevando a que estos métodos ignoren la clase minoritaria (la clase con el menor número de muestras). Este trabajo propone un clasificador novedoso, llamado enhanced twin support vector machine–(ETWSVM), que representa las muestras de entrada en un espacio de características de alta dimensionalidad, posiblemente infinita, durante la construcción de una frontera de decisión bajo la filosofía del twin support vector machine–(TWSVM). También, usamos un método basado en centered kernel alignment–(CKA) para aprender la función kernel con el fin de contrarrestar los problemas inherentes del desbalance y mejorar la separabilidad de los datos. Además, adoptamos las estrategias One-versus-Rest y One-versus-One para extender la formulación del ETWSVM a tareas de clasificación multiclase. De los resultados obtenidos sobre bases de datos sintéticas y reales, nuestra propuesta supera métodos del estado del arte con respecto al desempeño (precisión, media geométrica, F-measure), y tiempo de entrenamiento. En efecto, después analizamos la sensibilidad de los parámetros libres para diferentes tasas de desbalance y traslape entre las clases, y sugerimos una variante del ETWSVMN automático que registra una indicada relación entre desempeño de clasificación y tiempo de entrenamiento

Clasificador de máquinas de vectores de soporte para problemas desbalanceados con selección automática de parámetros

La mayoría de los métodos de clasificación asumen que el número de muestras en las clases estudiadas son las mismas (balanceadas). Sin embargo, realizar esta asunción puede llevar a desempeños sesgados, ya que, la mayoría de aplicaciones y bases de datos reales no son balanceadas, llevando a que estos métodos ignoren la clase minoritaria (la clase con el menor número de muestras). Este trabajo propone un clasificador novedoso, llamado enhanced twin support vector machine–(ETWSVM), que representa las muestras de entrada en un espacio de características de alta dimensionalidad, posiblemente infinita, durante la construcción de una frontera de decisión bajo la filosofía del twin support vector machine–(TWSVM). También, usamos un método basado en centered kernel alignment–(CKA) para aprender la función kernel con el fin de contrarrestar los problemas inherentes del desbalance y mejorar la separabilidad de los datos. Además, adoptamos las estrategias One-versus-Rest y One-versus-One para extender la formulación del ETWSVM a tareas de clasificación multiclase. De los resultados obtenidos sobre bases de datos sintéticas y reales, nuestra propuesta supera métodos del estado del arte con respecto al desempeño (precisión, media geométrica, F-measure), y tiempo de entrenamiento. En efecto, después analizamos la sensibilidad de los parámetros libres para diferentes tasas de desbalance y traslape entre las clases, y sugerimos una variante del ETWSVMN automático que registra una indicada relación entre desempeño de clasificación y tiempo de entrenamiento

Pitch Angle Control for a Small-Scale Darrieus Vertical Axis Wind Turbine with Straight Blades (H-type VAWT)

Unlike the horizontal axis wind turbines, only a few studies have been conducted recently to improve the performance of a Darrieus Vertical Axis Wind Turbine with straight blades (H-type VAWT). Pitch angle control technique is used to enhance the performance of an H-type VAWT in terms of power output and self-starting capability. This thesis aims to investigate the performance of an H-type VAWT using an intelligent blade pitch control system. Computational Fluid Dynamics (CFD) is used to determine the optimum pitch angles and study their effects on the aerodynamic performance of a 2D H-type VAWT at different Tip Speed Ratios (TSRs) by calculating the power coefficient (Cp). The results obtained from the CFD model are used to construct the aerodynamic model of an H-type VAWT rotor, which is required to design an intelligent pitch angle controller based on Multi-Layer Perceptron Artificial Neural Networks (MLP-ANN) method. The performance of the blade pitch controller is investigated by adding a conventional controller (PID) to the MLP-ANN controller (i.e., Hybrid controller). For stability analysis, an H-type VAWT is modeled in nonlinear state space by determining the mathematical models for an H-type VAWT components along with Hybrid control scheme. The effectiveness of proposed pitch control system and the CFD results are validated by building an H-type VAWT prototype. This prototype is tested outdoor extensively at different wind conditions for both fixed and variable pitch angle configurations. Results demonstrate that the blade pitching technique enhanced the performance of an H-type VAWT in terms of power output by around 22%

SIMMEC 2016

Nota de responsabilidade: os autores s?o os ?nicos respons?veis pelo material reproduzido nesse artigo.O Simp?sio de Mec?nica Computacional (SIMMEC) ? um evento multidisciplinar de ?mbito nacional realizado desde 1991 como evento da Associa??o Brasileira de M?todos Computacionais em Engenharia (ABMEC). Seu objetivo ? a divulga??o da produ??o t?cnica e cient?fica na ?rea de m?todos computacionais aplicados a diversas ?reas da engenharia, incentivando a gera??o de conhecimento, parcerias e produtos. O XII SIMMEC foi realizado de 23 a 25 de maio de 2016 na cidade de Diamantina, Minas Gerais, cidade Patrim?nio Cultural da Humanidade desde 1999. Esta edi??o foi organizada pelo Instituto de Ci?ncia e Tecnologia da Universidade Federal dos Vales do Jequitinhonha e Mucuri. Nesta edi??o o SIMMEC contou com contribui??es nas seguintes ?reas tem?ticas: biomec?nica, computa??o cient?fica, din?mica e vibra??o, fen?menos de transporte, mec?nica dos s?lidos, m?todos num?ricos e otimiza??o.Coordena??o de Aperfei?oamento de Pessoal de N?vel Superior (CAPES)Funda??o de Amparo ? Pesquisa do Estado de Minas Gerais (FAPEMIG

Error back-propagation algorithm for classification of imbalanced data, Neurocomputing 74 (6

Abstract Classification of imbalanced data is pervasive but it is a difficult problem to solve. In order to improve the classification of imbalanced data, this letter proposes a new error function for the error back-propagation algorithm of multilayer perceptrons. The error function intensifies weight-updating for the minority class and weakens weight-updating for the majority class. We verify the effectiveness of the proposed method through simulations on mammography and thyroid data sets