Evolutions in artificial intelligence (AI) techniques and their applications to physics have made it feasible to develop and implement new modeling techniques for high-energy interactions. In particular, AI techniques of artificial neural networks (ANN) have recently been used to design and implement more effective models. The neural network (NN) model and parton two fireball model (PTFM) have ...

Recommender systems are now widely used in e-commerce applications to assist customers to find relevant products from the many that are frequently available. Collaborative filtering (CF) is a key component of many of these systems, in which recommendations are made to users based on the opinions of similar users in a system. This paper presents a model-based approach to CF by using supervised A...

Two different artificial neural networks (NN) are used for estimating a time dependent boundary condition (x = 0) in a slab: multilayer perceptron (MP) and radial base function (RBF). The input for the NN is the temperature time-series obtained from a probe next to boundary of interest. Our numerical experiments follow the work of Krejsa et al. [4]. The NNs were trainned considering 5 per cent ...

The use of financial statement data to predict the future jlnancial health of an economic entity is generally considered a complex problem where non-linear pattern recognition methods such as neural networks (NN) can provide a performance advantage. In fact, bankruptcy prediction has emerged as a popular benchmark for neural network performance. However, the use of neural networks in bankruptcy...

This paper presents an accurate Differential Global Positioning System (DGPS) using multi-layered Neural Networks (NNs) based on the Back Propagation (BP) and Imperialistic Competition Algorithm (ICA) in order to predict the DGPS corrections for accurate positioning. Simulation results allowed us to optimize the NN performance in term of residual mean square error. We compare results obtained b...

This paper addresses the problem of inverting Ground Penetrating Radar (GPR) data, to find the buried inclusions characteristics depth and radii considering a nonhomogenous host media by using neural networks (NN). The aim is the detection and characterization of inclusions in concrete structures. A novel asynchronous model is proposed to the NN arrangement. The model is shown to outperform the...

Answer selection plays a key role in community question answering (CQA). Previous research on answer selection usually ignores the problems of redundancy and noise prevalent in CQA. In this paper, we propose to treat different text segments differently and design a novel attentive interactive neural network (AI-NN) to focus on those text segments useful to answer selection. The representations ...

Hierarchical RBF networks consist of multiple RBF networks assembled in different level or cascade architecture. In this paper, an evolved hierarchical RBF network was employed to detect the breast cancel. For evolving a hierarchical RBF network model, Extended Compact Genetic Programming (ECGP), a tree-structure based evolutionary algorithm and the Differential Evolution (DE) are used to find ...

: This paper presents the application of non-destructive evaluation by eddy currents for the determination of the geometrical and physical parameters of metal sheet, obedient to a sensor of a double coil (method of Adding-Opposing (A O)). The forward problem is solved by using an analytical model. The electrical impedance for coil is measured for two frequencies ranging from 1 kHz and 1 MHz. Th...

Neural networks have recently been proposed for multi-label classification because they are able to capture and model label dependencies in the output layer. In this work, we investigate limitations of BP-MLL, a neural network (NN) architecture that aims at minimizing pairwise ranking error. Instead, we propose to use a comparably simple NN approach with recently proposed learning techniques fo...