Abstract Automated machine learning (AutoML) and artificial neural networks (ANNs) have revolutionized the field of intelligence by yielding incredibly high-performing models to solve a myriad inductive tasks. In spite their successes, little guidance exists on when use one versus other. Furthermore, relatively few tools exist that allow integration both AutoML ANNs in same analysis yield resul...

A search for neutral supersymmetric Higgs bosons and work relating to the improvement of the b-tagging and trigger capabilities at the DØ detector during Run II of the Fermilab Tevatron collider is presented. The search for evidence of the Higgs sector in the Standard Model (SM) and supersymmetric extensions of the SM are a high priority for the DØ collaboration, and b-tagging and good triggers...

This paper deals with the problem of combination of Neural Networks (NN) and traditional statistical pattern classiiers. It is shown that a Neural Network can be used to replace the vector quantizer (VQ) and some feature extraction and feature reduction modules in a discrete pattern recognition system. A criterion for training the NN-weights and the classiier jointly is derived, leading to the ...

Artificial Neural Networks or widely known as Neural networks (ANNs or NNs) is a computational paradigm that comprises of mathematical, statistical, biological sciences and philosophy. These paradigms formulate a formula to form a brain like function, called artificial neuron. Artificial neuron comprises of large number of computational processing elements called units, nodes or cells. Analogou...

In this paper, an optimized hierarchical B-spline network was employed to detect the breast cancel. For evolving a hierarchical B-spline network model, a tree-structure based evolutionary algorithm and the Particle Swarm Optimization (PSO) are used to find an optimal detection model. The performance of proposed method was then compared with Flexible Neural Tree (FNT), Neural Network (NN), and W...

We describe how multi-stage non-Markovian decision problems can be solved using actor-critic reinforcement learning by assuming that a discrete version of CohenGrossberg node dynamics describes the node-activation computations of a neural network (NN). Our NN (i.e., agent) is capable of rendering the process Markovian implicitly and automatically in a totally model-free fashion without learning...

With the popularity of deep learning (DL), artificial intelligence (AI) has been applied in many areas of human life. Artificial neural network or neural network (NN), the main technique behind DL, has been extensively studied to facilitate computer vision and natural language processing. However, the more we rely on information technology, the more vulnerable we are. That is, malicious NNs cou...

After production and operations, finance and investments are one of the most frequent areas of neural network applications in business. The lack of standardized paradigms that can determine the efficiency of certain NN architectures in a particular problem domain is still present. The selection of NN architecture needs to take into consideration the type of the problem, the nature of the data i...

For managing data in a smart card’s limited memory, containing medical and biometric images, images compression is resorted to. For image retrieval, it is necessary that the classification algorithm be efficient to search and locate the image in a compressed domain. This study proposes a novel training algorithm for Multi-Layer Perceptron Neural Network (MLP-NN) to classify compressed images. M...

In this paper, Neural network model has been used to estimate the feedgap which is one of the design parameters of circular monopole antenna (CMA) required to make it operate in a particular frequency band. A Neural Network (NN) model is prepared using Feed Forward Back Propagation Algorithm which can be further used for designing a CMA operating between 2GHz and 12 GHz. The results obtained by...