Neural Network Applications

Artificial neural networks, also called neural networks, have been used successfully in many fields including engineering, science and business. This paper presents the implementation of several neural network simulators and their applications in character recognition and other engineering area

On the partition sum of the NS five-brane

We study the Type IIA NS five-brane wrapped on a Calabi-Yau manifold X in a double-scaled decoupling limit. We calculate the euclidean partition function in the presence of a flat RR 3-form field. The classical contribution is given by a sum over fluxes of the self-dual tensor field which reduces to a theta-function. The quantum contributions are computed using a T-dual IIB background where the five-branes are replaced by an ALE singularity. Using the supergravity effective action we find that the loop corrections to the free energy are given by B-model topological string amplitudes. This seems to provide a direct link between the double-scaled little strings on the five-brane worldvolume and topological strings. Both the classical and quantum contributions to the partition function satisfy (conjugate) holomorphic anomaly equations, which explains an observation of Witten relating topological string theory to the quantization of three-form fields.Comment: 35 page

Neural Networks: Implementations and Applications

Artificial neural networks, also called neural networks, have been used successfully in many fields including engineering, science and business. This paper presents the implementation of several neural network simulators and their applications in character recognition and other engineering area

Using genetic algorithms with grammar encoding to generate neural networks

Kitano's approach to neural network design is extended in the sense that not just the neural network structure, but also the values of the weights are coded in the chromosome. Experimental results are presented demonstrating the capability of the technique in the solution of a standard test problem

Integrating Evolutionary Computation with Neural Networks

There is a tremendous interest in the development of the evolutionary computation techniques as they are well suited to deal with optimization of functions containing a large number of variables. This paper presents a brief review of evolutionary computing techniques. It also discusses briefly the hybridization of evolutionary computation and neural networks and presents a solution of a classical problem using neural computing and evolutionary computing technique

Cytochrome P4501-inhibiting chemicals amplify aryl hydrocarbon receptor activation and IL-22 production in T helper 17 cells

The aryl hydrocarbon receptor (AHR)controls interleukin 22 production by T helper 17 cells (Th17). IL-22contributes to intestinalhomeostasis but has also been implicated inchronic inflammatory disorders and colorectal cancer, highlighting the need for appropriate regulation of IL-22 production. Upon activation, the AHR induces expression of cytochrome P4501 (CYP1) enzymes that in turn play an important feedback role that curtails the duration of AHR signaling by metabolizingAHRligands. Recently we described how agents that inhibit CYP1 function potentiate AHR signalingby disruptingmetabolic clearance of the endogenous ligand 6-formylindolo[3,2-b]carbazole (FICZ). In the present study, we investigated the immune-modulating effects of environmental pollutants such as polycyclic aromatic hydrocarbons on Th17 differentiation and IL-22 production. Using Th17 cells deficient in CYP1 enzymes (Cyp1a1/1a2/1b1-/-)we show that these chemicals potentiate AHR activation through inhibition of CYP1 enzymes which leads to increases in intracellular AHR agonists. Our findings demonstrate that IL-22 production by Th17 cellsis profoundly enhanced by impaired CYP1-function and strongly suggest that chemicals able to modify CYP1 function or expression may disrupt AHR-mediated immune regulation by altering the levels of endogenous AHR agonist(s)

Automatic General of a Neural Network Architecture Using Evolutionary Computation

This paper reports the application of evolutionary computation in the automatic generation of a neural network architecture. It is a usual practice to use trial and error to find a suitable neural network architecture. This is not only time consuming but may not generate an optimal solution for a given problem. The use of evolutionary computation is a step towards automation in architecture generation. In this paper a brief introduction to the field is given as well as an implementation of automatic neural network generation using genetic programmin