Artificial neural network (ANN) based prediction of the response of a microbend fiber optic sensor is presented. To the best of our knowledge no similar work has been previously reported in the literature. Parallel corrugated plates with three deformation cycles, 6 mm thickness of the spacer material and 16 mm mechanical periodicity between deformations were used in the microbend sensor. Multil...

Artificial Neural Network (ANN) is an effective technique of Soft Computing can model ComputerAided Diagnosis (CAD) system efficiently. CAD system is an essential for the prediction of Malignancy in Cervical Cancer. Cervical Cancer can be cured if it is diagnosed in early stages. Hence, for the effective screening of cancer lesions in the Cervical cell images which are captured using Pap smear ...

1 2 m 1 2 k 1 2 n Hash bin address in binary hash Key Compress to a shorter binary code Figure 1: Architecture of Neuro-Hasher consists of a two layer feedforward neural net. The generic function of a feedforward multilayer perceptron (MLP) network is to map patterns from one space to another. This mapping function, determined by the set of examples used to train the network, may be viewed as a...

Blind image quality assessment can be modeled as feature extraction followed by score prediction. It necessitates considerable expertise and efforts to handcraft features for optimal representation of perceptual image quality. This paper addresses blind image sharpness assessment by using a shallow convolutional neural network (CNN). The network takes single feature layer to unearth intrinsic f...

A computationally efficient artificial neural network (ANN) for the purpose of dynamic nonlinear system identification is proposed. The major drawback of feedforward neural networks, such as multilayer perceptrons (MLPs) trained with the backpropagation (BP) algorithm, is that they require a large amount of computation for learning. We propose a single-layer functional-link ANN (FLANN) in which...

We address a contrastive study between the well known Multi-Layer Perceptron (MLP) and Radial Basis Function (RBF) neural networks and a SOM based supervised architecture in a number of data classification tasks. Well known databases like Breast Cancer, Parkinson and Iris were used to evaluate the three architectures by constructing confusion matrices. The results are encouraging and indicate t...

This paper presents a comparative study of different neural network models for forecasting the weather of Vancouver, British Columbia, Canada. For developing the models, we used one year’s data comprising of daily maximum and minimum temperature, and wind-speed. We used Multi-Layered Perceptron (MLP) and an Elman Recurrent Neural Network (ERNN), which were trained using the one-step-secant and ...

In this study a new artificial neural network based approach to automatic or semi-automatic colorization of black and white film footages is introduced. Different features of black and white images are tried as the input of a MLP neural network which has been trained to colorize the movie using its first frame as the ground truth. Amongst the features tried, e.g. position, relaxed position, lum...

Introduction: Repetitive strain injuries are one of the most prevalent problems in occupational diseases. Repetition, vibration and bad postures of the extremities are physical risk factors related to work that can cause chronic musculoskeletal disorders. Repetitive work on a computer with low level contraction requires the posture to be maintained for a long time, which can cause muscle fatigu...

AI 057 Abstract The Supervised Growing Neural Gas algorithm (SGNG) provides an interesting alternative to standard multilayer perceptrons (MLP). A comparison is drawn between the performance of SGNG and MLP in the domain of function mapping. A further eld of interest is classiication power, which has been investigated with real data taken by PS197 at CERN. The characteristics of the two network...