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This paper presents the development process of the SUST-Bangla Handwritten Numeral Database (SUST-BHND). We extracted handwritten Bengali digits from twenty-one hundred pre-designed form filled by different people. After data retrieval, cleaning, processing and error analysis we have created a database consisting of 101065 sample images. It provides a basic database for Bangla OCR and script identification...
We compare the performance of multilayer perceptrons (MLPs) obtained using back propagation (BP), decision boundary making (DBM) algorithm and extreme learning machine (ELM), and investigate better method for developing aware agents (A-agent) that are suitable for implementation in portable/wearable computing devices (P/WCD). The DBM has been proposed by us for inducing compact and high performance...
The finite impulse response multilayer perceptron (FIRMLP), a class of temporal processing neural networks, is a multilayer perceptron where the static weights (synapses) have been replaced by finite impulse response filters. Thus FIRMLPs are a type of convolutional neural network and different synapse types can be considered. We compare the performance of different network configurations for the...
The field of artificial neural networks has a long history of several decades, where the theoretical contributions have progressed with advances in terms of power and memory in present day computers. Some old methods are now rebranded or represented, taking advantage of the power of present day computers. More particularly, we consider the current trend of Random Vector Functional Link Networks, which...
Retinal vessel segmentation has been widely used for screening, diagnosis and treatment of cardiovascular and ophthalmologic diseases. In this paper, we propose an automated approach for vessel segmentation in digital retinal images based on de-noising auto-encoders layer-wise initialized neural networks. The proposed method utilized a deep neural network, which is layer-wise initialized by de-noising...
This paper presents an attempt to solve the challenging problem of Devanagari numeral and character recognition. It uses structural and geometric features to represent the Devanagari numerals and characters. Each image is zoned in 9 blocks and 8 structural features are extracted from each block. Similarly 9 global geometric features are extracted. These 81 features are used for representing the image...
The paper presents application of Artificial Neural networks (ANN) in classification system of medical data set, which can be helpful in diagnosis procedures. As a neural network, the perceptron feed forward architecture has been applied. The proposed ANN architecture contains one hidden layer. The elaborated models of ANN, computer simulations and results presented in this paper, have been made by...
This paper proposes a new method to identify people using Electrocardiogram (ECG), particularly the QRS complex which has been proven to be stable against heart rate variability and convenient to be used alone as a biometric feature. 324 QRS complexes are extracted from ECGs of 18 subjects in Physionet's MIT-BIH Normal Sinus Rhythm Database (NSRDB). Multilayer Perceptron (MLP) and Radial Basis Function...
The fully connected feed-forward neural networks are commonly used in almost all neural networks applications, since such architecture provides the best generalisation power. However, they need large computing resources and have low speed when they are applied to large databases. The aim of this paper is to assess the effectiveness of an alternative approach, based on a partially connected neural...
Predicting the class label using neural networks through attribute relevance analysis is presented in this paper. This method has the advantage that the number of units required can be reduced so that we can increase the speed of neural network technique for predicting the class label of the new tuples. In this proposed paper attribute relevance analysis is used to eliminate irrelevant attributes...
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