Efficient Wind Speed Forecasting Using Discrete Wavelet Transform and Artificial Neural Networks

In the present chapter, the widely common problem of handwritten character recognition has been tackled with multiresolution technique using discrete wavelet transform and artificial neural networks. The technique has been tested and found to be more accurate and economic in respect of the recognition process time of the system. Features of the handwritten character images are extracted by discrete wavelet transform used with appropriate level of multiresolution technique, then the artificial neural networks is trained by extracted features. The unknown input handwritten character images are recognized by trained artificial neural networks system. The proposed method provides good recognition accuracy for handwritten characters with less training time, less no. of samples and less no. of iterations.

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Wavelet-Based Recognition of Handwritten Characters Using Artificial Neural Network

Handbook of Research on Generalized and Hybrid Set Structures and Applications for Soft Computing - Advances in Computational Intelligence and Robotics ◽

10.4018/978-1-4666-9798-0.ch022 ◽

2016 ◽

pp. 473-489

Author(s):

D. K. Patel ◽

T. Som ◽

M. K. Singh

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Wavelet Transform ◽

Discrete Wavelet Transform ◽

Character Recognition ◽

Process Time ◽

Discrete Wavelet ◽

Training Time ◽

Handwritten Character ◽

Artificial Neural

In the present chapter, the widely common problem of handwritten character recognition has been tackled with multiresolution technique using discrete wavelet transform and artificial neural networks. The technique has been tested and found to be more accurate and economic in respect of the recognition process time of the system. Features of the handwritten character images are extracted by discrete wavelet transform used with appropriate level of multiresolution technique, then the artificial neural networks is trained by extracted features. The unknown input handwritten character images are recognized by trained artificial neural networks system. The proposed method provides good recognition accuracy for handwritten characters with less training time, less no. of samples and less no. of iterations.

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Face Identification Based on Discrete Wavelet Transform and Neural Networks

International Journal of Image and Graphics ◽

10.1142/s0219467819500220 ◽

2019 ◽

Vol 19 (04) ◽

pp. 1950022

Author(s):

Samrat P. Khadilkar ◽

Sunil R. Das ◽

Mansour H. Assaf ◽

Satyendra N. Biswas

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Wavelet Transform ◽

Discrete Wavelet Transform ◽

Identification Problem ◽

Recognition System ◽

Face Identification ◽

Discrete Wavelet ◽

Localization Property ◽

Artificial Neural

The subject paper presents implementation of a new automatic face recognition system. To formulate an automated framework for the recognition of human faces is a highly challenging endeavor. The face identification problem is particularly very crucial in the context of today’s rapid emergence of technological advancements with ever expansive requirements. It has also significant relevance in the related engineering disciplines of computer graphics, pattern recognition, psychology, image processing and artificial neural networks. This paper proposes a side-view face authentication approach based on discrete wavelet transform and artificial neural networks for the solution of the problem. A subset determination strategy that expands on the number of training samples and permits protection of the global information is discussed. The authentication technique involves image profile extraction, decomposition of the wavelets, splitting of the subsets and finally neural network verification. The procedure exploits the localization property of the wavelets in both the frequency and spatial domains, while maintaining the generalized properties of the neural networks. The realization strategy of the methodology was executed using MATLAB, demonstrating that the performance of the technique is quite satisfactory.

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Three-day forecasting of greenhouse gas CH4 in the atmosphere of the Arctic Belyy Island using discrete wavelet transform and artificial neural networks

Neural Computing and Applications ◽

10.1007/s00521-021-05792-3 ◽

2021 ◽

Author(s):

Anna Rakhmatova ◽

Alexander Sergeev ◽

Andrey Shichkin ◽

Alexander Buevich ◽

Elena Baglaeva

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Wavelet Transform ◽

Discrete Wavelet Transform ◽

Greenhouse Gas ◽

The Arctic ◽

Discrete Wavelet ◽

Artificial Neural

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Classification of fetal abnormalities based on CTG signal

Baghdad Science Journal ◽

10.21123/bsj.11.2.681-689 ◽

2014 ◽

Vol 11 (2) ◽

pp. 681-689

Author(s):

Baghdad Science Journal

Keyword(s):

Neural Networks ◽

Heart Rate ◽

Fuzzy Logic ◽

Artificial Neural Networks ◽

Wavelet Transform ◽

Discrete Wavelet Transform ◽

Fetal Heart Rate ◽

Fetal Heart ◽

Discrete Wavelet ◽

Artificial Neural

The fetal heart rate (FHR) signal processing based on Artificial Neural Networks (ANN),Fuzzy Logic (FL) and frequency domain Discrete Wavelet Transform(DWT) were analysis in order to perform automatic analysis using personal computers. Cardiotocography (CTG) is a primary biophysical method of fetal monitoring. The assessment of the printed CTG traces was based on the visual analysis of patterns that describing the variability of fetal heart rate signal. Fetal heart rate data of pregnant women with pregnancy between 38 and 40 weeks of gestation were studied. The first stage in the system was to convert the cardiotocograghy (CTG) tracing in to digital series so that the system can be analyzed ,while the second stage ,the FHR time series was transformed using transform domains Discrete Wavelet Transform(DWT) in order to obtain the system features .At the last stage the approximation coefficients result from the Discrete Wavelet Transform were fed to the Artificial Neural Networks and to the Fuzzy Logic, then compared between two results to obtain the best for classifying fetal heart rate.

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