Hybrid GMDH model for handwritten character recognition

Dhawan, Pranaab; Dongre, Snehlata; Tidke, D. J.

doi:10.1109/imac4s.2013.6526498

Cited by 5 publications

(2 citation statements)

References 10 publications

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“…The Convolution neural network combines outputs of various neural networks and gives better results [7]. Convolutional neural networks have multiple layers of collection of small neuron which look at the small parts of the input image.…”

Section: Classificationmentioning

confidence: 99%

A Hybrid Approach towards Cost Effective Model for Handwritten Character Recognition

Chauhan¹,

Sharma²,

Singh³

2014

IJCA

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Handwritten character is gaining a lot of attention in the area of pattern recognition as its applications in various fields are increasing day by day. HCR system is providing us with a key factor to a paperless environment. Feature Extraction is a key part for a cost effective model for handwritten character recognition. Effective features improve the recognition rate and misclassification. A hybrid model provides better performance in comparison of the individual. Convolution neural networks are viewed to be more efficient to optimize the recognition ability of HCR system.

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Section: Classificationmentioning

confidence: 99%

A Hybrid Approach towards Cost Effective Model for Handwritten Character Recognition

Chauhan¹,

Sharma²,

Singh³

2014

IJCA

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“…Also, there may be a possibility of multicocolinearity, which occurs in the learning calculation of neurons, leading to instability in the prediction values of time series (Kondo, Ueno & Takao, 2013). Currently, there are several reviews about the hybrid modelling, which suggest that hybrid system obtained better performance and accuracy level as compared to the traditional or conventional system (Kim, Seo & Park, 2009;Samsudin, Saad & Shabri, 2011;Dhawan, Dongre & Tidke, 2013;Shabri & Samsudin, 2014a;Basheer & Khamis, 2017).…”

Section: Introductionmentioning

confidence: 99%

Modelling and Forecasting of Monthly Crude Palm Oil Price of Malaysia using Hybrid Wavelet-Modified GMDH Model

Basheer¹,

Khamis²

2022

ASMScJ

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This study comprises developing a more appropriate hybrid wavelet-modified GMDH model for forecasting the monthly crude palm oil (CPO) price of Malaysia. In the proposed hybrid model, the complex data of monthly CPO price is decomposed into different sub series using discrete wavelet transform (DWT) and then it has been linked with modified GMDH model. Sigmoid, radial basis, tangent and polynomial functions are selected as transfer functions in modified GMDH for the best fit and correct model compared to conventional GMDH. The monthly CPO data were taken from Malaysian Palm Oil Board (MPOB) spanning the period January 1983 to November 2019. The capabilities of modified GMDH and hybrid wavelet-modified GMDH in modelling and forecasting the monthly CPO price are determined by MAE, RMSE, MAPE, R and R2. The MAPE of the proposed hybrid wavelet-modified GMDH model for the monthly CPO price of Malaysia is less than 4 % and coefficient of correlation (R) is 0.99, which show an excellent fit compared to the individual modified GMDH model. The proposed hybrid model provides the best alternative tools to help the Malaysian industry deal with price variations and assists Malaysia in playing a dominant role worldwide in the international market.

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Transfer Learning in GMDH-Type Neural Networks

Abdullahi¹,

Akter

2018

Cryptology and Network Security

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Hybrid GMDH model for handwritten character recognition

Cited by 5 publications

References 10 publications

A Hybrid Approach towards Cost Effective Model for Handwritten Character Recognition

A Hybrid Approach towards Cost Effective Model for Handwritten Character Recognition

Modelling and Forecasting of Monthly Crude Palm Oil Price of Malaysia using Hybrid Wavelet-Modified GMDH Model

Transfer Learning in GMDH-Type Neural Networks

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