An Improved Scheme for Digital Watermarking Using Functional Link Artificial Neural Network

Majhi, Banshidhar; Shalabi, Hasan

doi:10.3844/jcssp.2005.169.174

Cited by 8 publications

(5 citation statements)

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“…Authors reported that the use of direct decision feedback can greatly improve the performance of FLANN structures. Majhi et al [40], has applied FLANN for digital watermarking (DwFLANN), their results shows that FLANN has better performance than other algorithms in this line. Purwar et al [61] have proposed a Chebyshev functional link neural network (SyiFLA dynamic non-linear discrete time systems.…”

Section: Flanns For Predictionmentioning

confidence: 98%

Towards Crafting a Smooth and Accurate Functional Link Artificial Neural Networks Based on Differential Evolution and Feature Selection for Noisy Database

Dash¹,

Dehuri

Cho

et al. 2015

IJCIS

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This work presents an accurate and smooth functional link artificial neural network (FLANN) for classification of noisy database. The accuracy and smoothness of the network is taken birth by suitably tuning the parameters of FLANN using differential evolution and filter based feature selection. We use Qclean algorithm for identification of noise, information gain theory for filtering irrelevant features, and then supplied the remaining relevant attributes to the functional expansion unit of FLANN, which in turn map lower to higher dimensional feature space for constructing a smooth and accurate classifier. In specific, the differential evolution is used to fine tune the weight vector of this network and some trigonometric functions are used in functional expansion unit. The proposed approach is validated with a few benchmarking highly skewed and balanced dataset retrieved from University of California, Irvine (UCI) repository with a range of 5-20% noise. The insightful experimental study signifies the propensity of noise in the classification accuracy of a database with a range of noise from 5-20%. Moreover, our method suggests that noisy samples along with irrelevant set of attributes are deceptive and weakening the reliability of the classifier, therefore, it is required to reduce its effect before or during the process of classification.

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Section: Flanns For Predictionmentioning

confidence: 98%

Towards Crafting a Smooth and Accurate Functional Link Artificial Neural Networks Based on Differential Evolution and Feature Selection for Noisy Database

Dash¹,

Dehuri

Cho

et al. 2015

IJCIS

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“…Because of simple linear structure and complex nonlinear mapping capability, FLANN and its variants have been applied in a variety of applications including forecasting, classification, system identification, channel equalisation, etc. Pao and Takefuji (1992), Patra et al (1999a), Marcu and Koppen-Seliger (2004), Patra and Pal (1995), Haring et al (1997), Dehuri et al (2008), Majhi and Shalabi (2005), Patra et al (2008), Dash et al (1999), Sing and Srivastava (2002), Hu (2008), Weng and Yen (2004), Hussain et al (1997), Patra et al (1999b), Mishra and Dehuri (2007), Patra et al (2009), Sun et al (2009), Nanda et al (2009), Chakravarty and Dash (2009), Majhi et al (2009), Emrani et al 2010 Backpropagation Pao and Takefuji (1992), …”

Section: Functional Link Artificial Neural Networkmentioning

confidence: 99%

“…,George and Panda (2012) Application OtherPanagiotopoulos et al (1999), Patra and van den Bos(2000),Majhi and Shalabi (2005),Patra et al (2008),Krishnaiah et al (2008),Sing and Srivastava (2002),Park and Pao (2000),Hu (2008),Chen et al (1998),Hussain et al (1997),Patra et al (1999b),Nanda et al (2009),Parija et al (2013),Cui et al …”

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confidence: 99%

Nonlinear time series forecasting using a novel self-adaptive TLBO-MFLANN model

Panigrahi

Behera

2019

IJCISTUDIES

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In this paper, we have proposed a multiplicative FLANN (MFLANN) model for time series forecasting. In addition, an improved version of teaching learning based optimisation (TLBO), called self-adaptive TLBO (SATLBO) has been proposed to train the MFLANN model. The proposed SATLBO uses the gradient descent learning algorithm in the teacher phase while uses the past experience to adapt the learners parameters. This unique integration of SATLBO with gradient descent learning algorithm is used to determine the near optimal weight set of MFLANN. The proposed method is implemented in MATLAB environment and the obtained results are compared with other methods (DE-based MFLANN, TLBO-based MFLANN, CRO-based MFLANN, Jaya-based MFLANN and ETLBO-JPSNN) considering 11 benchmark univariate time series datasets. Extensive statistical analysis on obtained results indicated that the proposed SATLBO-MFLANN method is better and statistically significant in comparison to its counterparts.

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“…Majhi et al [91] has applied FLNN for digital watermarking (DwFLNN), their results shows that FLNN has better performance than other algorithms in this line. In Neural Comput & Applic [96], a comparative performance of three artificial neural networks has given for the detection and classification of gear faults.…”

Section: Functional Link Neural Network: a Road Mapmentioning

confidence: 99%

A comprehensive survey on functional link neural networks and an adaptive PSO–BP learning for CFLNN

Dehuri

Cho

2009

Neural Comput & Applic

144

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Functional link neural network (FLNN) is a class of higher order neural networks (HONs) and have gained extensive popularity in recent years. FLNN have been successfully used in many applications such as system identification, channel equalization, short-term electricload forecasting, and some of the tasks of data mining. The goals of this paper are to: (1) provide readers who are novice to this area with a basis of understanding FLNN and a comprehensive survey, while offering specialists an updated picture of the depth and breadth of the theory and applications; (2) present a new hybrid learning scheme for Chebyshev functional link neural network (CFLNN); and (3) suggest possible remedies and guidelines for practical applications in data mining. We then validate the proposed learning scheme for CFLNN in classification by an extensive simulation study. Comprehensive performance comparisons with a number of existing methods are presented.

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An Improved Scheme for Digital Watermarking Using Functional Link Artificial Neural Network

Cited by 8 publications

References 0 publications

Towards Crafting a Smooth and Accurate Functional Link Artificial Neural Networks Based on Differential Evolution and Feature Selection for Noisy Database

Towards Crafting a Smooth and Accurate Functional Link Artificial Neural Networks Based on Differential Evolution and Feature Selection for Noisy Database

Nonlinear time series forecasting using a novel self-adaptive TLBO-MFLANN model

A comprehensive survey on functional link neural networks and an adaptive PSO–BP learning for CFLNN

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