The Use of Computational Intelligence in Digital Watermarking: Review, Challenges, and New Trends

Darwish, Ashraf; Abraham, Ajith

doi:10.14311/nnw.2011.21.017

Cited by 6 publications

(2 citation statements)

References 60 publications

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“…It is also defined as adaptive non-linear and self-organized processing algorithms. It has multiple processing units called neurons, which are interconnected and distributed in different layers of the network, which have the ability to learn based on their inputs and are adapted according to the learning obtained from their environment [26].…”

Section: Introductionmentioning

confidence: 99%

An approach on the implementation of full batch, online and mini-batch learning on a Mamdani based neuro-fuzzy system with center-of-sets defuzzification: Analysis and evaluation about its functionality, performance, and behavior

et al. 2019

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Due to the rapid technological evolution and communications accessibility, data generated from different sources of information show an exponential growth behavior. That is, volume of data samples that need to be analyzed are getting larger, so the methods for its processing have to adapt to this condition, focusing mainly on ensuring the computation is efficient, especially when the analysis tools are based on computational intelligence techniques. As we know, if you do not have a good control of the handling of the volume of the data, some techniques that are based on learning iterative processes could represent an excessive load of computation and could take a prohibitive time in trying to find a solution that could not come close to desired. There are learning methods known as full batch, online and mini-batch, and they represent a good strategy to this problem since they are oriented to the processing of data according to the size or volume of available data samples that require analysis. In this first approach, synthetic datasets with a small and medium volume were used, since the main objective is to define its implementation and in experimentation phase through regression analysis obtain information that allows us to assess the performance and behavior of different learning methods under distinct conditions. To carry out this study, a Mamdani based neuro-fuzzy system with center-of-sets defuzzification with support of multiple inputs and outputs was designed and implemented that had the flexibility to use any of the three learning methods, which were implemented within the training process. Finally, results show that the learning method with best performances was Mini-Batch when compared to full batch and online learning methods. The results obtained by mini-batch learning method are as follows; mean correlation coefficient with 0.8268 and coefficient of determination with 0.7444, and is also the method with better control of the dispersion between the results obtained from the 30 experiments executed per each dataset processed.

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Section: Introductionmentioning

confidence: 99%

An approach on the implementation of full batch, online and mini-batch learning on a Mamdani based neuro-fuzzy system with center-of-sets defuzzification: Analysis and evaluation about its functionality, performance, and behavior

et al. 2019

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“…Machine learning is effective in an exploratory analysis scenario where there are no predetermined notions about what will constitute an interesting outcome [12]. Various techniques have been employed including support vector machines [13], rough set theory [14], decision trees [15] and neural networks [16,17].…”

Section: Introductionmentioning

confidence: 99%

A Relevance Vector Machine With Rough Set Theory Model in Analyzing the Life Cycle of New Economic Firms

Hsu

Pai

Chung³

2013

NNW

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Abstract:Most of the traditional clustering algorithms are poor for clustering more complex structures other than the convex spherical sample space. In the past few years, several spectral clustering algorithms were proposed to cluster arbitrarily shaped data in various real applications. However, spectral clustering relies on the dataset where each cluster is approximately well separated to a certain extent. In the case that the cluster has an obvious inflection point within a non-convex space, the spectral clustering algorithm would mistakenly recognize one cluster to be different clusters. In this paper, we propose a novel spectral clustering algorithm called HSC combined with hierarchical method, which obviates the disadvantage of the spectral clustering by not using the misleading information of the noisy neighboring data points. The simple clustering procedure is applied to eliminate the misleading information, and thus the HSC algorithm could cluster both convex shaped data and arbitrarily shaped data more efficiently and accurately. The experiments on both synthetic data sets and real data sets show that HSC outperforms other popular clustering algorithms. Furthermore, we observed that HSC can also be used for the estimation of the number of clusters.

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Watermarking 3D Triangular Mesh Models Using Intelligent Vertex Selection

Soliman

Hassanien

Onsi

2016

Proceedings of the Second International Afro-European Conference for Industrial Advancement AECIA 2015

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The Use of Computational Intelligence in Digital Watermarking: Review, Challenges, and New Trends

Cited by 6 publications

References 60 publications

An approach on the implementation of full batch, online and mini-batch learning on a Mamdani based neuro-fuzzy system with center-of-sets defuzzification: Analysis and evaluation about its functionality, performance, and behavior

An approach on the implementation of full batch, online and mini-batch learning on a Mamdani based neuro-fuzzy system with center-of-sets defuzzification: Analysis and evaluation about its functionality, performance, and behavior

A Relevance Vector Machine With Rough Set Theory Model in Analyzing the Life Cycle of New Economic Firms

Watermarking 3D Triangular Mesh Models Using Intelligent Vertex Selection

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