QuantumIS: A Qualia Consciousness Awareness and Information Theory Quale Approach to Reducing Strategic Decision-Making Entropy

Rodger, James A.

doi:10.3390/e21020125

Cited by 12 publications

(10 citation statements)

References 47 publications

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“…Regarding the SSEQ method, we found that SpacEn SKEW and both SpecEn -derived features can be useful to distinguish images of adequate quality from inadequate quality ones. This result is consistent with other studies that successfully employed entropy-based measurements in the context of RIQA [ 20 , 24 , 26 ], biomedical signal processing [ 41 , 42 ], and strategic decision making [ 43 ].…”

Section: Discussionsupporting

confidence: 91%

“…Entropy-based evaluation is one of the most extended techniques to assess information content in a system. As a consequence, it has been explored in different contexts, including biomedical signal processing [ 41 , 42 ], strategic decision making [ 43 ], and image quality assessment [ 35 , 37 , 44 ]. SSEQ is a NR-IQA method based on features related to spatial and spectral entropies in small non-overlapped regions (blocks) of the image [ 35 ].…”

Section: Methodsmentioning

confidence: 99%

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Combination of Global Features for the Automatic Quality Assessment of Retinal Images

Jiménez-García

Romero-Oraá

García

et al. 2019

Entropy

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Diabetic retinopathy (DR) is one of the most common causes of visual loss in developed countries. Computer-aided diagnosis systems aimed at detecting DR can reduce the workload of ophthalmologists in screening programs. Nevertheless, a large number of retinal images cannot be analyzed by physicians and automatic methods due to poor quality. Automatic retinal image quality assessment (RIQA) is needed before image analysis. The purpose of this study was to combine novel generic quality features to develop a RIQA method. Several features were calculated from retinal images to achieve this goal. Features derived from the spatial and spectral entropy-based quality (SSEQ) and the natural images quality evaluator (NIQE) methods were extracted. They were combined with novel sharpness and luminosity measures based on the continuous wavelet transform (CWT) and the hue saturation value (HSV) color model, respectively. A subset of non-redundant features was selected using the fast correlation-based filter (FCBF) method. Subsequently, a multilayer perceptron (MLP) neural network was used to obtain the quality of images from the selected features. Classification results achieved 91.46% accuracy, 92.04% sensitivity, and 87.92% specificity. Results suggest that the proposed RIQA method could be applied in a more general computer-aided diagnosis system aimed at detecting a variety of retinal pathologies such as DR and age-related macular degeneration.

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Section: Discussionsupporting

confidence: 91%

Section: Methodsmentioning

confidence: 99%

Combination of Global Features for the Automatic Quality Assessment of Retinal Images

Jiménez-García

Romero-Oraá

García

et al. 2019

Entropy

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“…Zhang [25] extended TOPSIS to multiple criteria decision making with Pythagorean fuzzy sets. They determine PIS and NIS according to the following Formula (20) and (21) based on the score function Equation 6and they defined.…”

Section: Illustrative Examplementioning

confidence: 99%

“…Entropy measure [17,18] and its complementary concept knowledge measure [19] are effective ways to determine the weights vector of attributes when making decisions. Rodger considered the decision-maker's intrinsic state and solved the decision-making problem by entropy principles [20]. Harish [21] proposed intuitionistic fuzzy entropy-based method to deal with MCDM problems with unknown criteria weights.…”

Section: Introductionmentioning

confidence: 99%

A New Method for MAGDM Based on Improved TOPSIS and a Novel Pythagorean Fuzzy Soft Entropy

et al. 2019

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A decision-making environment is full of uncertainty and complexity. Existing tools include fuzzy sets, soft sets, intuitionistic fuzzy sets, Pythagorean fuzzy sets (PFSs) and so on. Compared with intuitionistic fuzzy sets (IFSs), PFSs proposed by Yager have advantages in handling vagueness in the real world and possess good symmetry. The entropy measure is the most widespread form of uncertainty measure. In this paper, we improve the technique for order preference by similarity to an ideal solution (TOPSIS) method to better deal with multiple-attribute group decision making (MAGDM) problems based on Pythagorean fuzzy soft sets (PFSSs). To better determine the weights of attributes, we firstly define a novel Pythagorean fuzzy soft entropy which is more reasonable and valid. Meanwhile the entropy has good symmetry. Entropy for PFSSs which is used to determine the subjective weights of attributes is also defined. Then we introduce a measure to calculate integrated weights by combining objective weights and subjective weights. Based on the integrated weights, the TOPSIS method is generalized and improved to solve the MAGDM problem. A distance measure taking into account the characteristics of Pythagorean fuzzy numbers (PFNs) is used to calculate distance between alternatives and ideal solutions. Finally, the proposed MAGDM method is applied in the case of selecting a missile position. Compared with other methods, it is shown that the proposed method can rank alternatives more reasonably and have higher distinguishability.

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“…We design an algorithm for CS Recovery with entropy theory [ 50 ]. We simultaneously learn the hidden parameters and do inference.…”

Section: Image Cs Recovery Via Nonlocal Residual Learning and D-ammentioning

confidence: 99%

An Entropy-Based Algorithm with Nonlocal Residual Learning for Image Compressive Sensing Recovery

Xie

Liu

Yang

2019

Entropy

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Image recovery from compressive sensing (CS) measurement data, especially noisy data has always been challenging due to its implicit ill-posed nature, thus, to seek a domain where a signal can exhibit a high degree of sparsity and to design an effective algorithm have drawn increasingly more attention. Among various sparsity-based models, structured or group sparsity often leads to more powerful signal reconstruction techniques. In this paper, we propose a novel entropy-based algorithm for CS recovery to enhance image sparsity through learning the group sparsity of residual. To reduce the residual of similar packed patches, the group sparsity of residual is described by a Laplacian scale mixture (LSM) model, therefore, each singular value of the residual of similar packed patches is modeled as a Laplacian distribution with a variable scale parameter, to exploit the benefits of high-order dependency among sparse coefficients. Due to the latent variables, the maximum a posteriori (MAP) estimation of the sparse coefficients cannot be obtained, thus, we design a loss function for expectation–maximization (EM) method based on relative entropy. In the frame of EM iteration, the sparse coefficients can be estimated with the denoising-based approximate message passing (D-AMP) algorithm. Experimental results have shown that the proposed algorithm can significantly outperform existing CS techniques for image recovery.

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QuantumIS: A Qualia Consciousness Awareness and Information Theory Quale Approach to Reducing Strategic Decision-Making Entropy

Cited by 12 publications

References 47 publications

Combination of Global Features for the Automatic Quality Assessment of Retinal Images

Combination of Global Features for the Automatic Quality Assessment of Retinal Images

A New Method for MAGDM Based on Improved TOPSIS and a Novel Pythagorean Fuzzy Soft Entropy

An Entropy-Based Algorithm with Nonlocal Residual Learning for Image Compressive Sensing Recovery

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