Similarity measures for intuitionistic fuzzy sets: State of the art

Baccour, Leila; Alimi, Adel M.; John, Robert

doi:10.3233/ifs-2012-0527

Cited by 63 publications

(21 citation statements)

References 27 publications

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“…To validate the effectiveness and quality of image enhancement of the proposed scheme, we have introduced some well-known image quality measurement matrices such as mean square error, mean absolute error (MAE), index of fuzziness, and so on. 23,31,32 In this article, MAE, 32 linear index of fuzziness (LIF), 32 Weber-law-based contrast measure (EMEE), 31 Michelson law measure of enhancement (AME), 10 Michelson law measure of enhancement by entropy (AMEE), 31 universal quality index (UQI), 23 structural similarity (SSIM), 27,31 fuzzy quality index (FSQI), 27 and intuitionistic fuzzy quality index (IFSQI) 23,27 have been utilized to evaluate the improved contrast quality as well as the visual clarity of the enhanced images. As mentioned above, all values of each metric of larger index indicate better enhancement result and clear visual quality except MAE.…”

Section: Performance Metrics and Baseline Methodsmentioning

confidence: 99%

“…In fuzzy plane, the membership function of foreground and background IFSS from Equations (12) to (13) can be considered as the bright pixels in foreground area with different degree of levels and dim pixels in background area for a gray level image. 27 So, to quantify the brightness variation and gray level variation, we slice IFSS  (x) and IFSS  (x) to -cut set using Equations (5) and 6as:…”

Section: Hesitant Fuzzy Setsmentioning

confidence: 99%

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Development of intuitionistic fuzzy special embedded convolutional neural network for mammography enhancement

Ghosh

Biswas

Ghosh³

2020

Computational Intelligence

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This article proposes a novel mammogram enhancement approach using adaptive intuitionistic fuzzy special set (IFSS) with deep convolutional neural network (called MECNNIFS) for visual interpretation of mammography lesions, lumps, and abnormal cells in low-dose X-ray images. The proposed MECNNIFS scheme utilizes the membership grade modification by IFSS on low-dose X-ray images (mammography). The suggested model attempts to increase the underexposed and abnormal structural regions such as breast lesions, lumps, and nodules on the mammogram. The proposed algorithm initially separates mammograms using convolutional neural networks (CNNs) into foreground and background areas and then fuzzifies the image by intuitionistic fuzzy set theory. Low-level features of a mammogram of the adjacent part are integrated with CNN in pixel classification during the separation task stage to improve the performance. Hyperbolic regularization and hesitant score have been applied on fuzzy plane to quantify the uncertainty and fuzziness in spatial domain for the proposed contrast enhancement. Finally, an enhanced mammogram is acquired through the process of defuzzification. The results show better quality and performance for improvement of contrast and Mammography Lesion Detection using IFSS and CNN.

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Section: Performance Metrics and Baseline Methodsmentioning

confidence: 99%

Section: Hesitant Fuzzy Setsmentioning

confidence: 99%

Development of intuitionistic fuzzy special embedded convolutional neural network for mammography enhancement

Ghosh

Biswas

Ghosh³

2020

Computational Intelligence

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“…n POF * (7)  The Spread (∆): The metric ∆ measures the diversity between consecutive solutions inside the Pareto front PF. Mathematically, ∆ is presented in equation 8:…”

Section: Performance Metricmentioning

confidence: 99%

Dynamic Multi Objective Particle Swarm Optimization Based on a New Environment Change Detection Strategy

Aboud

Fdhila

Alimi

2017

Lecture Notes in Computer Science

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The dynamic of real-world optimization problems raises new challenges to the traditional particle swarm optimization (PSO). Responding to these challenges, the dynamic optimization has received considerable attention over the past decade. This paper introduces a new dynamic multi-objective optimization based particle swarm optimization (Dynamic-MOPSO).The main idea of this paper is to solve such dynamic problem based on a new environment change detection strategy using the advantage of the particle swarm optimization. In this way, our approach has been developed not just to obtain the optimal solution, but also to have a capability to detect the environment changes. Thereby, Dynamic-MOPSO ensures the balance between the exploration and the exploitation in dynamic research space. Our approach is tested through the most popularized dynamic benchmark's functions to evaluate its performance as a good method.

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“…The optimization can be incorporated into other intelligent tools of soft computing such as the neural network [9,10] and the fuzzy system [11] to produce better and faster result. In fact, Swarm intelligence (SI) is considered as an adaptable concept for the optimization problem.…”

Section: Introductionmentioning

confidence: 99%

Multi Objective Particle Swarm Optimization Based Cooperative Agents with Automated Negotiation

Kouka

Fdhila

Alimi

2017

Neural Information Processing

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This paper investigates a new hybridization of multi-objective particle swarm optimization (MOPSO) and cooperative agents (MOPSO-CA) to handle the problem of stagnation encounters in MOPSO, which leads solutions to trap in local optima. The proposed approach involves a new distribution strategy based on the idea of having a set of a sub-population, each of which is processed by one agent. The number of the sub-population and agents are adjusted dynamically through the Pareto ranking. This method allocates a dynamic number of sub-population as required to improve diversity in the search space. Additionally, agents are used for better management for the exploitation within a sub-population, and for exploration among sub-populations. Furthermore, we investigate the automated negotiation within agents in order to share the best knowledge. To validate our approach, several benchmarks are performed. The results show that the introduced variant ensures the trade-off between the exploitation and exploration with respect to the comparative algorithms.

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Similarity measures for intuitionistic fuzzy sets: State of the art

Cited by 63 publications

References 27 publications

Development of intuitionistic fuzzy special embedded convolutional neural network for mammography enhancement

Development of intuitionistic fuzzy special embedded convolutional neural network for mammography enhancement

Dynamic Multi Objective Particle Swarm Optimization Based on a New Environment Change Detection Strategy

Multi Objective Particle Swarm Optimization Based Cooperative Agents with Automated Negotiation

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