Detection of diabetic retinopathy using partial swarm optimization (PSO) and Gaussian interval type-2 fuzzy membership functions (GIT2FMFS)

Recently, high resolution remote sensing image segmentation is a hot issue in image procesing procedures. However, it is a difficult task. The difficulties derive from the uncertainties of pixel segmentation and decision-making model. To this end, we take spatial relationship into consideration when constructing the interval type-2 fuzzy neural networks for high resolution remote sensing image segmentation. First, the proposed algorithm constructs a Gaussian model as a type-1 fuzzy model to describe the uncertainty contained in the image. Second, interval type-2 fuzzy model is obtained by blurring the mean and variance in type-1 model. The proposed interval type-2 model can strengthen the expression of uncertainty and simultaneously decrease the uncertainty in the decision model. Then the fuzzy membership function itself and its upper and lower fuzzy membership functions of the training samples are used as the input of neuron network which acts as the decision model in proposed algorithm. Finally, the relationship of neighbour pixels is taken into consideration and the fuzzy membership functions of the detected pixel and its neighbourhood are used to decide the class of each pixel to get the final segmentation result. The proposed algorithm, FCM and HMRF-FCM algorithm and an interval type-2 fuzzy neuron networks without spatial relationships are performed on synthetic and real high resolution remote sensing images. The qualitative and quantitative analyses demonstrate the efficient of the proposed algorithm, especially for homogeneous regions which contains a great difference in its gray level (for example forest).

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Solving Interval Type-2 Fuzzy Reliability-Redundancy Allocation Systems With Efficient PSO Algorithm

Advances in Systems Analysis, Software Engineering, and High Performance Computing - Advancements in Fuzzy Reliability Theory ◽

10.4018/978-1-7998-7564-2.ch007 ◽

2021 ◽

pp. 135-165

Author(s):

Zubair Ashraf ◽

Md. Gulzarul Hasan ◽

Mohd Shoaib Khan

Keyword(s):

System Reliability ◽

Pso Algorithm ◽

Total System ◽

Fuzzy Reliability ◽

Swarm Optimization ◽

Redundancy Allocation ◽

Fuzzy Membership Functions ◽

New Type ◽

Interval Type

A new type-2 fuzzy reliability-redundancy allocation problem (IT2FRRAP) is introduced in this study, which maximizes system reliability underneath the type-2 fuzzy uncertainty. Throughout the design process, estimation of system parameters with type-2 fuzzy sets is very rational, as many experts may have varying views on the numerous risks involved with the multi-state framework. The authors consider fuzzy membership functions of interval type-2 to fuzzy set to reflect the reliability of the sub-system. By using the extension theory, they evaluate the total system reliability as per respective configurations (i.e., series, parallel, non-parallel, and bridge). They developed a novel solution method focused on particle swarm optimization to address the IT2FRRAP. They carried out the computational simulations on various numerical databases to show the gravity of the proposed formulations. To compare its productiveness, a detailed analysis of the proposed solution was made with the genetic algorithm (GA).

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