scholarly journals Variational Mode Decomposition Based Retinal Area Detection and Merging Of Superpixels in SLO Image

2020 ◽  
Vol 15 ◽  
Author(s):  
Suchetha.M ◽  
Deepika.V
Keyword(s):  
Fuzzy Inference ◽  
Retinal Area ◽  
Variational Mode Decomposition ◽  
Inference System ◽  
Novel Approach ◽  
Mode Decomposition ◽  
Regional Gradient ◽  
Artificial Neural Network Ann ◽  
Processing Techniques

: Scanning Laser Ophthalmoscope (SLO) image can be used to detect retinal diseases. However identifying retinal area is a major task as retinal artefacts such as eyelashes and eyelids are also captured. Major part of retina can be viewed if detection is done with the help of images of SLO. In this paper our novel technique helps in detecting the true retinal area based on image processing techniques. To the SLO image two dimensional Variational Mode Decomposition (VMD) is applied. As a result of this different modes are obtained. Mode-1 is choosed because it has high frequency. Then mode1 is pre-processed using median filtering. After this preprocessed mode1 image is grouped into pixels based on regional size and compactness called superpixels. Superpixels are generated to reduce complexity. Superpixel merging is done next to Superpixel generation. It is done to reduce further difficulty and to enhance the speed. From the merged superpixels feature generation is performed using Regional, Gradient and textural features. It is done to eliminate artefacts and to detect the retinal area. Also feature selection will reduce the processing time and increase the speed. A classifier is constructed using Adaptive Network Fuzzy Inference System (ANFIS)for classification of features and its performance is compared with Artificial Neural Network(ANN). By this novel approach we got an classification accuracy of 98.5%.

Power Quality Disturbance Classification Based on Adaptive Neuro-Fuzzy System

2009 ◽  
Vol 10 (3) ◽  
Author(s):  
Thai Nguyen ◽  
Yuan Liao
Keyword(s):  
Power Quality ◽  
Fuzzy Inference ◽  
Evaluation Studies ◽  
Inference System ◽  
Neuro Fuzzy ◽  
S Transform ◽  
Power Quality Disturbance ◽  
Processing Techniques ◽  
Signal Processing Techniques

This paper presents an adaptive neuro-fuzzy inference system and a set of novel features for classification of power quality disturbances. The most common types of disturbances including flickers, harmonics, impulses, notches, outages, sags, swells, and switching transients are considered in this research. The proposed method employs voltage waveforms for analysis. The features are extracted utilizing the signal processing techniques such as the windowed discrete Fourier transform and S-transform. Evaluation studies based on both simulated and field data are reported.

An Intuitionistic Fuzzy Based Novel Approach to CPU Scheduler

2020 ◽  
Vol 16 (4) ◽  
pp. 316-328
Author(s):  
Supriya Raheja
Keyword(s):  
Fuzzy Inference ◽  
The Other ◽  
Simulation Environment ◽  
Inference System ◽  
Intuitionistic Fuzzy ◽  
Novel Approach ◽  
Continuous Feedback ◽  
Simulation Results ◽  
Effectiveness And Efficiency ◽  
Unique Capability

Background: The extension of CPU schedulers with fuzzy has been ascertained better because of its unique capability of handling imprecise information. Though, other generalized forms of fuzzy can be used which can further extend the performance of the scheduler. Objectives: This paper introduces a novel approach to design an intuitionistic fuzzy inference system for CPU scheduler. Methods: The proposed inference system is implemented with a priority scheduler. The proposed scheduler has the ability to dynamically handle the impreciseness of both priority and estimated execution time. It also makes the system adaptive based on the continuous feedback. The proposed scheduler is also capable enough to schedule the tasks according to dynamically generated priority. To demonstrate the performance of proposed scheduler, a simulation environment has been implemented and the performance of proposed scheduler is compared with the other three baseline schedulers (conventional priority scheduler, fuzzy based priority scheduler and vague based priority scheduler). Results: Proposed scheduler is also compared with the shortest job first CPU scheduler as it is known to be an optimized solution for the schedulers. Conclusion: Simulation results prove the effectiveness and efficiency of intuitionistic fuzzy based priority scheduler. Moreover, it provides optimised results as its results are comparable to the results of shortest job first.

scholarly journals Global Image Thresholding Adaptive Neuro-Fuzzy Inference System Trained with Fuzzy Inclusion and Entropy Measures

Symmetry ◽  
2019 ◽  
Vol 11 (2) ◽  
pp. 286 ◽  
Author(s):  
Athanasios Bogiatzis ◽  
Basil Papadopoulos
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Initial Step ◽  
Local Area ◽  
Mathematical Functions ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Entropy Measures ◽  
Complex Procedure ◽  
Artificial Neural Network Ann

Thresholding algorithms segment an image into two parts (foreground and background) by producing a binary version of our initial input. It is a complex procedure (due to the distinctive characteristics of each image) which often constitutes the initial step of other image processing or computer vision applications. Global techniques calculate a single threshold for the whole image while local techniques calculate a different threshold for each pixel based on specific attributes of its local area. In some of our previous work, we introduced some specific fuzzy inclusion and entropy measures which we efficiently managed to use on both global and local thresholding. The general method which we presented was an open and adaptable procedure, it was free of sensitivity or bias parameters and it involved image classification, mathematical functions, a fuzzy symmetrical triangular number and some criteria of choosing between two possible thresholds. Here, we continue this research and try to avoid all these by automatically connecting our measures with the wanted threshold using some Artificial Neural Network (ANN). Using an ANN in image segmentation is not uncommon especially in the domain of medical images. However, our proposition involves the use of an Adaptive Neuro-Fuzzy Inference System (ANFIS) which means that all we need is a proper database. It is a simple and immediate method which could provide researchers with an alternative approach to the thresholding problem considering that they probably have at their disposal some appropriate and specialized data.

Adaptive Neuro-Fuzzy Inference System for Classification of ACL-Ruptured Knees Using Arthrometric Data

2008 ◽  
Vol 36 (9) ◽  
pp. 1449-1457 ◽  
Author(s):  
Zoya Heydari ◽  
Farzam Farahmand ◽  
Hossein Arabalibeik ◽  
Mohamad Parnianpour
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Inference System ◽  
Neuro Fuzzy
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