Fuzzy Classification of Animal Fibers Using Neuro-Fuzzy Classifier

2008 ◽  
Author(s):  
Xian-Jun Shi ◽  
Wei-Dong Yu
Keyword(s):  
Fuzzy Classification ◽  
Fuzzy Classifier ◽  
Neuro Fuzzy

scholarly journals Polymer characterization with a fuzzy classification algorithm

1994 ◽  
Vol 16 (5) ◽  
pp. 161-165
Author(s):  
D. J. Ramsbottom ◽  
M. J. Adams ◽  
J. Carroll
Keyword(s):  
Cluster Algorithm ◽  
Classification Algorithm ◽  
Fuzzy Classification ◽  
Fuzzy Classifier ◽  
Polymer Characterization ◽  
Pure Polymer ◽  
Fuzzy C Means ◽  
Infra Red

The classification of polymer samples from their infra-red spectra has been achieved by the application of a fuzzy c-means cluster algorithm. The generation of a fuzzy classifier allows the characterization of samples which are a combination of more than one pure polymer.

scholarly journals A Framework of A Ship Domain-Based Near-Miss Detection Method Using Mamdani Neuro-Fuzzy Classification

2018 ◽  
Vol 25 (s1) ◽  
pp. 14-21 ◽  
Author(s):  
Rafał Szłapczyński ◽  
Tacjana Niksa-Rynkiewicz
Keyword(s):  
Risk Measures ◽  
Risk Measure ◽  
Near Miss ◽  
Fuzzy Classification ◽  
Automatic Identification ◽  
Identification System ◽  
Collision Risk ◽  
Neuro Fuzzy ◽  
Fuzzy Network

Abstract Safety analysis of navigation over a given area may cover application of various risk measures for ship collisions. One of them is percentage of the so called near-miss situations (potential collision situations). In this article a method of automatic detection of such situations based on the data from Automatic Identification System (AIS), is proposed. The method utilizes input parameters such as: collision risk measure based on ship’s domain concept, relative speed between ships as well as their course difference. For classification of ships encounters, there is used a neuro-fuzzy network which estimates a degree of collision hazard on the basis of a set of rules. The worked out method makes it possibile to apply an arbitrary ship’s domain as well as to learn the classifier on the basis of opinions of experts interpreting the data from the AIS.

Comparison of Fundamental Learning Strategies for CBIR using Deep Learning Methods

2021 ◽  
Vol 14 ◽  
Author(s):  
Meenakshi Garg ◽  
Manisha Malhotra ◽  
Harpal Singh
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Learning Strategies ◽  
Deep Neural Network ◽  
Fuzzy Classification ◽  
Digital Archive ◽  
Large Database ◽  
Fuzzy Classifier ◽  
Neuro Fuzzy ◽  
Network Methods

Background: Photo retrieval based on contents is primarily used to retrieve photographs from a broad database. CBIR, also named "search by image," is an al-lowing technology that handles computerized images by its recognizable attributes. Methods: In other words, CBIR is a method for recovery of images that does not rely on annotations or keywords but on the characteristics of the images directly taken from the pictures. CBIR systems rely on the use of machine display methods in broad datasets for the image retrieval issue. The CBIR technology is the retrieval from a cluster of photos or archive of the most visually similar photographs to a particular query file.It is really useful for scanning photos, medical research etc. in other fields such as photography. It may be hard to visually find the images by inserting the metadata or keywords into a large database and cannot catch the keyword for identifying this image. CBIR allows the extraction of similar photographs from a digital archive with no labeling of photographs. The Deep Neural Network and Neuro-Fuzzy classification are contrasted in this article. They both have numerous findings and numerous tests to forecast the picture. Results: The analysis of the neuro-fuzzy and deep neural network methods we suggest reveals that the precision is increased. Conclusion: Accuracy values for DNN and Neuro-Fuzzy Classifier process are74.6% and 75.4%. For the validity of the proposed process, the visual and qualitative findings are provided.

Detection of Sleep Apnea Disorder Using an Adaptive Neuro-Fuzzy Classification Method

2020 ◽  
Vol 17 (9) ◽  
pp. 4229-4234
Author(s):  
Jyoti Bali ◽  
Anilkumar V. Nandi ◽  
P. S. Hiremath ◽  
Poornima Patil
Keyword(s):  
Feature Extraction ◽  
Sleep Apnea ◽  
Principal Component ◽  
Feature Reduction ◽  
Fuzzy Classification ◽  
Ecg Signal ◽  
Night Time ◽  
Fuzzy Classifier ◽  
Diagnostic Modality ◽  
Neuro Fuzzy

The proposed work aims at developing a solution for the detection of sleep apnea disorder using ECG signal analysis, which is an established diagnostic modality. Under this work, the standard research resource, ECG-Apnea database from MIT’s Physionet.org., having ECG signal night time recordings, is used. The sequential procedure of Preprocessing, Peak or QRS complex detection, Feature extraction, Feature reduction, and Classification is used. Preprocessing of the ECG signal is performed to free it from noise resulted from baseline wander, power-line interference, and muscle artifacts. Thus, the improved signal quality is estimated in terms of its Signal to Noise Ratio (SNR) and entropy value. QRS detection is implemented using the popular Pan-Tompkins algorithm that provides the reference for the feature extraction process. The performance of the detection algorithm is measured in terms of the average values of accuracy and specificity as 98% and 96%, respectively. Feature extraction algorithm involves the collection of selected 30 feature values related to the time domain and the frequency domain gathered from each of the test recordings of the ECG database, minute-wise for 7 hours. Feature reduction technique is followed to reduce the data size to a set of 20 ECG signal features using Principal Component Analysis (PCA) and avoid redundancy. Hence the trained Adaptive Neuro-Fuzzy Classifier is used on the output feature set derived from PCA to detect the presence or absence of Sleep apnea disorder with an estimated accuracy and specificity as 95% and 96%, respectively.

scholarly journals REGION-BASED CLASSIFICATION OF ELECTRO-OPTICAL IMAGES WITH NEURAL NETWORKS AND FUZZY LOGIC

2014 ◽  
pp. 117-123
Author(s):  
Iryna Petrosyuk ◽  
Yuri Zaichenko
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Classification ◽  
Unification Algorithm ◽  
Sensing Systems ◽  
Optical Images ◽  
Novel Approach ◽  
The Neural Network ◽  
Neuro Fuzzy ◽  
Remote Sensing Systems

This paper reports on a novel approach to the optical information processing for the hyperspectral remote sensing systems by means of developed unification algorithm of the two mathematical tools: the fuzzy logic and the neural network. New neuro-fuzzy classification algorithm for hyperspectral remote sensed images has been proposed. It is able to replace complicated empirical formulae, which require the knowledge of dependences of many input parameters that rapidly change during of range time and difficult for crisp determination.

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