Search
Search Results
-
Possibilistic picture fuzzy product partition C-means clustering incorporating rich local information for medical image segmentation
Picture fuzzy C-means clustering is a new computational intelligence method that has more significant potential advantages than fuzzy clustering in...
-
Fuzzy C-Means for image segmentation: challenges and solutions
Image segmentation is considered a pertinent prerequisite for numerous tasks in digital image processing. The procedure through which identical...
-
Road crack detection using pixel classification and intensity-based distinctive fuzzy C-means clustering
Road cracks are quickly becoming one of the world's most serious concerns. It may have an impact on traffic safety and increase the likelihood of...
-
Integrating fuzzy C-means clustering and fuzzy inference system for audiovisual quality of experience
Estimating the perceived quality and quality of experience of audio-visual signals is critical for several multimedia networks and audio-visual...
-
Automatic liver tumor detection and classification using the hyper tangent fuzzy C-Means and improved fuzzy SVM
Globally liver diseases are the most life-threatening diseases, and according to global cancer statistics, liver cancer is the most common. Early...
-
Performance of the K-means and fuzzy C-means algorithms in big data analytics
Nowadays, cloud computing is used by most organizations to utilize cloud resources and services in dealing with big data. Besides, machine learning...
-
Hybrid fuzzy clustering technique to enhance the performance based on a fusion of intuitionistic modified fuzzy c-means and improved genetic algorithm
In the modern era, there is a sudden rise in data due to the wide use of the web, social networks and so on. Now, it becomes difficult to explore...
-
Local feature driven fuzzy local information C-means clustering with kernel metric for blurred and noisy image segmentation
Kernel fuzzy weighted local information C-means clustering is a widely used robust segmentation algorithm for noisy images. However, it cannot...
-
An equidistance index intuitionistic fuzzy c-means clustering algorithm based on local density and membership degree boundary
Fuzzy c-means (FCM) algorithm is an unsupervised clustering algorithm that effectively expresses complex real world information by integrating fuzzy...
-
Entropy-Based Fuzzy C-Ordered-Means Clustering Algorithm
Fuzzy C -Means is a well-known fuzzy clustering technique. Although FCM can cover the uncertainty problem by forming overlap** clusters, it involves...
-
Parameter-Free Auto-Weighted Possibilistic Fuzzy C-Means Clustering with Kernel Metric and Robust Algorithm
To further improve the clustering quality on high dimensional data, this paper makes two improvements of weighted possibilistic fuzzy C-means (WPFCM)...
-
A Biologically-Inspired Sparse Self-Representation Approach for Projected Fuzzy Double C-Means Clustering
Data redundancy is frequently encountered in biologically data. Locality preserving projection (LPP) is a dimensionality reduction approach to...
-
Probabilistic intuitionistic fuzzy c-means algorithm with spatial constraint for human brain MRI segmentation
Segmentation of brain MRI images becomes a challenging task due to spatially distributed noise and uncertainty present between boundaries of soft...
-
Detection and classification of breast cancer in mammogram images using entropy-based Fuzzy C-Means Clustering and RMCNN
Radiologists employ mammograms for the detection of breast cancer in patients, particularly as breast cancer exhibits higher incidence rates in...
-
Soybean Genome Clustering Using Quantum-Based Fuzzy C-Means Algorithm
Bioinformatics is a new area of research in which many computer scientists are working to extract some useful information from genome sequences in a...
-
Brain tumor segmentation based on kernel fuzzy c-means and penguin search optimization algorithm
Brain tumor is the irregular growth of cells in the brain that can develop into malignant or benign tumors. However, the prediction of brain tumors...
-
A coincidental correctness test case identification framework with fuzzy C-means clustering
Cleansing coincidental correctness test cases has been proven to be useful in software fault localization. However, k -means clustering-based...
-
Fuzzy c-means based medical image retrieval for identifying most clinically relevant images
Rapid growth of multimedia, storage systems and digital computers has resulted in repositories of multimedia content and large image in recent years....
-
Robust joint learning of superpixel generation and superpixel-based image segmentation using fuzzy C-multiple-means clustering
In recent years, many superpixel-based image segmentation algorithms have been presented. However, most of these algorithms face issues of high model...
-
A proficient video recommendation framework using hybrid fuzzy C means clustering and Kullback-Leibler divergence algorithms
A video recommendation framework for e-commerce clients is proposed using the collaborative filtering (CF) process. One of the most important...
