Fuzzy c-means clustering: A review of applications in breast cancer detection
This paper reviews the potential use of fuzzy c-means clustering (FCM) and explores
modifications to the distance function and centroid initialization methods to enhance image …
modifications to the distance function and centroid initialization methods to enhance image …
Nature and biologically inspired image segmentation techniques
Image processing is among the significant areas of growth in the current scenario. It consist
of a set of techniques typically used to enhance the raw image obtained from different …
of a set of techniques typically used to enhance the raw image obtained from different …
Scene semantic recognition based on modified fuzzy C-mean and maximum entropy using object-to-object relations
With advances in machine vision systems (eg, artificial eye, unmanned aerial vehicles,
surveillance monitoring) scene semantic recognition (SSR) technology has attracted much …
surveillance monitoring) scene semantic recognition (SSR) technology has attracted much …
CGFFCM: Cluster-weight and Group-local Feature-weight learning in Fuzzy C-Means clustering algorithm for color image segmentation
The fuzzy c-means (FCM) algorithm is a popular method for data clustering and image
segmentation. However, the main problem of this algorithm is that it is very sensitive to the …
segmentation. However, the main problem of this algorithm is that it is very sensitive to the …
A survey on the utilization of Superpixel image for clustering based image segmentation
Superpixel become increasingly popular in image segmentation field as it greatly helps
image segmentation techniques to segment the region of interest accurately in noisy …
image segmentation techniques to segment the region of interest accurately in noisy …
UFFDFR: Undersampling framework with denoising, fuzzy c-means clustering, and representative sample selection for imbalanced data classification
In the field of artificial intelligence, classification algorithms tend to be biased toward the
majority class samples when encountering imbalanced data, resulting in low recognition …
majority class samples when encountering imbalanced data, resulting in low recognition …
Aquila-particle swarm based cooperative search optimizer with superpixel techniques for epithelial layer segmentation
The segmentation of epithelial layers from oral histopathology images plays a crucial role for
early detection of oral cancer disease. As a result, more accurate segmentation of this layer …
early detection of oral cancer disease. As a result, more accurate segmentation of this layer …
A new smoke segmentation method based on improved adaptive density peak clustering
Z Ma, Y Cao, L Song, F Hao, J Zhao - Applied Sciences, 2023 - mdpi.com
Smoke image segmentation plays a vital role in the accuracy of target extraction. In order to
improve the performance of the traditional fire image segmentation algorithm, a new smoke …
improve the performance of the traditional fire image segmentation algorithm, a new smoke …
Lévy–Cauchy arithmetic optimization algorithm combined with rough K-means for image segmentation
Abstract Rough K-Means (RKM) is a well-known unsupervised clustering algorithm based
on rough set logic that is utilized in a wide range of applications. However, when dealing …
on rough set logic that is utilized in a wide range of applications. However, when dealing …
Dynamic real–time abnormal energy consumption detection and energy efficiency optimization analysis considering uncertainty
S Yin, H Yang, K Xu, C Zhu, S Zhang, G Liu - Applied Energy, 2022 - Elsevier
Uncertain working conditions will lead to abnormal energy consumption and energy loss of
high energy consumption machines. Therefore, it is necessary to detect abnormal energy …
high energy consumption machines. Therefore, it is necessary to detect abnormal energy …