[HTML][HTML] Improving K-means clustering with enhanced Firefly Algorithms
In this research, we propose two variants of the Firefly Algorithm (FA), namely inward
intensified exploration FA (IIEFA) and compound intensified exploration FA (CIEFA), for …
intensified exploration FA (IIEFA) and compound intensified exploration FA (CIEFA), for …
CT liver tumor segmentation hybrid approach using neutrosophic sets, fast fuzzy c-means and adaptive watershed algorithm
AM Anter, AE Hassenian - Artificial intelligence in medicine, 2019 - Elsevier
Liver tumor segmentation from computed tomography (CT) images is a critical and
challenging task. Due to the fuzziness in the liver pixel range, the neighboring organs of the …
challenging task. Due to the fuzziness in the liver pixel range, the neighboring organs of the …
A novel hybrid model for multi-step ahead photovoltaic power prediction based on conditional time series generative adversarial networks
F Li, H Zheng, X Li - Renewable Energy, 2022 - Elsevier
The accuracy of photovoltaic power forecasting is crucial to the revenue of new energy
generation projects in electricity market trading. However, due to the highly stochastic …
generation projects in electricity market trading. However, due to the highly stochastic …
Density-based IFCM along with its interval valued and probabilistic extensions, and a review of intuitionistic fuzzy clustering methods
Fuzzy clustering has been useful in capturing the uncertainty present in the data during
clustering. Most of the c-Means algorithms such as FCM (Fuzzy c-Means), IFCM …
clustering. Most of the c-Means algorithms such as FCM (Fuzzy c-Means), IFCM …
Fuzzy subspace clustering noisy image segmentation algorithm with adaptive local variance & non-local information and mean membership linking
T Wei, X Wang, X Li, S Zhu - Engineering Applications of Artificial …, 2022 - Elsevier
Abstract The Fuzzy C-means (FCM) clustering algorithm is an effective method for image
segmentation. Non-local spatial information considers more redundant information of the …
segmentation. Non-local spatial information considers more redundant information of the …
A Novel Brain MRI Image Segmentation Method Using an Improved Multi-View Fuzzy c-Means Clustering Algorithm
Background: The brain magnetic resonance imaging (MRI) image segmentation method
mainly refers to the division of brain tissue, which can be divided into tissue parts such as …
mainly refers to the division of brain tissue, which can be divided into tissue parts such as …
Residual-driven fuzzy C-means clustering for image segmentation
In this paper, we elaborate on residual-driven Fuzzy C-Means (FCM) for image
segmentation, which is the first approach that realizes accurate residual (noise/outliers) …
segmentation, which is the first approach that realizes accurate residual (noise/outliers) …
Robust fuzzy c-means clustering algorithm with adaptive spatial & intensity constraint and membership linking for noise image segmentation
Q Wang, X Wang, C Fang, W Yang - Applied Soft Computing, 2020 - Elsevier
The fuzzy C-means (FCM) clustering method is proven to be an efficient method to segment
images. However, the FCM method is not robustness and less accurate for noise images. In …
images. However, the FCM method is not robustness and less accurate for noise images. In …
An improved fast fuzzy c-means using crow search optimization algorithm for crop identification in agricultural
In this article is introduced an improved version of Fast Fuzzy C-Means (FFCM) by using the
Crow Search optimization Algorithm (CSA) for the task of data clustering. In the proposed …
Crow Search optimization Algorithm (CSA) for the task of data clustering. In the proposed …
Wavelet Frame-Based Fuzzy C-Means Clustering for Segmenting Images on Graphs
In recent years, image processing in a Euclidean domain has been well studied. Practical
problems in computer vision and geometric modeling involve image data defined in irregular …
problems in computer vision and geometric modeling involve image data defined in irregular …