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View article: Conditional spatial biased intuitionistic clustering technique for brain MRI image segmentation
Conditional spatial biased intuitionistic clustering technique for brain MRI image segmentation Open
In clinical research, it is crucial to segment the magnetic resonance (MR) brain image for studying the internal tissues of the brain. To address this challenge in a sustainable manner, a novel approach has been proposed leveraging the pow…
View article: An Enhanced Integrated Method for Healthcare Data Classification with Incompleteness
An Enhanced Integrated Method for Healthcare Data Classification with Incompleteness Open
View article: A New Suppression-based Possibilistic Fuzzy c-means Clustering Algorithm
A New Suppression-based Possibilistic Fuzzy c-means Clustering Algorithm Open
Possibilistic fuzzy c-means (PFCM) is one of the most widely used clustering algorithm that solves the noise sensitivity problem of Fuzzy c-means (FCM) and coincident clusters problem of possibilistic c-means (PCM). Though PFCM is a highly…
View article: MCBC-SMOTE: A Majority Clustering Model for Classification of營mbalanced Data
MCBC-SMOTE: A Majority Clustering Model for Classification of營mbalanced Data Open
Datasets with the imbalanced class distribution are difficult to handle with the standard classification algorithms. In supervised learning, dealing with the problem of class imbalance is still considered to be a challenging research probl…
View article: An Enhanced Spatial Intuitionistic Fuzzy C-means Clustering for Image Segmentation
An Enhanced Spatial Intuitionistic Fuzzy C-means Clustering for Image Segmentation Open
Intuitionistic based Fuzzy clustering is a popular method in the field of image segmentation. The widely used Intuitionistic Fuzzy C-means (IFCM) based image segmentation is sensitive to noise since it uses only distance criterion in the f…
View article: Improving Semi-Supervised Classification using Clustering
Improving Semi-Supervised Classification using Clustering Open
Supervised classification techniques, broadly depend on the availability of labeled data. However, collecting this labeled data is always a tedious and costly process. To reduce these efforts and improve the performance of classification p…
View article: A new Semi-Supervised Intuitionistic Fuzzy C-means Clustering
A new Semi-Supervised Intuitionistic Fuzzy C-means Clustering Open
Semi-supervised clustering algorithms aim to increase the accuracy of unsupervised clustering process by effectively exploring the limited supervision available in the form of labelled data. Also the intuitionistic fuzzy sets, a generaliza…