Applications and challenges of artificial intelligence in space missions
PA Oche, GA Ewa, N Ibekwe - IEEE Access, 2021 - ieeexplore.ieee.org
Artificial Intelligence (AI) is increasingly finding acceptance in the space community,
especially Machine Learning (ML), a subdomain of AI. ML algorithms now find numerous …
especially Machine Learning (ML), a subdomain of AI. ML algorithms now find numerous …
A comprehensive review of clustering techniques in artificial intelligence for knowledge discovery: Taxonomy, challenges, applications and future prospects
Clustering is a set of essential mathematical techniques in artificial intelligence and machine
learning for analyzing massive amounts of data generated by applications. Clustering uses …
learning for analyzing massive amounts of data generated by applications. Clustering uses …
A survey on brain tumor detection techniques for MR images
PK Chahal, S Pandey, S Goel - Multimedia Tools and Applications, 2020 - Springer
One of the most crucial tasks in any brain tumor detection system is the isolation of abnormal
tissues from normal brain tissues. Interestingly, domain of brain tumor analysis has …
tissues from normal brain tissues. Interestingly, domain of brain tumor analysis has …
Image segmentation using computational intelligence techniques
Image segmentation methodology is a part of nearly all computer schemes as a pre-
processing phase to excerpt more meaningful and useful information for analysing the …
processing phase to excerpt more meaningful and useful information for analysing the …
Graphical image region extraction with k-means clustering and watershed
With a wide range of applications, image segmentation is a complex and difficult
preprocessing step that plays an important role in automatic visual systems, which accuracy …
preprocessing step that plays an important role in automatic visual systems, which accuracy …
Fast and robust spatial fuzzy bounded k-plane clustering method for human brain MRI image segmentation
Fuzzy k-plane clustering (FkPC) is a soft plane-based clustering that efficiently clusters non-
spherically distributed data. However, the FkPC method is sensitive to noise and provides …
spherically distributed data. However, the FkPC method is sensitive to noise and provides …
Hybrid intelligent approach for diagnosis of the lung nodule from CT images using spatial kernelized fuzzy c-means and ensemble learning
Lung cancer is one of the most common forms of cancer leading to over a million deaths per
year throughout the world. The aim of this paper is to identify the pulmonary nodules in …
year throughout the world. The aim of this paper is to identify the pulmonary nodules in …
Generalized possibilistic fuzzy c-means with novel cluster validity indices for clustering noisy data
A generalized form of Possibilistic Fuzzy C-Means (PFCM) algorithm (GPFCM) is presented
for clustering noisy data. A function of distance is used instead of the distance itself to damp …
for clustering noisy data. A function of distance is used instead of the distance itself to damp …
Online monitoring of green pellet size distribution in haze-degraded images based on VGG16-LU-Net and haze judgment
J Duan, X Liu - IEEE Transactions on Instrumentation and …, 2021 - ieeexplore.ieee.org
Online monitoring of pellet size distribution (PSD) of green pellets is an important work in
product quality control of pelletization process. Conventionally, image segmentation …
product quality control of pelletization process. Conventionally, image segmentation …
A modified interval type-2 fuzzy C-means algorithm with application in MR image segmentation
C Qiu, J **ao, L Yu, L Han, MN Iqbal - Pattern Recognition Letters, 2013 - Elsevier
The fuzzy C-means (FCM) algorithm has significant importance compared to other methods
in Medical image segmentation. Conventional FCM algorithm is sensitive to noise especially …
in Medical image segmentation. Conventional FCM algorithm is sensitive to noise especially …