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Feature selection via a novel chaotic crow search algorithm
GI Sayed, AE Hassanien, AT Azar - Neural computing and applications, 2019 - Springer
Crow search algorithm (CSA) is a new natural inspired algorithm proposed by Askarzadeh
in 2016. The main inspiration of CSA came from crow search mechanism for hiding their …
in 2016. The main inspiration of CSA came from crow search mechanism for hiding their …
Spatial hazard assessment of the PM10 using machine learning models in Barcelona, Spain
B Choubin, M Abdolshahnejad, E Moradi… - Science of The Total …, 2020 - Elsevier
Air pollution, and especially atmospheric particulate matter (PM), has a profound impact on
human mortality and morbidity, environment, and ecological system. Accordingly, it is very …
human mortality and morbidity, environment, and ecological system. Accordingly, it is very …
An unsupervised cluster-based VANET-oriented evolving graph (CVoEG) model and associated reliable routing scheme
In vehicular ad hoc networks (VANETs), communication links break more frequently due to
the high-speed vehicles. In this paper, a novel cluster-based VANET oriented evolving …
the high-speed vehicles. In this paper, a novel cluster-based VANET oriented evolving …
Deep face clustering using residual graph convolutional network
C Qi, J Zhang, H Jia, Q Mao, L Wang, H Song - Knowledge-Based Systems, 2021 - Elsevier
Face clustering has important applications in image retrieval and criminal investigation.
Face images can be seen as the nodes of a graph and the possibility of links between the …
Face images can be seen as the nodes of a graph and the possibility of links between the …
An entropy-based density peaks clustering algorithm for mixed type data employing fuzzy neighborhood
Most clustering algorithms rely on the assumption that data simply contains numerical
values. In fact, however, data sets containing both numerical and categorical attributes are …
values. In fact, however, data sets containing both numerical and categorical attributes are …
Two-pronged feature reduction in spectral clustering with optimized landmark selection
Spectral clustering is widely employed for clustering data points, particularly for non-linear
and non-convex structures in high-dimensional spaces. However, it faces challenges due to …
and non-convex structures in high-dimensional spaces. However, it faces challenges due to …
Multi-view document clustering based on geometrical similarity measurement
B Diallo, J Hu, T Li, GA Khan, AS Hussein - International Journal of …, 2022 - Springer
Numerous works implemented multi-view clustering algorithms in document clustering. A
challenging problem in document clustering is the similarity metric. Existing multi-view …
challenging problem in document clustering is the similarity metric. Existing multi-view …
Handling missing data via max-entropy regularized graph autoencoder
Graph neural networks (GNNs) are popular weapons for modeling relational data. Existing
GNNs are not specified for attribute-incomplete graphs, making missing attribute imputation …
GNNs are not specified for attribute-incomplete graphs, making missing attribute imputation …
Density peaks clustering using geodesic distances
Density peaks clustering (DPC) algorithm is a novel clustering algorithm based on density. It
needs neither iterative process nor more parameters. However, it cannot effectively group …
needs neither iterative process nor more parameters. However, it cannot effectively group …
RESKM: A general framework to accelerate large-scale spectral clustering
Spectral Clustering is an effective preprocessing method in communities for its excellent
performance, but its scalability still is a challenge. Many efforts have been made to face this …
performance, but its scalability still is a challenge. Many efforts have been made to face this …