Density peak clustering algorithms: A review on the decade 2014–2023
Density peak clustering (DPC) algorithm has become a well-known clustering method
during the last decade, The research communities believe that DPC is a powerful tool …
during the last decade, The research communities believe that DPC is a powerful tool …
A multi-objective evolutionary algorithm with interval based initialization and self-adaptive crossover operator for large-scale feature selection in classification
Feature selection (FS) is an important data pre-processing technique in classification. In
most cases, FS can improve classification accuracy and reduce feature dimension, so it can …
most cases, FS can improve classification accuracy and reduce feature dimension, so it can …
Predictive maintenance planning for industry 4.0 using machine learning for sustainable manufacturing
MH Abidi, MK Mohammed, H Alkhalefah - Sustainability, 2022 - mdpi.com
With the advent of the fourth industrial revolution, the application of artificial intelligence in
the manufacturing domain is becoming prevalent. Maintenance is one of the important …
the manufacturing domain is becoming prevalent. Maintenance is one of the important …
An overview on density peaks clustering
X Wei, M Peng, H Huang, Y Zhou - Neurocomputing, 2023 - Elsevier
Density peaks clustering (DPC) algorithm is a new algorithm based on density clustering
analysis, which can quickly obtain the cluster centers by drawing the decision diagram by …
analysis, which can quickly obtain the cluster centers by drawing the decision diagram by …
Big data cleaning based on mobile edge computing in industrial sensor-cloud
With the advent of 5G, the industrial Internet of Things has developed rapidly. The industrial
sensor-cloud system (SCS) has also received widespread attention. In the future, a large …
sensor-cloud system (SCS) has also received widespread attention. In the future, a large …
BLOCK-DBSCAN: Fast clustering for large scale data
We analyze the drawbacks of DBSCAN and its variants, and find the grid technique, which is
used in Fast-DBSCAN and ρ-approximate DBSCAN, is almost useless in high dimensional …
used in Fast-DBSCAN and ρ-approximate DBSCAN, is almost useless in high dimensional …
Optimal 5G network slicing using machine learning and deep learning concepts
MH Abidi, H Alkhalefah, K Moiduddin, M Alazab… - Computer Standards & …, 2021 - Elsevier
Network slicing is predetermined to hold up the diversity of emerging applications with
enhanced performance and flexibility requirements in the way of splitting the physical …
enhanced performance and flexibility requirements in the way of splitting the physical …
A five-layer deep convolutional neural network with stochastic pooling for chest CT-based COVID-19 diagnosis
Abstract Till August 17, 2020, COVID-19 has caused 21.59 million confirmed cases in more
than 227 countries and territories, and 26 naval ships. Chest CT is an effective way to detect …
than 227 countries and territories, and 26 naval ships. Chest CT is an effective way to detect …
KNN-BLOCK DBSCAN: Fast clustering for large-scale data
Large-scale data clustering is an essential key for big data problem. However, no current
existing approach is “optimal” for big data due to high complexity, which remains it a great …
existing approach is “optimal” for big data due to high complexity, which remains it a great …
A three-way density peak clustering method based on evidence theory
Density peaks clustering (DPC) algorithm is an efficient and simple clustering method
attracting the attention of many researchers. However, its strategy of assigning each non …
attracting the attention of many researchers. However, its strategy of assigning each non …