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Cluster Analysis

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A Deep Learning and Clustering Extraction Mechanism for Recognizing the Actions of Athletes in Sports.

Computational intelligence and neuroscience
In sports, the essence of a complete technical action is a complete information structure pattern and the athlete's judgment of the action is actually the identification of the movement information structure pattern. Action recognition refers to the ...

Artificial Intelligence-Enabled Medical Analysis for Intracranial Cerebral Hemorrhage Detection and Classification.

Journal of healthcare engineering
Intracranial hemorrhage (ICH) becomes a crucial healthcare emergency, which requires earlier detection and accurate assessment. Owing to the increased death rate (around 40%), the earlier recognition and classification of disease using computed tomog...

Sports Action Recognition Based on Deep Learning and Clustering Extraction Algorithm.

Computational intelligence and neuroscience
This paper constructs a sports action recognition model based on deep learning (DL) and clustering extraction algorithm. For the input detection image frame, athletes' movements are detected through DL network, and then athletes' sports movements are...

Computational Intelligence Approaches in Developing Cyberattack Detection System.

Computational intelligence and neuroscience
The Internet plays a fundamental part in relentless correspondence, so its applicability can decrease the impact of intrusions. Intrusions are defined as movements that unfavorably influence the focus of a computer. Intrusions may sacrifice the reput...

Development of cluster analysis methodology for identification of model rainfall hyetographs and its application at an urban precipitation field scale.

The Science of the total environment
Despite growing access to precipitation time series records at a high temporal scale, in hydrology, and particularly urban hydrology, engineers still design and model drainage systems using scenarios of rainfall temporal distributions predefined by m...

A Graph Partition-Based Large-Scale Distribution Network Reconfiguration Method.

Computational intelligence and neuroscience
This article focuses on the analysis of large-scale distribution network reconstruction fused with graph theory and graph partitioning algorithms. Graph theory and graph segmentation algorithms have been rushed by many researchers in the fields of me...

Deep contrastive learning based tissue clustering for annotation-free histopathology image analysis.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
BACKGROUND: Deep convolutional neural networks (CNNs) have yielded promising results in automatic whole slide images (WSIs) processing for digital pathology in recent years. Training supervised CNNs usually requires a large amount of annotated sample...

Water quality forecasting based on data decomposition, fuzzy clustering and deep learning neural network.

Environmental pollution (Barking, Essex : 1987)
Water quality forecasting can provide useful information for public health protection and support water resources management. In order to forecast water quality more accurately, this paper proposes a novel hybrid model by combining data decomposition...

Missing data imputation in clinical trials using recurrent neural network facilitated by clustering and oversampling.

Biometrical journal. Biometrische Zeitschrift
In clinical practice, the composition of missing data may be complex, for example, a mixture of missing at random (MAR) and missing not at random (MNAR) assumptions. Many methods under the assumption of MAR are available. Under the assumption of MNAR...

A copula based topology preserving graph convolution network for clustering of single-cell RNA-seq data.

PLoS computational biology
Annotation of cells in single-cell clustering requires a homogeneous grouping of cell populations. There are various issues in single cell sequencing that effect homogeneous grouping (clustering) of cells, such as small amount of starting RNA, limite...