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Cluster Analysis

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Clustering at the Disposal of Industry 4.0: Automatic Extraction of Plant Behaviors.

Sensors (Basel, Switzerland)
For two centuries, the industrial sector has never stopped evolving. Since the dawn of the Fourth Industrial Revolution, commonly known as Industry 4.0, deep and accurate understandings of systems have become essential for real-time monitoring, predi...

An Intelligent and Reliable Hyperparameter Optimization Machine Learning Model for Early Heart Disease Assessment Using Imperative Risk Attributes.

Journal of healthcare engineering
Heart disease is a severe disorder, which inflicts an adverse burden on all societies and leads to prolonged suffering and disability. We developed a risk evaluation model based on visible low-cost significant noninvasive attributes using hyperparame...

DGCyTOF: Deep learning with graphic cluster visualization to predict cell types of single cell mass cytometry data.

PLoS computational biology
Single-cell mass cytometry, also known as cytometry by time of flight (CyTOF) is a powerful high-throughput technology that allows analysis of up to 50 protein markers per cell for the quantification and classification of single cells. Traditional ma...

A hybrid resampling algorithms SMOTE and ENN based deep learning models for identification of Marburg virus inhibitors.

Future medicinal chemistry
Marburg virus (MARV) is a sporadic outbreak of a zoonotic disease that causes lethal hemorrhagic fever in humans. We propose a deep learning model with resampling techniques and predict the inhibitory activity of MARV from unknown compounds in the v...

Dynamically Generated Hierarchical Neural Networks Designed With the Aid of Multiple Support Vector Regressors and PNN Architecture With Probabilistic Selection.

IEEE transactions on neural networks and learning systems
The two issues on dynamically generated hierarchical neural networks such as the sort of basic neurons and how to compose a layer are considered in this article. On the first issue, a variant version of the least-square support vector regression (SVR...

Structure Guided Deep Neural Network for Unsupervised Active Learning.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Unsupervised active learning has become an active research topic in the machine learning and computer vision communities, whose goal is to choose a subset of representative samples to be labeled in an unsupervised setting. Most of existing approaches...

An analysis framework for clustering algorithm selection with applications to spectroscopy.

PloS one
Cluster analysis is a valuable unsupervised machine learning technique that is applied in a multitude of domains to identify similarities or clusters in unlabelled data. However, its performance is dependent of the characteristics of the data it is b...

Research on Keyword Extraction Algorithm in English Text Based on Cluster Analysis.

Computational intelligence and neuroscience
How to facilitate users to quickly and accurately search for the text information they need is a current research hotspot. Text clustering can improve the efficiency of information search and is an effective text retrieval method. Keyword extraction ...

Effective CBMIR System Using Hybrid Features-Based Independent Condensed Nearest Neighbor Model.

Journal of healthcare engineering
In recent times, a large number of medical images are generated, due to the evolution of digital imaging modalities and computer vision application. Due to variation in the shape and size of the images, the retrieval task becomes more tedious in the ...

An automated process for supporting decisions in clustering-based data analysis.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Metrics are commonly used by biomedical researchers and practitioners to measure and evaluate properties of individuals, instruments, models, methods, or datasets. Due to the lack of a standardized validation procedure for a...