AI Medical Compendium Topic:
Cluster Analysis

Clear Filters Showing 821 to 830 of 1337 articles

Rehab-Net: Deep Learning Framework for Arm Movement Classification Using Wearable Sensors for Stroke Rehabilitation.

IEEE transactions on bio-medical engineering
In this paper, we present a deep learning framework "Rehab-Net" for effectively classifying three upper limb movements of the human arm, involving extension, flexion, and rotation of the forearm, which, over the time, could provide a measure of rehab...

CirGO: an alternative circular way of visualising gene ontology terms.

BMC bioinformatics
BACKGROUND: Prioritisation of gene ontology terms from differential gene expression analyses in a two-dimensional format remains a challenge with exponentially growing data volumes. Typically, gene ontology terms are represented as tree-maps that enc...

Support vector machine with quantile hyper-spheres for pattern classification.

PloS one
This paper formulates a support vector machine with quantile hyper-spheres (QHSVM) for pattern classification. The idea of QHSVM is to build two quantile hyper-spheres with the same center for positive or negative training samples. Every quantile hyp...

Application of adaptive-network-based fuzzy inference systems to the parameter optimization of a biochemical rule-based model.

Computers in biology and medicine
In this study, the binding of allergens to antibody-receptor complexes was investigated. This process is important in understanding the allergic response. A BioNetGen model that simulates this process, combined with a novel method for encoding steric...

Deep divergence-based approach to clustering.

Neural networks : the official journal of the International Neural Network Society
A promising direction in deep learning research consists in learning representations and simultaneously discovering cluster structure in unlabeled data by optimizing a discriminative loss function. As opposed to supervised deep learning, this line of...

Finite-time cluster synchronization for a class of fuzzy cellular neural networks via non-chattering quantized controllers.

Neural networks : the official journal of the International Neural Network Society
This paper considers the finite-time cluster synchronization (FTCS) of coupled fuzzy cellular neural networks (FCNNs) with Markovian switching topology, discontinuous activation functions, proportional leakage, and time-varying unbounded delays. Nove...

SAR study on inhibitors of GIIA secreted phospholipase A using machine learning methods.

Chemical biology & drug design
GIIA secreted phospholipase A (GIIA sPLA ) is a potent target for drug discovery. To distinguish the activity level of the inhibitors of GIIA sPLA , we built 24 classification models by three machine learning algorithms including support vector machi...

A survey of neural network-based cancer prediction models from microarray data.

Artificial intelligence in medicine
Neural networks are powerful tools used widely for building cancer prediction models from microarray data. We review the most recently proposed models to highlight the roles of neural networks in predicting cancer from gene expression data. We identi...

Melanoma lesion detection and segmentation using deep region based convolutional neural network and fuzzy C-means clustering.

International journal of medical informatics
OBJECTIVE: Melanoma is a dangerous form of the skin cancer responsible for thousands of deaths every year. Early detection of melanoma is possible through visual inspection of pigmented lesions over the skin, treated with simple excision of the cance...

Clustering algorithms: A comparative approach.

PloS one
Many real-world systems can be studied in terms of pattern recognition tasks, so that proper use (and understanding) of machine learning methods in practical applications becomes essential. While many classification methods have been proposed, there ...