AIMC Topic: Pattern Recognition, Automated

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Hybrid Gradient Descent Grey Wolf Optimizer for Optimal Feature Selection.

BioMed research international
Feature selection is the process of decreasing the number of features in a dataset by removing redundant, irrelevant, and randomly class-corrected data features. By applying feature selection on large and highly dimensional datasets, the redundant fe...

Computer vision based individual fish identification using skin dot pattern.

Scientific reports
Precision fish farming is an emerging concept in aquaculture research and industry, which combines new technologies and data processing methods to enable data-based decision making in fish farming. The concept is based on the automated monitoring of ...

Identifying resting state differences salient for resilience to chronic pain based on machine learning multivariate pattern analysis.

Psychophysiology
Studies have documented behavior differences between more versus less resilient adults with chronic pain (CP), but the presence and nature of underlying neurophysiological differences have received scant attention. In this study, we attempted to iden...

Construction of Genealogical Knowledge Graphs From Obituaries: Multitask Neural Network Extraction System.

Journal of medical Internet research
BACKGROUND: Genealogical information, such as that found in family trees, is imperative for biomedical research such as disease heritability and risk prediction. Researchers have used policyholder and their dependent information in medical claims dat...

EHR-Oriented Knowledge Graph System: Toward Efficient Utilization of Non-Used Information Buried in Routine Clinical Practice.

IEEE journal of biomedical and health informatics
Non-used clinical information has negative implications on healthcare quality. Clinicians pay priority attention to clinical information relevant to their specialties during routine clinical practices but may be insensitive or less concerned about in...

Disease ontologies for knowledge graphs.

BMC bioinformatics
BACKGROUND: Data integration to build a biomedical knowledge graph is a challenging task. There are multiple disease ontologies used in data sources and publications, each having its hierarchy. A common task is to map between ontologies, find disease...

An empirical survey of data augmentation for time series classification with neural networks.

PloS one
In recent times, deep artificial neural networks have achieved many successes in pattern recognition. Part of this success can be attributed to the reliance on big data to increase generalization. However, in the field of time series recognition, man...

Time-Frequency Representation and Convolutional Neural Network-Based Emotion Recognition.

IEEE transactions on neural networks and learning systems
Emotions composed of cognizant logical reactions toward various situations. Such mental responses stem from physiological, cognitive, and behavioral changes. Electroencephalogram (EEG) signals provide a noninvasive and nonradioactive solution for emo...

Deep CNNs Meet Global Covariance Pooling: Better Representation and Generalization.

IEEE transactions on pattern analysis and machine intelligence
Compared with global average pooling in existing deep convolutional neural networks (CNNs), global covariance pooling can capture richer statistics of deep features, having potential for improving representation and generalization abilities of deep C...

Toward a Coronavirus Knowledge Graph.

Genes
This study builds a coronavirus knowledge graph (KG) by merging two information sources. The first source is Analytical Graph (AG), which integrates more than 20 different public datasets related to drug discovery. The second source is CORD-19, a col...