AIMC Topic: Pattern Recognition, Automated

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A knowledge graph-based data harmonization framework for secondary data reuse.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: The adoption of new technologies in clinical care systems has propitiated the availability of a great amount of valuable data. However, this data is usually heterogeneous, requiring its harmonization to be integrated and ana...

Knowledge graph and CBR-based approach for automated analysis of bridge operational accidents: Case representation and retrieval.

PloS one
Bridge operational accident analysis is a critical process in bridge operational risk management. It provides valuable knowledge support for responding to newly occurring accidents. However, there are three issues: (1) research specifically focused o...

Protocol to implement a computational pipeline for biomedical discovery based on a biomedical knowledge graph.

STAR protocols
Biomedical knowledge graphs (BKGs) provide a new paradigm for managing abundant biomedical knowledge efficiently. Today's artificial intelligence techniques enable mining BKGs to discover new knowledge. Here, we present a protocol for implementing a ...

A large-scale evaluation of NLP-derived chemical-gene/protein relationships from the scientific literature: Implications for knowledge graph construction.

PloS one
One area of active research is the use of natural language processing (NLP) to mine biomedical texts for sets of triples (subject-predicate-object) for knowledge graph (KG) construction. While statistical methods to mine co-occurrences of entities wi...

Feature-aware unsupervised lesion segmentation for brain tumor images using fast data density functional transform.

Scientific reports
We demonstrate that isomorphically mapping gray-level medical image matrices onto energy spaces underlying the framework of fast data density functional transform (fDDFT) can achieve the unsupervised recognition of lesion morphology. By introducing t...

Multi-domain knowledge graph embeddings for gene-disease association prediction.

Journal of biomedical semantics
BACKGROUND: Predicting gene-disease associations typically requires exploring diverse sources of information as well as sophisticated computational approaches. Knowledge graph embeddings can help tackle these challenges by creating representations of...

High-Order Correlation-Guided Slide-Level Histology Retrieval With Self-Supervised Hashing.

IEEE transactions on pattern analysis and machine intelligence
Histopathological Whole Slide Images (WSIs) play a crucial role in cancer diagnosis. It is of significant importance for pathologists to search for images sharing similar content with the query WSI, especially in the case-based diagnosis. While slide...

Optimized Classifier Learning for Face Recognition Performance Boost in Security and Surveillance Applications.

Sensors (Basel, Switzerland)
Face recognition has become an integral part of modern security processes. This paper introduces an optimization approach for the quantile interval method (QIM), a promising classifier learning technique used in face recognition to create face templa...

Combining Knowledge Graph and Word Embeddings for Spherical Topic Modeling.

IEEE transactions on neural networks and learning systems
Probabilistic topic models are considered as an effective framework for text analysis that uncovers the main topics in an unlabeled set of documents. However, the inferred topics by traditional topic models are often unclear and not easy to interpret...

Leveraging Knowledge Graphs and Natural Language Processing for Automated Web Resource Labeling and Knowledge Mobilization in Neurodevelopmental Disorders: Development and Usability Study.

Journal of medical Internet research
BACKGROUND: Patients and families need to be provided with trusted information more than ever with the abundance of online information. Several organizations aim to build databases that can be searched based on the needs of target groups. One such gr...