AIMC Topic: Algorithms

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New stability results for Takagi-Sugeno fuzzy Cohen-Grossberg neural networks with multiple delays.

Neural networks : the official journal of the International Neural Network Society
This work focuses on global asymptotic stability of Takagi-Sugeno fuzzy Cohen-Grossberg neural networks with multiple time delays. By using the standard Lyapunov stability techniques and nonsingular M-matrix condition of matrices together with employ...

Product Module Network Modeling and Evolution Analysis.

Computational intelligence and neuroscience
Modular technology for product design and manufacturing is an effective way to solve mass customization problems. One difficulty in the application of modular technology is that the characteristics of mass customization, such as multi batch and small...

Tooth detection and numbering in panoramic radiographs using convolutional neural networks.

Dento maxillo facial radiology
OBJECTIVES: Analysis of dental radiographs is an important part of the diagnostic process in daily clinical practice. Interpretation by an expert includes teeth detection and numbering. In this project, a novel solution based on convolutional neural ...

Improving Reliability of Myocontrol Using Formal Verification.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
In the context of assistive robotics, myocontrol is one of the so-far unsolved problems of upper-limb prosthetics. It consists of swiftly, naturally, and reliably converting biosignals, non-invasively gathered from an upper-limb disabled subject, int...

KGDDS: A System for Drug-Drug Similarity Measure in Therapeutic Substitution based on Knowledge Graph Curation.

Journal of medical systems
Measuring drug-drug similarity is important but challenging. Significant progresses have been made in drugs whose labeled training data is sufficient and available. However, handling data skewness and incompleteness with domain-specific knowledge gra...

Addressing priority challenges in the detection and assessment of colorectal polyps from capsule endoscopy and colonoscopy in colorectal cancer screening using machine learning.

Acta oncologica (Stockholm, Sweden)
BACKGROUND: Colorectal capsule endoscopy (CCE) is a potentially valuable patient-friendly technique for colorectal cancer screening in large populations. Before it can be widely applied, significant research priorities need to be addressed. We presen...

Virtual histological staining of unlabelled tissue-autofluorescence images via deep learning.

Nature biomedical engineering
The histological analysis of tissue samples, widely used for disease diagnosis, involves lengthy and laborious tissue preparation. Here, we show that a convolutional neural network trained using a generative adversarial-network model can transform wi...

Feature extraction using traditional image processing and convolutional neural network methods to classify white blood cells: a study.

Australasian physical & engineering sciences in medicine
White blood cells play a vital role in monitoring health condition of a person. Change in count and/or appearance of these cells indicate hematological disorders. Manual microscopic evaluation of white blood cells is the gold standard method, but the...

Applications of machine learning in addiction studies: A systematic review.

Psychiatry research
This study aims to provide a systematic review of the applications of machine learning methods in addiction research. In this study, multiple searches on MEDLINE, Embase and the Cochrane Database of Systematic Reviews were performed. 23 full-text art...

Portable brain-computer interface based on novel convolutional neural network.

Computers in biology and medicine
Electroencephalography (EEG) is a powerful, noninvasive tool that provides a high temporal resolution to directly reflect brain activities. Conventional electrodes require skin preparation and the use of conductive gels, while subjects must wear unco...