The factors that determine Serum Thyrotropin (TSH) levels have been examined through different methods, using different covariates. However, the use of machine learning methods has so far not been studied in population databases like NHANES (National...
Machine learning (ML) as a novel approach could help clinicians address the challenge of accurate stability assessment of unruptured intracranial aneurysms (IAs). We developed multiple ML models for IA stability assessment and compare their performan...
IEEE transactions on biomedical circuits and systems
May 19, 2020
This paper proposes novel methods for making embryonic bio-inspired hardware efficient against faults through self-healing, fault prediction, and fault-prediction assisted self-healing. The proposed self-healing recovers a faulty embryonic cell throu...
Neural networks : the official journal of the International Neural Network Society
May 19, 2020
RGB-Infrared (IR) person re-identification is very challenging due to the large cross-modality variations between RGB and IR images. Considering no correspondence labels between every pair of RGB and IR images, most methods try to alleviate the varia...
Neural networks : the official journal of the International Neural Network Society
May 19, 2020
Multi-class classification for highly imbalanced data is a challenging task in which multiple issues must be resolved simultaneously, including (i) accuracy on classifying highly imbalanced multi-class data; (ii) training efficiency for large data; a...
The use of machine learning (ML) in medicine is becoming increasingly fundamental to analyse complex problems by discovering associations among different types of information and to generate knowledge for medical decision support. Many regulatory and...
International journal of molecular sciences
May 19, 2020
Computational methods for predicting the macromolecular targets of drugs and drug-like compounds have evolved as a key technology in drug discovery. However, the established validation protocols leave several key questions regarding the performance a...
Deep neural networks (DNN) are state-of-the-art machine learning algorithms that can be learned to self-extract significant features of the electrocardiogram (ECG) and can generally provide high-output diagnostic accuracy if subjected to robust train...
Effective computational prediction of complex or novel molecule syntheses can greatly help organic and medicinal chemistry. Retrosynthetic analysis is a method employed by chemists to predict synthetic routes to target compounds. The target compounds...
BACKGROUND: Previous attempts to identify predictors of treatment outcomes in body dysmorphic disorder (BDD) have yielded inconsistent findings. One way to increase precision and clinical utility could be to use machine learning methods, which can in...
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