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Semi-supervised random forest regression model based on co-training and grouping with information entropy for evaluation of depression symptoms severity.

Mathematical biosciences and engineering : MBE
Semi-supervised learning has always been a hot topic in machine learning. It uses a large number of unlabeled data to improve the performance of the model. This paper combines the co-training strategy and random forest to propose a novel semi-supervi...

Deep learning supported disease detection with multi-modality image fusion.

Journal of X-ray science and technology
Multi-modal image fusion techniques aid the medical experts in better disease diagnosis by providing adequate complementary information from multi-modal medical images. These techniques enhance the effectiveness of medical disorder analysis and class...

Fuzzy support vector machine with joint optimization of genetic algorithm and fuzzy c-means.

Technology and health care : official journal of the European Society for Engineering and Medicine
BACKGROUND: Motor imagery electroencephalogram (MI-EEG) play an important role in the field of neurorehabilitation, and a fuzzy support vector machine (FSVM) is one of the most used classifiers. Specifically, a fuzzy c-means (FCM) algorithm was used ...

Confronting pitfalls of AI-augmented molecular dynamics using statistical physics.

The Journal of chemical physics
Artificial intelligence (AI)-based approaches have had indubitable impact across the sciences through the ability to extract relevant information from raw data. Recently, AI has also found use in enhancing the efficiency of molecular simulations, whe...

[Multi-index optimization of extraction process of Fengyin Decoction based on BAS-GA-BP neural network combined with entropy weight method].

Zhongguo Zhong yao za zhi = Zhongguo zhongyao zazhi = China journal of Chinese materia medica
To optimize the ethanol extraction technology parameters of Fengyin Decoction by orthogonal experiment combined with beetle antennae search(BAS)-genetic algorithm(GA)-back propagation neural network(BPNN). Based on single factor investigation, the ex...

Depression Scale Prediction with Cross-Sample Entropy and Deep Learning.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
A two-stage deep learning-based scheme is presented to predict the Hamilton Depression Scale (HAM-D) in this study. First, the cross-sample entropy (CSE) that allows assessing the degree of similarity of two data series are evaluated for the 90 brain...

Optical Coherence Tomography Vulnerable Plaque Segmentation Based on Deep Residual U-Net.

Reviews in cardiovascular medicine
Automatic and accurate segmentation of intravascular optical coherence tomography imagery is of great importance in computer-aided diagnosis and in treatment of cardiovascular diseases. However, this task has not been well addressed for two reasons. ...

Cellular automata as convolutional neural networks.

Physical review. E
Deep-learning techniques have recently demonstrated broad success in predicting complex dynamical systems ranging from turbulence to human speech, motivating broader questions about how neural networks encode and represent dynamical rules. We explore...

Cost function network-based design of protein-protein interactions: predicting changes in binding affinity.

Bioinformatics (Oxford, England)
MOTIVATION: Accurate and economic methods to predict change in protein binding free energy upon mutation are imperative to accelerate the design of proteins for a wide range of applications. Free energy is defined by enthalpic and entropic contributi...

Ensemble learning based on overlapping clusters of subjects to predict microsleep states from EEG.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Microsleeps are brief and involuntary instances of complete loss of sleep-related consciousness. We present a novel approach of creating overlapping clusters of subjects and training of an ensemble classifier to enhance the prediction of microsleep s...