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Application of Machine Learning Techniques for Enuresis Prediction in Children.

European journal of pediatric surgery : official journal of Austrian Association of Pediatric Surgery ... [et al] = Zeitschrift fur Kinderchirurgie
INTRODUCTION:  As a subset of artificial intelligence, machine learning techniques (MLTs) may evaluate very large and raw datasets. In this study, the aim is to establish a model by MLT for the prediction of enuresis in children.

Automatic lung segmentation in routine imaging is primarily a data diversity problem, not a methodology problem.

European radiology experimental
BACKGROUND: Automated segmentation of anatomical structures is a crucial step in image analysis. For lung segmentation in computed tomography, a variety of approaches exists, involving sophisticated pipelines trained and validated on different datase...

Bubbly flow prediction with randomized neural cells artificial learning and fuzzy systems based on k-ε turbulence and Eulerian model data set.

Scientific reports
Computing gas and liquid interactions based on interfacial force models require a proper turbulence model that accurately resolve the turbulent scales such as turbulence kinetic energy and turbulence dissipation rate with cheap computational resource...

New convolutional neural network model for screening and diagnosis of mammograms.

PloS one
Breast cancer is the most common cancer in women and poses a great threat to women's life and health. Mammography is an effective method for the diagnosis of breast cancer, but the results are largely limited by the clinical experience of radiologist...

Direct cortical thickness estimation using deep learning-based anatomy segmentation and cortex parcellation.

Human brain mapping
Accurate and reliable measures of cortical thickness from magnetic resonance imaging are an important biomarker to study neurodegenerative and neurological disorders. Diffeomorphic registration-based cortical thickness (DiReCT) is a known technique t...

cnnAlpha: Protein disordered regions prediction by reduced amino acid alphabets and convolutional neural networks.

Proteins
Intrinsically disordered regions (IDR) play an important role in key biological processes and are closely related to human diseases. IDRs have great potential to serve as targets for drug discovery, most notably in disordered binding regions. Accurat...

Remaining Useful Life Estimation Using Deep Convolutional Generative Adversarial Networks Based on an Autoencoder Scheme.

Computational intelligence and neuroscience
Accurate predictions of remaining useful life (RUL) of important components play a crucial role in system reliability, which is the basis of prognostics and health management (PHM). This paper proposed an integrated deep learning approach for RUL pre...

Design and Performance Evaluation of a Deep Neural Network for Spectrum Recognition of Underwater Targets.

Computational intelligence and neuroscience
Due to the complexity of the underwater environment, underwater acoustic target recognition (UATR) has always been challenging. Although deep neural networks (DNN) have been used in UATR and some achievements have been made, the performance is not sa...

A New Image Classification Approach via Improved MobileNet Models with Local Receptive Field Expansion in Shallow Layers.

Computational intelligence and neuroscience
Because deep neural networks (DNNs) are both memory-intensive and computation-intensive, they are difficult to apply to embedded systems with limited hardware resources. Therefore, DNN models need to be compressed and accelerated. By applying depthwi...