AIMC Topic: Neural Networks, Computer

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Machine learning for modeling animal movement.

PloS one
Animal movement drives important ecological processes such as migration and the spread of infectious disease. Current approaches to modeling animal tracking data focus on parametric models used to understand environmental effects on movement behavior...

An automated detection system for colonoscopy images using a dual encoder-decoder model.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Conventional computer-aided detection systems (CADs) for colonoscopic images utilize shape, texture, or temporal information to detect polyps, so they have limited sensitivity and specificity. This study proposes a method to extract possible polyp fe...

Left ventricle quantification with sample-level confidence estimation via Bayesian neural network.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Quantification of cardiac left ventricle has become a hot topic due to its great significance in clinical practice. Many efforts have been devoted to LV quantification and obtained promising performance with the help of various deep neural networks w...

Digital Pharmaceutical Sciences.

AAPS PharmSciTech
Artificial intelligence (AI) and machine learning, in particular, have gained significant interest in many fields, including pharmaceutical sciences. The enormous growth of data from several sources, the recent advances in various analytical tools, a...

Three-Dimensional Neural Network to Automatically Assess Liver Tumor Burden Change on Consecutive Liver MRIs.

Journal of the American College of Radiology : JACR
BACKGROUND: Tumor response to therapy is often assessed by measuring change in liver lesion size between consecutive MRIs. However, these evaluations are both tedious and time-consuming for clinical radiologists.

Exploration of total synchronous fluorescence spectroscopy combined with pre-trained convolutional neural network in the identification and quantification of vegetable oil.

Food chemistry
In order to distinguish different vegetable oils, adulterated vegetable oils, and to identify and quantify counterfeit vegetable oils, a method based on a small sample size of total synchronous fluorescence (TSyF) spectra combined with convolutional ...

Guidelines for Recurrent Neural Network Transfer Learning-Based Molecular Generation of Focused Libraries.

Journal of chemical information and modeling
Deep learning approaches have become popular in recent years in the field of molecular design. While a variety of different methods are available, it is still a challenge to assess and compare their performance. A particularly promising approach for...

Performance comparison of wavelet neural network and adaptive neuro-fuzzy inference system with small data sets.

Journal of molecular graphics & modelling
In this work, performance of wavelet neural network (WNN) and adaptive neuro-fuzzy inference system (ANFIS) models were compared with small data sets by different criteria such as second order corrected Akaike information criterion (AICc), Bayesian i...

Depth in convolutional neural networks solves scene segmentation.

PLoS computational biology
Feed-forward deep convolutional neural networks (DCNNs) are, under specific conditions, matching and even surpassing human performance in object recognition in natural scenes. This performance suggests that the analysis of a loose collection of image...

Multi-sensor information fusion detection system for fire robot through back propagation neural network.

PloS one
OBJECTIVE: To reduce the danger for firefighters and ensure the safety of firefighters as much as possible, based on the back propagation neural network (BPNN) the fire sensor multi-sensor information fusion detection system is investigated.