AIMC Topic: Neural Networks, Computer

Clear Filters Showing 31041 to 31050 of 31376 articles

ProQ3D: improved model quality assessments using deep learning.

Bioinformatics (Oxford, England)
SUMMARY: Protein quality assessment is a long-standing problem in bioinformatics. For more than a decade we have developed state-of-art predictors by carefully selecting and optimising inputs to a machine learning method. The correlation has increase...

TSSPlant: a new tool for prediction of plant Pol II promoters.

Nucleic acids research
Our current knowledge of eukaryotic promoters indicates their complex architecture that is often composed of numerous functional motifs. Most of known promoters include multiple and in some cases mutually exclusive transcription start sites (TSSs). M...

Phytoremediation of palm oil mill secondary effluent (POMSE) by Chrysopogon zizanioides (L.) using artificial neural networks.

International journal of phytoremediation
Artificial neural networks (ANNs) have been widely used to solve the problems because of their reliable, robust, and salient characteristics in capturing the nonlinear relationships between variables in complex systems. In this study, ANN was applied...

De-identification of patient notes with recurrent neural networks.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: Patient notes in electronic health records (EHRs) may contain critical information for medical investigations. However, the vast majority of medical investigators can only access de-identified notes, in order to protect the confidentiality...

Training and Validating a Deep Convolutional Neural Network for Computer-Aided Detection and Classification of Abnormalities on Frontal Chest Radiographs.

Investigative radiology
OBJECTIVES: Convolutional neural networks (CNNs) are a subtype of artificial neural network that have shown strong performance in computer vision tasks including image classification. To date, there has been limited application of CNNs to chest radio...

[A hyperspectral assessment model for leaf chlorophyll content of Pinus massoniana based on neural network].

Ying yong sheng tai xue bao = The journal of applied ecology
The relationships between the leaf chlorophyll content (LCC) of Pinus massoniana at different growth stages and their chlorophyll content were analyzed. 7 of 36 red edge-based parameters were finally selected as the typical spectral response paramete...

River flood prediction using fuzzy neural networks: an investigation on automated network architecture.

Water science and technology : a journal of the International Association on Water Pollution Research
Urban floods are one of the most devastating natural disasters globally and improved flood prediction is essential for better flood management. Today, high-resolution real-time datasets for flood-related variables are widely available. These data can...

Central and peripheral vision for scene recognition: A neurocomputational modeling exploration.

Journal of vision
What are the roles of central and peripheral vision in human scene recognition? Larson and Loschky (2009) showed that peripheral vision contributes more than central vision in obtaining maximum scene recognition accuracy. However, central vision is m...

Transfer Learning with Convolutional Neural Networks for Classification of Abdominal Ultrasound Images.

Journal of digital imaging
The purpose of this study is to evaluate transfer learning with deep convolutional neural networks for the classification of abdominal ultrasound images. Grayscale images from 185 consecutive clinical abdominal ultrasound studies were categorized int...