AIMC Topic: Neural Networks, Computer

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Computer-Aided Colon Polyp Detection on High Resolution Colonoscopy Using Transfer Learning Techniques.

Sensors (Basel, Switzerland)
Colonoscopies reduce the incidence of colorectal cancer through early recognition and resecting of the colon polyps. However, the colon polyp miss detection rate is as high as 26% in conventional colonoscopy. The search for methods to decrease the po...

Student Behavior Recognition System for the Classroom Environment Based on Skeleton Pose Estimation and Person Detection.

Sensors (Basel, Switzerland)
Human action recognition has attracted considerable research attention in the field of computer vision, especially for classroom environments. However, most relevant studies have focused on one specific behavior of students. Therefore, this paper pro...

Target Adaptive Tracking Based on GOTURN Algorithm with Convolutional Neural Network and Data Fusion.

Computational intelligence and neuroscience
With the advent of the artificial intelligence era, target adaptive tracking technology has been rapidly developed in the fields of human-computer interaction, intelligent monitoring, and autonomous driving. Aiming at the problem of low tracking accu...

Improving stock trading decisions based on pattern recognition using machine learning technology.

PloS one
PRML, a novel candlestick pattern recognition model using machine learning methods, is proposed to improve stock trading decisions. Four popular machine learning methods and 11 different features types are applied to all possible combinations of dail...

Spatially-Aware Context Neural Networks.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
A variety of computer vision tasks benefit significantly from increasingly powerful deep convolutional neural networks. However, the inherently local property of convolution operations prevents most existing models from capturing long-range feature i...

LUNAR :Drug Screening for Novel Coronavirus Based on Representation Learning Graph Convolutional Network.

IEEE/ACM transactions on computational biology and bioinformatics
An outbreak of COVID-19 that began in late 2019 was caused by a novel coronavirus(SARS-CoV-2). It has become a global pandemic. As of June 9, 2020, it has infected nearly 7 million people and killed more than 400,000, but there is no specific drug. T...

KenDTI: An Ensemble Model for Predicting Drug-Target Interaction by Integrating Multi-Source Information.

IEEE/ACM transactions on computational biology and bioinformatics
The identification of drug-target interactions (DTIs) is an essential step in the process of drug discovery. As experimental validation suffers from high cost and low success rate, various computational models have been exploited to infer potential D...

DeepDRBP-2L: A New Genome Annotation Predictor for Identifying DNA-Binding Proteins and RNA-Binding Proteins Using Convolutional Neural Network and Long Short-Term Memory.

IEEE/ACM transactions on computational biology and bioinformatics
DNA-binding proteins (DBPs) and RNA-binding proteins (RBPs) are two kinds of crucial proteins, which are associated with various cellule activities and some important diseases. Accurate identification of DBPs and RBPs facilitate both theoretical rese...

A Convolutional Neural Network System to Discriminate Drug-Target Interactions.

IEEE/ACM transactions on computational biology and bioinformatics
Biological targets are most commonly proteins such as enzymes, ion channels, and receptors. They are anything within a living organism to bind with some other entities (like an endogenous ligand or a drug), resulting in change in their behaviors or f...

Can AI distinguish a bone radiograph from photos of flowers or cars? Evaluation of bone age deep learning model on inappropriate data inputs.

Skeletal radiology
OBJECTIVE: To evaluate the behavior of a publicly available deep convolutional neural network (DCNN) bone age algorithm when presented with inappropriate data inputs in both radiological and non-radiological domains.