AIMC Topic: Neural Networks, Computer

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Graph-Convolutional Neural Net Model of the Statistical Torsion Profiles for Small Organic Molecules.

Journal of chemical information and modeling
We present a graph-convolutional neural network (GCNN)-based method for learning and prediction of statistical torsional profiles (STP) in small organic molecules based on the experimental X-ray structure data. A specialized GCNN torsion profile mode...

Exploration of Chemical Space Guided by PixelCNN for Fragment-Based De Novo Drug Discovery.

Journal of chemical information and modeling
We report a novel framework for achieving fragment-based molecular design using pixel convolutional neural network (PixelCNN) combined with the simplified molecular input line entry system (SMILES) as molecular representation. While a widely used rec...

A Spatiotemporal Deep Neural Network Useful for Defect Identification and Reconstruction of Artworks Using Infrared Thermography.

Sensors (Basel, Switzerland)
Assessment of cultural heritage assets is now extremely important all around the world. Non-destructive inspection is essential for preserving the integrity of artworks while avoiding the loss of any precious materials that make them up. The use of I...

Non-Contact Vibro-Acoustic Object Recognition Using Laser Doppler Vibrometry and Convolutional Neural Networks.

Sensors (Basel, Switzerland)
Laser Doppler vibrometers (LDVs) have been widely adopted due to their large number of benefits in comparison to traditional contacting vibration transducers. Their high sensitivity, among other unique characteristics, has also led to their use as op...

Deep problems with neural network models of human vision.

The Behavioral and brain sciences
Deep neural networks (DNNs) have had extraordinary successes in classifying photographic images of objects and are often described as the best models of biological vision. This conclusion is largely based on three sets of findings: (1) DNNs are more ...

Prediction of radiosensitivity and radiocurability using a novel supervised artificial neural network.

BMC cancer
BACKGROUND: Radiotherapy has been widely used to treat various cancers, but its efficacy depends on the individual involved. Traditional gene-based machine-learning models have been widely used to predict radiosensitivity. However, there is still a l...

Ensemble deep learning enhanced with self-attention for predicting immunotherapeutic responses to cancers.

Frontiers in immunology
INTRODUCTION: Despite the many benefits immunotherapy has brought to patients with different cancers, its clinical applications and improvements are still hindered by drug resistance. Fostering a reliable approach to identifying sufferers who are sen...

Efficient framework for brain tumor detection using different deep learning techniques.

Frontiers in public health
The brain tumor is an urgent malignancy caused by unregulated cell division. Tumors are classified using a biopsy, which is normally performed after the final brain surgery. Deep learning technology advancements have assisted the health professionals...

Optimization for a New XY Positioning Mechanism by Artificial Neural Network-Based Metaheuristic Algorithms.

Computational intelligence and neuroscience
This paper devotes a new method in modeling and optimizing to handle the optimization of the XY positioning mechanism. The fitness functions and constraints of the mechanism are formulated via proposing a combination of artificial neural network (ANN...

Artificial intelligence for localization of the acute ischemic stroke by non-contrast computed tomography.

PloS one
A non-contrast cranial computer tomography (ncCT) is often employed for the diagnosis of the early stage of the ischemic stroke. However, the number of false negatives is high. More accurate results are obtained by an MRI. However, the MRI is not ava...