AIMC Topic: Neural Networks, Computer

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Optimized Bags of Artificial Neural Networks Can Predict the Viability of Organisms Exposed to Nanoparticles.

The journal of physical chemistry. A
Prediction of organismal viability upon exposure to a nanoparticle in varying environments─as fully specified at the molecular scale─has emerged as a useful figure of merit in the design of engineered nanoparticles. We build on our earlier finding th...

Teaching robots the art of human social synchrony.

Science robotics
Humanoid robots can now learn the art of social synchrony using neural networks.

Raman spectroscopy and one-dimensional convolutional neural network modeling as a real-time monitoring tool for in vitro transaminase-catalyzed synthesis of a pharmaceutically relevant amine precursor.

Biotechnology progress
Raman spectroscopy has been used to measure the concentration of a pharmaceutically relevant model amine intermediate for positive allosteric modulators of nicotinic acetylcholine receptor in a ω-transaminase-catalyzed conversion. A model based on a ...

GraphMHC: Neoantigen prediction model applying the graph neural network to molecular structure.

PloS one
Neoantigens are tumor-derived peptides and are biomarkers that can predict prognosis related to immune checkpoint inhibition by estimating their binding to major histocompatibility complex (MHC) proteins. Although deep neural networks have been prima...

A lightweight and gradient-stable neural layer.

Neural networks : the official journal of the International Neural Network Society
To enhance resource efficiency and model deployability of neural networks, we propose a neural-layer architecture based on Householder weighting and absolute-value activating, called Householder-absolute neural layer or simply Han-layer. Compared to ...

Layerwised multimodal knowledge distillation for vision-language pretrained model.

Neural networks : the official journal of the International Neural Network Society
The transformer-based model can simultaneously learn the representation for both images and text, providing excellent performance for multimodal applications. Practically, the large scale of parameters may hinder its deployment in resource-constraine...

Deep local-to-global feature learning for medical image super-resolution.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Medical images play a vital role in medical analysis by providing crucial information about patients' pathological conditions. However, the quality of these images can be compromised by many factors, such as limited resolution of the instruments, art...

iAFPs-Mv-BiTCN: Predicting antifungal peptides using self-attention transformer embedding and transform evolutionary based multi-view features with bidirectional temporal convolutional networks.

Artificial intelligence in medicine
Globally, fungal infections have become a major health concern in humans. Fungal diseases generally occur due to the invading fungus appearing on a specific portion of the body and becoming hard for the human immune system to resist. The recent emerg...

Precise tooth design using deep learning-based templates.

Journal of dentistry
OBJECTIVES: In prosthodontic procedures, traditional computer-aided design (CAD) is often time-consuming and lacks accuracy in shape restoration. In this study, we combined implicit template and deep learning (DL) to construct a precise neural networ...

Optimizing neural network algorithms for submerged membrane bioreactor: A comparative study of OVAT and RSM hyperparameter optimization techniques.

Water science and technology : a journal of the International Association on Water Pollution Research
Hyperparameter tuning is an important process to maximize the performance of any neural network model. This present study proposed the factorial design of experiment for screening and response surface methodology to optimize the hyperparameter of two...