AIMC Topic: Neural Networks, Computer

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Predefined-time synchronization of coupled neural networks with switching parameters and disturbed by Brownian motion.

Neural networks : the official journal of the International Neural Network Society
This article focuses on predefined time synchronization problem for a class of signal switching neural networks with time-varying delays. In the network models, we not only consider the coupling characteristics in the following networks, but also con...

Train-induced vibration attenuation measurements and prediction from ground soil to building column.

Environmental science and pollution research international
The investigation of the influence of soil-structure coupling on the vibration propagation pattern is the key to ensuring the reliability of the prediction of train-induced building vibration. This study selects different metro depots with over-track...

FDE-net: Frequency-domain enhancement network using dynamic-scale dilated convolution for thyroid nodule segmentation.

Computers in biology and medicine
Thyroid nodules, a common disease of endocrine system, have a probability of nearly 10% to turn into malignant nodules and thus pose a serious threat to health. Automatic segmentation of thyroid nodules is of great importance for clinicopathological ...

MM-StackEns: A new deep multimodal stacked generalization approach for protein-protein interaction prediction.

Computers in biology and medicine
Accurate in-silico identification of protein-protein interactions (PPIs) is a long-standing problem in biology, with important implications in protein function prediction and drug design. Current computational approaches predominantly use a single da...

Artificial intelligence and machine learning approaches in composting process: A review.

Bioresource technology
Studies on developing strategies to predict the stability and performance of the composting process have increased in recent years. Machine learning (ML) has focused on process optimization, prediction of missing data, detection of non-conformities, ...

Using machine learning to predict the effects and consequences of mutations in proteins.

Current opinion in structural biology
Machine and deep learning approaches can leverage the increasingly available massive datasets of protein sequences, structures, and mutational effects to predict variants with improved fitness. Many different approaches are being developed, but syste...

Computational Predictions of Nonclinical Pharmacokinetics at the Drug Design Stage.

Journal of chemical information and modeling
Although computational predictions of pharmacokinetics (PK) are desirable at the drug design stage, existing approaches are often limited by prediction accuracy and human interpretability. Using a discovery data set of mouse and rat PK studies at Roc...

Prediction of Kinetic Product Ratios: Investigation of a Dynamically Controlled Case.

The journal of physical chemistry. A
Of the various factors influencing kinetically controlled product ratios, the role of nonstatistical dynamics is arguably the least well understood. In this paper, reactions were chosen in which dynamics played a dominant role in product selection, b...

Development and Validation of a Deep Learning-Based Synthetic Bone-Suppressed Model for Pulmonary Nodule Detection in Chest Radiographs.

JAMA network open
IMPORTANCE: Dual-energy chest radiography exhibits better sensitivity than single-energy chest radiography, partly due to its ability to remove overlying anatomical structures.

Deep Learning Framework for Controlling Work Sequence in Collaborative Human-Robot Assembly Processes.

Sensors (Basel, Switzerland)
The human-robot collaboration (HRC) solutions presented so far have the disadvantage that the interaction between humans and robots is based on the human's state or on specific gestures purposely performed by the human, thus increasing the time requi...