AIMC Topic: Neural Networks, Computer

Clear Filters Showing 28531 to 28540 of 31376 articles

Modelling the biological treatment process aeration efficiency: application of the artificial neural network algorithm.

Water science and technology : a journal of the International Association on Water Pollution Research
The biological treatment process (BTP) is responsible for removing chemical oxygen demand (COD) and ammonia using microorganisms present in wastewater. The BTP consumes large quantities of energy due to the transfer of oxygen using air pumps/blowers....

The deep radiomic analytics pipeline.

Veterinary radiology & ultrasound : the official journal of the American College of Veterinary Radiology and the International Veterinary Radiology Association
Radiomics refers to the process of extracting useful imaging features from radiological data. Conventional radiomics like standard uptake value, intensity histograms, or phase images involve hand-crafted (manual) or automated regions of interest (com...

Pathologic Image Classification of Flat Urothelial Lesions Using Pathologic Criteria-Based Deep Learning.

American journal of clinical pathology
OBJECTIVES: Pathologic diagnosis of flat urothelial lesions is subject to high interobserver variability. We expected that deep learning could improve the accuracy and consistency of such pathologic diagnosis, although the learning process is a black...

Neural Network Approaches for Soft Biological Tissue and Organ Simulations.

Journal of biomechanical engineering
Given the functional complexities of soft tissues and organs, it is clear that computational simulations are critical in their understanding and for the rational basis for the development of therapies and replacements. A key aspect of such simulation...

BindWeb: A web server for ligand binding residue and pocket prediction from protein structures.

Protein science : a publication of the Protein Society
Knowledge of protein-ligand interactions is beneficial for biological process analysis and drug design. Given the complexity of the interactions and the inadequacy of experimental data, accurate ligand binding residue and pocket prediction remains ch...

Universal Solution Manifold Networks (USM-Nets): Non-Intrusive Mesh-Free Surrogate Models for Problems in Variable Domains.

Journal of biomechanical engineering
We introduce universal solution manifold network (USM-Net), a novel surrogate model, based on artificial neural networks (ANNs), which applies to differential problems whose solution depends on physical and geometrical parameters. We employ a mesh-le...

A Feature-Encoded Physics-Informed Parameter Identification Neural Network for Musculoskeletal Systems.

Journal of biomechanical engineering
Identification of muscle-tendon force generation properties and muscle activities from physiological measurements, e.g., motion data and raw surface electromyography (sEMG), offers opportunities to construct a subject-specific musculoskeletal (MSK) d...

Deep learning for assessing liver fibrosis based on acoustic nonlinearity maps: an in vivo study of rabbits.

Computer assisted surgery (Abingdon, England)
This study aimed to assess liver fibrosis in rabbits by deep learning models based on acoustic nonlinearity maps. Injection of carbon tetrachloride was used to induce liver fibrosis. Acoustic nonlinearity maps, which were built by data of echo signal...

[Automatic Delineation of Clinical Target Volume and Organ at Risk by Deep Learning for Prostate Cancer Adaptive Radiotherapy].

Zhongguo yi liao qi xie za zhi = Chinese journal of medical instrumentation
Adaptive radiotherapy can modify the treatment plan online based on the clinical target volume (CTV) and organ at risk (OAR) contours on the cone-beam CT (CBCT) before treatment, improving the accuracy of radiotherapy. However, manual delineation of ...

Binary salt structure classification with convolutional neural networks: Application to crystal nucleation and melting point calculations.

The Journal of chemical physics
Convolutional neural networks are constructed and validated for the crystal structure classification of simple binary salts such as the alkali halides. The inputs of the neural network classifiers are the local bond orientational order parameters of ...