AIMC Topic: Models, Theoretical

Clear Filters Showing 1721 to 1730 of 1953 articles

High-generalization deep sparse pattern reconstruction: feature extraction of speckles using self-attention armed convolutional neural networks.

Optics express
Light scattering is a pervasive problem in many areas. Recently, deep learning was implemented in speckle reconstruction. To better investigate the key feature extraction and generalization abilities of the networks for sparse pattern reconstruction,...

Automatic contour segmentation of cervical cancer using artificial intelligence.

Journal of radiation research
In cervical cancer treatment, radiation therapy is selected based on the degree of tumor progression, and radiation oncologists are required to delineate tumor contours. To reduce the burden on radiation oncologists, an automatic segmentation of the ...

Integrating multi-scale neighbouring topologies and cross-modal similarities for drug-protein interaction prediction.

Briefings in bioinformatics
MOTIVATION: Identifying the proteins that interact with drugs can reduce the cost and time of drug development. Existing computerized methods focus on integrating drug-related and protein-related data from multiple sources to predict candidate drug-t...

Venn diagrams in bioinformatics.

Briefings in bioinformatics
Venn diagrams are widely used tools for graphical depiction of the unions, intersections and distinctions among multiple datasets, and a large number of programs have been developed to generate Venn diagrams for applications in various research areas...

Feature extraction approaches for biological sequences: a comparative study of mathematical features.

Briefings in bioinformatics
As consequence of the various genomic sequencing projects, an increasing volume of biological sequence data is being produced. Although machine learning algorithms have been successfully applied to a large number of genomic sequence-related problems,...

Hysteresis modeling and compensation of a rotary series elastic actuator with nonlinear stiffness.

The Review of scientific instruments
Series elastic actuators (SEAs) have widely been adapted in robots where safe human-robot interaction is required for accurate and robust force control. Recent research on the SEAs has shown that the SEA with a user-defined variable stiffness possess...

Evaluation of the prediction of CoVID-19 recovered and unrecovered cases using symptoms and patient's meta data based on support vector machine, neural network, CHAID and QUEST Models.

European review for medical and pharmacological sciences
OBJECTIVE: This paper aims to develop four prediction models for recovered and unrecovered cases using descriptive data of patients and symptoms of CoVID-19 patients. The developed prediction models aim to extract the important variables in predictin...

Auto informing COVID-19 detection result from x-ray/CT images based on deep learning.

The Review of scientific instruments
It is no secret to all that the corona pandemic has caused a decline in all aspects of the world. Therefore, offering an accurate automatic diagnostic system is very important. This paper proposed an accurate COVID-19 system by testing various deep l...

Unsupervised flow cytometry analysis in hematological malignancies: A new paradigm.

International journal of laboratory hematology
Ever since hematopoietic cells became "events" enumerated and characterized in suspension by cell counters or flow cytometers, researchers and engineers have strived to refine the acquisition and display of the electronic signals generated. A large a...

Predicting the Need For Vasopressors in the Intensive Care Unit Using an Attention Based Deep Learning Model.

Shock (Augusta, Ga.)
BACKGROUND: Previous models on prediction of shock mostly focused on septic shock and often required laboratory results in their models. The purpose of this study was to use deep learning approaches to predict vasopressor requirement for critically i...