AIMC Topic: Neural Networks, Computer

Clear Filters Showing 16101 to 16110 of 31376 articles

Ultrasonic Defect Characterization Using the Scattering Matrix: A Performance Comparison Study of Bayesian Inversion and Machine Learning Schemas.

IEEE transactions on ultrasonics, ferroelectrics, and frequency control
Accurate defect characterization is desirable in the ultrasonic nondestructive evaluation as it can provide quantitative information about the defect type and geometry. For defect characterization using ultrasonic arrays, high-resolution images can p...

Identifying individuals with recent COVID-19 through voice classification using deep learning.

Scientific reports
Recently deep learning has attained a breakthrough in model accuracy for the classification of images due mainly to convolutional neural networks. In the present study, we attempted to investigate the presence of subclinical voice feature alteration ...

Artificial intelligence-based image analysis can predict outcome in high-grade serous carcinoma via histology alone.

Scientific reports
High-grade extrauterine serous carcinoma (HGSC) is an aggressive tumor with high rates of recurrence, frequent chemotherapy resistance, and overall 5-year survival of less than 50%. Beyond determining and confirming the diagnosis itself, pathologist ...

Deep learning in cancer diagnosis, prognosis and treatment selection.

Genome medicine
Deep learning is a subdiscipline of artificial intelligence that uses a machine learning technique called artificial neural networks to extract patterns and make predictions from large data sets. The increasing adoption of deep learning across health...

Image learning to accurately identify complex mixture components.

The Analyst
The study of complex mixtures is very important for exploring the evolution of natural phenomena, but the complexity of the mixtures greatly increases the difficulty of material information extraction. Image perception-based machine-learning techniqu...

Deep learning based classification of unsegmented phonocardiogram spectrograms leveraging transfer learning.

Physiological measurement
Cardiovascular diseases (CVDs) are a main cause of deaths all over the world. This research focuses on computer-aided analysis of phonocardiogram (PCG) signals based on deep learning that can enable improved and timely detection of heart abnormalitie...

XENet: Using a new graph convolution to accelerate the timeline for protein design on quantum computers.

PLoS computational biology
Graph representations are traditionally used to represent protein structures in sequence design protocols in which the protein backbone conformation is known. This infrequently extends to machine learning projects: existing graph convolution algorith...

Claims-based algorithms for common chronic conditions were efficiently constructed using machine learning methods.

PloS one
Identification of medical conditions using claims data is generally conducted with algorithms based on subject-matter knowledge. However, these claims-based algorithms (CBAs) are highly dependent on the knowledge level and not necessarily optimized f...

Static Attitude Determination Using Convolutional Neural Networks.

Sensors (Basel, Switzerland)
The need to estimate the orientation between frames of reference is crucial in spacecraft navigation. Robust algorithms for this type of problem have been built by following algebraic approaches, but data-driven solutions are becoming more appealing ...

Improving Ponzi Scheme Contract Detection Using Multi-Channel TextCNN and Transformer.

Sensors (Basel, Switzerland)
With the development of blockchain technologies, many Ponzi schemes disguise themselves under the veil of smart contracts. The Ponzi scheme contracts cause serious financial losses, which has a bad effect on the blockchain. Existing Ponzi scheme cont...