AIMC Topic: Algorithms

Clear Filters Showing 16051 to 16060 of 28713 articles

Automatic prediction of left cardiac chamber enlargement from chest radiographs using convolutional neural network.

European radiology
OBJECTIVE: To develop deep learning-based cardiac chamber enlargement-detection algorithms for left atrial (DLCE-LAE) and ventricular enlargement (DLCE-LVE), on chest radiographs METHODS: For training and internal validation of DLCE-LAE and -LVE, 5,0...

The Translational Machine: A novel machine-learning approach to illuminate complex genetic architectures.

Genetic epidemiology
The Translational Machine (TM) is a machine learning (ML)-based analytic pipeline that translates genotypic/variant call data into biologically contextualized features that richly characterize complex variant architectures and permit greater interpre...

The Function of Color and Structure Based on EEG Features in Landscape Recognition.

International journal of environmental research and public health
Both color and structure make important contributions to human visual perception, as well as the evaluation of landscape quality and landscape aesthetics. The EEG equipment liveamp32 was used to record the EEG signals of humans when viewing landscape...

Identification of Cotton Leaf Lesions Using Deep Learning Techniques.

Sensors (Basel, Switzerland)
The use of deep learning models to identify lesions on cotton leaves on the basis of images of the crop in the field is proposed in this article. Cultivated in most of the world, cotton is one of the economically most important agricultural crops. It...

Development and Validation of a Deep Learning Model to Quantify Interstitial Fibrosis and Tubular Atrophy From Kidney Ultrasonography Images.

JAMA network open
IMPORTANCE: Interstitial fibrosis and tubular atrophy (IFTA) is a strong indicator of decline in kidney function and is measured using histopathological assessment of kidney biopsy core. At present, a noninvasive test to assess IFTA is not available.

Artificial intelligence improves the accuracy of residents in the diagnosis of hip fractures: a multicenter study.

BMC musculoskeletal disorders
BACKGROUND: Less experienced clinicians sometimes make misdiagnosis of hip fractures. We developed computer-aided diagnosis (CAD) system for hip fractures on plain X-rays using a deep learning model trained on a large dataset. In this study, we exami...

Artificial Evolution Network: A Computational Perspective on the Expansibility of the Nervous System.

IEEE transactions on neural networks and learning systems
Neurobiologists recently found the brain can use sudden emerged channels to process information. Based on this finding, we put forward a question whether we can build a computation model that is able to integrate a sudden emerged new type of perceptu...

Qualitative Analysis and Bifurcation in a Neuron System With Memristor Characteristics and Time Delay.

IEEE transactions on neural networks and learning systems
This article focuses on the hybrid effects of memristor characteristics, time delay, and biochemical parameters on neural networks. First, we propose a novel neuron system with memristor and time delays in which the memristor is characterized by a sm...

Automated Diagnosis of COVID-19 Using Deep Features and Parameter Free BAT Optimization.

IEEE journal of translational engineering in health and medicine
Accurate and fast diagnosis of COVID-19 is very important to manage the medical conditions of affected persons. The task is challenging owing to shortage and ineffectiveness of clinical testing kits. However, the existing problems can be improved by...