AIMC Topic: Deep Learning

Clear Filters Showing 14121 to 14130 of 28423 articles

Development of an Early Embryo Detection Methodology for Quail Eggs Using a Thermal Micro Camera and the YOLO Deep Learning Algorithm.

Sensors (Basel, Switzerland)
Poultry production utilizes many available technologies in terms of farm-industry automation and sanitary control. However, there is a lack of robust techniques and affordable equipment for avian embryo detection and sexual segregation at the early s...

A deep learning framework for identifying essential proteins based on multiple biological information.

BMC bioinformatics
BACKGROUND: Essential Proteins are demonstrated to exert vital functions on cellular processes and are indispensable for the survival and reproduction of the organism. Traditional centrality methods perform poorly on complex protein-protein interacti...

Wetland Ecotourism Development Using Deep Learning and Grey Clustering Algorithm from the Perspective of Sustainable Development.

Journal of environmental and public health
The purpose is to promote the sustainable development of wetland ecotourism in China and plan the passenger flow in different tourism periods. This work selects Zhangye Heihe wetland ecotourism spot as the research object. Firstly, the two single wet...

Diagnosing Diabetic Retinopathy in OCTA Images Based on Multilevel Information Fusion Using a Deep Learning Framework.

Computational and mathematical methods in medicine
OBJECTIVE: As an extension of optical coherence tomography (OCT), optical coherence tomographic angiography (OCTA) provides information on the blood flow status at the microlevel and is sensitive to changes in the fundus vessels. However, due to the ...

Biomechanical Analysis of Volleyball Players' Spike Swing Based on Deep Learning.

Computational intelligence and neuroscience
Deep learning is to learn the inherent laws and representation levels of sample data. The information obtained during these learning processes is of great help in the interpretation of data such as text, images, and sounds. Through the deep learning ...

Hyperspectral Image Classification Model Using Squeeze and Excitation Network with Deep Learning.

Computational intelligence and neuroscience
In the domain of remote sensing, the classification of hyperspectral image (HSI) has become a popular topic. In general, the complicated features of hyperspectral data cause the precise classification difficult for standard machine learning approache...

BrainNet: Optimal Deep Learning Feature Fusion for Brain Tumor Classification.

Computational intelligence and neuroscience
Early detection of brain tumors can save precious human life. This work presents a fully automated design to classify brain tumors. The proposed scheme employs optimal deep learning features for the classification of FLAIR, T1, T2, and T1CE tumors. I...

Deep Learning-Aided Automated Pneumonia Detection and Classification Using CXR Scans.

Computational intelligence and neuroscience
The COVID-19 pandemic has caused a worldwide catastrophe and widespread devastation that reeled almost all countries. The pandemic has mounted pressure on the existing healthcare system and caused panic and desperation. The gold testing standard for ...

A shallow deep learning approach to classify skin cancer using down-scaling method to minimize time and space complexity.

PloS one
The complex feature characteristics and low contrast of cancer lesions, a high degree of inter-class resemblance between malignant and benign lesions, and the presence of various artifacts including hairs make automated melanoma recognition in dermos...

A Transformer-Based Approach Combining Deep Learning Network and Spatial-Temporal Information for Raw EEG Classification.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
The attention mechanism of the Transformer has the advantage of extracting feature correlation in the long-sequence data and visualizing the model. As time-series data, the spatial and temporal dependencies of the EEG signals between the time points ...