AIMC Topic: Deep Learning

Clear Filters Showing 10421 to 10430 of 28423 articles

Deep learning in wastewater treatment: a critical review.

Water research
Modeling wastewater processes supports tasks such as process prediction, soft sensing, data analysis and computer assisted design of wastewater systems. Wastewater treatment processes are large, complex processes, with multiple controlling mechanisms...

DeepAmes: A deep learning-powered Ames test predictive model with potential for regulatory application.

Regulatory toxicology and pharmacology : RTP
The Ames assay is required by the regulatory agencies worldwide to assess the mutagenic potential risk of consumer products. As well as this in vitro assay, in silico approaches have been widely used to predict Ames test results as outlined in the In...

Comprehensive study of algal blooms variation in Jiaozhou Bay based on google earth engine and deep learning.

Scientific reports
The Jiaozhou Bay ecosystem, a crucial marine ecosystem in China, has been plagued by frequent harmful algal blooms as due to deteriorating water quality and eutrophication. This study analyzed the temporal and spatial changes of harmful algal blooms ...

Integration of pre-trained protein language models into geometric deep learning networks.

Communications biology
Geometric deep learning has recently achieved great success in non-Euclidean domains, and learning on 3D structures of large biomolecules is emerging as a distinct research area. However, its efficacy is largely constrained due to the limited quantit...

Deep learning and wing interferential patterns identify Anopheles species and discriminate amongst Gambiae complex species.

Scientific reports
We present a new and innovative identification method based on deep learning of the wing interferential patterns carried by mosquitoes of the Anopheles genus to classify and assign 20 Anopheles species, including 13 malaria vectors. We provide additi...

Small immunological clocks identified by deep learning and gradient boosting.

Frontiers in immunology
BACKGROUND: The aging process affects all systems of the human body, and the observed increase in inflammatory components affecting the immune system in old age can lead to the development of age-associated diseases and systemic inflammation.

Use of artificial intelligence in triaging of chest radiographs to reduce radiologists' workload.

European radiology
OBJECTIVES: To evaluate whether deep learning-based detection algorithms (DLD)-based triaging can reduce outpatient chest radiograph interpretation workload while maintaining noninferior sensitivity.

Multimodal deep learning as a next challenge in nutrition research: tailoring fermented dairy products based on -mediated lipid metabolism.

Critical reviews in food science and nutrition
Deep learning is evolving in nutritional epidemiology to address challenges including precise nutrition and data-driven disease modeling. Fermented dairy products consumption as the implementation of specific dietary priority contributes to a lower r...

Indocyanine green-based fluorescence imaging improved by deep learning.

Journal of biophotonics
Intraoperative identification of malignancies using indocyanine green (ICG)-based fluorescence imaging could provide real-time guidance for surgeons. Existing ICG-based fluorescence imaging mostly operates in the near-infrared (NIR)-I (700-1000 nm) o...

Deep Learning-Based Evaluation of Ultrasound Images for Benign Skin Tumors.

Sensors (Basel, Switzerland)
In this study, a combined convolutional neural network for the diagnosis of three benign skin tumors was designed, and its effectiveness was verified through quantitative and statistical analysis. To this end, 698 sonographic images were taken and di...