AIMC Topic: Neural Networks, Computer

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Detecting the sources of chemicals in the Black Sea using non-target screening and deep learning convolutional neural networks.

The Science of the total environment
The Black Sea is an important ecosystem, which is affected by various anthropogenic pressures, such as shipping activities and wastewater inputs from large coastal cities. Significant loads of chemical pollutants are being continuously brought in by ...

LS-NTP: Unifying long- and short-range spatial correlations for near-surface temperature prediction.

Neural networks : the official journal of the International Neural Network Society
The near-surface temperature prediction (NTP) is an important spatial-temporal forecast problem, which can be used to prevent temperature crises. Most of the previous approaches fail to explicitly model the long- and short-range spatial correlations ...

Finite-time stability of state-dependent delayed systems and application to coupled neural networks.

Neural networks : the official journal of the International Neural Network Society
Finite-time stability and stabilization problems of state-dependent delayed systems are studied in this paper. Different from discrete delays and time-dependent delays which can be well estimated over time, the information of state-dependent delays i...

Unsupervised domain adaptation method for segmenting cross-sectional CCA images.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVES: Automatic vessel segmentation in ultrasound is challenging due to the quality of the ultrasound images, which is affected by attenuation, high level of speckle noise and acoustic shadowing. Recently, deep convolutional neur...

Development and validation of a meta-learning-based multi-modal deep learning algorithm for detection of peritoneal metastasis.

International journal of computer assisted radiology and surgery
PURPOSE: The existing medical imaging tools have a detection accuracy of 97% for peritoneal metastasis(PM) bigger than 0.5 cm, but only 29% for that smaller than 0.5 cm, the early detection of PM is still a difficult problem. This study is aiming at ...

Ultrasound enhanced butyric acid-lauric acid designer lipid synthesis: Based on artificial neural network and changes in enzymatic structure.

Ultrasonics sonochemistry
Ultrasound is a green technology for intensifying enzymatic reactions. In this study, an ultrasonic water bath with equipment parameters of 28 kHz, 1750.1 W/m, 60% duty cycle was used to assist the synthesis of butyric acid-lauric acid designer lipid...

Adaptive Modular Convolutional Neural Network for Image Recognition.

Sensors (Basel, Switzerland)
Image recognition has long been one of the research hotspots in computer vision tasks. The development of deep learning is rapid in recent years, and convolutional neural networks usually need to be designed with fixed resources. If sufficient resour...

Deep Learning-Based Segmentation of Post-Mortem Human's Olfactory Bulb Structures in X-ray Phase-Contrast Tomography.

Tomography (Ann Arbor, Mich.)
The human olfactory bulb (OB) has a laminar structure. The segregation of cell populations in the OB image poses a significant challenge because of indistinct boundaries of the layers. Standard 3D visualization tools usually have a low resolution and...

Limited generalizability of single deep neural network for surgical instrument segmentation in different surgical environments.

Scientific reports
Clarifying the generalizability of deep-learning-based surgical-instrument segmentation networks in diverse surgical environments is important in recognizing the challenges of overfitting in surgical-device development. This study comprehensively eva...

End-to-end deep learning framework for printed circuit board manufacturing defect classification.

Scientific reports
We report a complete deep-learning framework using a single-step object detection model in order to quickly and accurately detect and classify the types of manufacturing defects present on Printed Circuit Board (PCBs). We describe the complete model ...