AIMC Topic: Neural Networks, Computer

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Remaining Useful Life Estimation of Aircraft Engines Using a Joint Deep Learning Model Based on TCNN and Transformer.

Computational intelligence and neuroscience
The remaining useful life estimation is a key technology in prognostics and health management (PHM) systems for a new generation of aircraft engines. With the increase in massive monitoring data, it brings new opportunities to improve the prediction ...

Intelligent Sports Video Classification Based on Deep Neural Network (DNN) Algorithm and Transfer Learning.

Computational intelligence and neuroscience
Traditional text annotation-based video retrieval is done by manually labeling videos with text, which is inefficient and highly subjective and generally cannot accurately describe the meaning of videos. Traditional content-based video retrieval uses...

Domain- and task-specific transfer learning for medical segmentation tasks.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVES: Transfer learning is a valuable approach to perform medical image segmentation in settings with limited cases available for training convolutional neural networks (CNN). Both the source task and the source domain influence ...

Optical Biopsy Using a Neural Network to Predict Gene Expression From Photos of Wounds.

The Journal of surgical research
BACKGROUND: The clinical characterization of the biological status of complex wounds remains a considerable challenge. Digital photography provides a non-invasive means of obtaining wound information and is currently employed to assess wounds qualita...

A blind randomized validated convolutional neural network for auto-segmentation of clinical target volume in rectal cancer patients receiving neoadjuvant radiotherapy.

Cancer medicine
BACKGROUND: Delineation of clinical target volume (CTV) for radiotherapy is a time-consuming and labor-intensive work. This study aims to propose a novel convolutional neural network (CNN)-based model for fast auto-segmentation of CTV. To evaluate it...

UV-adVISor: Attention-Based Recurrent Neural Networks to Predict UV-Vis Spectra.

Analytical chemistry
Ultraviolet-visible (UV-Vis) absorption spectra are routinely collected as part of high-performance liquid chromatography (HPLC) analysis systems and can be used to identify chemical reaction products by comparison to the reference spectra. Here, we ...

An Upgraded Siamese Neural Network for Motion Tracking in Ultrasound Image Sequences.

IEEE transactions on ultrasonics, ferroelectrics, and frequency control
Deep learning is heavily being borrowed to solve problems in medical imaging applications, and Siamese neural networks are the front runners of motion tracking. In this article, we propose to upgrade one such Siamese architecture-based neural network...

Deep Learning for Ultrasound Image Formation: CUBDL Evaluation Framework and Open Datasets.

IEEE transactions on ultrasonics, ferroelectrics, and frequency control
Deep learning for ultrasound image formation is rapidly garnering research support and attention, quickly rising as the latest frontier in ultrasound image formation, with much promise to balance both image quality and display speed. Despite this pro...

Estimation of Lower Limb Kinematics during Squat Task in Different Loading Using sEMG Activity and Deep Recurrent Neural Networks.

Sensors (Basel, Switzerland)
The aim of the present study was to predict the kinematics of the knee and the ankle joints during a squat training task of different intensities. Lower limb surface electromyographic (sEMG) signals and the 3-D kinematics of lower extremity joints we...

Network analysis of trauma in patients with early-stage psychosis.

Scientific reports
Childhood trauma (ChT) is a risk factor for psychosis. Negative lifestyle factors such as rumination, negative schemas, and poor diet and exercise are common in psychosis. The present study aimed to perform a network analysis of interactions between ...