AIMC Topic: Neural Networks, Computer

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Aperiodic switching event-triggered stabilization of continuous memristive neural networks with interval delays.

Neural networks : the official journal of the International Neural Network Society
The stabilization problem is studied for memristive neural networks with interval delays under aperiodic switching event-triggered control. Note that, most of delayed memristive neural networks models studied are discontinuous, which are not the real...

Physics-informed neural networks for transcranial ultrasound wave propagation.

Ultrasonics
Transcranial ultrasound imaging has been playing an increasingly important role in the non-invasive treatment of brain disorders. However, the conventional mesh-based numerical wave solvers, which are an integral part of imaging algorithms, suffer fr...

Real-Time Automatic Assisted Detection of Uterine Fibroid in Ultrasound Images Using a Deep Learning Detector.

Ultrasound in medicine & biology
OBJECTIVE: Uterine smooth muscle hyperplasia causes a tumor called a uterine fibroid. With an incidence of up to 30%, it is one of the most prevalent tumors in women and has the third highest prevalence of all gynecological illnesses. Although uterin...

Leak detection and localization in water distribution networks using conditional deep convolutional generative adversarial networks.

Water research
This paper explores the use of 'conditional convolutional generative adversarial networks' (CDCGAN) for image-based leak detection and localization (LD&L) in water distribution networks (WDNs). The method employs pressure measurements and is based on...

Machine learning-based detection and mapping of riverine litter utilizing Sentinel-2 imagery.

Environmental science and pollution research international
Despite the substantial impact of rivers on the global marine litter problem, riverine litter has been accorded inadequate consideration. Therefore, our objective was to detect riverine litter by utilizing middle-scale multispectral satellite images ...

Can Machine Learning Be Better than Biased Readers?

Tomography (Ann Arbor, Mich.)
Training machine learning (ML) models in medical imaging requires large amounts of labeled data. To minimize labeling workload, it is common to divide training data among multiple readers for separate annotation without consensus and then combine th...

A Deep Learning Framework for Processing and Classification of Hyperspectral Rice Seed Images Grown under High Day and Night Temperatures.

Sensors (Basel, Switzerland)
A framework combining two powerful tools of hyperspectral imaging and deep learning for the processing and classification of hyperspectral images (HSI) of rice seeds is presented. A seed-based approach that trains a three-dimensional convolutional ne...

Flamingo-Optimization-Based Deep Convolutional Neural Network for IoT-Based Arrhythmia Classification.

Sensors (Basel, Switzerland)
Cardiac arrhythmia is a deadly disease that threatens the lives of millions of people, which shows the need for earlier detection and classification. An abnormal signal in the heart causing arrhythmia can be detected at an earlier stage when the heal...

Preliminary evaluation of deep learning for first-line diagnostic prediction of tumor mutational status.

Scientific reports
The detection of tumour gene mutations by DNA or RNA sequencing is crucial for the prescription of effective targeted therapies. Recent developments showed promising results for tumoral mutational status prediction using new deep learning based metho...

Analysis of therapeutic effect of subliminal cognition combined with hypnotherapy on anxiety disorder via neural network.

Biotechnology & genetic engineering reviews
Hypnotherapy combined with cognitive therapy is an effective way to intervene anxiety problems, which also responds to the call that using hypnotherapy to treat somatic disorders should become a trend in the future. This paper constructs an evaluatio...