AIMC Topic: Neural Networks, Computer

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Novel methods to global Mittag-Leffler stability of delayed fractional-order quaternion-valued neural networks.

Neural networks : the official journal of the International Neural Network Society
In this paper, a type of fractional-order quaternion-valued neural networks (FOQVNNs) with leakage and time-varying delays is established to simulate real-world situations, and the global Mittag-Leffler stability of the system is investigated by usin...

A Comprehensive Study of Data Augmentation Strategies for Prostate Cancer Detection in Diffusion-Weighted MRI Using Convolutional Neural Networks.

Journal of digital imaging
Data augmentation refers to a group of techniques whose goal is to battle limited amount of available data to improve model generalization and push sample distribution toward the true distribution. While different augmentation strategies and their co...

Molecule Edit Graph Attention Network: Modeling Chemical Reactions as Sequences of Graph Edits.

Journal of chemical information and modeling
The central challenge in automated synthesis planning is to be able to generate and predict outcomes of a diverse set of chemical reactions. In particular, in many cases, the most likely synthesis pathway cannot be applied due to additional constrain...

MolGpka: A Web Server for Small Molecule p Prediction Using a Graph-Convolutional Neural Network.

Journal of chemical information and modeling
p is an important property in the lead optimization process since the charge state of a molecule in physiologic pH plays a critical role in its biological activity, solubility, membrane permeability, metabolism, and toxicity. Accurate and fast estima...

Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Future.

Sensors (Basel, Switzerland)
With the advances of data-driven machine learning research, a wide variety of prediction problems have been tackled. It has become critical to explore how machine learning and specifically deep learning methods can be exploited to analyse healthcare ...

Object Detection and Depth Estimation Approach Based on Deep Convolutional Neural Networks.

Sensors (Basel, Switzerland)
In this paper, we present a real-time object detection and depth estimation approach based on deep convolutional neural networks (CNNs). We improve object detection through the incorporation of transfer connection blocks (TCBs), in particular, to det...

A Survey of Deep Convolutional Neural Networks Applied for Prediction of Plant Leaf Diseases.

Sensors (Basel, Switzerland)
In the modern era, deep learning techniques have emerged as powerful tools in image recognition. Convolutional Neural Networks, one of the deep learning tools, have attained an impressive outcome in this area. Applications such as identifying objects...

Deep learning for COVID-19 detection based on CT images.

Scientific reports
COVID-19 has tremendously impacted patients and medical systems globally. Computed tomography images can effectively complement the reverse transcription-polymerase chain reaction testing. This study adopted a convolutional neural network for COVID-1...

An interpretable multiple-instance approach for the detection of referable diabetic retinopathy in fundus images.

Scientific reports
Diabetic retinopathy (DR) is one of the leading causes of vision loss across the world. Yet despite its wide prevalence, the majority of affected people lack access to the specialized ophthalmologists and equipment required for monitoring their condi...

Interpretable deep learning for the remote characterisation of ambulation in multiple sclerosis using smartphones.

Scientific reports
The emergence of digital technologies such as smartphones in healthcare applications have demonstrated the possibility of developing rich, continuous, and objective measures of multiple sclerosis (MS) disability that can be administered remotely and ...