AIMC Topic: Neural Networks, Computer

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Evolutionary gravitational neocognitron neural network optimized with marine predators optimization algorithm for MRI brain tumor classification.

Electromagnetic biology and medicine
Magnetic resonance imaging (MRI) is a powerful tool for tumor diagnosis in human brain. Here, the MRI images are considered to detect the brain tumor and classify the regions as meningioma, glioma, pituitary and normal types. Numerous existing method...

A continuous-time neurodynamic approach in matrix form for rank minimization.

Neural networks : the official journal of the International Neural Network Society
This article proposes a continuous-time neurodynamic approach for solving the rank minimization under affine constraints. As opposed to the traditional neurodynamic approach, the proposed neurodynamic approach extends the form of the variables from t...

Multiple instance learning for digital pathology: A review of the state-of-the-art, limitations & future potential.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Digital whole slides images contain an enormous amount of information providing a strong motivation for the development of automated image analysis tools. Particularly deep neural networks show high potential with respect to various tasks in the fiel...

Predicting memorability of face photographs with deep neural networks.

Scientific reports
With the advent of social media in our daily life, we are exposed to a plethora of images, particularly face photographs, every day. Recent behavioural studies have shown that some of these photographs stick in the mind better than others. Previous r...

Event-triggered hybrid impulsive control for synchronization of fractional-order multilayer signed networks under cyber attacks.

Neural networks : the official journal of the International Neural Network Society
In this paper, we consider the exponential bipartite synchronization (EBS) problem of fractional-order multilayer signed networks with time-varying delays (FO-MSNT) under random cyber attacks. In contrast to the existing literature, the proposed hybr...

PeNet: A feature excitation learning approach to advertisement click-through rate prediction.

Neural networks : the official journal of the International Neural Network Society
Since the physical meaning of the fields of the dataset is unknown, we have to use the feature interaction method to select the correlated features and exclude uncorrelated features. The current state-of-the-art methods employ various methods based o...

Modelling dataset bias in machine-learned theories of economic decision-making.

Nature human behaviour
Normative and descriptive models have long vied to explain and predict human risky choices, such as those between goods or gambles. A recent study reported the discovery of a new, more accurate model of human decision-making by training neural networ...

Analysis of neural networks for routine classification of sixteen ultrasound upper abdominal cross sections.

Abdominal radiology (New York)
PURPOSE: Abdominal ultrasound screening requires the capture of multiple standardized plane views as per clinical guidelines. Currently, the extent of adherence to such guidelines is dependent entirely on the skills of the sonographer. The use of neu...

Cross-User Electromyography Pattern Recognition Based on a Novel Spatial-Temporal Graph Convolutional Network.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
With the goal of promoting the development of myoelectric control technology, this paper focuses on exploring graph neural network (GNN) based robust electromyography (EMG) pattern recognition solutions. Given that high-density surface EMG (HD-sEMG) ...