AIMC Topic: Neural Networks, Computer

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A Novel Affective Analysis System Modeling Method Integrating Affective Cognitive Model and Bi-LSTM Neural Network.

Computational intelligence and neuroscience
The severity of mental health issues among college students has increased over the past few years, having a significant negative impact on not only their academic performance but also on their families and even society as a whole. Therefore, one of t...

Computing Topological Invariants of Deep Neural Networks.

Computational intelligence and neuroscience
A deep neural network has multiple layers to learn more complex patterns and is built to simulate the activity of the human brain. Currently, it provides the best solutions to many problems in image recognition, speech recognition, and natural langua...

Vehicle Driving Risk Prediction Model by Reverse Artificial Intelligence Neural Network.

Computational intelligence and neuroscience
The popularity of private cars has brought great convenience to citizens' travel. However, the number of private cars in society is increasing yearly, and the traffic pressure on the road is also increasing. The number of traffic accidents is increas...

ViSpa (Vision Spaces): A computer-vision-based representation system for individual images and concept prototypes, with large-scale evaluation.

Psychological review
Quantitative, data-driven models for mental representations have long enjoyed popularity and success in psychology (e.g., distributional semantic models in the language domain), but have largely been missing for the visual domain. To overcome this, w...

Rationalized deep learning super-resolution microscopy for sustained live imaging of rapid subcellular processes.

Nature biotechnology
The goal when imaging bioprocesses with optical microscopy is to acquire the most spatiotemporal information with the least invasiveness. Deep neural networks have substantially improved optical microscopy, including image super-resolution and restor...

A novel adaptive cubic quasi-Newton optimizer for deep learning based medical image analysis tasks, validated on detection of COVID-19 and segmentation for COVID-19 lung infection, liver tumor, and optic disc/cup.

Medical physics
BACKGROUND: Most of existing deep learning research in medical image analysis is focused on networks with stronger performance. These networks have achieved success, while their architectures are complex and even contain massive parameters ranging fr...

Deep learning-based neural networks for day-ahead power load probability density forecasting.

Environmental science and pollution research international
Energy efficiency is crucial to greenhouse gas (GHG) emission pathways reported by the Intergovernmental Panel on Climate Change. Electrical overload frequently occurs and causes unwanted outages in distribution networks, which reduces energy utiliza...

Estimation of biosurfactant production parameters and yields without conducting additional experiments on a larger production scale.

Journal of microbiological methods
In this study, a Plackett-Burman design was applied to investigate critical factors for surface tension. After adding a new factor called "production scale", a central composite design (CCD) was constructed to examine nonlinear relations among factor...

Transformer Based Binocular Disparity Prediction with Occlusion Predict and Novel Full Connection Layers.

Sensors (Basel, Switzerland)
The depth estimation algorithm based on the convolutional neural network has many limitations and defects by constructing matching cost volume to calculate the disparity: using a limited disparity range, the authentic disparity beyond the predetermin...

A Robust End-to-End Deep Learning-Based Approach for Effective and Reliable BTD Using MR Images.

Sensors (Basel, Switzerland)
Detection of a brain tumor in the early stages is critical for clinical practice and survival rate. Brain tumors arise in multiple shapes, sizes, and features with various treatment options. Tumor detection manually is challenging, time-consuming, an...