AIMC Topic: Neural Networks, Computer

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Application of GIS Technology-Supported Cross Media Fusion Method Based on Deep Learning in Landscape Performance Evaluation.

Computational intelligence and neuroscience
GIS technology can provide reasonable and sustainable data support for landscape planning and ecological development and make wetland landscape planning consider the spatial layout of landscape and the optimal allocation of resources more. The key te...

Frequency-Tuned Universal Adversarial Attacks on Texture Recognition.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Although deep neural networks (DNNs) have been shown to be susceptible to image-agnostic adversarial attacks on natural image classification problems, the effects of such attacks on DNN-based texture recognition have yet to be explored. As part of ou...

Biologically plausible single-layer networks for nonnegative independent component analysis.

Biological cybernetics
An important problem in neuroscience is to understand how brains extract relevant signals from mixtures of unknown sources, i.e., perform blind source separation. To model how the brain performs this task, we seek a biologically plausible single-laye...

Artificial Intelligence in the Imaging of Diffuse Lung Disease.

Radiologic clinics of North America
Diffuse lung diseases are a heterogeneous group of disorders that can be difficult to differentiate by imaging using traditional methods of evaluation. The overlap between various disorders results in difficulty when medical professionals attempt to ...

Continuous learning of spiking networks trained with local rules.

Neural networks : the official journal of the International Neural Network Society
Artificial neural networks (ANNs) experience catastrophic forgetting (CF) during sequential learning. In contrast, the brain can learn continuously without any signs of catastrophic forgetting. Spiking neural networks (SNNs) are the next generation o...

HVIOnet: A deep learning based hybrid visual-inertial odometry approach for unmanned aerial system position estimation.

Neural networks : the official journal of the International Neural Network Society
Sensor fusion is used to solve the localization problem in autonomous mobile robotics applications by integrating complementary data acquired from various sensors. In this study, we adopt Visual-Inertial Odometry (VIO), a low-cost sensor fusion metho...

Exploration of deep learning models for real-time monitoring of state and performance of anaerobic digestion with online sensors.

Bioresource technology
The immediate response to the state disturbances of anaerobic digestion is essential to prevent anaerobic digestion failure. However, frequent monitoring of the state and performance of anaerobic digestion is challenging. Thus, deep learning models w...

Individual Identification by Late Information Fusion of EmgCNN and EmgLSTM from Electromyogram Signals.

Sensors (Basel, Switzerland)
This paper is concerned with individual identification by late fusion of two-stream deep networks from Electromyogram (EMG) signals. EMG signal has more advantages on security compared to other biosignals exposed visually, such as the face, iris, and...

Automated Fluid Intake Detection Using RGB Videos.

Sensors (Basel, Switzerland)
Dehydration is a common, serious issue among older adults. It is important to drink fluid to prevent dehydration and the complications that come with it. As many older adults forget to drink regularly, there is a need for an automated approach, track...

GeneralizedDTA: combining pre-training and multi-task learning to predict drug-target binding affinity for unknown drug discovery.

BMC bioinformatics
BACKGROUND: Accurately predicting drug-target binding affinity (DTA) in silico plays an important role in drug discovery. Most of the computational methods developed for predicting DTA use machine learning models, especially deep neural networks, and...