AIMC Topic: Neural Networks, Computer

Clear Filters Showing 13931 to 13940 of 31376 articles

Vision Transformer and Deep Sequence Learning for Human Activity Recognition in Surveillance Videos.

Computational intelligence and neuroscience
Human Activity Recognition is an active research area with several Convolutional Neural Network (CNN) based features extraction and classification methods employed for surveillance and other applications. However, accurate identification of HAR from ...

Two-Stage CNN Model for Joint Demosaicing and Denoising of Burst Bayer Images.

Computational intelligence and neuroscience
In the classical image processing pipeline, demosaicing and denoising are separated steps that may interfere with each other. Joint demosaicing and denoising utilizes the shared image prior information to guide the image recovery process. It is expec...

Structure Guided Deep Neural Network for Unsupervised Active Learning.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Unsupervised active learning has become an active research topic in the machine learning and computer vision communities, whose goal is to choose a subset of representative samples to be labeled in an unsupervised setting. Most of existing approaches...

Deep Learning Methods for Multi-Channel EEG-Based Emotion Recognition.

International journal of neural systems
Currently, Fourier-based, wavelet-based, and Hilbert-based time-frequency techniques have generated considerable interest in classification studies for emotion recognition in human-computer interface investigations. Empirical mode decomposition (EMD)...

Deep Learning-Based Indoor Localization Using Multi-View BLE Signal.

Sensors (Basel, Switzerland)
In this paper, we present a novel Deep Neural Network-based indoor localization method that estimates the position of a Bluetooth Low Energy (BLE) transmitter (tag) by using the received signals' characteristics at multiple Anchor Points (APs). We us...

State-of-the-Art Capability of Convolutional Neural Networks to Distinguish the Signal in the Ionosphere.

Sensors (Basel, Switzerland)
Recovering and distinguishing different ionospheric layers and signals usually requires slow and complicated procedures. In this work, we construct and train five convolutional neural network (CNN) models: DeepLab, fully convolutional DenseNet24 (FC-...

Contextual associations represented both in neural networks and human behavior.

Scientific reports
Contextual associations facilitate object recognition in human vision. However, the role of context in artificial vision remains elusive as does the characteristics that humans use to define context. We investigated whether contextually related objec...

An advanced computational intelligent framework to predict shear sonic velocity with application to mechanical rock classification.

Scientific reports
Shear sonic wave velocity (Vs) has a wide variety of implications, from reservoir management and development to geomechanical and geophysical studies. In the current study, two approaches were adopted to predict shear sonic wave velocities (Vs) from ...

Reducing prediction volatility in the surgical workflow recognition of endoscopic pituitary surgery.

International journal of computer assisted radiology and surgery
PURPOSE: Workflow recognition can aid surgeons before an operation when used as a training tool, during an operation by increasing operating room efficiency, and after an operation in the completion of operation notes. Although several methods have b...

Generalizability assessment of COVID-19 3D CT data for deep learning-based disease detection.

Computers in biology and medicine
BACKGROUND: Artificial intelligence technologies in classification/detection of COVID-19 positive cases suffer from generalizability. Moreover, accessing and preparing another large dataset is not always feasible and time-consuming. Several studies h...