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Recognition, Psychology

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Multihydrophone Fusion Network for Modulation Recognition.

Sensors (Basel, Switzerland)
Deep learning (DL)-based modulation recognition methods of underwater acoustic communication signals are mostly applied to a single hydrophone reception scenario. In this paper, we propose a novel end-to-end multihydrophone fusion network (MHFNet) fo...

A Comprehensive Review of Recent Deep Learning Techniques for Human Activity Recognition.

Computational intelligence and neuroscience
Human action recognition is an important field in computer vision that has attracted remarkable attention from researchers. This survey aims to provide a comprehensive overview of recent human action recognition approaches based on deep learning usin...

An Automated Image-Based Multivariant Concrete Defect Recognition Using a Convolutional Neural Network with an Integrated Pooling Module.

Sensors (Basel, Switzerland)
Buildings and infrastructure in congested metropolitan areas are continuously deteriorating. Various structural flaws such as surface cracks, spalling, delamination, and other defects are found, and keep on progressing. Traditionally, the assessment ...

Emotion Recognition from Physiological Channels Using Graph Neural Network.

Sensors (Basel, Switzerland)
In recent years, a number of new research papers have emerged on the application of neural networks in affective computing. One of the newest trends observed is the utilization of graph neural networks (GNNs) to recognize emotions. The study presente...

A New Approach for Abnormal Human Activities Recognition Based on ConvLSTM Architecture.

Sensors (Basel, Switzerland)
Recognizing various abnormal human activities from video is very challenging. This problem is also greatly influenced by the lack of datasets containing various abnormal human activities. The available datasets contain various human activities, but o...

Constructing Accurate and Efficient Deep Spiking Neural Networks With Double-Threshold and Augmented Schemes.

IEEE transactions on neural networks and learning systems
Spiking neural networks (SNNs) are considered as a potential candidate to overcome current challenges, such as the high-power consumption encountered by artificial neural networks (ANNs); however, there is still a gap between them with respect to the...

Adversarial Attack on Skeleton-Based Human Action Recognition.

IEEE transactions on neural networks and learning systems
Deep learning models achieve impressive performance for skeleton-based human action recognition. Graph convolutional networks (GCNs) are particularly suitable for this task due to the graph-structured nature of skeleton data. However, the robustness ...

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 ...

KinectGaitNet: Kinect-Based Gait Recognition Using Deep Convolutional Neural Network.

Sensors (Basel, Switzerland)
Over the past decade, gait recognition had gained a lot of attention in various research and industrial domains. These include remote surveillance, border control, medical rehabilitation, emotion detection from posture, fall detection, and sports tra...

A Prediction Model Analysis of Behavior Recognition Based on Genetic Algorithm and Neural Network.

Computational intelligence and neuroscience
With the extensive application of virtual technology and simulation algorithm, motion behavior recognition is widely used in various fields. The original neural network algorithm cannot solve the problem of data redundancy in behavior recognition, an...