Vision-based semantic segmentation in scene understanding for autonomous driving: Recent achievements, challenges, and outlooks
Scene understanding plays a crucial role in autonomous driving by utilizing sensory data for
contextual information extraction and decision making. Beyond modeling advances, the …
contextual information extraction and decision making. Beyond modeling advances, the …
Mapping the landscape and roadmap of geospatial artificial intelligence (GeoAI) in quantitative human geography: An extensive systematic review
This paper brings a comprehensive systematic review of the application of geospatial
artificial intelligence (GeoAI) in quantitative human geography studies, including the …
artificial intelligence (GeoAI) in quantitative human geography studies, including the …
Human activity recognition via hybrid deep learning based model
In recent years, Human Activity Recognition (HAR) has become one of the most important
research topics in the domains of health and human-machine interaction. Many Artificial …
research topics in the domains of health and human-machine interaction. Many Artificial …
Human action recognition using attention based LSTM network with dilated CNN features
Human action recognition in videos is an active area of research in computer vision and
pattern recognition. Nowadays, artificial intelligence (AI) based systems are needed for …
pattern recognition. Nowadays, artificial intelligence (AI) based systems are needed for …
Artificial Intelligence of Things-assisted two-stream neural network for anomaly detection in surveillance Big Video Data
In the last few years, visual sensors are deployed almost everywhere, generating a massive
amount of surveillance video data in smart cities that can be inspected intelligently to …
amount of surveillance video data in smart cities that can be inspected intelligently to …
Light-DehazeNet: a novel lightweight CNN architecture for single image dehazing
Due to the rapid development of artificial intelligence technology, industrial sectors are
revolutionizing in automation, reliability, and robustness, thereby significantly increasing …
revolutionizing in automation, reliability, and robustness, thereby significantly increasing …
DB-Net: A novel dilated CNN based multi-step forecasting model for power consumption in integrated local energy systems
In the era of cutting edge technology, excessive demand for electricity is rising day by day,
due to the exponential growth of population, electricity reliant vehicles, and home …
due to the exponential growth of population, electricity reliant vehicles, and home …
Randomly initialized CNN with densely connected stacked autoencoder for efficient fire detection
Vision sensors-based fire detection is an interesting and useful research domain with
significant alleviated attention from computer vision experts. The baseline research is based …
significant alleviated attention from computer vision experts. The baseline research is based …
Optimal feature selection based speech emotion recognition using two‐stream deep convolutional neural network
Speech signal processing is an active area of research, the most dominant source of
exchanging information among human beings, and the best way for human–computer …
exchanging information among human beings, and the best way for human–computer …
CLSTM: Deep feature-based speech emotion recognition using the hierarchical ConvLSTM network
Artificial intelligence, deep learning, and machine learning are dominant sources to use in
order to make a system smarter. Nowadays, the smart speech emotion recognition (SER) …
order to make a system smarter. Nowadays, the smart speech emotion recognition (SER) …