A review on explainability in multimodal deep neural nets
Artificial Intelligence techniques powered by deep neural nets have achieved much success
in several application domains, most significantly and notably in the Computer Vision …
in several application domains, most significantly and notably in the Computer Vision …
Detecting human driver inattentive and aggressive driving behavior using deep learning: Recent advances, requirements and open challenges
MH Alkinani, WZ Khan, Q Arshad - Ieee Access, 2020 - ieeexplore.ieee.org
Human drivers have different driving styles, experiences, and emotions due to unique
driving characteristics, exhibiting their own driving behaviors and habits. Various research …
driving characteristics, exhibiting their own driving behaviors and habits. Various research …
Automatic driver stress level classification using multimodal deep learning
Stress has been identified as one of the contributing factors to vehicle crashes which create
a significant cost in terms of loss of life and productivity for governments and societies …
a significant cost in terms of loss of life and productivity for governments and societies …
Analyzing factors influencing situation awareness in autonomous vehicles—A survey
Autonomous driving of higher automation levels asks for optimal execution of critical
maneuvers in all environments. A crucial prerequisite for such optimal decision-making …
maneuvers in all environments. A crucial prerequisite for such optimal decision-making …
A review of multimodal deep learning methods for genomic-enabled prediction in plant breeding
OA Montesinos-López, M Chavira-Flores, Kismiantini… - Genetics, 2024 - academic.oup.com
Deep learning methods have been applied when working to enhance the prediction
accuracy of traditional statistical methods in the field of plant breeding. Although deep …
accuracy of traditional statistical methods in the field of plant breeding. Although deep …
Software/hardware co-design for multi-modal multi-task learning in autonomous systems
Optimizing the quality of result (QoR) and the quality of service (QoS) of AI-empowered
autonomous systems simultaneously is very challenging. First, there are multiple input …
autonomous systems simultaneously is very challenging. First, there are multiple input …
[PDF][PDF] A New Indonesian Traffic Obstacle Dataset and Performance Evaluation of YOLOv4 for ADAS
Intelligent transport systems (ITS) are a promising area of studies. One implementation of
ITS are advanced driver assistance systems (ADAS), involving the problem of obstacle …
ITS are advanced driver assistance systems (ADAS), involving the problem of obstacle …
A conceptual multi-layer framework for the detection of nighttime pedestrian in autonomous vehicles using deep reinforcement learning
The major challenge faced by autonomous vehicles today is driving through busy roads
without getting into an accident, especially with a pedestrian. To avoid collision with …
without getting into an accident, especially with a pedestrian. To avoid collision with …
[PDF][PDF] A review of multimodal deep learning methods for genomic-enabled prediction in plant breeding
R Fritsche-Neto, K Al-Nowibet, A Montesinos-López… - …, 2024 - cgspace.cgiar.org
Deep learning methods have been applied when working to enhance the prediction
accuracy of traditional statistical methods in the field of plant breeding. Although deep …
accuracy of traditional statistical methods in the field of plant breeding. Although deep …
Driver stress level detection based on multimodal measurements
MN Rastgoo - 2019 - eprints.qut.edu.au
Successful driver performance is fundamental in preventing vehicle crashes. Stress can
negatively affect driver performance and significantly increase the risk of a crash. Therefore …
negatively affect driver performance and significantly increase the risk of a crash. Therefore …