[HTML][HTML] A survey of multimodal information fusion for smart healthcare: Map** the journey from data to wisdom
Multimodal medical data fusion has emerged as a transformative approach in smart
healthcare, enabling a comprehensive understanding of patient health and personalized …
healthcare, enabling a comprehensive understanding of patient health and personalized …
Multimodal co-learning: Challenges, applications with datasets, recent advances and future directions
Multimodal deep learning systems that employ multiple modalities like text, image, audio,
video, etc., are showing better performance than individual modalities (ie, unimodal) …
video, etc., are showing better performance than individual modalities (ie, unimodal) …
[HTML][HTML] Examining the interplay between artificial intelligence and the agri-food industry
Artificial intelligence (AI) has advanced at an astounding rate and transformed numerous
economic sectors. Nevertheless, a comprehensive understanding of how AI can improve the …
economic sectors. Nevertheless, a comprehensive understanding of how AI can improve the …
Deep evidential fusion with uncertainty quantification and reliability learning for multimodal medical image segmentation
Single-modality medical images generally do not contain enough information to reach an
accurate and reliable diagnosis. For this reason, physicians commonly rely on multimodal …
accurate and reliable diagnosis. For this reason, physicians commonly rely on multimodal …
[HTML][HTML] Towards a data collection methodology for Responsible Artificial Intelligence in health: A prospective and qualitative study in pregnancy
A medical field that is increasingly benefiting from Artificial Intelligence applications is Gyne-
cology and Obstetrics. In previous work, we exposed that Artificial Intelligence (AI) …
cology and Obstetrics. In previous work, we exposed that Artificial Intelligence (AI) …
Recognition of the mental workloads of pilots in the cockpit using EEG signals
The commercial flightdeck is a naturally multi-tasking work environment, one in which
interruptions are frequent come in various forms, contributing in many cases to aviation …
interruptions are frequent come in various forms, contributing in many cases to aviation …
A deep cross-modal neural cognitive diagnosis framework for modeling student performance
In intelligent education systems, one fundamental task is to predict student performance on
new exercises and estimate the knowledge proficiency of students on knowledge concepts …
new exercises and estimate the knowledge proficiency of students on knowledge concepts …
Multi-agent reinforcement learning based on representational communication for large-scale traffic signal control
Traffic signal control (TSC) is a challenging problem within intelligent transportation systems
and has been tackled using multi-agent reinforcement learning (MARL). While centralized …
and has been tackled using multi-agent reinforcement learning (MARL). While centralized …
A deep learning-based approach for assessment of bridge condition through fusion of multi-type inspection data
Bridges typically undergo regular inspections to assess their structural conditions. However,
relying solely on numerical data overlooks valuable information from other data types …
relying solely on numerical data overlooks valuable information from other data types …
[HTML][HTML] Cross-modal generative models for multi-modal plastic sorting
Automated sorting through chemometric analysis of plastic spectral data could be a key
strategy towards improving plastic waste management. Deep learning is a promising …
strategy towards improving plastic waste management. Deep learning is a promising …