Multimodal data fusion for systems improvement: A review

N Gaw, S Yousefi, MR Gahrooei - … from the Air Force Institute of …, 2022 - taylorfrancis.com
In recent years, information available from multiple data modalities has become increasingly
common for industrial engineering and operations research applications. There have been a …

A textual-based featuring approach for depression detection using machine learning classifiers and social media texts

R Chiong, GS Budhi, S Dhakal, F Chiong - Computers in Biology and …, 2021 - Elsevier
Depression is one of the leading causes of suicide worldwide. However, a large percentage
of cases of depression go undiagnosed and, thus, untreated. Previous studies have found …

[HTML][HTML] Information fusion and artificial intelligence for smart healthcare: a bibliometric study

X Chen, H ** review on the multimodal classification of depression and experimental study on existing multimodal models
U Arioz, U Smrke, N Plohl, I Mlakar - Diagnostics, 2022 - mdpi.com
Depression is a prevalent comorbidity in patients with severe physical disorders, such as
cancer, stroke, and coronary diseases. Although it can significantly impact the course of the …

[PDF][PDF] Improving depression prediction accuracy using fisher score-based feature selection and dynamic ensemble selection approach based on acoustic features of …

N Janardhan, N Kumaresh - Traitement du Signal, 2022 - researchgate.net
Accepted: 13 February 2022 Depression affects over 322 million people, and it is the most
common source of disability worldwide. Literature in speech processing revealed that …

[PDF][PDF] Detecting signs of depression using social media texts through an ensemble of ensemble classifiers

R Chiong, G Budhi, E Cambria - IEEE Trans. Affect. Comput, 2024 - ww.sentic.net
Artificial intelligence-based machine learning models have been widely used to explore and
address various mental health-related problems in recent years, including depression. In …