Results 241 to 250 of about 609,343 (292)

Machine learning models using multimodal data accurately predict chemotherapy-induced cardiotoxicity in breast cancer. [PDF]

Front Cardiovasc Med
Chen K, An Y, Wang Z, Nie F.
europepmc   +1 more source

Multimodal data-driven multitask learning for enhanced identification and classification of chronic obstructive pulmonary disease: a retrospective study. [PDF]

J Glob Health
Wu Q, Guo H, Li R, Han J, Zhang Z, Jingesi A, Kang S.   +6 more
europepmc   +1 more source

Systematic evaluation of single-cell multimodal data integration enhances cell type resolution and discovery of clinically relevant states in complex tissues. [PDF]

Genome Biol
Acera-Mateos M, Adiconis X, Li JK, Marchese D, Caratù G, Hon CC, Tiwari P, Kojima M, Vieth B, Murphy MA, Simmons SK, Lefevre T, Claes I, O'Connor CL, Menon R, Otto EA, Ando Y, Vandereyken K, Kretzler M, Bitzer M, Fraenkel E, Voet T, Enard W, Carninci P, Heyn H, Levin JZ, Mereu E.   +26 more
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Language may be all omics needs: Harmonizing multimodal data for omics understanding with CellHermes

Gao Y, Wang W, Zhao Y, Dong K, Shan C, Zheng W, Richter T, Li Z, Chen S, Theis FJ, Liu Q.   +10 more
europepmc   +1 more source

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Modality adaptation in multimodal data

Expert Systems with Applications, 2021
Abstract Recently, multimodal data has received much attention. In classical machine learning, it is assumed that all data comes from one modality while in multimodal machine learning, the information comes from different modalities. In multimodal machine learning, transiting, or fusing knowledge from different modalities is an important step. Hence,
Parvin Razzaghi, Karim Abbasi, Mahmoud Shirazi, Niloofar Shabani   +3 more
openaire   +1 more source

Adaptive monitoring of multimodal data

Computers & Industrial Engineering, 2018
Abstract Multimodal process data that include several subpopulations appear frequently in many complex applications due to process heterogeneity. Different from the most existing control charts that are only applicable to unimodal data, a new adaptive monitoring method is proposed in this paper for multimodal data from heterogeneous processes ...
Kai Wang 0046, Jian Li 0023, Fugee Tsung
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Deep Multimodal Data Fusion

ACM Computing Surveys
Multimodal Artificial Intelligence (Multimodal AI), in general, involves various types of data (e.g., images, texts, or data collected from different sensors), feature engineering (e.g., extraction, combination/fusion), and decision-making (e.g., majority vote).
Fei Zhao, Chengcui Zhang, Baocheng Geng
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Data-driven multimodal synthesis

Speech Communication, 2005
This paper is a report on current efforts at the Department of Speech, Music and Hearing, KTH, on data-driven multimodal synthesis including both visual speech synthesis and acoustic modeling. In the research we try to combine both corpus based methods with knowledge based models and to explore the best of the two approaches. In the paper an attempt to
Rolf Carlson, Björn Granström
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Collecting multimodal data in the wild

Proceedings of the 2012 ACM international conference on Intelligent User Interfaces, 2012
Multimodal interaction allows users to specify commands using combinations of inputs from multiple different modalities. For example, in a local search application, a user might say "gas stations" while simultaneously tracing a route on a touchscreen display.
Michael Johnston, Patrick Ehlen
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Analysis of Multimodal Neuroimaging Data

IEEE Reviews in Biomedical Engineering, 2011
Each method for imaging brain activity has technical or physiological limits. Thus, combinations of neuroimaging modalities that can alleviate these limitations such as simultaneous recordings of neurophysiological and hemodynamic activity have become increasingly popular.
Felix, Biessmann, Sergey, Plis, Frank C, Meinecke, Tom, Eichele, Klaus-Robert, Müller   +4 more
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