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Journal articles on the topic 'Multimodal processing'

Author: Grafiati

Published: 1 June 2024

Last updated: 31 July 2025

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1

Ng, Vincent, and Shengjie Li. "Multimodal Propaganda Processing." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 13 (2023): 15368–75. http://dx.doi.org/10.1609/aaai.v37i13.26792.

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Propaganda campaigns have long been used to influence public opinion via disseminating biased and/or misleading information. Despite the increasing prevalence of propaganda content on the Internet, few attempts have been made by AI researchers to analyze such content. We introduce the task of multimodal propaganda processing, where the goal is to automatically analyze propaganda content. We believe that this task presents a long-term challenge to AI researchers and that successful processing of propaganda could bring machine understanding one important step closer to human understanding. We di

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Azizbek Akmal ugli, Akramov, and Rakhimberdiev Sanjarbek Alisher ugli. "MULTIMODAL SPEECH PROCESSING." INTERNATIONAL JOURNAL OF SCIENCE AND TECHNOLOGY 2, no. 07 (2025): 33–38. https://doi.org/10.70728/tech.v2.i07.0012.

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The aim of this research is to evaluate the effectiveness of multimodal speech processing techniques in improving speech recognition accuracy, addressing the key issue of integrating audio, visual, and contextual cues in real-time applications; to solve this problem, data is required from diverse speech datasets that encompass various modalities, including video input, audio recordings, and contextual language information. This dissertation examines the effectiveness of multimodal speech processing techniques in enhancing speech recognition accuracy, focusing particularly on the integration of

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Sinke, Christopher, Janina Neufeld, Daniel Wiswede, Hinderk M. Emrich, Stefan Bleich, and Gregor R. Szycik. "Multisensory processing in synesthesia — differences in the EEG signal during uni- and multimodal processing." Seeing and Perceiving 25 (2012): 53. http://dx.doi.org/10.1163/187847612x646749.

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Synesthesia is a condition in which stimulation in one processing stream (e.g., letters or music) leads to perception in an unstimulated processing stream (e.g., colors). Behavioral differences in mutisensory processing have been shown for multimodal illusions, but the differences in neural processing are still unclear. In the present study, we examined uni- and multimodal processing in 14 people with synesthesia and 13 controls using EEG recordings and a simple detection task. Stimuli were either presented acoustically, visually or multimodaly (simultaneous visual and auditory stimulation). I

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D'Ulizia, Arianna, Fernando Ferri, and Patrizia Grifoni. "Generating Multimodal Grammars for Multimodal Dialogue Processing." IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans 40, no. 6 (2010): 1130–45. http://dx.doi.org/10.1109/tsmca.2010.2041227.

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Barricelli, Barbara Rita, Piero Mussio, Marco Padula, and Paolo Luigi Scala. "TMS for multimodal information processing." Multimedia Tools and Applications 54, no. 1 (2010): 97–120. http://dx.doi.org/10.1007/s11042-010-0527-x.

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Parsons, Aaron D., Stephen W. T. Price, Nicola Wadeson, et al. "Automatic processing of multimodal tomography datasets." Journal of Synchrotron Radiation 24, no. 1 (2017): 248–56. http://dx.doi.org/10.1107/s1600577516017756.

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With the development of fourth-generation high-brightness synchrotrons on the horizon, the already large volume of data that will be collected on imaging and mapping beamlines is set to increase by orders of magnitude. As such, an easy and accessible way of dealing with such large datasets as quickly as possible is required in order to be able to address the core scientific problems during the experimental data collection. Savu is an accessible and flexible big data processing framework that is able to deal with both the variety and the volume of data of multimodal and multidimensional scienti

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Holler, Judith, and Stephen C. Levinson. "Multimodal Language Processing in Human Communication." Trends in Cognitive Sciences 23, no. 8 (2019): 639–52. http://dx.doi.org/10.1016/j.tics.2019.05.006.

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Farzin, Faraz, Eric P. Charles, and Susan M. Rivera. "Development of Multimodal Processing in Infancy." Infancy 14, no. 5 (2009): 563–78. http://dx.doi.org/10.1080/15250000903144207.

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Zhang, Ge, Tianxiang Luo, Witold Pedrycz, Mohammed A. El-Meligy, Mohamed Abdel Fattah Sharaf, and Zhiwu Li. "Outlier Processing in Multimodal Emotion Recognition." IEEE Access 8 (2020): 55688–701. http://dx.doi.org/10.1109/access.2020.2981760.

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Metaxakis, Athanasios, Dionysia Petratou, and Nektarios Tavernarakis. "Multimodal sensory processing in Caenorhabditis elegans." Open Biology 8, no. 6 (2018): 180049. http://dx.doi.org/10.1098/rsob.180049.

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Multisensory integration is a mechanism that allows organisms to simultaneously sense and understand external stimuli from different modalities. These distinct signals are transduced into neuronal signals that converge into decision-making neuronal entities. Such decision-making centres receive information through neuromodulators regarding the organism's physiological state and accordingly trigger behavioural responses. Despite the importance of multisensory integration for efficient functioning of the nervous system, and also the implication of dysfunctional multisensory integration in the ae

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Nock, Harriet J., Giridharan Iyengar, and Chalapathy Neti. "Multimodal processing by finding common cause." Communications of the ACM 47, no. 1 (2004): 51. http://dx.doi.org/10.1145/962081.962105.

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Glasbey, C. A., and N. J. Martin. "Multimodal microscopy by digital image processing." Journal of Microscopy 181, no. 3 (1996): 225–37. http://dx.doi.org/10.1046/j.1365-2818.1996.91372.x.

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Kyselova, A. H., G. D. Kiselov, A. A. Serhyeyev, and A. V. Shalaginov. "Processing input data in multimodal applications." Electronics and Communications 16, no. 2 (2011): 86–92. http://dx.doi.org/10.20535/2312-1807.2011.16.2.268253.

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Kuhnke, Philipp, Markus Kiefer, and Gesa Hartwigsen. "Task-Dependent Functional and Effective Connectivity during Conceptual Processing." Cerebral Cortex 31, no. 7 (2021): 3475–93. http://dx.doi.org/10.1093/cercor/bhab026.

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Abstract Conceptual knowledge is central to cognition. Previous neuroimaging research indicates that conceptual processing involves both modality-specific perceptual-motor areas and multimodal convergence zones. For example, our previous functional magnetic resonance imaging (fMRI) study revealed that both modality-specific and multimodal regions respond to sound and action features of concepts in a task-dependent fashion (Kuhnke P, Kiefer M, Hartwigsen G. 2020b. Task-dependent recruitment of modality-specific and multimodal regions during conceptual processing. Cereb Cortex. 30:3938–3959.). H

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Boyko, Nataliya. "Models and Algorithms for Multimodal Data Processing." WSEAS TRANSACTIONS ON INFORMATION SCIENCE AND APPLICATIONS 20 (March 14, 2023): 87–97. http://dx.doi.org/10.37394/23209.2023.20.11.

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Information technologies and computer equipment are used in almost all areas of activity, which is why new areas of their use are emerging, and the level of ICT implementation is deepening, with more and more functions that were the prerogative of humans being assigned to computers. As science and technology develop, new technologies and technical means are emerging that enable a human-centered approach to software development, better adaptation of human-machine interfaces to user needs, and an increase in the ergonomics of software products, etc. These measures contribute to the formation of

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Chen, Mujun. "Automatic Image Processing Algorithm for Light Environment Optimization Based on Multimodal Neural Network Model." Computational Intelligence and Neuroscience 2022 (June 3, 2022): 1–12. http://dx.doi.org/10.1155/2022/5156532.

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In this paper, we conduct an in-depth study and analysis of the automatic image processing algorithm based on a multimodal Recurrent Neural Network (m-RNN) for light environment optimization. By analyzing the structure of m-RNN and combining the current research frontiers of image processing and natural language processing, we find out the problem of the ineffectiveness of m-RNN for some image generation descriptions, starting from both the image feature extraction part and text sequence data processing. Unlike traditional image automatic processing algorithms, this algorithm does not need to

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Tanaka, Yukari, Hirokata Fukushima, Kazuo Okanoya, and Masako Myowa-Yamakoshi. "Mothers' multimodal information processing is modulated by multimodal interactions with their infants." International Journal of Psychophysiology 94, no. 2 (2014): 174. http://dx.doi.org/10.1016/j.ijpsycho.2014.08.744.

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Chmiel, Agnieszka, Przemysław Janikowski, and Agnieszka Lijewska. "Multimodal processing in simultaneous interpreting with text." Target. International Journal of Translation Studies 32, no. 1 (2020): 37–58. http://dx.doi.org/10.1075/target.18157.chm.

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Abstract The present study focuses on (in)congruence of input between the visual and the auditory modality in simultaneous interpreting with text. We asked twenty-four professional conference interpreters to simultaneously interpret an aurally and visually presented text with controlled incongruences in three categories (numbers, names and control words), while measuring interpreting accuracy and eye movements. The results provide evidence for the dominance of the visual modality, which goes against the professional standard of following the auditory modality in the case of incongruence. Numbe

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Brunet, Paul M., Roddy Cowie, Dirk Heylen, Anton Nijholt, and Marc Schröder. "Conceptual frameworks for multimodal social signal processing." Journal on Multimodal User Interfaces 6, no. 3-4 (2012): 95–99. http://dx.doi.org/10.1007/s12193-012-0099-3.

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Basystiuk, Oleh, and Nataliya Melnykova. "Development of the Multimodal Handling Interface Based on Google API." Computer Design Systems. Theory and Practice 6, no. 1 (2024): 216–23. http://dx.doi.org/10.23939/cds2024.01.216.

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Today, Artificial Intelligence is a daily routine, becoming deeply entrenched in our lives. One of the most popular and rapidly advancing technologies is speech recognition, which forms an integral part of the broader concept of multimodal data handling. Multimodal data encompasses voice, audio, and text data, constituting a multifaceted approach to understanding and processing information. This paper presents the development of a multimodal handling interface leveraging Google API technologies. The interface aims to facilitate seamless integration and management of diverse data modalities, in

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Penia, Oleksandr, та Yevgeniya Sulema. "ЗАСТОСУВАННЯ ГЛИБОКИХ ШТУЧНИХ НЕЙРОННИХ МЕРЕЖ ДЛЯ КЛАСИФІКАЦІЇ МУЛЬТИМОДАЛЬНИХ ДАНИХ". System technologies 6, № 149 (2024): 11–22. http://dx.doi.org/10.34185/1562-9945-6-149-2023-02.

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Multimodal data analysis is gaining attention in recent research. Pu Liang et al. (2023) provide a comprehensive overview on multimodal machine learning, highlighting its founda-tions, challenges and achievements in recent years. More problem-oriented works propose new methods and applications for multimodal ML, such a Ngiam et al. (2011) propose to use joint audio and video data to improve speech recognition accuracy; Sun, Wand and Li (2018) describe application of multimodal classification for breast cancer prognosis prediction; Mao et al. (2014) propose an architecture of multimodal recurre

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Basystiuk, Oleh, Zoriana Rybchak, Iryna Zavushchak, and Uliana Marikutsa. "EVALUATION OF MULTIMODAL DATA SYNCHRONIZATION TOOLS." Computer Design Systems. Theory and Practice 6, no. 3 (2024): 104–11. https://doi.org/10.23939/cds2024.03.104.

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The constant growth of data volumes requires the development of effective methods for managing, processing, and storing information. Additionally, it is advisable to apply multimodal approaches for knowledge aggregation to extract additional knowledge. Usually, the problem of efficient processing of multimodal data is associated with high-quality data preprocessing. One of the most critical preprocessing steps is synchronizing multimodal data stre- ams to analyze complex interactions in different data types. In this article, we evaluate existing approaches to synchronization, focusing on strat

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Zou, Zhuo. "Performance analysis of using multimodal embedding and word embedding transferred to sentiment classification." Applied and Computational Engineering 5, no. 1 (2023): 417–22. http://dx.doi.org/10.54254/2755-2721/5/20230610.

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Multimodal machine learning is one of artificial intelligence's most important research topics. Contrastive Language-Image Pretraining (CLIP) is one of the applications of multimodal machine Learning and is well applied to computer vision. However, there is a research gap in applying CLIP in natural language processing. Therefore, based on IMDB, this paper applies the multimodal features of CLIP and three other pre-trained word vectors, Glove, Word2vec, and BERT, to compare their effects on sentiment classification of natural language processing, to test the performance of CLIP multimodal feat

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Zhang, Ye, Diego Frassinelli, Jyrki Tuomainen, Jeremy I. Skipper, and Gabriella Vigliocco. "More than words: word predictability, prosody, gesture and mouth movements in natural language comprehension." Proceedings of the Royal Society B: Biological Sciences 288, no. 1955 (2021): 20210500. http://dx.doi.org/10.1098/rspb.2021.0500.

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The ecology of human language is face-to-face interaction, comprising cues such as prosody, co-speech gestures and mouth movements. Yet, the multimodal context is usually stripped away in experiments as dominant paradigms focus on linguistic processing only. In two studies we presented video-clips of an actress producing naturalistic passages to participants while recording their electroencephalogram. We quantified multimodal cues (prosody, gestures, mouth movements) and measured their effect on a well-established electroencephalographic marker of processing load in comprehension (N400). We fo

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García, Adolfo M., Eugenia Hesse, Agustina Birba, et al. "Time to Face Language: Embodied Mechanisms Underpin the Inception of Face-Related Meanings in the Human Brain." Cerebral Cortex 30, no. 11 (2020): 6051–68. http://dx.doi.org/10.1093/cercor/bhaa178.

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Abstract In construing meaning, the brain recruits multimodal (conceptual) systems and embodied (modality-specific) mechanisms. Yet, no consensus exists on how crucial the latter are for the inception of semantic distinctions. To address this issue, we combined electroencephalographic (EEG) and intracranial EEG (iEEG) to examine when nouns denoting facial body parts (FBPs) and nonFBPs are discriminated in face-processing and multimodal networks. First, FBP words increased N170 amplitude (a hallmark of early facial processing). Second, they triggered fast (~100 ms) activity boosts within the fa

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Paulmann, Silke, Sarah Jessen, and Sonja A. Kotz. "Investigating the Multimodal Nature of Human Communication." Journal of Psychophysiology 23, no. 2 (2009): 63–76. http://dx.doi.org/10.1027/0269-8803.23.2.63.

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The multimodal nature of human communication has been well established. Yet few empirical studies have systematically examined the widely held belief that this form of perception is facilitated in comparison to unimodal or bimodal perception. In the current experiment we first explored the processing of unimodally presented facial expressions. Furthermore, auditory (prosodic and/or lexical-semantic) information was presented together with the visual information to investigate the processing of bimodal (facial and prosodic cues) and multimodal (facial, lexic, and prosodic cues) human communicat

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Yogesh J. Gaikwad. "Stress Detection using Multimodal Representation Learning, Fusion Techniques, and Applications." Journal of Information Systems Engineering and Management 10, no. 16s (2025): 245–70. https://doi.org/10.52783/jisem.v10i16s.2593.

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The fields of speech recognition, image identification, and natural language processing have undergone a paradigm shift with the advent of machine learning and deep learning approaches. Although these tasks rely primarily on a single modality for input signals, the artificial intelligence field has various applications that necessitate the use of several modalities. In recent years, academics have placed a growing emphasis on the intricate topic of modelling and learning across various modalities. This has attracted the interest of the scientific community. This technical article provides a co

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Hecht, David, Miriam Reiner, and Gad Halevy. "Multimodal Virtual Environments: Response Times, Attention, and Presence." Presence: Teleoperators and Virtual Environments 15, no. 5 (2006): 515–23. http://dx.doi.org/10.1162/pres.15.5.515.

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Multimodal virtual environments (VE) succeed better than single-sensory technologies in creating a sense of presence. We hypothesize that the underlying cognitive mechanism is related to a faster mental processing of multimodal events. Comparing simple detection times of unimodal (auditory, visual, and haptic) events, with bimodal and trimodal combinations, we show that mental processing times are in the following order: unimodal > bimodal > trimodal. Given this processing-speed advantage, multimodal VE users start their cognitive process faster, thus, in a similar exposure time they can

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Diekuu, John-Bosco. "Harnessing Multimodal Image Processing for Cross-Domain Object Recognition in Personalized Healthcare Applications." International Journal of Research Publication and Reviews 6, no. 2 (2025): 219–35. https://doi.org/10.55248/gengpi.6.0225.0713.

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Daulay, Nahdyah Sari, SitiIsma Sari Lubis, and Widya Wulandari. "MULTIMODAL METAPHOR IN ADVERTISEMENT." AICLL: ANNUAL INTERNATIONAL CONFERENCE ON LANGUAGE AND LITERATURE 1, no. 1 (2018): 170–75. http://dx.doi.org/10.30743/aicll.v1i1.24.

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Metaphor based on the cognitive linguistic view can be defined as a tool which allows us to understand one conceptual domain in terms of another. What usually happens is that we use a physical. What we need to comprehend, is the target domain. It means that human cognition is organized in conceptual schema. Rodriguez (2015) stated that multimodal needs a mental comprehension process which differs from processing visual or verbal concepts alone. Metaphor has been used in many advertising. The metaphor can be interpreted differently from one to others. This paper was to present an analysis of vi

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Salamone, Paula C., Agustina Legaz, Lucas Sedeño, et al. "Interoception Primes Emotional Processing: Multimodal Evidence from Neurodegeneration." Journal of Neuroscience 41, no. 19 (2021): 4276–92. http://dx.doi.org/10.1523/jneurosci.2578-20.2021.

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Nakamura, S. "Statistical multimodal integration for audio-visual speech processing." IEEE Transactions on Neural Networks 13, no. 4 (2002): 854–66. http://dx.doi.org/10.1109/tnn.2002.1021886.

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Li Deng, Kuansan Wang, A. Acero, et al. "Distributed speech processing in miPad's multimodal user interface." IEEE Transactions on Speech and Audio Processing 10, no. 8 (2002): 605–19. http://dx.doi.org/10.1109/tsa.2002.804538.

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Samman, Shatha N., Kay M. Stanney, Joseph Dalton, Ali M. Ahmad, Clint Bowers, and Valerie Sims. "Multimodal Interaction: Multi-Capacity Processing Beyond 7 +/− 2." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 48, no. 3 (2004): 386–90. http://dx.doi.org/10.1177/154193120404800324.

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Bengio, Samy. "Multimodal speech processing using asynchronous Hidden Markov Models." Information Fusion 5, no. 2 (2004): 81–89. http://dx.doi.org/10.1016/j.inffus.2003.04.001.

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Ruan, Ludan, Anwen Hu, Yuqing Song, Liang Zhang, Sipeng Zheng, and Qin Jin. "Accommodating Audio Modality in CLIP for Multimodal Processing." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 8 (2023): 9641–49. http://dx.doi.org/10.1609/aaai.v37i8.26153.

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Multimodal processing has attracted much attention lately especially with the success of pre-training. However, the exploration has mainly focused on vision-language pre-training, as introducing more modalities can greatly complicate model design and optimization. In this paper, we extend the state-of-the-art Vision-Language model CLIP to accommodate the audio modality for Vision-Language-Audio multimodal processing. Specifically, we apply inter-modal and intra-modal contrastive learning to explore the correlation between audio and other modalities in addition to the inner characteristics of t

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Mingyu, Ji, Zhou Jiawei, and Wei Ning. "AFR-BERT: Attention-based mechanism feature relevance fusion multimodal sentiment analysis model." PLOS ONE 17, no. 9 (2022): e0273936. http://dx.doi.org/10.1371/journal.pone.0273936.

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Multimodal sentiment analysis is an essential task in natural language processing which refers to the fact that machines can analyze and recognize emotions through logical reasoning and mathematical operations after learning multimodal emotional features. For the problem of how to consider the effective fusion of multimodal data and the relevance of multimodal data in multimodal sentiment analysis, we propose an attention-based mechanism feature relevance fusion multimodal sentiment analysis model (AFR-BERT). In the data pre-processing stage, text features are extracted using the pre-trained l

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Qi, Qingfu, Liyuan Lin, and Rui Zhang. "Feature Extraction Network with Attention Mechanism for Data Enhancement and Recombination Fusion for Multimodal Sentiment Analysis." Information 12, no. 9 (2021): 342. http://dx.doi.org/10.3390/info12090342.

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Multimodal sentiment analysis and emotion recognition represent a major research direction in natural language processing (NLP). With the rapid development of online media, people often express their emotions on a topic in the form of video, and the signals it transmits are multimodal, including language, visual, and audio. Therefore, the traditional unimodal sentiment analysis method is no longer applicable, which requires the establishment of a fusion model of multimodal information to obtain sentiment understanding. In previous studies, scholars used the feature vector cascade method when f

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Rajalingam B. and Priya R. "Enhancement of Hybrid Multimodal Medical Image Fusion Techniques for Clinical Disease Analysis." International Journal of Computer Vision and Image Processing 8, no. 3 (2018): 16–40. http://dx.doi.org/10.4018/ijcvip.2018070102.

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Multimodal medical image fusion is one the most significant and useful disease analytic techniques. This research article proposes the hybrid multimodality medical image fusion methods and discusses the most essential advantages and disadvantages of these methods. The hybrid multimodal medical image fusion algorithms are used to improve the quality of fused multimodality medical image. Magnetic resonance imaging, positron emission tomography, and single photon emission computed tomography are the input multimodal therapeutic images used for fusion process. An experimental result of proposed hy

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Fahrner, Harald, Stefan Kirrmann, Mark Gainey, Marianne Schmucker, Martin Vogel, and Felix Ernst Heinemann. "Multimodal Document Management in Radiotherapy, an Update." Journal of Radiation Oncology Informatics 10, no. 1 (2019): 9. http://dx.doi.org/10.5166/jroi-10-1-1.

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Background: In 2013, we presented a study entitled “Multimodal document management in radiotherapy”, demonstrating the excellent routine performance of the system about four years after its initiation by evaluating a sample of n=500 documents. During this time the system saw additional developments and significant improvements: the most important innovative step being the automatic document processing. This has been completely reworked, to minimize staff-machine interaction, to increase processing speed and to further simplify the overall document handling. This improved system has been runnin

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Song, Chunlai. "Enhancing Multimodal Understanding With LIUS." Journal of Organizational and End User Computing 36, no. 1 (2024): 1–17. http://dx.doi.org/10.4018/joeuc.336276.

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VQA (visual question and answer) is the task of enabling a computer to generate accurate textual answers based on given images and related questions. It integrates computer vision and natural language processing and requires a model that is able to understand not only the image content but also the question in order to generate appropriate linguistic answers. However, current limitations in cross-modal understanding often result in models that struggle to accurately capture the complex relationships between images and questions, leading to inaccurate or ambiguous answers. This research aims to

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Sumalatha, Dr P. "MOON: Multimodal Omniscient Operational Network." International Scientific Journal of Engineering and Management 04, no. 05 (2025): 1–7. https://doi.org/10.55041/isjem03324.

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Abstract—The advancement of artificial intelligence (AI) has significantly accelerated the development of multimodal virtual assistants that integrate diverse sensory modalities to enrich human-computer interaction. This paper introduces MOON (Multimodal Omniscient Operational Network), an AI assistant designed to seamlessly combine voice recognition, computer vision, gesture control, and environmental analysis within an adaptive and intuitive interface. Built upon frameworks such as MediaPipe for gesture recognition, YOLOv3 for real-time object detection, and spaCy for natural language proces

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Liang, Chu, Jiajie Xu, Jie Zhao, Ying Chen, and Jiwei Huang. "Deep Learning-Based Construction and Processing of Multimodal Corpus for IoT Devices in Mobile Edge Computing." Computational Intelligence and Neuroscience 2022 (August 5, 2022): 1–10. http://dx.doi.org/10.1155/2022/2241310.

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Dialogue sentiment analysis is a hot topic in the field of artificial intelligence in recent years, in which the construction of multimodal corpus is the key part of dialogue sentiment analysis. With the rapid development of the Internet of Things (IoT), it provides a new means to collect the multiparty dialogues to construct a multimodal corpus. The rapid development of Mobile Edge Computing (MEC) provides a new platform for the construction of multimodal corpus. In this paper, we construct a multimodal corpus on MEC servers to make full use of the storage space distributed at the edge of the

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Tsai, Hui-Yu, and Ming-Hseng Tseng. "Evaluating Multimodal Techniques for Predicting Visibility in the Atmosphere Using Satellite Images and Environmental Data." Electronics 13, no. 13 (2024): 2585. http://dx.doi.org/10.3390/electronics13132585.

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Visibility is a measure of the atmospheric transparency at an observation point, expressed as the maximum horizontal distance over which a person can see and identify objects. Low atmospheric visibility often occurs in conjunction with air pollution, posing hazards to both traffic safety and human health. In this study, we combined satellite remote sensing images with environmental data to explore the classification performance of two distinct multimodal data processing techniques. The first approach involves developing four multimodal data classification models using deep learning. The second

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Pinto, Inês F., Maria Raquel Aires-Barros, and Ana M. Azevedo. "Multimodal chromatography: debottlenecking the downstream processing of monoclonal antibodies." Pharmaceutical Bioprocessing 3, no. 3 (2015): 263–79. http://dx.doi.org/10.4155/pbp.15.7.

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Engebretsen, Martin. "From Decoding a Graph to Processing a Multimodal Message." Nordicom Review 41, no. 1 (2020): 33–50. http://dx.doi.org/10.2478/nor-2020-0004.

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AbstractData visualisation – in the forms of graphs, charts, and maps – represents a text type growing in prevalence and impact in many cultural domains; education, journalism, business, PR, and more. Research on data visualisation reception is scarce, particularly that related to interactive and dynamic forms of data visualisation in digital media. Taking an approach inspired by grounded theory, in this article I investigate the ways in which young students interact with data visualisations found in digital news media. Combining observations from reading sessions with ten in-depth interviews,

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Li, Tianyun, and Bicheng Fan. "Attention-Sharing Initiative of Multimodal Processing in Simultaneous Interpreting." International Journal of Translation, Interpretation, and Applied Linguistics 2, no. 2 (2020): 42–53. http://dx.doi.org/10.4018/ijtial.20200701.oa4.

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This study sets out to describe simultaneous interpreters' attention-sharing initiatives when exposed under input from both videotaped speech recording and real-time transcriptions. Separation of mental energy in acquiring visual input accords with the human brain's statistic optimization principle where the same property of an object is presented through diverse fashions. In examining professional interpreters' initiatives, the authors invited five professional English-Chinese conference interpreters to simultaneously interpret a videotaped speech with real-time captions generated by speech r

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Zhang, Tao, and Martin McKinney. "Multimodal signal processing and machine learning for hearing instruments." Journal of the Acoustical Society of America 143, no. 3 (2018): 1745. http://dx.doi.org/10.1121/1.5035696.

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Porta, Alberto, Federico Aletti, Frederic Vallais, and Giuseppe Baselli. "Multimodal signal processing for the analysis of cardiovascular variability." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 367, no. 1887 (2008): 391–409. http://dx.doi.org/10.1098/rsta.2008.0229.

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Cardiovascular (CV) variability as a primary vital sign carrying information about CV regulation systems is reviewed by pointing out the role of the main rhythms and the various control and functional systems involved. The high complexity of the addressed phenomena fosters a multimodal approach that relies on data analysis models and deals with the ongoing interactions of many signals at a time. The importance of closed-loop identification and causal analysis is remarked upon and basic properties, application conditions and methods are recalled. The need of further integration of CV signals re

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Wang, Peijin, Zhiyuan Yan, Xuee Rong, et al. "Review of multimodal data processing techniques with limited data." Journal of Image and Graphics 27, no. 10 (2022): 2803–34. http://dx.doi.org/10.11834/jig.220049.

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