Predicting information usefulness in health information identification from modal behaviors

Chen, Jing; Zhang, Lu; Lu, Quan; Liu, Hui; Chen, Shuaipu

doi:10.1016/j.ipm.2022.103220

Cited by 3 publications

(4 citation statements)

References 23 publications

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“…The F1 scores of these models were all higher than 77%, and they may be used in a variety of contexts, including the identification of health records. In addition, the gesture-based model could accommodate stringent technological or legal requirements; the gazebased model was well-suited to AR, VR, and metaverse uses; and the combined model provided an option for multimodal human-computer interaction [8].…”

Section: Purpose Of Metaversementioning

confidence: 99%

A Review of Current Metaverse Applications as a Tool for Reshaping Human Behaviour in Health Communication

Miharaini Md Ghani,

Mohd Nizam Osman,

Siti Zobidah Omar

et al. 2023

ARASET

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In line with the objectives of Industry 4.0, the Health 4.0 initiative promotes the integration of cutting-edge technology into medical practice. The Health 4.0 concept includes virtual reality (VR) as a component that has the potential to play an important role. Even though VR in the medical field is a hot issue right now, there is still a lack of understanding about VR-assisted treatment from a broad viewpoint. The combination of healthcare with the metaverse will result in more efficient resource distribution and usage within the healthcare system. The data pertaining to healthcare in the metaverse are kept on a public server, and Artificial Intelligence (AI)-based devices are used to retrieve it. Through the use of metaverse's healthcare systems, patients get a diagnosis in a timely manner. Because the metaverse is meant to be used for specific things, its effects on society, the economy, and culture are also determined by those things. The main findings of this review are to highlight the metaverse's goal of encouraging connectivity, collaboration, and new experiences directly affects how people interact, work, and interact with digital environments and how important it is for responsible and ethical development to make sure that all of these elements work well together. This analysis is going to be broken down into two distinct categories, the first two of which are purpose and impact. In conclusion, the findings of this research will help to improve strategic decision-making as well as policy-making processes in order to pursue the creation of commercial opportunities via the deployment of a metaverse service in healthcare and other comparable contexts.

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Section: Purpose Of Metaversementioning

confidence: 99%

A Review of Current Metaverse Applications as a Tool for Reshaping Human Behaviour in Health Communication

Miharaini Md Ghani,

Mohd Nizam Osman,

Siti Zobidah Omar

et al. 2023

ARASET

View full text Add to dashboard Cite

show abstract

“…For example, to train a two‐channel neural network for behavior classification, some methods use RGB images and optical flow information 15 . Machine learning theory and practice have confirmed that knowledge can be transferred and shared between related machine‐learning task, and that learning multiple tasks together can lead to better performance than learning each task separately 16‐18 . Video behavior detection mainly focuses on the task of video behavior detection.…”

Section: Introductionmentioning

confidence: 99%

“…This text presents a novel approach to addressing complex problems that are challenging to solve using single‐modal learning in real‐world scenarios. Examples of such problems include audio‐visual speech recognition, 17 multi‐modal emotion recognition, 18 multi‐modal machine translation, 19 visual question answering (VQA), 20 multi‐modal retrieval, 21 and multi‐modal behavior recognition. They then compute feature descriptors and utilize the bag‐of‐visual‐words model to describe behavior, and build a vocabulary of visual words to strengthen the description of behavior 22 .…”

Section: Introductionmentioning

confidence: 99%

“…15 Machine learning theory and practice have confirmed that knowledge can be transferred and shared between related machine-learning task, and that learning multiple tasks together can lead to better performance than learning each task separately. [16][17][18] Video behavior detection mainly focuses on the task of video behavior detection. When dealing with a video that displays motion behavior, two objectives need to be met: classifying the behavior and locating the motion in either the spatial, temporal, or spatiotemporal domain.…”

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confidence: 99%

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A face retrieval technique combining large models and artificial neural networks

2024

Concurrency and Computation

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SummaryMultimodal sentiment analysis is a popular research direction in the field of affective computing. It extends unimodal‐based sentiment analysis to the environment based on multimodal information exchange. Face retrieval is the most critical technology in the current multimodal sentiment analysis field. Traditional methods for face retrieval rely on large amounts of data to train the matching relationship between text and face images. This approach suffers from several issues, including high data acquisition costs, insufficient or poor quality training data, and high computational costs for training models. To address the aforementioned issues, this paper proposes a face retrieval technique that merges a large language model with a visual base model. This technique explores the use of zero samples for training, eliminating the need to collect data and train the model to achieve face retrieval. As a result, it significantly reduces the cost requirements of data and computational power. First, an accurate natural text of face description is generated using a large language model based on cue engineering based on face‐independent features. Second, the text and images are encoded using a visual base model and mapped into the same multidimensional space for matching. The experimental results indicate that this method performs well on the CelebA dataset. Extracting 31 different sets of face feature descriptions from the CelebA dataset for retrieval achieves 72% on top1 accuracy and 93% on top3 accuracy. Compared to traditional methods, the method proposed in this paper achieves considerable face retrieval accuracy without collecting text image pairs of data and without training the model.

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Haar-wavelet based texture inpainting for human pose transfer

Wu,

He,

Duan

et al. 2024

Information Processing & Management

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Predicting information usefulness in health information identification from modal behaviors

Cited by 3 publications

References 23 publications

A Review of Current Metaverse Applications as a Tool for Reshaping Human Behaviour in Health Communication

A Review of Current Metaverse Applications as a Tool for Reshaping Human Behaviour in Health Communication

A face retrieval technique combining large models and artificial neural networks

Haar-wavelet based texture inpainting for human pose transfer

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