Channel Prediction with Liquid Time-Constant Networks: An Online and Adaptive Approach

Yin, Hao; Zhou, You; Cao, Liu; Xu, Yifei

doi:10.1109/vtc2021-fall52928.2021.9625323

Cited by 3 publications

(1 citation statement)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Journal of Disability Research 2024classification, and video analysis, where spatial and temporal patterns in input data are crucial(Wongpatikaseree et al, 2021;Yin et al, 2021;Bidollahkhani et al, 2023). With LNN parallel processing, distributed representations, and temporal dynamics, researchers can develop efficient and effective image processing solutions with reduced computation time and better performance compared to traditional architectures.…”

mentioning

confidence: 99%

Developing a Pain Identification Model Using a Deep Learning Technique

Wahab Sait,

Dutta

2024

Journal of Disability Research

View full text Add to dashboard Cite

In this study, the authors proposed a pain identification model using facial expressions. An image extraction technique was developed using the liquid neural network to extract diverse images from the video files. The authors used the DenseNet 201 and MobileNet V3 models to build a hybrid feature engineering technique. They applied quantization aware training to improve the efficiency of the models. The Prkachin and Solomon Pain Intensity score was used for the image classification. They fine-tuned the LightGBM model using the random search algorithm for identifying pain from the facial images. The authors used the Denver Intensity of Spontaneous Facial Action dataset to generalize the proposed model. The performance evaluation outlined the significant performance of the proposed model in identifying pain using the images. In addition, it demands limited computational resources to identify pain. Healthcare and rehabilitation centers can implement the proposed model to provide adequate services to disabled individuals.

show abstract

mentioning

confidence: 99%