Using a Selective Ensemble Support Vector Machine to Fuse Multimodal Features for Human Action Recognition

Tang, Chao; Tong, Anyang; Zheng, Aihua; Peng, Hua; Li, Wei

doi:10.1155/2022/1877464

Cited by 7 publications

(7 citation statements)

References 42 publications

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“…Bayesian classification is a machine learning method based on Bayes’ theorem, which classifies samples by calculating the probability that a sample belongs to a certain class ( Asafu-Adjei and Betensky, 2015 ; Ramanujam and Kaliappan, 2016 ). Support vector machine is a kind of classification and regression algorithm that can project data into a high-dimensional space; by finding the optimal segmentation plane in the high-dimensional space, the data can be classified or regressed ( Agyapong et al, 2022 ; Tang et al, 2022 ). Neural networks are artificial networks that mimic the workings of neural networks in the human brain and can be used to solve classification, regression, and a variety of other machine learning problems ( Checcucci et al, 2020 ; Laudicella et al, 2021 ).…”

Section: Methodsmentioning

confidence: 99%

A lung cancer risk warning model based on tongue images

et al. 2023

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Objective: To investigate the tongue image features of patients with lung cancer and benign pulmonary nodules and to construct a lung cancer risk warning model using machine learning methods.Methods: From July 2020 to March 2022, we collected 862 participants including 263 patients with lung cancer, 292 patients with benign pulmonary nodules, and 307 healthy subjects. The TFDA-1 digital tongue diagnosis instrument was used to capture tongue images, using feature extraction technology to obtain the index of the tongue images. The statistical characteristics and correlations of the tongue index were analyzed, and six machine learning algorithms were used to build prediction models of lung cancer based on different data sets.Results: Patients with benign pulmonary nodules had different statistical characteristics and correlations of tongue image data than patients with lung cancer. Among the models based on tongue image data, the random forest prediction model performed the best, with a model accuracy of 0.679 ± 0.048 and an AUC of 0.752 ± 0.051. The accuracy for the logistic regression, decision tree, SVM, random forest, neural network, and naïve bayes models based on both the baseline and tongue image data were 0.760 ± 0.021, 0.764 ± 0.043, 0.774 ± 0.029, 0.770 ± 0.050, 0.762 ± 0.059, and 0.709 ± 0.052, respectively, while the corresponding AUCs were 0.808 ± 0.031, 0.764 ± 0.033, 0.755 ± 0.027, 0.804 ± 0.029, 0.777 ± 0.044, and 0.795 ± 0.039, respectively.Conclusion: The tongue diagnosis data under the guidance of traditional Chinese medicine diagnostic theory was useful. The performance of models built on tongue image and baseline data was superior to that of the models built using only the tongue image data or the baseline data. Adding objective tongue image data to baseline data can significantly improve the efficacy of lung cancer prediction models.

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Section: Methodsmentioning

confidence: 99%

A lung cancer risk warning model based on tongue images

et al. 2023

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“…e method described above is where the data samples are linearly separable. Still, if the vector distribution is linearly inseparable, then slack variables must be introduced to solve this problem [24][25][26]. e specific method is to take a positive number for the introduced slack variable, select a nonlinear mapping function ϕ(x), and convert the original problem from a two-dimensional to a high-dimensional space for processing so that the nonlinear samples can be linearly divided in the high-dimensional space.…”

Section: Classificationmentioning

confidence: 99%

“…If a function can satisfy the Mercer condition, it can be used as a kernel function [ 25 , 29 ]. Currently, many scholars are devoted to the research of kernel function construction.…”

Section: Support Vector Machine Kernel Function Selection and Paramet...mentioning

confidence: 99%

Optimization Design of Drilling Fluid Chemical Formula Based on Artificial Intelligence

Chen

2022

Computational Intelligence and Neuroscience

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Through the research and development of the regression prediction function of support vector machine, this paper applies it to the prediction of drilling fluid performance parameters and the formulation design of drilling fluid. The research in this paper can reduce the experimental workload and improve the efficiency of drilling fluid formulation design. The apparent viscosity (AV), plastic viscosity (PV), API filter loss (FLAPI), and roll recovery (R) of the drilling fluid were selected as the inspection objects of the drilling fluid performance parameters, and the support vector machine was used to establish a model for predicting the drilling fluid performance parameters. This predictive model was used as part of the overall drilling fluid formulation optimization design model. For a given drilling fluid performance parameter requirement, this model can be applied to reverse the addition of various treatment agents, and finally, the prediction accuracy of the model is verified by experiments.

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“…This development has led to the emergence of multi-modality datasets, which include 3D skeleton information on human actions. These datasets have garnered attention in the research field and have been shown to achieve higher accuracy [4]. Several multi-modality datasets exist for human action analysis, such as Toyota Smarthome [5], NTU RGB+D [6], [7], Northwestern-UCLA [8], and PKU-MMD [9].…”

Section: Introductionmentioning

confidence: 99%

DGU-HAO: A Dataset With Daily Life Objects for Comprehensive 3D Human Action Analysis

Park,

Kim,

Gil

et al. 2024

IEEE Access

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The importance of a high-quality dataset availability in 3D human action analysis research cannot be overstated. This paper introduces DGU-HAO (Human Action analysis dataset with daily life Objects). This novel 3D human action multi-modality dataset encompasses four distinct data modalities accompanied by annotation data, including motion capture data, RGB video data, image data, and 3D object modeling data. It features 63 action classes involving interactions with 60 common furniture and electronic devices. Each action class comprises approximately 1, 000 motion capture data representing 3D skeleton data, along with corresponding RGB video and 3D object modeling data, resulting in 67, 505 motion capture data samples. It offers comprehensive 3D structural information of the human, RGB images and videos, and point cloud data for 60 objects, collected through the participation of 126 subjects to ensure inclusivity and account for diverse human body types. To validate our dataset, we leveraged MMNet, a 3D human action recognition model, achieving Top-1 accuracy of 91.51% and 92.29% using the skeleton joint and bone methods, respectively. Beyond human action recognition, our versatile dataset is valuable for various research endeavors in 3D human action analysis. INDEX TERMS 3D human action analysis, human action recognition, human activity understanding, motion capture, multi-modal datasetThis article has been accepted for publication in IEEE Access.

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Using a Selective Ensemble Support Vector Machine to Fuse Multimodal Features for Human Action Recognition

Cited by 7 publications

References 42 publications

A lung cancer risk warning model based on tongue images

A lung cancer risk warning model based on tongue images

Optimization Design of Drilling Fluid Chemical Formula Based on Artificial Intelligence

DGU-HAO: A Dataset With Daily Life Objects for Comprehensive 3D Human Action Analysis

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