Interdisciplinary Evolution of the Machine Brain

Wang, Wenfeng; Cai, Hengjin; Deng, Xiangyang; Lu, Chenguang; Zhang, Limin

doi:10.1007/978-981-33-4244-6_6

Cited by 3 publications

(2 citation statements)

References 57 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A sketch of the learning processes is shown in Figure 2, which construct cells in the machine brain (O'Doherty et al, 2011;Wang et al, 2020;Wang et al, 2021;Wang et al, 2022). We can understand the LSTM learning process with the cells' states.…”

Section: The Learning Processesmentioning

confidence: 99%

Machine learning brings new insights for reducing salinization disaster

Wang²,

Chen³

et al. 2023

Front. Earth Sci.

View full text Add to dashboard Cite

This study constructs a machine learning system to examine the predictors of soil salinity in deserts. We conclude that soil humidity and subterranean CO2 concentration are two leading controls of soil salinity—respectively explain 71.33%, 13.83% in the data. The (R2, root-mean-square error, RPD) values at the training stage, validation stage and testing stage are (0.9924, 0.0123, and 8.282), (0.9931, 0.0872, and 7.0918), (0.9826, 0.1079, and 6.0418), respectively. Based on the underlining mechanisms, we conjecture that subterranean CO2 sequestration could reduce salinization disaster in deserts.

show abstract

Section: The Learning Processesmentioning

confidence: 99%

Machine learning brings new insights for reducing salinization disaster

Wang²,

Chen³

et al. 2023

Front. Earth Sci.

View full text Add to dashboard Cite

show abstract

“…The potential engineering applications of our proposed model will not be limited to the patients hospitalized with CAP [29][30][31][32]. We used RF and XGB methods after learning the sample characteristics of the data by BLS [33][34][35][36][37][38]. This approach is novel compared to the previous studies on predicting the risk of death among CAP cases [39][40][41][42][43].…”

Section: 4mentioning

confidence: 99%

A Broad Learning System to Predict the 28-Day Mortality of Patients Hospitalized with Community-Acquired Pneumonia: A Case-Control Study

Yuan

Liu

Wang

et al. 2022

Computational and Mathematical Methods in Medicine

View full text Add to dashboard Cite

This study was to conduct a model based on the broad learning system (BLS) for predicting the 28-day mortality of patients hospitalized with community-acquired pneumonia (CAP). A total of 1,210 eligible CAP cases from Chifeng Municipal Hospital were finally included in this retrospective case-control study. Random forest (RF) and an eXtreme Gradient Boosting (XGB) models were used to develop the prediction models. The data features extracted from BLS are utilized in RF and XGB models to predict the 28-day mortality of CAP patients, which established two integrated models BLS-RF and BLS-XGB. Our results showed the integrated model BLS-XGB as an efficient broad learning system (BLS) for predicting the death risk of patients, which not only performed better than the two basic models but also performed better than the integrated model BLS-RF and two well-known deep learning systems-deep neural network (DNN) and convolutional neural network (CNN). In conclusion, BLS-XGB may be recommended as an efficient model for predicting the 28-day mortality of CAP patients after hospital admission.

show abstract