Journal: Molecules

Article Title: A Review of Machine Learning and QSAR/QSPR Predictions for Complexes of Organic Molecules with Cyclodextrins

doi: 10.3390/molecules29133159

Figure Lengend Snippet: Summary of predictions for Δ G , ln ⁡ K , and log ⁡ K of complex formation with α-CD, arranged in chronological order.

Article Snippet: Δ G , about 200 [ , ] , 75%/25%, R package caret , PaDEL , ensemble of: Cubist, GBM, MARSplines, RF, polynomial SVR, RBF SVR (Rstudio) , for single models: R 2 test from 0.63 to 0.78, Q 2 LOO from 0.50 to 0.64 , + , [ ] .

Techniques: Software

Journal: Molecules

Article Title: A Review of Machine Learning and QSAR/QSPR Predictions for Complexes of Organic Molecules with Cyclodextrins

doi: 10.3390/molecules29133159

Figure Lengend Snippet: Summary of predictions for Δ G , ln ⁡ K , and log ⁡ K of complex formation with β-CD, arranged in chronological order.

Article Snippet: Δ G , about 200 [ , ] , 75%/25%, R package caret , PaDEL , ensemble of: Cubist, GBM, MARSplines, RF, polynomial SVR, RBF SVR (Rstudio) , for single models: R 2 test from 0.63 to 0.78, Q 2 LOO from 0.50 to 0.64 , + , [ ] .

Techniques: Software, Derivative Assay, Biomarker Discovery, Activity Assay

Journal: Digital Health

Article Title: Construction of a predictive model based on MIV-SVR for prognosis and length of stay in patients with traumatic brain injury: Retrospective cohort study

doi: 10.1177/20552076231217814

Figure Lengend Snippet: Error of five models in predicting GOS scores in external datasets.

Article Snippet: To further bolster the empirical validation of the SVR model’s efficacy in predicting the length of hospital stay among patients with craniocerebral trauma, we utilized an external validation set of 111 patients sourced from the First Affiliated Hospital of Anhui Medical University.

Techniques:

Journal: Digital Health

Article Title: Construction of a predictive model based on MIV-SVR for prognosis and length of stay in patients with traumatic brain injury: Retrospective cohort study

doi: 10.1177/20552076231217814

Figure Lengend Snippet: Error of five models in predicting length of stay in external datasets.

Article Snippet: To further bolster the empirical validation of the SVR model’s efficacy in predicting the length of hospital stay among patients with craniocerebral trauma, we utilized an external validation set of 111 patients sourced from the First Affiliated Hospital of Anhui Medical University.

Techniques:

Journal: Digital Health

Article Title: Construction of a predictive model based on MIV-SVR for prognosis and length of stay in patients with traumatic brain injury: Retrospective cohort study

doi: 10.1177/20552076231217814

Figure Lengend Snippet: Error of the five models not screened by MIV in predicting GOS scores and length of stay.

Article Snippet: To further bolster the empirical validation of the SVR model’s efficacy in predicting the length of hospital stay among patients with craniocerebral trauma, we utilized an external validation set of 111 patients sourced from the First Affiliated Hospital of Anhui Medical University.

Techniques:

Journal: PeerJ Computer Science

Article Title: REDf: a deep learning model for short-term load forecasting to facilitate renewable integration and attaining the SDGs 7, 9, and 13

doi: 10.7717/peerj-cs.2819

Figure Lengend Snippet: Architecture of the proposed forecasting model, REDf.

Article Snippet: This discussion is also supported by the visual representation of the comparison of the results shown in . presents an integrated performance comparison of various forecasting models, including REDf, SVR, Prophet, and RFR, across multiple datasets (AEP, COMED, DAYTON, and PJME).

Techniques:

Journal: PeerJ Computer Science

Article Title: REDf: a deep learning model for short-term load forecasting to facilitate renewable integration and attaining the SDGs 7, 9, and 13

doi: 10.7717/peerj-cs.2819

Figure Lengend Snippet: (A) Proposed REDf model, (b) SVR model, (C) Facebook Prophet model, and (D) RFR model.

Article Snippet: This discussion is also supported by the visual representation of the comparison of the results shown in . presents an integrated performance comparison of various forecasting models, including REDf, SVR, Prophet, and RFR, across multiple datasets (AEP, COMED, DAYTON, and PJME).

Techniques:

Journal: Neural Computing & Applications

Article Title: Black box modeling of PIDs implemented in PLCs without structural information: a support vector regression approach

doi: 10.1007/s00521-014-1754-2

Figure Lengend Snippet: Output of the PID algorithm (PLC GE VersaMax ‘PID ISA’ function block) as well as the corresponding simulated outputs of the SVR for K C = 0.66, T I = 5.8, T D = 0.1

Article Snippet: Furthermore, the minor difference between the results of the training and testing of the SVR (90.93–86.96 % for parallel form GE, 91.26–88.62 % for ISA GE, 92.00–89.67 % for ISA Siemens) demonstrates good generalization properties.

Techniques: Blocking Assay

Journal: Neural Computing & Applications

Article Title: Black box modeling of PIDs implemented in PLCs without structural information: a support vector regression approach

doi: 10.1007/s00521-014-1754-2

Figure Lengend Snippet: Results for the SVR, Tf model and NNs, ISA GE

Article Snippet: Furthermore, the minor difference between the results of the training and testing of the SVR (90.93–86.96 % for parallel form GE, 91.26–88.62 % for ISA GE, 92.00–89.67 % for ISA Siemens) demonstrates good generalization properties.

Techniques:

Journal: Neural Computing & Applications

Article Title: Black box modeling of PIDs implemented in PLCs without structural information: a support vector regression approach

doi: 10.1007/s00521-014-1754-2

Figure Lengend Snippet: Results for the SVR, Tf model and NNs, ISA Siemens

Article Snippet: Furthermore, the minor difference between the results of the training and testing of the SVR (90.93–86.96 % for parallel form GE, 91.26–88.62 % for ISA GE, 92.00–89.67 % for ISA Siemens) demonstrates good generalization properties.

Techniques: