Data_Sheet_1_Adaptive restraint design for a diverse population through machine learning.pdf by Wenbo Sun (531308)

Key data on the three country case studies. by Nicole Fraser-Hurt (2636833)

Subjects:

Overview of the five stages of the HIPtool application. by Nicole Fraser-Hurt (2636833)

Subjects:

S1 Data - by Guangwei Liu (181992)

“…The primary objective of MSHHOTSA is to address the limitations of the tunicate swarm algorithm, which include slow optimization speed, low accuracy, and premature convergence when dealing with complex problems. …”

Curve of step response signal of 6 algorithms. by Guangwei Liu (181992)

“…The primary objective of MSHHOTSA is to address the limitations of the tunicate swarm algorithm, which include slow optimization speed, low accuracy, and premature convergence when dealing with complex problems. …”

Data_Sheet_1_Optimizing clinical outcomes in polypharmacy through poly-de-prescribing: a longitudinal study.docx by Doron Garfinkel (18465243)

Data_Sheet_1_Phase-wise evaluation and optimization of non-pharmaceutical interventions to contain the COVID-19 pandemic in the U.S..pdf by Xiao Zhou (122661)

Table_1_One-Time Optimization of Advanced T Cell Culture Media Using a Machine Learning Pipeline.DOCX by Paul Grzesik (11136582)

“…<p>The growing application of cell and gene therapies in humans leads to a need for cell type-optimized culture media. Design of Experiments (DoE) is a successful and well known tool for the development and optimization of cell culture media for bioprocessing. …”

Wilcoxon’s rank sum test results. by Guangwei Liu (181992)

“…The primary objective of MSHHOTSA is to address the limitations of the tunicate swarm algorithm, which include slow optimization speed, low accuracy, and premature convergence when dealing with complex problems. …”

Flowchart of MSHHOTSA. by Guangwei Liu (181992)

“…The primary objective of MSHHOTSA is to address the limitations of the tunicate swarm algorithm, which include slow optimization speed, low accuracy, and premature convergence when dealing with complex problems. …”

Flowchart of TSA [43]. by Guangwei Liu (181992)

“…The primary objective of MSHHOTSA is to address the limitations of the tunicate swarm algorithm, which include slow optimization speed, low accuracy, and premature convergence when dealing with complex problems. …”

The proportion integral derivative controller. by Guangwei Liu (181992)

“…The primary objective of MSHHOTSA is to address the limitations of the tunicate swarm algorithm, which include slow optimization speed, low accuracy, and premature convergence when dealing with complex problems. …”

Random parameter factor. by Guangwei Liu (181992)

“…The primary objective of MSHHOTSA is to address the limitations of the tunicate swarm algorithm, which include slow optimization speed, low accuracy, and premature convergence when dealing with complex problems. …”

Eight commonly used benchmark functions. by Guangwei Liu (181992)

“…The primary objective of MSHHOTSA is to address the limitations of the tunicate swarm algorithm, which include slow optimization speed, low accuracy, and premature convergence when dealing with complex problems. …”

Hyperbolic tangent row domain. by Guangwei Liu (181992)

“…The primary objective of MSHHOTSA is to address the limitations of the tunicate swarm algorithm, which include slow optimization speed, low accuracy, and premature convergence when dealing with complex problems. …”

Parameter settings. by Guangwei Liu (181992)

“…The primary objective of MSHHOTSA is to address the limitations of the tunicate swarm algorithm, which include slow optimization speed, low accuracy, and premature convergence when dealing with complex problems. …”

Nonlinear fast convergence factor. by Guangwei Liu (181992)

“…The primary objective of MSHHOTSA is to address the limitations of the tunicate swarm algorithm, which include slow optimization speed, low accuracy, and premature convergence when dealing with complex problems. …”

CEC2019 benchmark functions. by Guangwei Liu (181992)

“…The primary objective of MSHHOTSA is to address the limitations of the tunicate swarm algorithm, which include slow optimization speed, low accuracy, and premature convergence when dealing with complex problems. …”

Image_1_The prognostic role of an optimal machine learning model based on clinical available indicators in HCC patients.TIF by Xiaoying Lou (9700261)

Image_2_The prognostic role of an optimal machine learning model based on clinical available indicators in HCC patients.TIF by Xiaoying Lou (9700261)