Controlling Toolbox für ein erfolgreiches ChangeManagement im Finance Shared Services Projekt

Neukirchen, Ralph; Vollmer, Marcell

doi:10.15358/0935-0381-2007-2-91

Cited by 2 publications

(2 citation statements)

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“…Machine learning toolbox is designed by researchers to help users solve many statistics tasks [34], which can [7], Tensorflow [1] and PyTorch [38]. Specifically, according to the application field, machine learning toolboxes can be generally divided into four categories: 1) Open source machine learning tools for computer vision, natural language processing and audio, such as the software system Detectron [12], which is developed by Facebook's artificial intelligence research company.…”

Section: Related Workmentioning

confidence: 99%

“…For example, to solve the problem of on-line portfolio selection, Li et al [21] proposed a comprehensive toolbox, which can evaluate the performance of different on-line portfolio selection algorithms and develop new algorithms. On the issue of finance shared services project, Neukirchen and Vollmer [34] proposed a change management controlling toolbox, which is a representative research contribution for business management. According to empirical research, Kim [16] proposed a statistical toolbox that can provide a range of statistical instruments for financial researchers.…”

Section: Related Workmentioning

confidence: 99%

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MLLBC: A Machine Learning Toolbox for Modeling the Loss Rate of the Lining Bearing Capacity

Zhang

Wu²,

Zhao

et al. 2020

IEEE Access

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Testing the health of tunnels, as a branch of highway operation, has an extremely important application in public property and even life safety. Among them, there are many factors that cause the tunnel to deform or collapse. The conventional methods use the finite element method (FEM) which are to simulate the bearing capacity loss rate of the lining by using the mechanical method. However, it takes a long time to calculate the stress-strain-situation of the lining model under each condition. This paper explores the machine learning to calculate the loss rate of the lining bearing capacity under more conditions based on FEM simulation data. Here, we establish a machine learning toolbox for modeling the loss rate of the lining bearing capacity named ''MLLBC'', which contains three main components: 1) data loading; 2) machine learning model deployment; 3) performance evaluation. To ensure the fairness of model evaluation, ten machine learning models use a unified code library. We also conduct experiments on our new dataset which is the loss rate of the lining bearing capacity with different data amounts, as well as experiments on the goodness of model fitting under different ranges of various variables. INDEX TERMS Toolbox, the loss rate of the lining bearing capacity, machine learning, tunnel health.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%