Artificial neural network tools for computerised data modeling and processing

Krenek, Jiri; Kuča, Kamil; Krejcar, Ondřej; Soběslav, Vladimír; Blažek, Pavel

doi:10.1109/cinti.2014.7028685

Cited by 8 publications

(6 citation statements)

References 16 publications

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“…Organizational processes and decision support can be captured in many ways, and for many areas, we can mention, for example : strategic management by: Maltz and Kohli (1996); Certo (2003); ; Maresova (2010); Tsakalidis et al (2019); product development research and innovation implementation, see Repenning (2002); Garcia (2005); IT and economic analyzes see Shane and Cable (2002); Dedrick et al (2003); Krenek et al (2014); Tomaskova et al (2015); Maresova et al (2016); Tomaskova et al (2016); Maresova et al (2017); Cheng et al (2019); Tomaskova et al (2019a,b); Tomaskova (2019, 2020); different simulation approaches analysis, see Sterman (1994); Kozlowski et al (2013); Cimler et al (2018) or non-standard optimization techniques by: Gavalec and Tomaskova (2010); Bacovsky et al (2013); Gavalec (2013, 2014); Gavalec et al (2014Gavalec et al ( , 2015; Cimler et al (2017); Oudah et al (2018).…”

Section: Related Work and Backgroundmentioning

confidence: 99%

Peer Review #2 of "Approaches combining methods of Operational Research with Business Process Model and Notation: A systematic review (v0.2)"

2020

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Section: Related Work and Backgroundmentioning

confidence: 99%

Peer Review #2 of "Approaches combining methods of Operational Research with Business Process Model and Notation: A systematic review (v0.2)"

2020

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“…They also highlighted the gap between current analytical methods for decision making and their incorporation into these key plans. We mention the following publications devoted to the optimization, analysis, or decision support of processes or plans; dynamic simulation [4][5][6][7][8][9]; strategic management [10][11][12][13][14][15][16]; economic analyses; or information technology [17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

Using a Process Approach to Pandemic Planning: A Case Study

Tomášková

Tirkolaee

2021

Applied Sciences

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The purpose of this article was to demonstrate the difference between a pandemic plan’s textual prescription and its effective processing using graphical notation. Before creating a case study of the Business Process Model and Notation (BPMN) of the Czech Republic’s pandemic plan, we conducted a systematic review of the process approach in pandemic planning and a document analysis of relevant public documents. The authors emphasized the opacity of hundreds of pages of text records in an explanatory case study and demonstrated the effectiveness of the process approach in reengineering and improving the response to such a critical situation. A potential extension to the automation and involvement of SMART technologies or process optimization through process mining techniques is presented as a future research topic.

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“…Linear and nonlinear methods, such as ANN, have several limitations when using non-stationary data. If the data processing phase is not sufficiently accurate for input data, various problems can be caused such as redundant, incomplete, or incorrect data with suitable method [30,31]. Recently, wavelet analysis has been found to provide very efficient and practical results when using non-stationary data along with ANN in hydrological and water-related fields [19].…”

Section: Introductionmentioning

confidence: 99%

Predicting Monthly Streamflow Using a Hybrid Wavelet Neural Network: Case Study of the Çoruh River Basin

Güneş¹,

Parim²,

Yildiz³

et al. 2021

Pol. J. Environ. Stud.

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In this study, a hybrid model combining discrete wavelet transforms (WTs) and artificial neural networks (ANNs) is used to estimate the monthly streamflow. The WT-ANN hybrid model was developed using the Daubechies main wavelet to predict the streamflow for three gauging stations on the Çoruh river basin one month in advance, with different combinations of air temperature, precipitation, and streamflow variables, and their wavelet transformations. Four different hybrid WT-ANN models were generated and compared with four different conventional ANN models. The dataset was chronologically divided into training, validation, and testing data. The results indicated that the WT-ANN hybrid models performed better than the traditional ANN models for all three stations. Furthermore, the chronologically divided dataset was used to examine the effects of changes in hydrological data over time on model performance. In conclusion, model performances in the training period deteriorated during the validation and testing periods due to structural changes in the hydrological data.

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Artificial neural network tools for computerised data modeling and processing

Cited by 8 publications

References 16 publications

Peer Review #2 of "Approaches combining methods of Operational Research with Business Process Model and Notation: A systematic review (v0.2)"

Peer Review #2 of "Approaches combining methods of Operational Research with Business Process Model and Notation: A systematic review (v0.2)"

Using a Process Approach to Pandemic Planning: A Case Study

Predicting Monthly Streamflow Using a Hybrid Wavelet Neural Network: Case Study of the Çoruh River Basin

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