Materiały konferencyjne SEP 2020

[26]McCarthy R. V., McCarthy M. M., Ceccucci W., L. Halawi L. 2019: Applying Predictive Analytics. Applying Predictive Analytics: 1–25. [27]Gilcrist G. 2019: Evolving estimation techniques for an evolving world class stratiform copper depos- it at Kamoa-Kakula, Democratic Republic of the Congo. In Mining Goes Digital: Proceedings of the 39th International Symposium’Application of Computers and Operations Research in the Mineral In- dustry’(APCOM 2019), June 4-6, 2019, Wroclaw, Poland,192. [28]Whitehouse I.W.S., Slabik W. 2019: Transforming exploration data through machine learning. In Mining Goes Digital: Proceedings of the 39th International Symposium’Application of Computers and Operations Research in the Mineral Industry’(APCOM 2019), June 4-6, 2019, Wroclaw, Poland, , 210. [29]Huang L., Balamurali M., Silversides K.L. 2019: Machine learning classification of geochemical and geophysical data. In Mining Goes Digital: Proceedings of the 39th International Symposium’ Appli- cation of Computers and Operations Research in the Mineral Industry’(APCOM 2019), June 4-6, 2019, Wroclaw, Poland, 101. [30]Bongers D.R., Hal G. 2008: Fault Detection and Identification for Longwall Machinery Using SCADA Data. Springer Series in Reliability Engineering 8: 611–41. [31]Wodecki J., Stefaniak P., Polak M., Zimroz R. (2018) Unsupervised Anomaly Detection for Convey- or Temperature SCADA Data. Advances in Condition Monitoring of Machinery in Non-Stationary Operations. Applied Condition Monitoring, vol 9. [32]Stefaniak P., Zimroz R., Król R., Górniak-Zimroz J. 2012: Some Remarks on Using Condition Moni- toring for Spatially Distributed Mechanical System Belt Conveyor Network in Underground Mine – A Case Study. [33]Błażej R., Sawicki M., Konieczna M., Kozłowski T., Kirjanów A. 2016: Automatic analysis of them- rograms as a means for estimating technical of a gear system, Diagnostyka. 17(2):43-48. [34]Stefaniak P., Wodecki M., Michalak A. 2017: Association Rules Discovery from Diagnostic Data application to Gearboxes Used in Mining Industry. Vibroengineering Procedia 13(September): 103–8. [35]Wodecki J. et al. 2018: Technical Condition Change Detection Using Anderson–Darling Statistic Approach for LHD Machines–Engine Overheating Problem. International Journal of Mining, Recla- mation and Environment 32(6): 392–400. [36]Kabiesz J., Sikora B., Sikora M., and Wróbel Ł. 2013: Application of Rule-Based Models for Seismic Hazard Prediction in Coal Mines. Acta Montanistica Slovaca 18(4): 262–77. [37]Kabiesz J. 2006: Effect of the Form of Data on the Quality of Mine Tremors Hazard Forecasting Us- ing Neural Networks. Geotechnical and Geological Engineering 24(5): 1131–47. [38]Rusek J., Witkowski M.. 2017: Wykorzystanie probabilistycznych sieci neuronowych do wyznacza- nia ryzyka powstania szkód w budynkach poddanych wstrząsom górniczym. Przegląd Górniczy, nr 1, str. 44–47. [39]Rusek J., Firek K. 2019: Zastosowanie wnioskowania Bayesa do oceny zagrożenia budynków wiel- koblokowych na terenach górniczych. Przegląd Górniczy, nr 2, str. 7–12. [40]Firek K., Rusek J., Wodyński A. 2016: Wybrane metody eksploracji danych i uczenia maszynowego w analizie stanu uszkodzeń oraz zużycia technicznego zabudowy terenów górniczych. Przegląd Gór- niczy, nr 1, str. 50-55. [41]Project Management Institute 2018: Success in Disruptive Times: Expanding the Value Delivery Landscape to Address the High Cost of Low Performance. Pulse of the Profession: 35. [42] Wach M., Chomiak-Orsa I. 2019: Improvement of Investment Processes in Mining Company by Implementation of Project Management System. Mining Goes Digital: Proceedings of the 39th Inter- national Symposium’Application of Computers and Operations Research in the Mineral Indus- try’(APCOM 2019), June 4-6, 2019, Wroclaw, Poland, 47. [43]Idrees. S. M., Alam M. A., Agarwal P., Ansari L. 2019: Effective Predictive Analytics and Modeling Based on Historical Data. Advances in Computing and Data Sciences: Springer Singapore, 552–64.