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Analysis of Pole Coordinate Data Predictions in the Earth Orientation Parameters Combination of Prediction Pilot Project

References Bizouard C and D. Gambis, 2009, The combined solution C04 for Earth Orientation Parameters, recent improvements, Springer Verlag series, Series International Association of Geodesy Symposia, Vol. 134 Drewes, Hermann (Ed.), 265-270. Freedman, A. P., J. A. Steppe, J. O. Dickey, T. M. Eubanks, and L.-Y. Sung, 1994, The short-term prediction of universal time and length of day using atmospheric angular momentum, J. Geophys. Res., 99, 6981-6996. Gambis D., 2004, Monitoring

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Employing Combination Procedures to Short-Time Eop Prediction

References Kalarus M., Schuh H., Kosek W., Akyilmaz O., Bizouard Ch., Gambis D., Gross R., Kumakshev S., Kutterer H., Mendes Cerveira P. J., Pasynok S., Zotov L., Achievements of the Earth orientation parameters prediction comparison campaign. J. Geodesy , Vol. 84, 587-596. Luzum B., Wooden W., McCarthy D., Schuh H., Kosek W., Kalarus M., (2007). Ensemble Prediction for Earth Orientation Parameters, Geophysical Research Abstracts , Vol. 9, EGU2007-A-04315. Malkin Z. (2009

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Comparative Analysis of Object-Oriented Software Maintainability Prediction Models

Technology, pp.285-289, (2008). [4] Aggarwal K.K., Singh Y., Kaur A., Sangwan O.P.: A Neural Net Based Approach To Test Oracle. In ACM SIGSOFT Software Engineering Notes, pp.1-6, (2004). [5] Anwar S., Ramzan M., Rauf A., Shahid A.A.: Software Maintenance Prediction Using Weighted Scenarios: An Architecture Perspective. In International Conference on Information Science and Applications (ICISA), pp.1-9, IEEE, Korea (South), (2010). [6] Anwar S.: Software Maintenance Prediction: An Architecture Perspective. PHD thesis, AST National University of Computer

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Bottlenecks in Software Defect Prediction Implementation in Industrial Projects

References [1] Catal, C. and Diri, B. (2009). A systematic review of software fault prediction studies. Expert Systems with Application , 36:7346-7354. [2] Fenton, N. and Neil, M. (1999). A critique of software defect prediction models. IEEE Transactions on Software Engineering , 25:675-689. [3] Hall, T., Beecham, S., Bowes, D., D., G., and Counsell, S. (2012). A systematic review of fault prediction performance in software engineering. IEEE Transactions on Software Engineering , 38:1276-1304. [4] Jureczko, M. and Madeyski, L. (2010). Towards

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Preeclampsia - Prediction and Monitoring Factors

., Thilaganathan, B., Illanes, S.E., Khan, K.S., Aquilina, J. and Thangaratinam S. (2014). First-trimester uterine artery Doppler and adverse pregnancy outcome: a meta-analysis involving 55,974 women. Ultrasound. Obstet. Gynecol., 43(5), 500-7. 13. Khong, S.L., Kane, S.C., Brennecke, S.P. & da Silva Costa, F. (2015). First-trimester uterine artery Doppler analysis in the prediction of later pregnancy complications. Dis. Markers., 2015, 679730. 14. Sanchez-Aranguren, L.C., Espinosa-Gonzalez, C.T., Gonzalez-Ortiz, L.M., Sanabria-Barrera, S.M., Riano

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Prediction of Default of Small Companies in the Slovak Republic

References Alaka, H. A., Oyedele, L. O., Owolabi, H. A., Kumar, V., Ajayi, S. O., Akinade, O. O., & Bilal, M. (2018). Systematic review of bankruptcy prediction models: Towards a framework for tool selection. Expert Systems with Applications, 94 , 164-184. doi:10.1016/j.eswa.2017.10.040 Alaminos, D., Castillo, A. D., & Fernández, M. Á. (2016). A Global Model for Bankruptcy Prediction. Plos One, 11 (11). doi:10.1371/journal.pone.0166693 Altman, E. I., Iwanicz-Drozdowska, M., Laitinen, E. K., & Suvas, A. (2014). Distressed Firm and Bankruptcy

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Prediction Methods for MPEG-4 and H.264 Video Transmission

References ABDENNOUR, A.: Short-term MPEG-4 video traffic prediction using ANFIS, International Journal of Network Management 6 No. 15 (2005), 377 392. GUOQIANG, M.—HUABING, L.: Real Time Variable Bit Rate Video Traffic Prediction, Int. Journal of Communication Systems 4 No. 20 (2007), 491 505. ESWARADASS, A.—SUN, X—WU, M.: A Neural Network Based Predictive Mechanism for Available Bandwidth, Proceedings of the 19th IEEE International Parallel and Distributed Processing Symposium

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Cost Effectiveness of Software Defect Prediction in an Industrial Project

References [1] Arisholm, E., Briand, L.C., Johannessen, E.B.: A Systematic and Comprehensive Investigation of Methods to Build and Evaluate Fault Prediction Models. The Journal of Systems and Software 83(1), 2–17 (2010) [2] Atlassian: JIRA Homepage (2016), https://www.atlassian.com/software/jira/ , accessed: 2016.01.06 [3] Bell, T.E., Thayer, T.A.: Software requirements: Are they really a problem? In: Proceedings of the 2nd international conference on Software engineering. pp. 61–68. IEEE Computer Society Press (1976) [4] Boehm, B

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Bankruptcy Prediction: A Survey on Evolution, Critiques, and Solutions

References Adnan, M.; Dar, A. H. (2006). Predicting corporate bankruptcy: where we stand? Corporate Governance 6(1): 18–33. Agarwal, V.; Taffler, R. (2008). Comparing the performance of market-based and accounting-based bankruptcy prediction models. Journal of Banking and Finance 32: 1541–1555. Alam, P.; Booth, D.; Lee, K.; Thordarson, T. (2000). The use of fuzzy clustering algorithm and self-organizing neural networks for identifying potentially failing banks: an experimental study. Expert Systems with Applications 18: 185

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Logit business failure prediction in V4 countries

Introduction Financial risk is the possibility that company shareholders will lose money if the corporate cash flows are not sufficient to meet financial obligations. Business failure prediction models are used to eliminate this potential risk. Their task is to evaluate the financial health of the company based on selected financial indicators or other characteristics of the company or the environment in which they operate ( Kovacova and Kliestik, 2017 ). The main aim of the paper is to present the business failure prediction model for companies that

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