Search Results

1 - 10 of 5,304 items :

  • "prediction" x
Clear All

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 Earth orientation using space-geodetic techniques: state-of-the-art and prospective

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). Improving short-term EOP prediction using combination procedures. in: Proc. Journées

-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 & Emerging Sciences, Islamabad

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 identifying

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 Prediction in an

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 (IPDPS'05) 1 No. l (April 2005). POPESCU, I.—CONSTANTINOU, P

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.W.: Software Engineering. IEEE

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: 185–199. Altman, E. I. (1968

.0.html. [15] N. Fenton, P. Krause, M. Neil, and C. Lane, “A Probabilistic Model for Software Defect Prediction,” 2001. [16] N. E. Fenton and M. Neil, “Software metrics: success, failures and new directions,” J. Syst. Softw., vol. 47, pp. 149-157, July 1999. [17] Agena, “Agenarisk Desktop.” <http://www.agenarisk.com>. [18] S. Demeyer, S. Tichelaar, and S. Ducasse, “FAMIX 2.1 - The FAMOOS Information Exchange Model,” tech. rep., University of Berne, 2001. [19] TIOBE, “Programming Community Index.” http://www.tiobe.com/index.php/content/paperinfo/tpci/index.html, 10 2013

phase. Proinflammatory mediators involved in occurrence of preeclampsia include: soluble fms-like tyrosine kinase-1 (sFlt-1), soluble endoglin (sEng), leptin, activin-A, corticotrophin releasing hormone (CRH), serum placental protein 13 (PP13), and pregnancy associated plasma protein A (PAPP-A) ( 14 , 15 , 16 , 17 ). There were various attempts to provide the best prediction algorithm for preeclampsia, as well as to set aside parameters which would enable the most adequate monitoring of preeclampsia which has already been diagnosed. UTERINE ARTERY DOPPLER IN