Fabio Angeli, Enrica Angeli, Antonella D’Antonio, Cristina Poltronieri and Giuseppe Ambrosio
pressure education program working group on high blood pressure in pregnancy. Am J ObstetGynecol 2000; 183: S1-S22. http://dx.doi.org/10.1067/mob.2000.107928
7. Milne F, Redman C, Walker J et al. The pre-eclampsia community guideline (precog): How to screen for and detect onset of pre-eclampsia in the community. BMJ 2005; 330: 576-80. http://dx.doi.org/10.1136/bmj.330.7491.576
8. Angeli F, Angeli E, Reboldi G, Verdecchia P. Hypertensive disorders during pregnancy: Clinical applicability of risk predictionmodels. J Hypertens 2011; 29: 2320-3. http
(1), 236-252. http://doi.org/10.1016/j.ejor.2016.03.008.
Grice J.S., Dugan M.T. 2001. The limitations of bankruptcy predictionmodels: Some cautions for the researcher. Review of Quantitative Finance and Accounting, 17 (2), 151-166.
Gruszczynski M. 2005. Strengths and Weaknesses of Bankruptcy Models. Materialy i Prace Instytutu Funkcjonowania Gospodarki Narodowej, 93, 185-187.
Hołda A. 2001. Prognozowanie bankructwa jednostki w warunkach gospodarki polskiej z wykorzystaniem funkcji dyskryminacyjnej ZH. Rachunkowość, 5
Alaka, H. A., Oyedele, L. O., Owolabi, H. A., Kumar, V., Ajayi, S. O., Akinade, O. O., & Bilal, M. (2018). Systematic review of bankruptcy predictionmodels: 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
Svitlana Sytnyk, Viktoriia Lovynska, Petro Lakyda and Katerina Maslikova
Colendarum Ratio et Industria Lignaria , 12 (4): 43–53.
V iana , H., A ranha , J., L opes , D., C ohen , W., 2012. Estimation of crown biomass of Pinus pinaster stands and shrubland above-ground biomass using forest inventory data, remotely sensed imagery and spatial predictionmodels. Ecological Modelling , 22 (6): 22–35.
V iherä -A arnio A., V elling , P., 2017. Growth, wood density and bark thickness of silver birch originating from the Baltic countries and Finland in two Finnish provenance trials. Silva Fennica , 51 (4): article ID 7731, 18 p.
1. Anagnostopoulos T., Anagnostopoulos Ch., Hadjiefthymiades S.: An adaptive location predictionmodel based on fuzzy control. Computer Communications. Vol. 34. Iss. 7, May 2011.
2. Ben-lin Dai, Yu-long He, Fei-hu Mu, Ning Xu, Zhen Wu: Development of a traffic noise predictionmodel on inland waterway of China using the FHWA. Science of The Total Environment. Vol. 482–483, June 2014.
3. Chu-Hui Lee, Yu-lung Lo, Yu-Hsiang Fu: A novel predictionmodel based on hierarchical characteristic of web site. Expert Systems with Applications
Chudson, W. (1945). The Pattern of Corporate Financial Structure. New York: National Bureau of Economic Research.
Dagilienė, L. et al. (2010). Bankroto prognozavimo svarba ir metodai.Verslas: teorija ir praktika, 11(2), p. 143-150.
Du Jardin, P. (2009). Bankruptcy predictionmodels: How to choose the most relevant variables? Bankers, Markets & Investors, issue 98, January-February, p. 39-46.
Grigaravičius, S. (2003). Corporate Failure Diagnosis: Reliability and Practice. Organizacijų vadyba
research works on microblog influence are abundant. However, research on the influence of microblog in specific fields, such as public health emergencies, is relatively insufficient. This study attempts to propose a microblog influence predictionmodel for public health emergencies, which is composed of user, time, and content features and which uses the random forest method ( Breiman, 2001 ) and the Best Match 25-based latent Dirichlet allocation model (LDA-BM25) ( Li, 2013 ). As this model is constructed specifically for public health emergencies, it highlights the
Lake level is one of the most important lake characteristics which allows the results of different effects to be identified and detected. In this work time series of the water levels of Belorussian lakes were analysed in order to detect pattern variations, to evaluate quantitatively the transformation of the hydrological regime of lake ecosystems and to develop prediction models. The possibility of plotting predicting models of lake water levels one year in advance was shown. The complication in plotting predicting models is in its individuality, the huge volume of initial data and the impossibility of immediate assessment of the results. Additional complications are caused by the inhomogeneity of time series of water levels in lakes.
Yunfeng Hu, Liping Zhang, Jinjin Wei and Zengyu Wei
, Y, Q., Lv, J, P. (2013).Establishment and Test of PredictionModel for UHT Milk Shelf Life. Scientia Agricultural Sinica . 46 (03): 586-594.
20. Yang, M, Z., Wang, X, L. (2015). Shelf-life prediction of instant flavor rice through kinetic models. Journal of Harbin University of Commerce, 31(06):710-714.
21. Liu, X, D., Jang, A., Kim, D, H., et al. (2009). Effect of combination of chitosan coating and irradiation on physicochemical and functional properties of chicken egg during room-temperature storage. Radiation Physics & Chemistry , 78(7): 589