Classification Models as Tools of Bankruptcy Prediction—Polish Experience

2018 ◽  
pp. 163-172
Author(s):  
Józef Pociecha ◽  
Barbara Pawełek ◽  
Mateusz Baryła ◽  
Sabina Augustyn
Keyword(s):  
Bankruptcy Prediction ◽  
Classification Models ◽  
Polish Experience

Influence of Age on Treatment Results in Children and Adolescence with Hodgkin's Lymphoma Polish Experience

2014 ◽  
Vol 226 (02) ◽  
Author(s):  
W Balwierz ◽  
T Klekawka ◽  
A Moryl-Bujakowska ◽  
M Matysiak ◽  
I Malinowska ◽  
...  
Keyword(s):  
Hodgkin’S Lymphoma ◽  
Hodgkin's Lymphoma ◽  
Treatment Results ◽  
Polish Experience ◽  
Children And Adolescence

Bankruptcy Prediction Models: A Bet on Artificial Neural Networks

2012 ◽  
Vol 3 (2) ◽  
pp. 48-50
Author(s):  
Ana Isabel Velasco Fernández ◽  
◽  
Ricardo José Rejas Muslera ◽  
Juan Padilla Fernández-Vega ◽  
María Isabel Cepeda González
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Prediction Models ◽  
Bankruptcy Prediction ◽  
Artificial Neural

Comparing the Speed and Accuracy of Multi-Label Classification Models

2015 ◽  
Vol 10 (8) ◽  
pp. 829
Author(s):  
Aswin Wibisurya ◽  
Ford Lumban Gaol ◽  
Kuncoro Wastuwibowo
Keyword(s):  
Classification Models ◽  
Speed And Accuracy

Payment Habits as a Determinant of Retail Payment Innovations Diffusion: the Case of Poland

2013 ◽  
Vol 1 (2) ◽  
pp. 86-102 ◽  
Author(s):  
Janina Harasim ◽  
Monika Klimontowicz
Keyword(s):  
Financial Institutions ◽  
Success Factors ◽  
Market Development ◽  
Opportunities To Learn ◽  
Payment Systems ◽  
Global Trends ◽  
Innovative Methods ◽  
Preferred Providers ◽  
Polish Experience ◽  
Empirical Foundation

New and innovative methods for electronic funds transfer are emerging globally. These new payment tools include extensions of the established payment systems as well as new payment methods that are substantially different from traditional transactions. They have made the retail payments faster, cheaper, easier and more convenient for customers. Simultaneously, these payment innovations influence retail payment market around the world. During the last few decades it has changed remarkably and has become a very competitive one. Financial institutions are increasingly in competition with technology companies and other organizations to be the preferred providers of consumer payment services. There are huge differences between retail payment markets in developing countries and those in the mature markets. Payment habits are mostly influenced by local cultural drivers, so global trends are few and far between. Nevertheless, as consumer expectations and habits are becoming more homogenized and financial institutions start to be interested in new markets, the opportunities to learn from the experiences of other economies appear. The paper discusses theoretical and empirical foundation of retail payment innovations diffusion, presents the retail payment taxonomy and the results of a survey held in Poland in 2013. It is concluded that Polish experience can be assessed as a benchmark for searching determinants of retail payment markets development. However, copying success factors for sustainable market development is rather impossible with regard to payment culture, experiences and habits.

Effectiveness of Supervised Classification Models for Hate Speech on Twitter

2020 ◽  
Author(s):  
Kunal Srivastava ◽  
Ryan Tabrizi ◽  
Ayaan Rahim ◽  
Lauryn Nakamitsu
Keyword(s):  
Supervised Classification ◽  
Hate Speech ◽  
The Internet ◽  
Classification Models ◽  
Personal Attack ◽  
Political Beliefs ◽  
The Many ◽  
Primary Medium ◽  
Offensive Speech

<div> <div> <div> <p>Abstract </p> <p>The ceaseless connectivity imposed by the internet has made many vulnerable to offensive comments, be it their physical appearance, political beliefs, or religion. Some define hate speech as any kind of personal attack on one’s identity or beliefs. Of the many sites that grant the ability to spread such offensive speech, Twitter has arguably become the primary medium for individuals and groups to spread these hurtful comments. Such comments typically fail to be detected by Twitter’s anti-hate system and can linger online for hours before finally being taken down. Through sentiment analysis, this algorithm is able to distinguish hate speech effectively through the classification of sentiment. </p> </div> </div> </div>

Bankruptcy Prediction

2009 ◽  
Author(s):  
Zeynep Topaloglu ◽  
Yildiray Yildirim
Keyword(s):  
Bankruptcy Prediction

Internet Appendix to Accompany 'Bankruptcy Prediction with Financial Systemic Risk'

2019 ◽  
Author(s):  
Zhehao Jia ◽  
Yukun Shi ◽  
Cheng Yan ◽  
Meryem Duygun
Keyword(s):  
Systemic Risk ◽  
Bankruptcy Prediction
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