Deep Structure Learning for Fraud Detection

Author(s):  
Haibo Wang ◽  
Chuan Zhou ◽  
Jia Wu ◽  
Weizhen Dang ◽  
Xingquan Zhu ◽  
...  
Author(s):  
Joanne Nakonechny ◽  
Shona Ellis

Throughout this chapter, the authors trace how the theoretical and practical understanding, interpretation, and interactions with e-portfolios and their implementation support, both individually and through group work, students’ abilities to engage in deeper structure learning, and their resulting growth as authentic science scholars. The bryofolio, an individual and group course e-portfolio, begins this online journey to facilitate deeper structure learning for 31 students in Biology 321, Bryophytes: Mosses, Hornworts and Liverworts. (“Bryfolio” is a contraction of “bryophytes” and “e-portfolio.”) Initially, the authors give a short introduction to science education and how constructivist learning theory can include the use of e-portfolios as a teaching method. Following this, e-portfolios are situated within the learning context by providing a definition, a condition, and discussion on the key e-portfolio element,of critical reflection. The authors continue by introducing the bryofolio, its major components, and our analysis of how the bryofolio encourages deep structure learning at both individual and group levels.


2005 ◽  
Vol 33 (4) ◽  
pp. 403-410 ◽  
Author(s):  
Rebecca L. Bordt

This article suggests ways in which a current research article on employment discrimination from the American Sociological Review can be used in the undergraduate classroom to facilitate deep structure learning (Roberts 1986, 2001, 2002). The exercises are designed for different levels of the undergraduate curriculum and adopt the strategies of benign disruption, inquiry-based learning, and role-taking so students can accomplish higher intellectual development.


Author(s):  
Shibo Shen ◽  
Rongpeng Li ◽  
Zhifeng Zhao ◽  
Qing Liu ◽  
Jing Liang ◽  
...  

2020 ◽  
Vol 40 (05) ◽  
pp. 268-271 ◽  
Author(s):  
Seba Susan ◽  
Jatin Malhotra

Ancient Indic languages were written in the Devanagari script from which most of the modern-day Indic writing systems have evolved. The digitisation of ancient Devanagari manuscripts, now archived in national museums, is a part of the language documentation and digital archiving initiative of the Government of India. The challenge in digitizing these handwritten scripts is the lack of adequate datasets for training machine learning models. In our work, we focus on the Devanagari script that has 46 categories of characters that makes training a difficult task, especially when the number of samples are few. We propose deep structure learning of image quadrants, based on learning the hidden state activations derived from convolutional neural networks that are trained separately on five image quadrants. The second phase of our learning module comprises of a deep neural network that learns the hidden state activations of the five convolutional neural networks, fused by concatenation. The experiments prove that the proposed deep structure learning outperforms the state of the art.


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