Design and Implementation of Intelligent Scoring System for Handwritten Short Answer Based on Deep Learning

2020 ◽  
Author(s):  
Yubei Lin ◽  
Liubin Zheng ◽  
Feng Chen ◽  
Shumao Sun ◽  
Zilong Lin ◽  
...  
Keyword(s):  
Deep Learning ◽  
Scoring System ◽  
Short Answer ◽  
Design And Implementation

scholarly journals Development and Verification of a Deep Learning Algorithm to Evaluate Small-Bowel Preparation Quality

Diagnostics ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 1127
Author(s):  
Ji Hyung Nam ◽  
Dong Jun Oh ◽  
Sumin Lee ◽  
Hyun Joo Song ◽  
Yun Jeong Lim
Keyword(s):  
Deep Learning ◽  
Small Bowel ◽  
Scoring System ◽  
Operating Characteristic ◽  
Clinical Evidence ◽  
Learning Algorithm ◽  
Characteristic Curve ◽  
External Validation ◽  
Test Results ◽  
Deep Learning Algorithm

Capsule endoscopy (CE) quality control requires an objective scoring system to evaluate the preparation of the small bowel (SB). We propose a deep learning algorithm to calculate SB cleansing scores and verify the algorithm’s performance. A 5-point scoring system based on clarity of mucosal visualization was used to develop the deep learning algorithm (400,000 frames; 280,000 for training and 120,000 for testing). External validation was performed using additional CE cases (n = 50), and average cleansing scores (1.0 to 5.0) calculated using the algorithm were compared to clinical grades (A to C) assigned by clinicians. Test results obtained using 120,000 frames exhibited 93% accuracy. The separate CE case exhibited substantial agreement between the deep learning algorithm scores and clinicians’ assessments (Cohen’s kappa: 0.672). In the external validation, the cleansing score decreased with worsening clinical grade (scores of 3.9, 3.2, and 2.5 for grades A, B, and C, respectively, p < 0.001). Receiver operating characteristic curve analysis revealed that a cleansing score cut-off of 2.95 indicated clinically adequate preparation. This algorithm provides an objective and automated cleansing score for evaluating SB preparation for CE. The results of this study will serve as clinical evidence supporting the practical use of deep learning algorithms for evaluating SB preparation quality.

Supporting Open-Ended Programming Assignments

2009 ◽  
pp. 368-380
Author(s):  
Caitlin Kelleher
Keyword(s):  
Deep Learning ◽  
Formal Education ◽  
Potential Model ◽  
Classroom Setting ◽  
Programming Environment ◽  
Traditional Curriculum ◽  
Design And Implementation ◽  
Basic Concepts ◽  
Student Projects ◽  
Future Work

Self-directed, open-ended projects can enable students to pursue their own interests and lead to deep learning. However, it can be difficult to incorporate these kinds of projects into a traditional curriculum in which all students must master a set of basic skills. In this chapter, the authors describe the design and implementation of Storytelling Alice, a programming environment that presents computer programming as a means to the end of creating animated stories. By studying the kinds of animated movies that students envision creating, the chapter’s authors were able to design the system such that typical student projects naturally motivate the set of basic concepts we want students to learn. The authors present a potential model for incorporating Storytelling Alice into a classroom setting using open-ended projects. The chapter concludes with a discussion of some directions for future work that may help to enable the use more open-ended projects in formal education.

scholarly journals Design and Implementation of FPGA Based Configurable AI Architecture with Deep Learning Algorithm

2019 ◽  
Vol 1362 ◽  
pp. 012046
Author(s):  
Tamizhelakkiya ◽  
Utkarsh Kumar ◽  
Dussa Vishnu Simha ◽  
Pratyush Prasanna Sahu
Keyword(s):  
Deep Learning ◽  
Learning Algorithm ◽  
Design And Implementation ◽  
Deep Learning Algorithm

Automatic sleep scoring system using a patch-type EEG sensor based on deep learning

2019 ◽  
Vol 64 ◽  
pp. S260-S261
Author(s):  
S. Matsumori ◽  
T. Norisugi ◽  
K. Ezure ◽  
S. Yoshimoto ◽  
H. Mizutani
Keyword(s):  
Deep Learning ◽  
Scoring System ◽  
Patch Type ◽  
Sleep Scoring
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