Assessment Metrics for Imbalanced Learning

The Dynamic Haptic Robotic Trainer (DHRT) was developed to minimize the up to 39% of adverse effects experienced by patients during Central Venous Catheterization (CVC) by standardizing CVC training, and provide automated assessments of performance. Specifically, this system was developed to replace manikin trainers that only simulate one patient anatomy and require a trained preceptor to evaluate the trainees’ performance. While the DHRT system provides automated feedback, the utility of this system with real-world scenarios and expertise has yet to be thoroughly investigated. Thus, the current study was developed to determine the validity of the current objective assessment metrics incorporated in the DHRT system through expert interviews. The main findings from this study are that experts do agree on perceptions of patient case difficulty, and that characterizations of patient case difficulty is based on anatomical characteristics, multiple needle insertions, and prior catheterization.

Download Full-text

A hybrid multi-class imbalanced learning method for predicting the quality level of diesel engines

Journal of Manufacturing Systems ◽

10.1016/j.jmsy.2021.03.014 ◽

2021 ◽

Author(s):

Wei Qin ◽

Zilong Zhuang ◽

Liangxun Guo ◽

Yanning Sun

Keyword(s):

Diesel Engines ◽

Quality Level ◽

Learning Method ◽

Imbalanced Learning

Download Full-text

Statistical assessment metrics for InSAR atmospheric correction: Applications to generic atmospheric correction online service for InSAR (GACOS) in Eastern China

International Journal of Applied Earth Observation and Geoinformation ◽

10.1016/j.jag.2020.102289 ◽

2021 ◽

Vol 96 ◽

pp. 102289

Author(s):

Ruya Xiao ◽

Chen Yu ◽

Zhenhong Li ◽

Xiufeng He

Keyword(s):

Eastern China ◽

Atmospheric Correction ◽

Online Service ◽

Statistical Assessment ◽

Assessment Metrics

Download Full-text

Assessing and Benchmarking Learning Outcomes of Robotics-Enabled STEM Education

Education Sciences ◽

10.3390/educsci11020084 ◽

2021 ◽

Vol 11 (2) ◽

pp. 84

Author(s):

S. M. Mizanoor Rahman

Keyword(s):

Learning Outcomes ◽

Science Teachers ◽

Stem Education ◽

User Study ◽

Research Approach ◽

Traditional Teaching ◽

Assessment Metrics ◽

Social Scientific ◽

Survey Responses ◽

Mathematics And Science Teachers

Experienced middle school mathematics and science teachers were recruited for a pilot study. The teachers separately responded to a survey related to determining expected learning outcomes based on their traditional teaching, classroom experiences and observations, and self-brainstorming. The teachers then received training on how to design, develop, and implement robotics-enabled lessons under a design-based research approach for experiential learning, and taught robotics-enabled lessons to a selected student population in classroom settings. The teachers then responded to the survey for the robotics-enabled teaching. For each case (traditional and robotics-enabled), the survey responses were analyzed, and a set of expected learning outcomes of math and science lessons was derived separately. The thematic analysis results showed that the expected learning outcomes for the robotics-enabled lessons were not only related to the educational gains (content knowledge) observed in traditional teaching, but also to the improvements in the behavioral, social, scientific, cognitive, and intellectual aptitudes of the students. Then, a set of metrics and methods were proposed for assessing the learning outcomes separately. To validate the assessment metrics and methods, teachers from different schools taught two selected robotics-enabled lessons (one math, one science) to same grade students, and separately assessed the learning outcomes of each student using the proposed metrics and methods. The learning outcomes were then compared and benchmarked between schools and subjects. The results of a user study with the teachers showed user acceptance, effectiveness, and suitability of the assessment metrics and methods. The proposed scheme of assessing learning outcomes can be used to assess and justify the benefits and advantages of robotics-enabled STEM education, benchmark the outcomes, help improve teaching preparations, motivate decision-makers to confer on robotics-enabled STEM education and curricula development, and promote robotics-enabled STEM education.

Download Full-text

Advertisement Click-Through Rate Prediction Based on the Weighted-ELM and Adaboost Algorithm

Scientific Programming ◽

10.1155/2017/2938369 ◽

2017 ◽

Vol 2017 ◽

pp. 1-8 ◽

Cited By ~ 1

Author(s):

Sen Zhang ◽

Qiang Fu ◽

Wendong Xiao

Keyword(s):

Real Time ◽

Prediction Accuracy ◽

Historical Data ◽

User Profile ◽

Imbalanced Learning ◽

Sample Distribution ◽

The Real ◽

Adaboost Algorithm ◽

Click Through Rate ◽

Prediction Approach

Accurate click-through rate (CTR) prediction can not only improve the advertisement company’s reputation and revenue, but also help the advertisers to optimize the advertising performance. There are two main unsolved problems of the CTR prediction: low prediction accuracy due to the imbalanced distribution of the advertising data and the lack of the real-time advertisement bidding implementation. In this paper, we will develop a novel online CTR prediction approach by incorporating the real-time bidding (RTB) advertising by the following strategies: user profile system is constructed from the historical data of the RTB advertising to describe the user features, the historical CTR features, the ID features, and the other numerical features. A novel CTR prediction approach is presented to address the imbalanced learning sample distribution by integrating the Weighted-ELM (WELM) and the Adaboost algorithm. Compared to the commonly used algorithms, the proposed approach can improve the CTR significantly.

Download Full-text