scholarly journals An application for classifying perceptions on my health bank in Taiwan using convolutional neural networks and web-based computerized adaptive testing

Medicine ◽  
2021 ◽  
Vol 100 (52) ◽  
pp. e28457
Author(s):  
Chen-Fang Hsu ◽  
Tsair-Wei Chien ◽  
Yu-Hua Yan
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Computerized Adaptive Testing ◽  
Adaptive Testing ◽  
Web Based

scholarly journals An App for Detecting Bullying of Nurses Using Convolutional Neural Networks and Web-Based Computerized Adaptive Testing: Development and Usability Study (Preprint)

2019 ◽  
Author(s):  
Shu-Ching Ma ◽  
Willy Chou ◽  
Tsair-Wei Chien ◽  
Julie Chi Chow ◽  
Yu-Tsen Yeh ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Workplace Bullying ◽  
Computerized Adaptive Testing ◽  
Early Stage ◽  
Characteristic Curve ◽  
Area Under The Curve ◽  
Adaptive Testing ◽  
Web Based ◽  
Nurse Bullying

BACKGROUND Workplace bullying has been measured in many studies to investigate its effects on mental health issues. However, none have used web-based computerized adaptive testing (CAT) with bully classifications and convolutional neural networks (CNN) for reporting the extent of individual bullying in the workplace. OBJECTIVE This study aims to build a model using CNN to develop an app for automatic detection and classification of nurse bullying-levels, incorporated with online Rasch computerized adaptive testing, to help assess nurse bullying at an earlier stage. METHODS We recruited 960 nurses working in a Taiwan Ch-Mei hospital group to fill out the 22-item Negative Acts Questionnaire-Revised (NAQ-R) in August 2012. The k-mean and the CNN were used as unsupervised and supervised learnings, respectively, for: (1) dividing nurses into three classes (n=918, 29, and 13 with suspicious mild, moderate, and severe extent of being bullied, respectively); and (2) building a bully prediction model to estimate 69 different parameters. Finally, data were separated into training and testing sets in a proportion of 70:30, where the former was used to predict the latter. We calculated the sensitivity, specificity, and receiver operating characteristic curve (area under the curve [AUC]), along with the accuracy across studies for comparison. An app predicting the respondent bullying-level was developed, involving the model’s 69 estimated parameters and the online Rasch CAT module as a website assessment. RESULTS We observed that: (1) the 22-item model yields higher accuracy rates for three categories, with an accuracy of 94% for the total 960 cases, and accuracies of 99% (AUC 0.99; 95% CI 0.99-1.00) and 83% (AUC 0.94; 95% CI 0.82-0.99) for the lower and upper groups (cutoff points at 49 and 66 points) based on the 947 cases and 42 cases, respectively; and (2) the 700-case training set, with 95% accuracy, predicts the 260-case testing set reaching an accuracy of 97. Thus, a NAQ-R app for nurses that predicts bullying-level was successfully developed and demonstrated in this study. CONCLUSIONS The 22-item CNN model, combined with the Rasch online CAT, is recommended for improving the accuracy of the nurse NAQ-R assessment. An app developed for helping nurses self-assess workplace bullying at an early stage is required for application in the future.

scholarly journals An App for Detecting Bullying of Nurses Using Convolutional Neural Networks and Web-Based Computerized Adaptive Testing: Development and Usability Study

10.2196/16747 ◽  
2020 ◽  
Vol 8 (5) ◽  
pp. e16747
Author(s):  
Shu-Ching Ma ◽  
Willy Chou ◽  
Tsair-Wei Chien ◽  
Julie Chi Chow ◽  
Yu-Tsen Yeh ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Workplace Bullying ◽  
Computerized Adaptive Testing ◽  
Early Stage ◽  
Characteristic Curve ◽  
Area Under The Curve ◽  
Adaptive Testing ◽  
Web Based ◽  
Nurse Bullying

Background Workplace bullying has been measured in many studies to investigate its effects on mental health issues. However, none have used web-based computerized adaptive testing (CAT) with bully classifications and convolutional neural networks (CNN) for reporting the extent of individual bullying in the workplace. Objective This study aims to build a model using CNN to develop an app for automatic detection and classification of nurse bullying-levels, incorporated with online Rasch computerized adaptive testing, to help assess nurse bullying at an earlier stage. Methods We recruited 960 nurses working in a Taiwan Ch-Mei hospital group to fill out the 22-item Negative Acts Questionnaire-Revised (NAQ-R) in August 2012. The k-mean and the CNN were used as unsupervised and supervised learnings, respectively, for: (1) dividing nurses into three classes (n=918, 29, and 13 with suspicious mild, moderate, and severe extent of being bullied, respectively); and (2) building a bully prediction model to estimate 69 different parameters. Finally, data were separated into training and testing sets in a proportion of 70:30, where the former was used to predict the latter. We calculated the sensitivity, specificity, and receiver operating characteristic curve (area under the curve [AUC]), along with the accuracy across studies for comparison. An app predicting the respondent bullying-level was developed, involving the model’s 69 estimated parameters and the online Rasch CAT module as a website assessment. Results We observed that: (1) the 22-item model yields higher accuracy rates for three categories, with an accuracy of 94% for the total 960 cases, and accuracies of 99% (AUC 0.99; 95% CI 0.99-1.00) and 83% (AUC 0.94; 95% CI 0.82-0.99) for the lower and upper groups (cutoff points at 49 and 66 points) based on the 947 cases and 42 cases, respectively; and (2) the 700-case training set, with 95% accuracy, predicts the 260-case testing set reaching an accuracy of 97. Thus, a NAQ-R app for nurses that predicts bullying-level was successfully developed and demonstrated in this study. Conclusions The 22-item CNN model, combined with the Rasch online CAT, is recommended for improving the accuracy of the nurse NAQ-R assessment. An app developed for helping nurses self-assess workplace bullying at an early stage is required for application in the future.

Usage of Machine Learning In Strategy Planning On A Web-Based Business Platforms

2021 ◽  
Vol 12 (1) ◽  
pp. 38-38
Author(s):  
Fedor Zagumennov ◽  
Andrei Bystrov ◽  
Alexey Radaykin ◽  
Paschenko V.V.
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Demand Analysis ◽  
Erp Systems ◽  
Web Based ◽  
Income Data ◽  
Strategy Planning ◽  
Business Functions

This paper describes the practical usage of 1D convolutional neural networks in business platforms for such tasks as income prediction, procurements and order demand analysis. The structure of the CNN model is provided, as well as the results of experiments with real orders, procurements and income data. According to the results, there are guidelines formulated for the implementation in the particular ERP systems or web business platforms. Currently web-based platforms featuring advanced business functions are rapidly growing. Their new functions can use classic and modern concepts. The comparison between several approaches, including machine learning and regression are provided. Technologies used in such platforms are provided and analyzed. The structures of a such specific web-platforms frontend and backend systems are observed. Other prospective ideas of usage are formulated. Keywords: Business, Neural, Networks, CNN, Platform

scholarly journals Classification and Regression Tree and Computer Adaptive Testing in Cardiac Rehabilitation: Instrument Validation Study (Preprint)

2018 ◽  
Author(s):  
Linda Peute ◽  
Thom Scheeve ◽  
Monique Jaspers
Keyword(s):  
Cardiac Rehabilitation ◽  
Data Quality ◽  
Sensitivity And Specificity ◽  
Computerized Adaptive Testing ◽  
Regression Tree ◽  
Adaptive Testing ◽  
Classification And Regression Tree ◽  
Web Based ◽  
Classification And Regression ◽  
Multiple Domains

BACKGROUND There is a need for shorter-length assessments that capture patient questionnaire data while attaining high data quality without an undue response burden on patients. Computerized adaptive testing (CAT) and classification and regression tree (CART) methods have the potential to meet these needs and can offer attractive options to shorten questionnaire lengths. OBJECTIVE The objective of this study was to test whether CAT or CART was best suited to reduce the number of questionnaire items in multiple domains (eg, anxiety, depression, quality of life, and social support) used for a needs assessment procedure (NAP) within the field of cardiac rehabilitation (CR) without the loss of data quality. METHODS NAP data of 2837 CR patients from a multicenter Cardiac Rehabilitation Decision Support System (CARDSS) Web-based program was used. Patients used a Web-based portal, MyCARDSS, to provide their data. CAT and CART were assessed based on their performances in shortening the NAP procedure and in terms of sensitivity and specificity. RESULTS With CAT and CART, an overall reduction of 36% and 72% of NAP questionnaire length, respectively, was achieved, with a mean sensitivity and specificity of 0.765 and 0.817 for CAT, 0.777 and 0.877 for classification trees, and 0.743 and 0.40 for regression trees, respectively. CONCLUSIONS Both CAT and CART can be used to shorten the questionnaires of the NAP used within the field of CR. CART, however, showed the best performance, with a twice as large overall decrease in the number of questionnaire items of the NAP compared to CAT and the highest sensitivity and specificity. To our knowledge, our study is the first to assess the differences in performance between CAT and CART for shortening questionnaire lengths. Future research should consider administering varied assessments of patients over time to monitor their progress in multiple domains. For CR professionals, CART integrated with MyCARDSS would provide a feedback loop that informs the rehabilitation progress of their patients by providing real-time patient measurements.

scholarly journals Hardware Module Design and Software Implementation of Multisensor Fire Detection and Notification System Using Fuzzy Logic and Convolutional Neural Networks (CNNs)

2020 ◽  
Vol 2020 ◽  
pp. 1-16 ◽  
Author(s):  
Robert A. Sowah ◽  
Kwaku Apeadu ◽  
Francis Gatsi ◽  
Kwame O. Ampadu ◽  
Baffour S. Mensah
Keyword(s):  
Neural Networks ◽  
Fuzzy Logic ◽  
Convolutional Neural Networks ◽  
Fire Detection ◽  
Web Based ◽  
Notification System ◽  
Multisensor Data Fusion ◽  
Standard Fire ◽  
Area Of Interest ◽  
Detection Technology

This paper presents the design and development of a fuzzy logic-based multisensor fire detection and a web-based notification system with trained convolutional neural networks for both proximity and wide-area fire detection. Until recently, most consumer-grade fire detection systems relied solely on smoke detectors. These offer limited protection due to the type of fire present and the detection technology at use. To solve this problem, we present a multisensor data fusion with convolutional neural network (CNN) fire detection and notification technology. Convolutional Neural Networks are mainstream methods of deep learning due to their ability to perform feature extraction and classification in the same architecture. The system is designed to enable early detection of fire in residential, commercial, and industrial environments by using multiple fire signatures such as flames, smoke, and heat. The incorporation of the convolutional neural networks enables broader coverage of the area of interest, using visuals from surveillance cameras. With access granted to the web-based system, the fire and rescue crew gets notified in real-time with location information. The efficiency of the fire detection and notification system employed by standard fire detectors and the multisensor remote-based notification approach adopted in this paper showed significant improvements with timely fire detection, alerting, and response time for firefighting. The final experimental and performance evaluation results showed that the accuracy rate of CNN was 94% and that of the fuzzy logic unit is 90%.

scholarly journals A Web-Based Computerized Adaptive Testing (CAT) to Assess Patient Perception in Hospitalization

2011 ◽  
Vol 13 (3) ◽  
pp. e61 ◽  
Author(s):  
Tsair-Wei Chien ◽  
Wen-Chung Wang ◽  
Sheng-Yun Huang ◽  
Wen-Pin Lai ◽  
Julie Chi Chow
Keyword(s):  
Computerized Adaptive Testing ◽  
Adaptive Testing ◽  
Patient Perception ◽  
Web Based

scholarly journals Classification and Regression Tree and Computer Adaptive Testing in Cardiac Rehabilitation: Instrument Validation Study

10.2196/12509 ◽  
2020 ◽  
Vol 22 (1) ◽  
pp. e12509
Author(s):  
Linda Peute ◽  
Thom Scheeve ◽  
Monique Jaspers
Keyword(s):  
Cardiac Rehabilitation ◽  
Data Quality ◽  
Sensitivity And Specificity ◽  
Computerized Adaptive Testing ◽  
Regression Tree ◽  
Adaptive Testing ◽  
Classification And Regression Tree ◽  
Web Based ◽  
Classification And Regression ◽  
Multiple Domains

Background There is a need for shorter-length assessments that capture patient questionnaire data while attaining high data quality without an undue response burden on patients. Computerized adaptive testing (CAT) and classification and regression tree (CART) methods have the potential to meet these needs and can offer attractive options to shorten questionnaire lengths. Objective The objective of this study was to test whether CAT or CART was best suited to reduce the number of questionnaire items in multiple domains (eg, anxiety, depression, quality of life, and social support) used for a needs assessment procedure (NAP) within the field of cardiac rehabilitation (CR) without the loss of data quality. Methods NAP data of 2837 CR patients from a multicenter Cardiac Rehabilitation Decision Support System (CARDSS) Web-based program was used. Patients used a Web-based portal, MyCARDSS, to provide their data. CAT and CART were assessed based on their performances in shortening the NAP procedure and in terms of sensitivity and specificity. Results With CAT and CART, an overall reduction of 36% and 72% of NAP questionnaire length, respectively, was achieved, with a mean sensitivity and specificity of 0.765 and 0.817 for CAT, 0.777 and 0.877 for classification trees, and 0.743 and 0.40 for regression trees, respectively. Conclusions Both CAT and CART can be used to shorten the questionnaires of the NAP used within the field of CR. CART, however, showed the best performance, with a twice as large overall decrease in the number of questionnaire items of the NAP compared to CAT and the highest sensitivity and specificity. To our knowledge, our study is the first to assess the differences in performance between CAT and CART for shortening questionnaire lengths. Future research should consider administering varied assessments of patients over time to monitor their progress in multiple domains. For CR professionals, CART integrated with MyCARDSS would provide a feedback loop that informs the rehabilitation progress of their patients by providing real-time patient measurements.

scholarly journals HASIL PENGUJIAN SOFTWARE COMPUTERIZED ADAPTIVE TESTING (CAT) MATA PELAJARAN PENDIDIKAN AGAMA ISLAM (PAI) DALAM MEMBUAT REKAM MEDIK PEMBELAJARAN (RMP) UNTUK MENDIAGNOSIS KEMAMPUAN SISWA DI SEKOLAH

INFERENSI ◽  
2012 ◽  
Vol 6 (2) ◽  
pp. 183
Author(s):  
Winarno Winarno
Keyword(s):  
Research And Development ◽  
Religious Education ◽  
Computerized Adaptive Testing ◽  
Adaptive Testing ◽  
Estimation Result ◽  
Web Based ◽  
Test Items ◽  
Islamic Religion ◽  
Development Approach ◽  
Quantitative Descriptive

This research aims at  producing a Computerized Adaptive Testing (CAT) software Islamic religion education subject (CAT PAI) for the year 7 students of public junior high schoo and  to see the results of CAT software in making rekam medik pembelajaran (RMP) to diagnose student’s ability. This research used the research and development approach. The data was collected by doing observation and documentation. Analyzedto use quantitative descriptive technique. The findings of the research shows the CAT can be developed web-based, and The CAT is able to in estimating student’s ability correct be seen RMP. Overall, the CAT software can perform the tasks well in order to select test items and to measure student’s ability from correlation value between ability estimation result (è) in CAT with  Islamic religious education subjects enough tall that is 0,69.

scholarly journals Estimation of an Examinee's Ability in the Web-Based Computerized Adaptive Testing Program IRT-CAT

2006 ◽  
Vol 3 ◽  
pp. 4 ◽  
Author(s):  
Yoon-Hwan Lee ◽  
Jung-Ho Park ◽  
In-Yong Park
Keyword(s):  
Rasch Model ◽  
Computerized Adaptive Testing ◽  
Logistic Models ◽  
Adaptive Testing ◽  
Database Management System ◽  
Web Based ◽  
User Input ◽  
System A ◽  
Computer Based ◽  
The Web

We developed a program to estimate an examinee's ability in order to provide freely available access to a web-based computerized adaptive testing (CAT) program. We used PHP and Java Script as the program languages, PostgresSQL as the database management system on an Apache web server and Linux as the operating system. A system which allows for user input and searching within inputted items and creates tests was constructed. We performed an ability estimation on each test based on a Rasch model and 2- or 3-parametric logistic models. Our system provides an algorithm for a web-based CAT, replacing previous personal computer-based ones, and makes it possible to estimate an examinee?占퐏 ability immediately at the end of test.

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