Artificial intelligence-based fuzzy logic systems for predicting radiation protection awareness levels among university population

The present study aims to assess the knowledge level of radiation protection among individuals of Princess Nourah bint Abdulrahman University (PNU) using artificial intelligence baesd fuzzy logic system. This crosssectional study included 428 PNU participants. They were asked to fill in the online q...

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Main Authors: Hamd, Zuhal Y., Almohammed, H.I., Lashin, Maha M.A., Yousef, M., Aljuaid, Hanan, Khawaji, Sawsan M., Alhussain, Norah I., Salami, Alanoud H., Alsowayan, Rand A., Alshaik, Fatima A., Alshehri, Tahani K., Aldossari, Dalal M., Albogami, Nouf F., Khandaker, Mayeen Uddin *
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Published: Elsevier 2023
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Online Access:http://eprints.sunway.edu.my/2268/
https://doi.org/10.1016/j.radphyschem.2023.110888
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spelling my.sunway.eprints.22682023-06-17T07:48:26Z http://eprints.sunway.edu.my/2268/ Artificial intelligence-based fuzzy logic systems for predicting radiation protection awareness levels among university population Hamd, Zuhal Y. Almohammed, H.I. Lashin, Maha M.A. Yousef, M. Aljuaid, Hanan Khawaji, Sawsan M. Alhussain, Norah I. Salami, Alanoud H. Alsowayan, Rand A. Alshaik, Fatima A. Alshehri, Tahani K. Aldossari, Dalal M. Albogami, Nouf F. Khandaker, Mayeen Uddin * Q Science (General) QA Mathematics TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering The present study aims to assess the knowledge level of radiation protection among individuals of Princess Nourah bint Abdulrahman University (PNU) using artificial intelligence baesd fuzzy logic system. This crosssectional study included 428 PNU participants. They were asked to fill in the online questionnaire, consisting of demographic data, education level, and radiation protection awareness. After informed consent was completed, a statistical package for the social sciences as well as fuzzy logic system was used for data analysis. The participant group consisted of 98.4% females, 96.3% individuals aged 18–28 years (the most common age group), 63.1% bachelor’s degree holders, and 65.7% medical participants. Specialty and radiation protection awareness exhibited significant association (P < 0.05). However, age, education level, and gender did not show a significant association (P > 0.05). PNU individuals in the medical field differed significantly (P > 0.05) with the non-medical individual in their knowledge of radiation protection. This study suggests that PNU individuals in the medical field have a reasonable awareness of radiation protection. However, the general knowledge of nonmedical individuals must be improved to raise awareness. Based on the obtained results by using fuzzy model, this study suggests that the tool can be used in the process of radiation protection awareness in other institutions and areas. Elsevier 2023-07 Article PeerReviewed Hamd, Zuhal Y. and Almohammed, H.I. and Lashin, Maha M.A. and Yousef, M. and Aljuaid, Hanan and Khawaji, Sawsan M. and Alhussain, Norah I. and Salami, Alanoud H. and Alsowayan, Rand A. and Alshaik, Fatima A. and Alshehri, Tahani K. and Aldossari, Dalal M. and Albogami, Nouf F. and Khandaker, Mayeen Uddin * (2023) Artificial intelligence-based fuzzy logic systems for predicting radiation protection awareness levels among university population. Radiation Physics and Chemistry, 28. ISSN 0969-806X https://doi.org/10.1016/j.radphyschem.2023.110888 10.1016/j.radphyschem.2023.110888
institution Sunway University
building Sunway Campus Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Sunway University
content_source Sunway Institutional Repository
url_provider http://eprints.sunway.edu.my/
topic Q Science (General)
QA Mathematics
TA Engineering (General). Civil engineering (General)
TK Electrical engineering. Electronics Nuclear engineering
spellingShingle Q Science (General)
QA Mathematics
TA Engineering (General). Civil engineering (General)
TK Electrical engineering. Electronics Nuclear engineering
Hamd, Zuhal Y.
Almohammed, H.I.
Lashin, Maha M.A.
Yousef, M.
Aljuaid, Hanan
Khawaji, Sawsan M.
Alhussain, Norah I.
Salami, Alanoud H.
Alsowayan, Rand A.
Alshaik, Fatima A.
Alshehri, Tahani K.
Aldossari, Dalal M.
Albogami, Nouf F.
Khandaker, Mayeen Uddin *
Artificial intelligence-based fuzzy logic systems for predicting radiation protection awareness levels among university population
description The present study aims to assess the knowledge level of radiation protection among individuals of Princess Nourah bint Abdulrahman University (PNU) using artificial intelligence baesd fuzzy logic system. This crosssectional study included 428 PNU participants. They were asked to fill in the online questionnaire, consisting of demographic data, education level, and radiation protection awareness. After informed consent was completed, a statistical package for the social sciences as well as fuzzy logic system was used for data analysis. The participant group consisted of 98.4% females, 96.3% individuals aged 18–28 years (the most common age group), 63.1% bachelor’s degree holders, and 65.7% medical participants. Specialty and radiation protection awareness exhibited significant association (P < 0.05). However, age, education level, and gender did not show a significant association (P > 0.05). PNU individuals in the medical field differed significantly (P > 0.05) with the non-medical individual in their knowledge of radiation protection. This study suggests that PNU individuals in the medical field have a reasonable awareness of radiation protection. However, the general knowledge of nonmedical individuals must be improved to raise awareness. Based on the obtained results by using fuzzy model, this study suggests that the tool can be used in the process of radiation protection awareness in other institutions and areas.
format Article
author Hamd, Zuhal Y.
Almohammed, H.I.
Lashin, Maha M.A.
Yousef, M.
Aljuaid, Hanan
Khawaji, Sawsan M.
Alhussain, Norah I.
Salami, Alanoud H.
Alsowayan, Rand A.
Alshaik, Fatima A.
Alshehri, Tahani K.
Aldossari, Dalal M.
Albogami, Nouf F.
Khandaker, Mayeen Uddin *
author_facet Hamd, Zuhal Y.
Almohammed, H.I.
Lashin, Maha M.A.
Yousef, M.
Aljuaid, Hanan
Khawaji, Sawsan M.
Alhussain, Norah I.
Salami, Alanoud H.
Alsowayan, Rand A.
Alshaik, Fatima A.
Alshehri, Tahani K.
Aldossari, Dalal M.
Albogami, Nouf F.
Khandaker, Mayeen Uddin *
author_sort Hamd, Zuhal Y.
title Artificial intelligence-based fuzzy logic systems for predicting radiation protection awareness levels among university population
title_short Artificial intelligence-based fuzzy logic systems for predicting radiation protection awareness levels among university population
title_full Artificial intelligence-based fuzzy logic systems for predicting radiation protection awareness levels among university population
title_fullStr Artificial intelligence-based fuzzy logic systems for predicting radiation protection awareness levels among university population
title_full_unstemmed Artificial intelligence-based fuzzy logic systems for predicting radiation protection awareness levels among university population
title_sort artificial intelligence-based fuzzy logic systems for predicting radiation protection awareness levels among university population
publisher Elsevier
publishDate 2023
url http://eprints.sunway.edu.my/2268/
https://doi.org/10.1016/j.radphyschem.2023.110888
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score 13.214268