Utilization of random forest classifier and artificial neural network for predicting the acceptance of reopening decommissioned nuclear power plant

Ong, Ardvin Kester S.; Prasetyo, Yogi Tri; Velasco, Kenzo Emmanuel C.; Abad, Eman David R.; Buencille, Adrian Louis B.; Estorninos, Ezekiel M.; Cahigas, Maela Madel L.; Chuenyindee, Thanatorn; Persada, Satria Fadil; Nadlifatin, Reny; Sittiwatethanasiri, Thaninrat

doi:10.1016/j.anucene.2022.109188

Cited by 27 publications

(56 citation statements)

References 69 publications

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“…Since MLAs were developed in accordance with statistical tools, it was seen that the consideration of MLA as an alternative tool to replace traditional statistical analysis has been the trend [44]. With the presented studies [21][22][23]42,43], it could be deduced that higher accuracy for the classification model would be obtained for RFC as compared to the basic decision tree. In addition, other classification tools are also available such as different types of regression.…”

Section: Machine Learning Algorithm and Related Studiesmentioning

confidence: 84%

“…They also highlighted that SEM was a limitation and suggested using a machine learning ensemble to validate the findings of the study [10]. Machine learning algorithms are not being utilized to evaluate human behavior and human factors [43].…”

Section: Application Usability and Related Studiesmentioning

confidence: 99%

“…Machine learning algorithms are of different types, wherein classification tools are commonly utilized for pattern recognition. It was stated from the study by Ong et al [42,43] how MLAs such as RFC and ANN have been recognized in the field of human factors to evaluate human behavior. However, Jamshidi et al [44] highlighted that the use of artificial intelligence and machine learning can be debatable when it comes to their application since their use is highly dependent on human input, the role of the individual, and collaboration in the roles of human.…”

Section: Machine Learning Algorithm and Related Studiesmentioning

confidence: 99%

“…With that being said, the goal of this study aims to predict factors affecting actual usage of the MorChana mobile application, and it was posited to consider the ANN and the RFC ensemble. Similarly, Ong et al [43] considered the same ensemble to predict factors affecting the acceptance of a technology by a population. It was seen that these algorithms would be best to predict factors creating a higher accuracy rate with less complex algorithm and calculation.…”

Section: Machine Learning Algorithm and Related Studiesmentioning

confidence: 99%

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Utilization of Random Forest Classifier and Artificial Neural Network for Predicting Factors Influencing the Perceived Usability of COVID-19 Contact Tracing “MorChana” in Thailand

Ong

Prasetyo

Yuduang

et al. 2022

IJERPH

Self Cite

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With the constant mutation of COVID-19 variants, the need to reduce the spread should be explored. MorChana is a mobile application utilized in Thailand to help mitigate the spread of the virus. This study aimed to explore factors affecting the actual use (AU) of the application through the use of machine learning algorithms (MLA) such as Random Forest Classifier (RFC) and Artificial Neural Network (ANN). An integrated Protection Motivation Theory (PMT) and the Unified Theory of Acceptance and Use of Technology (UTAUT) were considered. Using convenience sampling, a total of 907 valid responses from those who answered the online survey were voluntarily gathered. With 93.00% and 98.12% accuracy from RFC and ANN, it was seen that hedonic motivation and facilitating conditions were seen to be factors affecting very high AU; while habit and understanding led to high AU. It was seen that when people understand the impact and causes of the COVID-19 pandemic’s aftermath, its severity, and also see a way to reduce it, it would lead to the actual usage of a system. The findings of this study could be used by developers, the government, and stakeholders to capitalize on using the health-related applications with the intention of increasing actual usage. The framework and methodology used presented a way to evaluate health-related technologies. Moreover, the developing trends of using MLA for evaluating human behavior-related studies were further justified in this study. It is suggested that MLA could be utilized to assess factors affecting human behavior and technology used worldwide.

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Section: Machine Learning Algorithm and Related Studiesmentioning

confidence: 84%

Section: Application Usability and Related Studiesmentioning

confidence: 99%

Section: Machine Learning Algorithm and Related Studiesmentioning

confidence: 99%

Section: Machine Learning Algorithm and Related Studiesmentioning

confidence: 99%

See 2 more Smart Citations

Utilization of Random Forest Classifier and Artificial Neural Network for Predicting Factors Influencing the Perceived Usability of COVID-19 Contact Tracing “MorChana” in Thailand

Ong

Prasetyo

Yuduang

et al. 2022

IJERPH

Self Cite

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“…In addition, the inclusion of other factors may be considered to assess the usability of mental health applications. Fourth, it is suggested that methodology focusing solely on machine learning algorithm is considered in order to assess the claims found in several studies focusing on consumer behavior [ 64 ] and technology usability [ 65 ]. Lastly, differentiation based on demographics was not conducted.…”

Section: Discussionmentioning

confidence: 99%

Utilizing Structural Equation Modeling–Artificial Neural Network Hybrid Approach in Determining Factors Affecting Perceived Usability of Mobile Mental Health Application in the Philippines

Yuduang

Ong

Vista

et al. 2022

IJERPH

Self Cite

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Mental health problems have emerged as one of the biggest problems in the world and one of the countries that has been seen to be highly impacted is the Philippines. Despite the increasing number of mentally ill Filipinos, it is one of the most neglected problems in the country. The purpose of this study was to determine the factors affecting the perceived usability of mobile mental health applications. A total of 251 respondents voluntarily participated in the online survey we conducted. A structural equation modeling and artificial neural network hybrid was applied to determine the perceived usability (PRU) such as the social influence (SI), service awareness (SA), technology self-efficacy (SE), perceived usefulness (PU), perceived ease of use (PEOU), convenience (CO), voluntariness (VO), user resistance (UR), intention to use (IU), and actual use (AU). Results indicate that VO had the highest score of importance, followed by CO, PEOU, SA, SE, SI, IU, PU, and ASU. Having the mobile application available and accessible made the users perceive it as highly beneficial and advantageous. This would lead to the continuous usage and patronage of the application. This result highlights the insignificance of UR. This study was the first study that considered the evaluation of mobile mental health applications. This study can be beneficial to people who have mental health disorders and symptoms, even to health government agencies. Finally, the results of this study could be applied and extended among other health-related mobile applications worldwide.

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Machine and Deep Learning: Their Roles in the Context of the Economic Growth Processes and Sustainability Assessment

Martinho

2023

SpringerBriefs in Applied Sciences and Technology

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Utilization of random forest classifier and artificial neural network for predicting the acceptance of reopening decommissioned nuclear power plant

Cited by 27 publications

References 69 publications

Utilization of Random Forest Classifier and Artificial Neural Network for Predicting Factors Influencing the Perceived Usability of COVID-19 Contact Tracing “MorChana” in Thailand

Utilization of Random Forest Classifier and Artificial Neural Network for Predicting Factors Influencing the Perceived Usability of COVID-19 Contact Tracing “MorChana” in Thailand

Utilizing Structural Equation Modeling–Artificial Neural Network Hybrid Approach in Determining Factors Affecting Perceived Usability of Mobile Mental Health Application in the Philippines

Machine and Deep Learning: Their Roles in the Context of the Economic Growth Processes and Sustainability Assessment

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