Classification modeling of intention to donate for victims of Typhoon Odette using deep learning neural network

Environmental Development, Journal Year: 2023, Volume and Issue: 45, P. 100823 - 100823

Published: Feb. 20, 2023

Language: Английский

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Global, regional, and local acceptance of solar power

Renewable and Sustainable Energy Reviews, Journal Year: 2024, Volume and Issue: 193, P. 114296 - 114296

Published: Jan. 21, 2024

This study aims to analyse solar power acceptance by different methods in various knowledge domains gain a holistic view of global, regional, and local acceptance. includes considering related aspects energy, including the overall concept, panel, device converting sunlight into electricity, photovoltaics, technology. multidisciplinary approach is possible through advancement artificial intelligence Technology sentiment, emotion, are concepts with slightly influences on technology deployment. Acceptance can be granted as social license affected how media discusses technologies. The further investment decisions wider adoption. Sentiment obtained machine or human-made analysis, which polarity (positive, negative, neutral) defined while levels indicative. applies opinion mining, chat generative pre-trained transformer, generalised aggregated lexical tables sentiment at levels. findings original contribution involve highlighting potential general Artificial appears capable providing fast indication both level power. Traditional mining seems more acknowledging trends. supports understanding market environment factors affecting development Decision-making benefit from indication.

Language: Английский

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Factors affecting the intention to prepare for tsunami in Thailand

Ocean & Coastal Management, Journal Year: 2022, Volume and Issue: 233, P. 106464 - 106464

Published: Dec. 20, 2022

Language: Английский

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Intelligent Alarm Analysis for Fault Diagnosis and Management in Nuclear Power Plants

Energies, Journal Year: 2025, Volume and Issue: 18(7), P. 1730 - 1730

Published: March 31, 2025

The safety and efficiency of nuclear power plants (NPPs) are heavily reliant on the timely accurate decisions made by operators in control room, particularly during fault scenarios. Traditional alarm systems often fall short identifying root causes faults, especially complex, multi-fault situations. This paper presents an advanced diagnosis method based multilevel flow model (MFM), which addresses limitations conventional linking alarms to system goals functions. MFM framework organizes hierarchically, aiding clear understanding causal relationships underlying faults. Additionally, this proposes a that combines longest path cause analysis, addressing both single- multiple-fault needs. approach improves diagnostic comprehensiveness, providing with systematic basis for analysis decision-making multi-alarm

Language: Английский

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Mastitis diagnosis with machine learning algorithms

Neural Computing and Applications, Journal Year: 2025, Volume and Issue: unknown

Published: March 29, 2025

Language: Английский

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Determinants of paratransit feeder service provision using AI-synthesized revealed preference data: a paratransit drivers’ perspective on PT reform using machine learning

Transportation Planning and Technology, Journal Year: 2024, Volume and Issue: unknown, P. 1 - 30

Published: Sept. 30, 2024

Language: Английский

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Investigating Factors Affecting Behavioral Intention among Gym-Goers to Visit Fitness Centers during the COVID-19 Pandemic: Integrating Physical Activity Maintenance Theory and Social Cognitive Theory

Sustainability, Journal Year: 2022, Volume and Issue: 14(19), P. 12020 - 12020

Published: Sept. 23, 2022

The COVID-19 pandemic has led to the closure of many fitness centers and significantly affected behavioral intentions gym-goers. This study aimed determine factors affecting gym-goers regarding during in Philippines by utilizing Physical Activity Maintenance Theory within framework Social Cognitive Theory. A total 1048 voluntarily answered an online, self-administered survey comprising 68 questions. Structural Equation Modeling indicated that physical activity maintenance through understanding self-motivation had highest significant effect on intentions. Interestingly, life stress was found have a negative direct maintenance. current is one first analyzed pandemic. Finally, application this provided accurate predictors intention. As result, integrated model could serve as theoretical foundation be applied extended assess among worldwide.

Language: Английский

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Utilizing a machine learning ensemble to evaluate the service quality and passenger satisfaction among public transportations

Journal of Public Transportation, Journal Year: 2023, Volume and Issue: 25, P. 100076 - 100076

Published: Jan. 1, 2023

Public transportation is an essential criterion that benefits several social sectors. Hence, most developing countries display increase in the demand for enhanced public utility vehicle (PUV) systems. PUVs are prevalent Philippines; however, research on passenger satisfaction and scarce. This aimed to assess passengers' future intentions regarding through utilizing various latent variables. study utilized online survey with a total of 600 respondents using Philippines who voluntarily answered questionnaire. The data were analyzed different Machine Learning Algorithms (MLA) such as Deep Neural Network (DLNN), Decision Tree (DT), Random Forest Classifier (RFC). indicated people vastly prefer route-efficient way traveling, safety, value money, expectations it highly affected intentions. theoretical basis this provided effective instrument resolving country's emerging traffic issues served foundation forming policy initiatives. Future may look into concentrate more particular types service quality factors provide in-depth analysis subject extend analysis. Researchers also utilize MLA provides efficient accurate factor sector. Finally, managerial insights could be elevated, including domains areas.

Language: Английский

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A machine learning ensemble approach to predicting factors affecting the intention and usage behavior towards online groceries applications in the Philippines

Heliyon, Journal Year: 2023, Volume and Issue: 9(10), P. e20644 - e20644

Published: Oct. 1, 2023

The emergence of e-commerce platforms, especially online grocery shopping, is heightened by the COVID-19 pandemic. Filipino consumers started to adapt due strict quarantine implementations in country. This study intended predict and evaluate factors influencing intention usage behavior towards groceries incorporating integrated Protection Motivation Theory an extended Unified Acceptance Use Technology applying machine learning ensemble. A total 373 responded survey evaluated under framework. Artificial Neural Network that 96.63 % accurate with aligned result Random Forest Classifier (96 accuracy 0.00 standard deviation) having Perceived Benefits as most significant factor followed Vulnerability, Behavioral Intention, Performance Expectancy, Perceived. These will lead very high applications. It was established algorithms can be used predicting consumer behavior. findings may applied serve a framework for government agencies grocers market convenient safe shopping globally.

Language: Английский

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Machine Learning Decision System on the Empirical Analysis of the Actual Usage of Interactive Entertainment: A Perspective of Sustainable Innovative Technology

Computers, Journal Year: 2024, Volume and Issue: 13(6), P. 128 - 128

Published: May 23, 2024

This study focused on the impact of Netflix’s interactive entertainment Filipino consumers, seamlessly combining vantage points from consumer behavior and employing data analytics. underlines revolutionary aspect in quickly expanding digital media ecosystem, particularly as Netflix pioneers fresh content distribution techniques. The main objective this was to find factors impacting real usage among viewers, filling a critical gap existing literature. major goal using advanced analytics techniques understand subtle dynamics affecting customer setting. Specifically, random forest classifier with hard soft classifiers assessed. compared LightGBM also employed, alongside different algorithms artificial neural network. Purposive sampling used obtain responses 258 people who had experienced entertainment, resulting comprehensive dataset. findings emphasized importance hedonic motivation, underlining requirement for highly engaging rewarding material. Customer service device compatibility, example, have significant user uptake. Furthermore, behavioral intention habit emerged key drivers, revealing entertainment’s long-term influence engagement. Practically, research recommends strategic platform suggestions that emphasize continuous innovation, user-friendly interfaces, user-centric methods. able fill literature which contributes better understanding consumption lays groundwork future dynamic field media. Moreover, offers essential insights into intricate interaction preferences, technology breakthroughs, societal influences ever-expanding environment entertainment. Lastly, comparative approach use machine learning provides works adopt employ human behavior-related studies.

Language: Английский

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