An extensive investigation on leveraging machine learning techniques for high-precision predictive modeling of CO 2 emission DOI
Van Giao Nguyen, Xuan Quang Duong, Lan Huong Nguyen

и другие.

Energy Sources Part A Recovery Utilization and Environmental Effects, Год журнала: 2023, Номер 45(3), С. 9149 - 9177

Опубликована: Июль 9, 2023

Predictive analytics utilizing machine learning algorithms play a pivotal role in various domains, including the profiling of carbon dioxide (CO2) emissions. This research paper delves into an extensive exploration different algorithms, encompassing neural networks with diverse architectures, optimization, training, ensemble, and specialized algorithms. The primary objective this is to evaluate efficacy supervised unsupervised Deep Belief Networks, Feed Forward Neural Gradient Boosting, Regression, as well Convolutional Gaussian, Grey, Markov models, clustering optimization study places particular emphasis on data-driven methodologies cross-validation techniques evaluation models entailing comprehensive validation, testing, employing metrics such R2, MAE, RMSE. employs correlation analysis examine relationship between input parameters emission characteristics. highlights advantageous attributes these accurately forecasting CO2 emissions, evaluating energy sources, improving prediction accuracy, estimating Notably, deep learning, Artificial Networks (ANN), Support Vector Machines (SVM) demonstrate effectiveness across industries, while Modified Regularized Fast Orthogonal-Extreme Learning Machine (MRFO-ELM) algorithm optimizes predictions specifically related coal chemical Hybrid accuracy predicting emissions consumption, whereas gray provide reliable estimates even limited data. However, it important acknowledge certain limitations, data requirements, potential inaccuracies arising from complex factors, constraints faced by developing countries, impact electric vehicle expansion power grid. To optimize survey conducted, involving customization rates, exploring performance model accuracy. outcomes contribute effective monitoring operational environments, thereby aiding executive decision-making processes.

Язык: Английский

Predicting the intention to use google glass: A comparative approach using machine learning models and PLS-SEM DOI Creative Commons

MAhmad Qasim Mohammad AlHamad,

Iman Akour, Muhammad Turki Alshurideh

и другие.

International Journal of Data and Network Science, Год журнала: 2021, Номер unknown, С. 311 - 320

Опубликована: Янв. 1, 2021

Technology-based education is the modern-day medium that widely being used by teachers and their students to exchange information over applications based on Information Communication Technology (ICT) such as Google Glass. There still resistance shown a few universities around globe when it comes shifting online mode of education. While have shifted Glass, others are yet do so. We base this study explore Glass Adoption in Gulf area. thought introducing all pros presents table might get attention considering using respective institutes. This paper structure framework depicting association between TAM other Influential factors. All all, investigation analyzes incorporation Acceptance Model (TAM) with major features associated method instructing learning facilitator, functionality, trust privacy improve correspondence among facilitators during process. A total 420 questionnaires were collected from various universities. The data was gathered through surveys employed for analysis research model Partial least squares-structural equation modeling (PLS-SEM) machine models. outcome showed factor functionality goes hand perceived usefulness ease use Both Factors, Perceived significant impact adoption. implies Trust adoption also offers practical implications outcomes future research.

Язык: Английский

Процитировано

241
Forecasting of transportation-related energy demand and CO2 emissions in Turkey with different machine learning algorithms DOI
Ümit Ağbulut

Sustainable Production and Consumption, Год журнала: 2021, Номер 29, С. 141 - 157

Опубликована: Окт. 7, 2021

Язык: Английский

Процитировано

189
Assessing Health Students' Attitudes and Usage of ChatGPT in Jordan: Validation Study DOI Creative Commons
Malik Sallam, Nesreen A. Salim, Muna Barakat

и другие.

JMIR Medical Education, Год журнала: 2023, Номер 9, С. e48254 - e48254

Опубликована: Авг. 14, 2023

ChatGPT is a conversational large language model that has the potential to revolutionize knowledge acquisition. However, impact of this technology on quality education still unknown considering risks and concerns surrounding use. Therefore, it necessary assess usability acceptability promising tool. As an innovative technology, intention use can be studied in context acceptance (TAM).

Язык: Английский

Процитировано

113
The Nexus Between Big Data and Decision-Making: A Study of Big Data Techniques and Technologies DOI

Rabab Naqvi,

Tariq Rahim Soomro, Haitham M. Alzoubi

и другие.

Advances in intelligent systems and computing, Год журнала: 2021, Номер unknown, С. 838 - 853

Опубликована: Янв. 1, 2021

Язык: Английский

Процитировано

108
Perspectives of Online Education in Pakistan: Post-covid Scenario DOI

Moattar Farrukh,

Tariq Rahim Soomro, Taher M. Ghazal

и другие.

Studies in computational intelligence, Год журнала: 2023, Номер unknown, С. 519 - 550

Опубликована: Янв. 1, 2023

Язык: Английский

Процитировано

63
A Roadmap for SMEs to Adopt an AI Based Cyber Threat Intelligence DOI

Abhilash J. Varma,

Nasser Taleb, Raed A. Said

и другие.

Studies in computational intelligence, Год журнала: 2023, Номер unknown, С. 1903 - 1926

Опубликована: Янв. 1, 2023

Язык: Английский

Процитировано

57
Educational Supply Chain Management: A View on Professional Development Success in Malaysia DOI
Khai Loon Lee, Gusman Nawanir, Jack Kie Cheng

и другие.

Studies in computational intelligence, Год журнала: 2023, Номер unknown, С. 2473 - 2490

Опубликована: Янв. 1, 2023

Язык: Английский

Процитировано

55
You Can Handle, You Can Teach It: Systematic Review on the Use of Extended Reality and Artificial Intelligence Technologies for Online Higher Education DOI Open Access
Gizéh Rangel‐de Lázaro, Josep M. Duart

Sustainability, Год журнала: 2023, Номер 15(4), С. 3507 - 3507

Опубликована: Фев. 14, 2023

Over the past year, defined by COVID-19 pandemic, we have witnessed a boom in applying key emerging technologies education. In such challenging situations, technology and education expanded their work together to strengthen interactively impact learning process online higher context. From pedagogical perspective, extended reality (XR) artificial intelligence (AI) were accessible toolboxes amplify an active learner-centered teaching method. Whether how activities will continue post-COVID-19 situation remains unclear. this systematic literature review, document application of XR AI settings build up accurate depiction influence after pandemic outbreak. A significant contribution thorough analysis conducted was corroboration growing interest these fast-emerging on learner agency outcomes, making more accessible, effective, engaging, collaborative, self-paced, adapted diverse academic trajectories. The momentum brought about has served as impulse for educators universities expand use progressively, meet new challenges, shape future

Язык: Английский

Процитировано

47
Factors Affecting the Use of Smart Mobile Examination Platforms by Universities’ Postgraduate Students during the COVID-19 Pandemic: An Empirical Study DOI Creative Commons
Muhammad Turki Alshurideh, Barween Al Kurdi, Ahmad AlHamad

и другие.

Informatics, Год журнала: 2021, Номер 8(2), С. 32 - 32

Опубликована: Апрель 30, 2021

Recent years have seen an increasingly widespread use of online learning technologies. This has prompted universities to make huge investments in technology augment their position the face extensive competition and enhance students’ experience efficiency. Numerous studies been carried out regarding mobile phone platforms. However, there are very few focusing on how university students will accept adopt smartphones as a new platform for taking examinations. Many reasons, but most recently importantly COVID-19 pandemic, educational institutions move toward using both techniques. study is pioneer examining intention exam platforms prerequisites such intention. The purpose this expand Technology Acceptance Model (TAM) by including four additional constructs: namely, content quality, service information system quality. A self-survey method was prepared obtain necessary basic data. In total, 566 from United Arab Emirates took part survey. Smart PLS used test constructs structural model. Results showed that all hypotheses supported confirmed effect TAM extension factors within UAE higher education setting. These outcomes suggest policymakers developers should consider assessment possible technological solution, especially when considering distance concept. It good bear mind initial designed explore student Furthermore, mixed-method research needed check effectiveness suitability examination platforms, postgraduate levels.

Язык: Английский

Процитировано

104
Examining the Factors Influencing the Mobile Learning Usage During COVID-19 Pandemic: An Integrated SEM-ANN Method DOI Creative Commons
Khadija Alhumaid, Mohammed Habes, Said A. Salloum

и другие.

IEEE Access, Год журнала: 2021, Номер 9, С. 102567 - 102578

Опубликована: Янв. 1, 2021

The way in which the emotion of fear affects technology adoption students and teachers amid COVID-19 pandemic is examined this study. Mobile Learning (ML) has been used study as an educational social platform at both public private higher-education institutes. key hypotheses are based on how influenced incorporation mobile learning brings about increase different kinds fear. major that teachers/instructors facing time include: because complete lockdown, experiencing education collapse having to give up relationships. proposed model was evaluated by developing a questionnaire survey distributed among 280 Zayed University, Abu Dhabi Campus, United Arab Emirates (UAE) with purpose collecting data from them. This uses new hybrid analysis approach combines SEM deep learning-based artificial neural networks (ANN). importance-performance map also determine significance performance every factor. Both ANN IPMA research showed Attitude (ATD) most important predictor intention use learning. According empirical findings, perceived ease use, usefulness, satisfaction, attitude, behavioral control, subjective norm played strongly significant role justified continuous usage. It found expectation confirmation were factors predicting Our field education, coronavirus pandemic, offered potential outcome for teaching learning; however, impact may be reduced losing friends, stressful family environment future results school. Therefore, during it examine appropriately so enable them handle situation emotionally. theoretically given enough details what influences ML viewpoint internet service variables individual basis. In practice, findings would allow higher decision formers experts decide should prioritized over others plan their policies appropriately. examines competence deciding non-linear relationships theoretical model, methodologically.

Язык: Английский

Процитировано

99