State-of-the-Art of Artificial Intelligence and Big Data Analytics Reviews in Five Different Domains: A Bibliometric Summary

Thayyib, P. V.; Mamilla, Rajesh; Khan, Mohsin; Fatima, Humaira; Asim, Mohammed; Anwar, Imran; Shamsudheen, M. K.; Khan, Mohd Adnan

doi:10.3390/su15054026

Cited by 55 publications

(29 citation statements)

References 139 publications

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“…By incorporating realtime data and leveraging predictive analytics, the model fosters a proactive rather than reactive operational stance. This shift is not just methodological but also cultural, encouraging a more data-conscious environment that values evidence-based strategies and decisions [54].…”

Section: Discussionmentioning

confidence: 99%

Machine Learning Enhanced Framework for Big Data Modeling with Application in Industry 4.0

Kazbekova,

Ismagulova,

Zhussipbek

et al. 2024

IJACSA

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In the dynamic milieu of Industry 4.0, characterized by the deluge of big data, this research promulgates a groundbreaking framework that harnesses machine learning (ML) to optimize big data modeling processes, addressing the intricate requirements and challenges of contemporary industrial domains. Traditional data processing mechanisms falter in the face of the sheer volume, velocity, and variety of big data, necessitating more robust, intelligent solutions. This paper delineates the development and application of an innovative ML-augmented framework, engineered to interpret and model complex, multifaceted data structures more efficiently and accurately than has been feasible with conventional methodologies. Central to our approach is the integration of advanced ML strategies-including but not limited to deep learning and neural networks-with sophisticated analytics tools, collectively capable of automated decisionmaking, predictive analysis, and trend identification in real-time scenarios. Beyond theoretical formulation, our research rigorously evaluates the framework through empirical analysis and industrial case studies, demonstrating tangible enhancements in data utility, predictive accuracy, operational efficiency, and scalability within various Industry 4.0 contexts. The results signify a marked improvement over existing models, particularly in handling high-dimensional data and facilitating actionable insights, thereby empowering industrial entities to navigate the complexities of digital transformation. This exploration underscores the potential of machine learning as a pivotal ally in evolving data strategies, setting a new precedent for data-driven decision-making paradigms in the era of Industry 4.0.

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Section: Discussionmentioning

confidence: 99%

Machine Learning Enhanced Framework for Big Data Modeling with Application in Industry 4.0

Kazbekova,

Ismagulova,

Zhussipbek

et al. 2024

IJACSA

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“…Including these terms highlights the ongoing digital transformation in tourism and how technology is reshaping how tourism destinations are managed and enjoyed. Also included in this list are the terms "smart destination" [91,92] and IoT [93], which suggest an interest in how tourism destinations can use technology to improve the tourist experience and destination management. This term is particularly related to the use of technology for the use of tourism information.…”

Section: State Of the Art: Bibliometrics Of Tourismmentioning

confidence: 99%

Defining the Balearic Islands’ Tourism Data Space: An Approach to Functional and Data Requirements

Ordóñez-Martínez,

Seguí-Pons,

Ruiz-Pérez

2024

Data

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The definition of a tourism data space (TDS) in the Balearic Islands is a complex process that involves identifying the types of questions to be addressed, including analytical tools, and determining the type of information to be incorporated. This study delves into the functional requirements of a Balearic Islands’ TDS based on the study of scientific research carried out in the field of tourism in the Balearic Islands and drawing comparisons with international scientific research in the field of tourism information. Utilizing a bibliometric analysis of the scientific literature, this study identifies the scientific requirements that should be met for the development of a robust, rigorous, and efficient TDS. The goal is to support excellent scientific research in tourism and facilitate the transfer of research results to the productive sector to maintain and improve the competitiveness of the Balearic Islands as a tourist destination. The results of the analysis provide a structured framework for the construction of the Balearic Islands’ TDS, outlining objectives, methods to be implemented, and information to be considered.

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“…In order to achieve this, accurate and precise deductions are highly desirable in data analysis. For this purpose, recent technological advances have induced the use of machine learning and artificial intelligence in various domains [24]; the use of machine learning in PA certainly constitutes one of these advances [25,26]. We hypothesized that, through this study, we would be able to discern specific patterns, characteristics, and subgroups of the population through machine learning, allowing us to better shape future policies targeting PA levels.…”

Section: Introductionmentioning

confidence: 97%

Physical Activity Levels and Predictors during COVID-19 Lockdown among Lebanese Adults: The Impacts of Sociodemographic Factors, Type of Physical Activity and Work Location

et al. 2023

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Background: Although effective against COVID-19, national lockdowns have several deleterious behavioral and health effects, including physical inactivity. The objective of this study is to assess physical activity (PA) levels during lockdown and the predictors of PA among Lebanese adults, while comparing classical statistics to machine learning models. Methods: Data were collected using an online questionnaire, with PA being evaluated through the “International Physical Activity Questionnaire” (IPAQ)—long form. Machine learning models were applied to predict total PA ≥ 600 MET·min/week. Results: Among 795 participants, while 67.5% auto-declared a decrease in PA level during lockdown, 36.2% did not meet the minimum recommendations for PA. Multivariate analysis showed that participants who went to their workplace during lockdown had significantly higher total and job-related PA, higher walking and moderate PA, and lower sitting time. PA level and intensity increased with age, while sitting time decreased. Participants who practiced a combination of both outdoor and at-home workouts had higher total, housework and leisure-related PA, and higher moderate and vigorous-intensity PA. Machine learning models confirmed these findings as well as the importance of outdoor activity for total PA levels, with Random Forest being the highest-performing model. Conclusions: Bringing to light the levels of physical inactivity and sedentary behavior, this study highlighted the importance of outdoor activity in contributing to PA.

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State-of-the-Art of Artificial Intelligence and Big Data Analytics Reviews in Five Different Domains: A Bibliometric Summary

Cited by 55 publications

References 139 publications

Machine Learning Enhanced Framework for Big Data Modeling with Application in Industry 4.0

Machine Learning Enhanced Framework for Big Data Modeling with Application in Industry 4.0

Defining the Balearic Islands’ Tourism Data Space: An Approach to Functional and Data Requirements

Physical Activity Levels and Predictors during COVID-19 Lockdown among Lebanese Adults: The Impacts of Sociodemographic Factors, Type of Physical Activity and Work Location

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