Exploring the Intersection of Machine Learning and Big Data: A Survey

Machine Learning and Knowledge Extraction, Journal Year: 2025, Volume and Issue: 7(1), P. 13 - 13

Published: Feb. 7, 2025

The integration of machine learning (ML) with big data has revolutionized industries by enabling the extraction valuable insights from vast and complex datasets. This convergence fueled advancements in various fields, leading to development sophisticated models capable addressing complicated problems. However, application ML environments presents significant challenges, including issues related scalability, quality, model interpretability, privacy, handling diverse high-velocity data. survey provides a comprehensive overview current state applications data, systematically identifying key challenges recent field. By critically analyzing existing methodologies, this paper highlights gaps research proposes future directions for scalable, interpretable, privacy-preserving techniques. Additionally, addresses ethical societal implications emphasizing need responsible equitable approaches harnessing these technologies. presented aim guide contribute ongoing discourse on

Language: Английский

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Post-Tornado Automated Building Damage Evaluation and Recovery Prediction by Integrating Remote Sensing, Deep Learning, and Restoration Models

Sustainable Cities and Society, Journal Year: 2025, Volume and Issue: unknown, P. 106286 - 106286

Published: March 1, 2025

Language: Английский

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Long-term tracking of urban structure and analysis of its impact on urban heat stress: a case study of Xi’an, China

Ecological Indicators, Journal Year: 2025, Volume and Issue: 174, P. 113418 - 113418

Published: April 9, 2025

Language: Английский

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Machine Learning Techniques to Map the Impact of Urban Heat Island: Investigating the City of Jeddah

Land, Journal Year: 2023, Volume and Issue: 12(6), P. 1159 - 1159

Published: May 31, 2023

Over the last decades, most agricultural land has been converted into residential colonies to accommodate rapid population expansion. Population growth and urbanization result in negative consequences on environment. Such experienced various environmental issues due increases. expansion a big impact worsening residences soon long term, as is projected increase more more. One such issue urban heat island (UHI), which computed based surface temperature (LST). The UHI effect fundamental anthropogenic impacts local areas, particularly rapidly growing cities. This unplanned shifts use cover (LUALC) at level, results climate condition variations. Therefore, proper planning concrete information best policy run remedy these issues. In this study, we attempt map out phenomena using machine learning (ML) algorithms, including bagging random subspace. proposed research also fulfills sustainable development goals (SDGs) requirement. We exploit correlation regression methods understand relationship between biophysical composition effect. Our findings indicate that megacity of Jeddah, Saudi Arabia, from 2000 2021, area enlarged by about 80%, while increased overall. Impervious surfaces significantly effect, vegetation water bodies have implications for More than 80% total parts Jeddah classified extremely high conditions, determined subspace models. particular, megacity’s south, north, central-east were categorized very conditions. not only expected assist understanding spatial patterns but planners policymakers planning. It will help ensure management improve life quality.

Language: Английский

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Revealing public attitudes toward mobile cabin hospitals during Covid-19 pandemic: Sentiment and topic analyses using social media data in China

Sustainable Cities and Society, Journal Year: 2024, Volume and Issue: 107, P. 105440 - 105440

Published: April 12, 2024

Language: Английский

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Analysis on the impact of smart city construction on urban greenness in China's megacities

Journal of Environmental Management, Journal Year: 2024, Volume and Issue: 355, P. 120568 - 120568

Published: March 1, 2024

Language: Английский

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Simultaneous extraction of spatial and attributional building information across large-scale urban landscapes from high-resolution satellite imagery

Sustainable Cities and Society, Journal Year: 2024, Volume and Issue: 106, P. 105393 - 105393

Published: April 4, 2024

Language: Английский

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The Role of Digital Technologies in Corporate Sustainability: A Bibliometric Review and Future Research Agenda

Journal of risk and financial management, Journal Year: 2024, Volume and Issue: 17(11), P. 509 - 509

Published: Nov. 14, 2024

This study aims to analyze trends, pioneers, emerging issues, and potential future research in the field of digital technologies such as blockchain, artificial intelligence, big data, fintech, transformation for corporate sustainability. Using VOSviewer, R-studio, BiblioMagika, this bibliometric review analyses 1251 articles published between 1995 2024 from Scopus database. It highlights gaps knowledge possible areas further Based on findings, it can be determined that recent scholarly work has focused topics digitalisation sustainability, AI sustainable development, blockchain environmental technology, financial technology green innovation, energy policy carbon emissions. is useful helping scholars identify understand current trends

Language: Английский

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Land-Use Change Prediction in Dam Catchment Using Logistic Regression-CA, ANN-CA and Random Forest Regression and Implications for Sustainable Land–Water Nexus

Sustainability, Journal Year: 2024, Volume and Issue: 16(4), P. 1699 - 1699

Published: Feb. 19, 2024

For sustainable water resource management within dam catchments, accurate knowledge of land-use and land-cover change (LULCC) the relationships with variability is necessary. To improve LULCC prediction, this study proposes use a random forest regression (RFR) model, in comparison logistic regression–cellular automata (LR-CA) artificial neural network–cellular (ANN-CA), for prediction (2019–2030) Gaborone catchment (Botswana). RFR proposed as it able to capture existing potential interactions between LULC intensity their nonlinear change-driving factors. forecasting, driving factors comprised physiographic variables (elevation, slope aspect) proximity-neighborhood (distances bodies, roads urban areas). In simulating historical (1986–2019) at 5-year time steps, outperformed ANN-CA LR-CA models respective percentage accuracies 84.9%, 62.1% 60.7%. Using predicted LULCCs were determined vegetation (−8.9%), bare soil (+8.9%), built-up (+2.49%) cropland (−2.8%), bodies exhibiting insignificant change. The correlation land (built-up areas) depicted an increasing population against decreasing capacity. approach has deriving land–water nexus, which can aid formulation monitoring development strategies.

Language: Английский

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The role of foreign MNEs in China’s twin transition: a study on the organization of green and digital innovation processes

Competitiveness Review An International Business Journal incorporating Journal of Global Competitiveness, Journal Year: 2024, Volume and Issue: 34(5), P. 879 - 895

Published: April 13, 2024

Purpose The purpose of this study is to shed light on the twin transition in China organization innovation processes artificial intelligence (AI) and green technology (GT) development understand role foreign multinationals Chinese systems. Design/methodology/approach A qualitative research approach used by interviewing executives from German with expertise AI GT China. In total, 11 semi-structured interviews were conducted companies, data analysed a thematic text analysis. Findings findings show that applications for are primarily developed cross-company projects led local regional authorities through industrial districts clusters. either being integrated, remaining autonomous or excluded these processes. Originality/value This paper aims fill gap literature providing one first towards exploring integration multinational enterprises cluster organizations. To best author’s knowledge, studies perspective emerging economies.

Language: Английский

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