Latest trends in land use and land cover monitoring using deep learning

Elsevier eBooks, Год журнала: 2025, Номер unknown, С. 237 - 248

Опубликована: Янв. 1, 2025

Язык: Английский

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Application of geographic information system and remote sensing technology in ecosystem services and biodiversity conservation

Elsevier eBooks, Год журнала: 2025, Номер unknown, С. 97 - 122

Опубликована: Янв. 1, 2025

Язык: Английский

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A Review of Current Trends in Greenhouse Gas Emission Prediction through Machine Intelligence Learning Techniques and Future Challenges

Опубликована: Фев. 21, 2025

Язык: Английский

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Carbon Dioxide Emissions Prediction of Selected Developing Countries Using Artificial Neural Network

Journal of the Knowledge Economy, Год журнала: 2025, Номер unknown

Опубликована: Март 25, 2025

Язык: Английский

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Long Short-Term Memory and Kolmogorov Arnold Network Theorem for epileptic seizure prediction

Engineering Applications of Artificial Intelligence, Год журнала: 2025, Номер 154, С. 110757 - 110757

Опубликована: Апрель 29, 2025

Язык: Английский

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Innovative Techniques for Image Clustering and Classification

Advances in computational intelligence and robotics book series, Год журнала: 2025, Номер unknown, С. 181 - 222

Опубликована: Март 7, 2025

The chapter is a review of techniques in deep leaning for tasks such as classification and clustering. Basically, due to the discussion two main topics learning, divided into parts, one discussing clustering methods first basic understanding method made then moving towards autoencoder based architectures that includes variational autoencoders (VAE), k-means with autoencoders, self-organizing maps, spectral DBSCAN. other part focused on methods, where architecture convolutional neural network (CNN) discussed, proceeding ResNet, DenseNet EfficientNet, little touch transformer-based CNN these vision transformers capsule networks are mentioned. A comparison both i.e., will make it clearer how different from another.

Язык: Английский

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Innovative Deep Learning Image Technologies

Advances in computational intelligence and robotics book series, Год журнала: 2025, Номер unknown, С. 145 - 180

Опубликована: Март 7, 2025

The chapter gives an overview of the applications deep learning and image processing in different industries medicine, automobiles, entertainment, security. Multiple advanced techniques such as CNN, GAN, ViT that have become handy analysis processing. From medical diagnostics to autonomous vehicles, environmental monitoring, surveillance, its show impact on accuracy efficiency. It also discusses critical ethical issues, data privacy, model biases, energy consumption, points out some possible solutions reduce those effects. In general, this contribution provides a advances related by potential for further innovative developments wide range applications.

Язык: Английский

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Application of CNN-LSTM Algorithm for PM2.5 Concentration Forecasting in the Beijing-Tianjin-Hebei Metropolitan Area

Atmosphere, Год журнала: 2023, Номер 14(9), С. 1392 - 1392

Опубликована: Сен. 3, 2023

Prolonged exposure to high concentrations of suspended particulate matter (SPM), especially aerodynamic fine that is ≤2.5 μm in diameter (PM2.5), can cause serious harm human health and life via the induction respiratory diseases lung cancer. Therefore, accurate prediction PM2.5 important for management governmental environmental decisions. However, time-series processing concentration based only on a single region special time period less explanatory, thus, spatial-temporal applicability model more restricted. To address this problem, paper constructs optimization Convolutional Neural Networks-Long Short-Term Memory (CNN-LSTM). Hourly data atmospheric pollutants, meteorological parameters, Precipitable Water Vapor (PWV) 10 cities Beijing-Tianjin-Hebei metropolitan area during 1–30 September 2021/2022 were used as training set, 1–7 October validation. The experimental results show CNN-LSTM optimizes average root mean square error (RMSE) by 25.52% 14.30%, absolute (MAE) 26.23% 15.01%, percentage (MAPE) 35.64% 16.98%, compared widely Back Propagation Network (BPNN) Long (LSTM) models. In summary, superior terms has highest accuracy area. study provide reference relevant departments predict its trend specific periods.

Язык: Английский

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The short- and long-run causal correlation between green finance, renewable energy consumption, and economic growth

Energy & Environment, Год журнала: 2023, Номер unknown

Опубликована: Июль 12, 2023

We propose a vector error correction model to explore the causal correlation between green finance, economic growth, and renewable energy consumption from both short- long-run perspectives empirically evaluate efficacy of finance policies. Based on time-series data 2000 2020, we use unit root test method examine time-varying trends cointegration for data. find that has negative relationship with emissions but is positively correlated growth. Green driving factor behind increasing utilization in China. CO 2 per GDP decreased by 1.077% every 1% increase development. Although share increased 1%, 0.55%. Therefore, significant decreasing emissions; it impact sector must be addressed financial policy, stability, sustainability. categorized which refers carbon innovations such as trusteeship, improve market demand eventually develop industries expand number emission-control industries.

Язык: Английский

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A bibliometric analysis of deep learning applications in climate change research using CiteSpace

J of Atmosphere and Oceanography Environment, Год журнала: 2024, Номер 11(1)

Опубликована: Янв. 4, 2024

<p>In recent years, artificial intelligence, particularly deep learning, has garnered significant attention among practitioners and scholars in meteorology atmospheric sciences, leading to a substantial body of literature. This study aims delineate the present research status trends climate innovation through CiteSpace visual analysis. To comprehend current landscape, prevalent terms, frontiers learning for change (DLCCR) within applications, we gathered 256 published papers spanning from 2018 2022 Web Science (WOS) core database. Employing these articles, conducted co-authorship, co-citation, keyword co-occurrence analyses. The findings unveiled steady rise DLCCR publications over last five years. However, correlation between high yield high-citation authorship appears inconsistent weak. Notably, prolific authors this domain included Zhang Z.L. Bonnet P. Furthermore, institutions such as Chinese Academy Sciences (China), le Centre National de la Recherche Scientifique (France), Nanjing University Information Technology (China) have played pivotal roles advancing DLCCR. primary contributors high-yield countries primarily cluster select group comprising China, USA, South Korea, Germany. Identifying information gaps numerical weather, physics processes, algorithm parametrizations, extreme events, our underscores necessity future researchers focus on related subjects. provides valuable insights into hotspots, developmental trajectories, emerging frontiers, thereby delineating knowledge structure field highlighting directions further research.</p>

Язык: Английский

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