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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Prediction of Greenhouse Indoor Air Temperature Using Artificial Intelligence (AI) Combined with Sensitivity Analysis

Horticulturae, Год журнала: 2023, Номер 9(8), С. 853 - 853

Опубликована: Июль 26, 2023

Greenhouses are essential for agricultural production in unfavorable climates. Accurate temperature predictions critical controlling Heating, Ventilation, Air-Conditioning, and Dehumidification (HVACD) lighting systems to optimize plant growth reduce financial losses. In this study, several machine models were employed predict indoor air an even-span Mediterranean greenhouse. Radial Basis Function (RBF), Support Vector Machine (SVM), Gaussian Process Regression (GPR) applied using external parameters such as outside air, relative humidity, wind speed, solar radiation. The results showed that RBF model with the LM learning algorithm outperformed SVM GPR models. had high accuracy reliability RMSE of 0.82 °C, MAPE 1.21%, TSSE 474.07 EF 1.00. prediction can help farmers manage their crops resources efficiently energy inefficiencies lower yields. integration into greenhouse control lead significant savings cost reductions.

Язык: Английский

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Advanced hybrid neural network techniques for minimizing gas turbine emissions

World Journal of Engineering, Год журнала: 2024, Номер unknown

Опубликована: Окт. 23, 2024

Purpose Emissions have significant environmental impacts. Hence, minimizing emissions is essential. This study aims to use a hybrid neural network model predict carbon monoxide (CO) and nitrogen oxide (NOx) from gas turbines (GTs) enhance emission prediction for GTs in predictive monitoring systems (PEMS). Design/methodology/approach The architecture combines convolutional networks (CNN) bidirectional long-short-term memory (Bi-LSTM) called CNN-BiLSTM with modified extrinsic attention regression. Over five years, data GT power plant was uploaded Google Colab, split into training testing sets (80:20), evaluated using test matrices. model’s performance benchmarked against state-of-the-art methodologies. Findings showed promising results CO NOx emissions. predictions had slight underestimation bias of −0.01, root mean-squared error (RMSE) 0.064, mean absolute (MAE) 0.04 R 2 0.82. an RMSE 0.051, MAE 0.036, 0.887 overestimation +0.01. Research limitations/implications While the demonstrates relative accuracy predictions, there potential further improvement future research. Practical implications Implementing real-time PEMS establishing continuous feedback loop will ensure real-world applications, functioning reduce emissions, fuel consumption running costs. Social Accurate support stricter standards, promote sustainable development goals healthier societal environment. Originality/value paper presents novel approach that integrates CNN Bi-LSTM networks. It considers both spatial temporal mitigate previous shortcomings.

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 impact of urbanization, energy consumption, industrialization on carbon emissions in SAARC countries: a policy recommendations to achieve sustainable development goals

Environment Development and Sustainability, Год журнала: 2024, Номер unknown

Опубликована: Сен. 5, 2024

Язык: Английский

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An examination of daily CO2 emissions prediction through a comparative analysis of Machine learning, Deep learning, and Statistical models

Research Square (Research Square), Год журнала: 2024, Номер unknown

Опубликована: Ноя. 19, 2024

Human-induced global warming, primarily attributed to the rise in atmospheric CO₂, poses a substantial risk survival of humanity. While most research focuses on predicting annual CO₂ emissions, which are crucial for setting long-term emission mitigation targets, precise prediction daily emissions is equally vital short-term targets. This study examines performance 14 models data from 1/1/2022 30/9/2023 across top four polluting regions (China, USA, India, and EU27&UK). The used comprise statistical (ARMA, ARIMA, SARMA, SARIMA), three machine learning (Support Vector Machine - SVM, Random Forest RF, Gradient Boosting GB), seven deep (Artificial Neural Network ANN, Recurrent variations such as Gated Unit GRU, Long Short-Term Memory LSTM, Bidirectional-LSTM BILSTM, hybrid combinations CNN-RNN). Performance evaluation employs metrics (R², MAE, RMSE, MAPE). results show that (ML) (DL) models, with higher R² (0.714–0.932) l ower RMSE (0.480 − 0.247) values, respectively, outperformed model, had (-0.060–0.719) (1.695 0.537) all regions. ML DL was further enhanced by differencing, technique improves accuracy ensuring stationarity creating additional features patterns model can learn from. Additionally, applying ensemble techniques bagging voting improved about 9.6%, while CNN-RNN RNN models. In summary, both relatively similar. However, due high computational requirements associated recommended using bagging. assist accurately forecasting aiding authorities targets reduction.

Язык: Английский

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