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Learning and prediction by LSTM model.

Journal: PLOS One

Article Title: A new method of off-site inverse carbon accounting and its application in agriculture carbon measurement

doi: 10.1371/journal.pone.0334270

Figure Lengend Snippet: Learning and prediction by LSTM model.

Article Snippet: To model and forecast these trajectories, we employed a univariate LSTM regression model implemented in MATLAB (Deep Learning Toolbox).

Techniques:

Samples generated by LSTM model.

Journal: PLOS One

Article Title: A new method of off-site inverse carbon accounting and its application in agriculture carbon measurement

doi: 10.1371/journal.pone.0334270

Figure Lengend Snippet: Samples generated by LSTM model.

Article Snippet: To model and forecast these trajectories, we employed a univariate LSTM regression model implemented in MATLAB (Deep Learning Toolbox).

Techniques: Generated

Proposed AI-empowered infrastructure resilience framework for urban systems. The framework incorporates multimodal sensors, real-time edge-AI preprocessing, deep learning fusion, such as LSTM + GNN, RSI scoring, anomaly detection, and feedback mechanisms according to the principles of SDG-9.

Journal: Frontiers in Artificial Intelligence

Article Title: Multimodal AI fusion for infrastructure resilience: real-time urban analytics framework aligned with SDG-9

doi: 10.3389/frai.2025.1612431

Figure Lengend Snippet: Proposed AI-empowered infrastructure resilience framework for urban systems. The framework incorporates multimodal sensors, real-time edge-AI preprocessing, deep learning fusion, such as LSTM + GNN, RSI scoring, anomaly detection, and feedback mechanisms according to the principles of SDG-9.

Article Snippet: Furthermore, by only requiring minor reconfiguration, the LSTM-GNN model from structured datasets is able to be fine-tuned for Chennai as well as Rotterdam.

Techniques:

Performance of the LSTM + GNN hybrid model over Singapore, Chennai, and Rotterdam. The framework achieves auto F1-scores across cities, indicating cross-regional generalizability.

Journal: Frontiers in Artificial Intelligence

Article Title: Multimodal AI fusion for infrastructure resilience: real-time urban analytics framework aligned with SDG-9

doi: 10.3389/frai.2025.1612431

Figure Lengend Snippet: Performance of the LSTM + GNN hybrid model over Singapore, Chennai, and Rotterdam. The framework achieves auto F1-scores across cities, indicating cross-regional generalizability.

Article Snippet: Furthermore, by only requiring minor reconfiguration, the LSTM-GNN model from structured datasets is able to be fine-tuned for Chennai as well as Rotterdam.

Techniques:

F1-score for five models on three structured and a real-world dataset for Chennai. The LSTM+ GNN hybrid model also shows that the performance change is small, which indicates that it is robust to the noise of the urban environment.

Journal: Frontiers in Artificial Intelligence

Article Title: Multimodal AI fusion for infrastructure resilience: real-time urban analytics framework aligned with SDG-9

doi: 10.3389/frai.2025.1612431

Figure Lengend Snippet: F1-score for five models on three structured and a real-world dataset for Chennai. The LSTM+ GNN hybrid model also shows that the performance change is small, which indicates that it is robust to the noise of the urban environment.

Article Snippet: Furthermore, by only requiring minor reconfiguration, the LSTM-GNN model from structured datasets is able to be fine-tuned for Chennai as well as Rotterdam.

Techniques: