DA-LSTM is a dual-attention LSTM framework used to predict viral particle concentrations and estimate removal efficiencies across wastewater matrices. The abstract states that it is designed to remain accurate on unseen matrices despite process drifts.
First-pass extracted concept
DA-LSTM
Candidate: toolkit itemType: computation method1 source documents6 linked claims
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Aliases
Dual-attention long short-term memory network
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Linked Claims
DA-LSTM zero-shot generalization using MMCM achieved mean average R2 values of 0.91 in sand wastewater matrices and 0.97 in MBR wastewater matrices in region R1, and 0.97 across the chlorinated effluent treatment process in region R2.
Quoted textsource-backed
The results showed that DA-LSTM zero-shot generalization achieved remarkable viral particles prediction performance using MMCM with a mean average coefficient of determination R2 of 0.91, and 0.97 across the sand, and MBR wastewater matrices in region R1, respectively, and R2 of 0.97 across the chlorinated effluent treatment process in region R2.
Tests on total viral prediction across municipal wastewater treatment plants in two additional Saudi Arabian regions confirmed the effectiveness of DA-LSTM for predicting viral particles across wastewater matrices and enhancing regional zero-shot generalization.
Quoted textsource-backed
Tests on total viral prediction across municipal WWTPs located in two other regions in Saudi Arabia confirmed the DA-LSTM's effectiveness in predicting viral particle across WMs and its ability to enhance zero-shot generalization performance at the regional level.
DA-LSTM combines attention mechanisms to adaptively adjust feature weights and increase long-term memory, enabling accuracy and robustness across unseen wastewater matrices.
Quoted textsource-backed
The DA-LSTM combines attention mechanisms to adaptively adjust the weights of the features and increase the long-term memory, enabling accuracy and robustness across unseen WMs.
DA-LSTM with generative models was proposed to predict viral particles and evaluate removal efficiencies across AeMBRs while handling effluent processing drifts.
Quoted textsource-backed
Dual-attention long short-term memory network (DA-LSTM) with new generative models was proposed to predict viral particles and evaluate the removal efficiencies across AeMBRs, thereby handling effluent processing drifts.
DA-LSTM demonstrated significant adaptability to unseen data across different wastewater matrices and maintained robust performance despite effluent drifts.
Quoted textsource-backed
The DA-LSTM model demonstrated significant adaptability to unseen data across different WMs, maintaining robust performance despite the effluent drifts.
Predicting viral particles on new unseen data across wastewater matrices in AeMBR-based wastewater treatment plants remains an open challenge because of process drifts during treatment stages.
Quoted textsource-backed
Predicting viral particles on new unseen data across wastewater matrices (WMs) in aerobic membrane bioreactor (AeMBR)-based wastewater treatment plants (WWTPs) remains an open challenge due to the process drifts involved in the treatment stages.