Research graph
References from A quantum-inspired multi-objective learning framework for real-time sustainable aquaculture water quality prediction. Local targets link to admitted publications; unresolved targets remain external evidence.
Energy optimization in large-scale recirculating aquaculture systems: implementation and performance analysis of a hybrid deep learning approach
10.1016/j.aquaeng.2025.102561 · 2025 · External reference
Harnessing crop science innovations for sustainable growth: building a predictive agriculture framework for long-term business development
10.56367/oag-041-11148 · 2024 · External reference
Water quality classification framework for IoT-enabled aquaculture ponds using deep learning based flexible temporal network model
10.1007/s12145-025-01857-2 · 2025 · External reference
Environmental parameters in aquaculture: temperature, pH, oxygen, and turbidity measurements
2024 · External reference
Intelligent prediction and continuous monitoring of water quality in aquaculture: integration of machine learning and internet of things for sustainable management
10.3390/w17010082 · 2025 · External reference
Rainfall prediction: a comparative analysis of modern machine learning algorithms for time-series forecasting
10.1016/j.mlwa.2021.100204 · 2022 · External reference
“Water quality parameter”
2023 · External reference
The contribution of fisheries and aquaculture to the global protein supply: aquaculture and global protein
10.1007/s12571-021-01246-9 · 2022 · External reference
IoT-based fish farm water quality monitoring system
10.3390/s22176700 · 2022 · External reference
Rapid real-time prediction techniques for ammonia and nitrite in high-density shrimp farming in recirculating aquaculture systems
10.3390/fishes9100386 · 2024 · External reference
Development of smart aquaculture farm management system using IoT and AI-based surrogate models
10.1016/j.jafr.2022.100357 · 2022 · External reference
Aquatic water management
2022 · External reference
An advanced deep learning model for predicting water quality index
10.1016/j.ecolind.2024.111806 · 2024 · External reference
Predictive modelling of aquaculture water quality using IoT and advanced machine learning algorithms
10.1016/j.rechem.2025.102456 · 2025 · External reference
Hybrid deep learning and machine learning framework for high-precision water quality prediction in urban systems
10.1007/s10668-025-06447-2 · 2025 · External reference
Assessment of water supply and demand in Peninsular Malaysia
10.4090/juee.2022.v16n2.101109 · 2022 · External reference
Data augmentation technique based on improved time-series generative adversarial networks for power load forecasting in recirculating aquaculture systems
10.3390/su162310721 · 2024 · External reference
Enhancing water quality index prediction accuracy in Mranti lake and rivers in Malaysia using regression forest model
10.1007/s13201-025-02705-w · 2025 · External reference
Redefining aquaculture safety with artificial intelligence: design innovations, trends, and future perspectives
10.3390/fishes10030088 · 2025 · External reference
Prediction of the dissolved oxygen content in aquaculture based on the CNN-GRU hybrid neural network
10.3390/w16243547 · 2024 · External reference
Freshwater fishpond multisensor water quality dataset for multiclass degradation classification (2153 samples)
2026 · External reference
Machine learning on low-cost edge devices for real-time water quality prediction in Tilapia aquaculture
10.3390/s25196159 · 2025 · External reference
Unresolved reference
External reference
Smart factory based on IIoT: applications, communication networks and cybersecurity
10.5815/ijwmt.2024.04.03 · 2024 · External reference
IoT-driven ensemble machine learning model for accurate dissolved oxygen prediction in aquaculture
10.1007/s43926-025-00201-w · 2025 · External reference
10.1038/s41598-025-25527-x
10.1038/s41598-025-25527-x · 2025 · External reference
An adaptive HMM method to simulate and forecast ocean chemistry data in aquaculture
10.1016/j.compag.2023.107767 · 2023 · External reference
The contribution of aquaculture systems to global aquaculture production
10.1111/jwas.12963 · 2023 · External reference
10.3390/su17115084
10.3390/su17115084 · 2025 · External reference
A Water quality prediction model based on long short-term memory networks and optimization algorithms
10.1109/access.2024.3487348 · 2024 · External reference
Application of a QPSO-optimized CNN-LSTM model in water quality prediction
10.1007/s43832-024-00161-2 · 2024 · External reference
A Combined model for water quality prediction based on VMD-TCN-ARIMA optimized by WSWOA
10.3390/w15244227 · 2023 · External reference
Hybrid deep learning and machine learning framework for high-precision water quality prediction in urban systems
10.1007/s10668-025-06447-2 · ExternalCitation · doi-reference
Water quality classification framework for IoT-enabled aquaculture ponds using deep learning based flexible temporal network model
10.1007/s12145-025-01857-2 · ExternalCitation · doi-reference
The contribution of fisheries and aquaculture to the global protein supply: aquaculture and global protein
10.1007/s12571-021-01246-9 · ExternalCitation · doi-reference
Enhancing water quality index prediction accuracy in Mranti lake and rivers in Malaysia using regression forest model
10.1007/s13201-025-02705-w · ExternalCitation · doi-reference
Application of a QPSO-optimized CNN-LSTM model in water quality prediction
10.1007/s43832-024-00161-2 · ExternalCitation · doi-reference
IoT-driven ensemble machine learning model for accurate dissolved oxygen prediction in aquaculture
10.1007/s43926-025-00201-w · ExternalCitation · doi-reference
Energy optimization in large-scale recirculating aquaculture systems: implementation and performance analysis of a hybrid deep learning approach
10.1016/j.aquaeng.2025.102561 · ExternalCitation · doi-reference
An adaptive HMM method to simulate and forecast ocean chemistry data in aquaculture
10.1016/j.compag.2023.107767 · ExternalCitation · doi-reference
An advanced deep learning model for predicting water quality index
10.1016/j.ecolind.2024.111806 · ExternalCitation · doi-reference
Development of smart aquaculture farm management system using IoT and AI-based surrogate models
10.1016/j.jafr.2022.100357 · ExternalCitation · doi-reference
Rainfall prediction: a comparative analysis of modern machine learning algorithms for time-series forecasting
10.1016/j.mlwa.2021.100204 · ExternalCitation · doi-reference
Predictive modelling of aquaculture water quality using IoT and advanced machine learning algorithms
10.1016/j.rechem.2025.102456 · ExternalCitation · doi-reference
10.1038/s41598-025-25527-x
10.1038/s41598-025-25527-x · ExternalCitation · doi-reference
A Water quality prediction model based on long short-term memory networks and optimization algorithms
10.1109/access.2024.3487348 · ExternalCitation · doi-reference
The contribution of aquaculture systems to global aquaculture production
10.1111/jwas.12963 · ExternalCitation · doi-reference
Redefining aquaculture safety with artificial intelligence: design innovations, trends, and future perspectives
10.3390/fishes10030088 · ExternalCitation · doi-reference
Rapid real-time prediction techniques for ammonia and nitrite in high-density shrimp farming in recirculating aquaculture systems
10.3390/fishes9100386 · ExternalCitation · doi-reference
IoT-based fish farm water quality monitoring system
10.3390/s22176700 · ExternalCitation · doi-reference
Machine learning on low-cost edge devices for real-time water quality prediction in Tilapia aquaculture
10.3390/s25196159 · ExternalCitation · doi-reference
Data augmentation technique based on improved time-series generative adversarial networks for power load forecasting in recirculating aquaculture systems
10.3390/su162310721 · ExternalCitation · doi-reference
10.3390/su17115084
10.3390/su17115084 · ExternalCitation · doi-reference
A Combined model for water quality prediction based on VMD-TCN-ARIMA optimized by WSWOA
10.3390/w15244227 · ExternalCitation · doi-reference
Prediction of the dissolved oxygen content in aquaculture based on the CNN-GRU hybrid neural network
10.3390/w16243547 · ExternalCitation · doi-reference
Intelligent prediction and continuous monitoring of water quality in aquaculture: integration of machine learning and internet of things for sustainable management
10.3390/w17010082 · ExternalCitation · doi-reference
Assessment of water supply and demand in Peninsular Malaysia
10.4090/juee.2022.v16n2.101109 · ExternalCitation · doi-reference
Harnessing crop science innovations for sustainable growth: building a predictive agriculture framework for long-term business development
10.56367/oag-041-11148 · ExternalCitation · doi-reference
Smart factory based on IIoT: applications, communication networks and cybersecurity
10.5815/ijwmt.2024.04.03 · ExternalCitation · doi-reference
