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| 1 | Extraction optimization of watermelon seed protein using response surface methodology显示文摘 | AliAbas Wani Devinder Kaur Idrees Ahmed | 2007 | Journal of Food Science and Technology2007，，10： | 1 |
| 2 | Extraction optimization of watermelon seed protein using response surface methodology显示文摘 | Ali Abas Wani Devinder Kaur Idrees Ahmed | 2008 | LWT-Food Science and Technology2008，41，8： | 1 |
| 3 | Effect of extraction conditions on lycopene extractions from tomato processing waste skin using response surface methodology显示文摘 | Devinder Kaur Ali Abas Wani D.P.S. Oberoi D.S. Sogi | 2007 | Food Chemistry2007，，2： | 1 |
| 4 | Effect of extraction conditions on lycopene extractions from tomato processing waste skin using response surface methodology 显示文摘 | Devinder Kaur Ali Abas Wani Oberoi D P S | 2008 | Food Chemistry2008，，2： | 1 |
| 5 | Extraction optimization of watermelon seed protein using response surface methodology显示文摘 | Ali Abas Wani Devinder Kaur Idrees Ahmed | 2008 | LWT-Food Science and Technology2008，41，8： | 1 |
| 6 | Extraction optimization of watermelon seed protein using response surface methodology显示文摘 | Ali Abas Wani Devinder Kaur Idress Ahmed | 2008 | Swiss Society of Food Science and Technology2008，41，： | 1 |
| 7 | extraction optimization of watermelon seed protein using response surface methodology显示文摘 | Ali Abas Wani Devinder Kaur Idrees Ahmed | 2007 | Journal of Food Science and Technology2007，10，： | 1 |
| 8 | Effect of extraction conditions on lycopene extractions from tomato processing waste skin using response surface methodology显示文摘 | Devinder Kaur Ali Abas Wani D.P.S. Oberoi D.S. Sogi | 2007 | Food Chemistry2007，，2： | 1 |
| 9 | Extraction optimization of watermelon seed protein using response surface methodology显示文摘 | Ali Abas Wani Devinder Kaur Idrees Ahmed D.S. Sogi | 2007 | LWT - Food Science and Technology2007，，8： | 1 |
| 10 | Flotation-cum- sedimentation system for skin and seed separation from to- mato pomace 显示文摘 | Devinder Kaur D S Sogi S K Garg | 2005 | Journal of Food Engineering2005，71，4： | 1 |
| 11 | A VAE-Bayesian deep learning scheme for solar power generation forecasting based on dimensionality reduction显示文摘The advancements in distributed generation(DG)technologies such as solar panels have led to a widespread integration of renewable power generation in modern power systems.However,the intermittent nature of renewable energy poses new challenges to the network operational planning with underlying uncertainties.This paper proposes a novel probabilistic scheme for renewable solar power generation forecasting by addressing data and model parameter uncertainties using Bayesian bidirectional long short-term memory(BiLSTM)neural networks,while handling the high dimensionality in weight parameters using variational auto-encoders(VAE).The forecasting performance of the proposed method is evaluated using various deterministic and probabilistic evaluation metrics such as root-mean square error(RMSE),Pinball loss,etc.Furthermore,reconstruction error and computational time are also monitored to evaluate the dimensionality reduction using the VAE component.When compared with benchmark methods,the proposed method leads to significant improvements in weight reduction,i.e.,from 76,4224 to 2,022 number of weight parameters,quantifying to 97.35%improvement in weight parameters reduction and 37.93%improvement in computational time for 6 months of solar power generation data. | Devinder Kaur Shama Naz Islam MdApel Mahmud Md.Enamul Haque Adnan Anwar | 2023 | Energy and AI2023，14，4： | 0 |
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