Citations (17)
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Diego Elustondo, Nirundorn Matan, Timothy Langrish & Shusheng Pang. (2023) Advances in wood drying research and development. Drying Technology 41:6, pages 890-914.
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Md. Imran H. Khan, Shyam S. Sablani, M. U. H. Joardder & M. A. Karim. (2022) Application of machine learning-based approach in food drying: opportunities and challenges. Drying Technology 40:6, pages 1051-1067.
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Alex Martynenko. (2018) Artificial intelligence: Is it a good fit for drying?. Drying Technology 36:8, pages 891-892.
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Juan Wu, Simon X. Yang & Fengchun Tian. (2017) An adaptive neuro-fuzzy approach to bulk tobacco flue-curing control process. Drying Technology 35:4, pages 465-477.
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Mortaza Aghbashlo, Soleiman Hosseinpour & Arun S. Mujumdar. (2015) Application of Artificial Neural Networks (ANNs) in Drying Technology: A Comprehensive Review. Drying Technology 33:12, pages 1397-1462.
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FábioB. Freire, GustavoN. A. Vieira, JoséT. Freire & ArunS. Mujumdar. (2014) Trends in Modeling and Sensing Approaches for Drying Control. Drying Technology 32:13, pages 1524-1532.
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Emel Çelik, Mehmet Akif Koç, Nezaket Parlak & Yusuf Çay. Prediction of the capacitance of the corn drying process parameter using adaptive- neuro-fuzzy intelligent technique with experimental validation. Drying Technology 0:0, pages 1-24.
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Articles from other publishers (10)
Ruchika Zalpouri, Manpreet Singh, Preetinder Kaur, Sukhmeet Singh, Satish Kumar & Amrit Kaur. (2023) Mathematical and artificial neural network modelling for refractance window drying kinetics of coriander (Coriandrum sativum L.) followed by the determination of energy consumption, mass transfer parameters and quality. Biomass Conversion and Biorefinery.
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Hongchao Wang, Gang Che, Lin Wan, Xin Wang & Hao Tang. (2022)
Combination of
LF‐NMR
and
BP‐ANN
to monitor the moisture content of rice during hot‐air drying
. Journal of Food Process Engineering 45:9.
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Joshua O. Ighalo, Adewale George Adeniyi & Chinenye Adaobi Igwegbe. 2022. Current Trends and Advances in Computer-Aided Intelligent Environmental Data Engineering. Current Trends and Advances in Computer-Aided Intelligent Environmental Data Engineering
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Maleki, Ghazvini, Ahmadi, Maddah & Shamshirband. (2019) Moisture Estimation in Cabinet Dryers with Thin-Layer Relationships Using a Genetic Algorithm and Neural Network. Mathematics 7:11, pages 1042.
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Silvia Bravo & Ángel H. Moreno. (2019) Prediction Model Based on Neural Networks for Microwave Drying Process of Amaranth Seeds. Prediction Model Based on Neural Networks for Microwave Drying Process of Amaranth Seeds.
Amin Omari, Nasser Behroozi-Khazaei & Faroogh Sharifian. (2018) Drying kinetic and artificial neural network modeling of mushroom drying process in microwave-hot air dryer. Journal of Food Process Engineering 41:7, pages e12849.
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Mohammad Kaveh, Reza Amiri Chayjan & Behrooz Khezri. (2018) Modeling Drying Properties of Pistachio Nuts, Squash and Cantaloupe Seeds under Fixed and Fluidized Bed Using Data-Driven Models and Artificial Neural Networks. International Journal of Food Engineering 14:1.
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Saban Pusat, Mustafa Tahir Akkoyunlu, Engin Pekel, Mehmet Cabir Akkoyunlu, Coşkun Özkan & Selin Soner Kara. (2016) Estimation of coal moisture content in convective drying process using ANFIS. Fuel Processing Technology 147, pages 12-17.
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S.M. Jafari, V. Ghanbari, M. Ganje & D. Dehnad. (2016) Modeling the Drying Kinetics of Green Bell Pepper in a Heat Pump Assisted Fluidized Bed Dryer. Journal of Food Quality 39:2, pages 98-108.
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Seid Mahdi Jafari, Mohammad Ganje, Danial Dehnad & Vahid Ghanbari. (2016) Mathematical, Fuzzy Logic and Artificial Neural Network Modeling Techniques to Predict Drying Kinetics of Onion. Journal of Food Processing and Preservation 40:2, pages 329-339.
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