A neural network based model to analyze rice parboiling process with small dataset

Behroozi‐Khazaei, Nasser; Nasirahmadi, Abozar

doi:10.1007/s13197-017-2701-x

Cited by 16 publications

(7 citation statements)

References 30 publications

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“…The plots between experimental and ANN predicted DG values under various TPs are shown in Figure 11. Behroozi‐Khazaei and Nasirahmadi (2017) reported similar application of neural network approach for the mapping of paddy parboiling process.…”

Section: Resultsmentioning

confidence: 95%

Neural network and computational fluid dynamics modeling for the gelatinization kinetics of instant controlled pressure drop treated parboiled rice

Chakraborty

Gautam

Hazarika

2020

J Food Process Engineering

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Effects of treatment pressure (TP) and treatment time (TT) on the degree of gelatinization (DG) and its impact on the quality attributes of instant controlled pressure drop (ICPD) treated parboiled rice were investigated. Water diffusion and gelatinization kinetics were established for controlling the quality attributes of parboiled rice. Fick's second law was implemented for the evaluation of water diffusion kinetics. DG was fitted with first order reaction kinetics, which showed a rising trend of reaction rate constant from 0.03 to 0.05 s−1 with the variation of TP. Simulation of gelatinization temperature front and its effect on the gelatinization kinetics were modeled by applying computational fluid dynamics (CFD) approach. Process modeling of DG as a function of TP, TT, and moisture content (MC) was accomplished on the basis of 3‐7‐1 artificial neural network (ANN) architecture. Parboiled rice treated at 0.6 MPa TP showed the best quality in terms of broken percentage, ease of cooking, micro‐structural characteristics, and improved cooking and pasting properties. Practical application Instant controlled pressure drop (ICPD) treated parboiling process is an innovative approach for producing rice with improved quality attributes. Investigation of water diffusion and gelatinization kinetics is a vital aspect in order to control the milling quality of ICPD treated parboiled rice. Further, mapping of gelatinization temperature front is also necessary in order to control DG with the variation of ICPD treatment pressure and time. Therefore, this study was conducted to enumerate the effects of water diffusion and gelatinization kinetics on the quality attributes of ICPD treated parboiled rice.

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Section: Resultsmentioning

confidence: 95%

Neural network and computational fluid dynamics modeling for the gelatinization kinetics of instant controlled pressure drop treated parboiled rice

Chakraborty

Gautam

Hazarika

2020

J Food Process Engineering

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“…So, to make better use of our corpus, we used 10-fold cross validation and the averages of precision, and the recall for all iterations of a single model were calculated and collected for analysis. Various research papers apply k-fold cross validation when dealing with smaller datasets [40][41][42][43][44][45][46].…”

Section: Discussionmentioning

confidence: 99%

Error Detection for Arabic Text Using Neural Sequence Labeling

Madi

Al‐Khalifa

2020

Applied Sciences

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The English language has, thus far, received the most attention in research concerning automatic grammar error correction and detection. However, these tasks have been less investigated for other languages. In this paper, we present the first experiments using neural network models for the task of error detection for Modern Standard Arabic (MSA) text. We investigate several neural network architectures and report the evaluation results acquired by applying cross-validation on the data. All experiments involve a corpus we created and augmented. The corpus has 494 sentences and 620 sentences after augmentation. Our models achieved a maximum precision of 78.09%, recall of 83.95%, and F0.5 score of 79.62% in the error detection task using SimpleRNN. Using an LSTM, we achieved a maximum precision of 79.21%, recall of 93.8%, and F0.5 score of 79.16%. Finally, the best results were achieved using a BiLSTM with a maximum precision of 80.74%, recall of 85.73%, and F0.5 score of 81.55%. We compared the results of the three models to a baseline, which is a commercially available Arabic grammar checker (Microsoft Word 2007). LSTM, BiLSTM, and SimpleRNN all outperformed the baseline in precision and F0.5. Our work shows preliminary results, demonstrating that neural network architectures for error detection through sequence labeling can successfully be applied to Arabic text.

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“…ANN models are applied to solve a wide range of problems, for example: acid concentration prediction for cold-rolled carbon steel strip pickling process [3], control the process of cellulosic material conversion into sugar [4], identification of rice parboiling process [5], validation of distillation column model [6], and simulation of water activity in freeze drying [7].…”

Section: Neural Network In Chemical Processes Controlmentioning

confidence: 99%

Resource and Energy Saving Neural Network-Based Control Approach for Continuous Carbon Steel Pickling Process

Bezsonov¹,

Ilyunin²,

Kaldybaeva³

et al. 2019

J. sustain. dev. energy water environ. syst.

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A neural network based model to analyze rice parboiling process with small dataset

Cited by 16 publications

References 30 publications

Neural network and computational fluid dynamics modeling for the gelatinization kinetics of instant controlled pressure drop treated parboiled rice

Neural network and computational fluid dynamics modeling for the gelatinization kinetics of instant controlled pressure drop treated parboiled rice

Error Detection for Arabic Text Using Neural Sequence Labeling

Resource and Energy Saving Neural Network-Based Control Approach for Continuous Carbon Steel Pickling Process

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