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Khadijeh Alibabaei
Department of Electromechanical Engineering, University of Beira Interior, Rua Marquês d’Ávila e Bolama, 6201-001 Covilhã, Portugal

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Journal article
Published: 29 May 2021 in Applied Sciences
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In recent years, deep learning algorithms have been successfully applied in the development of decision support systems in various aspects of agriculture, such as yield estimation, crop diseases, weed detection, etc. Agriculture is the largest consumer of freshwater. Due to challenges such as lack of natural resources and climate change, an efficient decision support system for irrigation is crucial. Evapotranspiration and soil water content are the most critical factors in irrigation scheduling. In this paper, the ability of Long Short-Term Memory (LSTM) and Bidirectional LSTM (BLSTM) to model daily reference evapotranspiration and soil water content is investigated. The application of these techniques to predict these parameters was tested for three sites in Portugal. A single-layer BLSTM with 512 nodes was selected. Bayesian optimization was used to determine the hyperparameters, such as learning rate, decay, batch size, and dropout size.The model achieved the values of mean square error values within the range of 0.014 to 0.056 and R2 ranging from 0.96 to 0.98. A Convolutional Neural Network (CNN) model was added to the LSTM to investigate potential performance improvement. Performance dropped in all datasets due to the complexity of the model. The performance of the models was also compared with CNN, traditional machine learning algorithms Support Vector Regression, and Random Forest. LSTM achieved the best performance. Finally, the impact of the loss function on the performance of the proposed models was investigated. The model with the mean square error as loss function performed better than the model with other loss functions.

ACS Style

Khadijeh Alibabaei; Pedro Gaspar; Tânia Lima. Modeling Soil Water Content and Reference Evapotranspiration from Climate Data Using Deep Learning Method. Applied Sciences 2021, 11, 5029 .

AMA Style

Khadijeh Alibabaei, Pedro Gaspar, Tânia Lima. Modeling Soil Water Content and Reference Evapotranspiration from Climate Data Using Deep Learning Method. Applied Sciences. 2021; 11 (11):5029.

Chicago/Turabian Style

Khadijeh Alibabaei; Pedro Gaspar; Tânia Lima. 2021. "Modeling Soil Water Content and Reference Evapotranspiration from Climate Data Using Deep Learning Method." Applied Sciences 11, no. 11: 5029.

Journal article
Published: 22 May 2021 in Energies
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Deep learning has already been successfully used in the development of decision support systems in various domains. Therefore, there is an incentive to apply it in other important domains such as agriculture. Fertilizers, electricity, chemicals, human labor, and water are the components of total energy consumption in agriculture. Yield estimates are critical for food security, crop management, irrigation scheduling, and estimating labor requirements for harvesting and storage. Therefore, estimating product yield can reduce energy consumption. Two deep learning models, Long Short-Term Memory and Gated Recurrent Units, have been developed for the analysis of time-series data such as agricultural datasets. In this paper, the capabilities of these models and their extensions, called Bidirectional Long Short-Term Memory and Bidirectional Gated Recurrent Units, to predict end-of-season yields are investigated. The models use historical data, including climate data, irrigation scheduling, and soil water content, to estimate end-of-season yield. The application of this technique was tested for tomato and potato yields at a site in Portugal. The Bidirectional Long Short-Term memory outperformed the Gated Recurrent Units network, the Long Short-Term Memory, and the Bidirectional Gated Recurrent Units network on the validation dataset. The model was able to capture the nonlinear relationship between irrigation amount, climate data, and soil water content and predict yield with an MSE of 0.017 to 0.039. The performance of the Bidirectional Long Short-Term Memory in the test was compared with the most commonly used deep learning method, the Convolutional Neural Network, and machine learning methods including a Multi-Layer Perceptrons model and Random Forest Regression. The Bidirectional Long Short-Term Memory outperformed the other models with an R2 score between 0.97 and 0.99. The results show that analyzing agricultural data with the Long Short-Term Memory model improves the performance of the model in terms of accuracy. The Convolutional Neural Network model achieved the second-best performance. Therefore, the deep learning model has a remarkable ability to predict the yield at the end of the season.

ACS Style

Khadijeh Alibabaei; Pedro Gaspar; Tânia Lima. Crop Yield Estimation Using Deep Learning Based on Climate Big Data and Irrigation Scheduling. Energies 2021, 14, 3004 .

AMA Style

Khadijeh Alibabaei, Pedro Gaspar, Tânia Lima. Crop Yield Estimation Using Deep Learning Based on Climate Big Data and Irrigation Scheduling. Energies. 2021; 14 (11):3004.

Chicago/Turabian Style

Khadijeh Alibabaei; Pedro Gaspar; Tânia Lima. 2021. "Crop Yield Estimation Using Deep Learning Based on Climate Big Data and Irrigation Scheduling." Energies 14, no. 11: 3004.

Journal article
Published: 26 August 2020 in International Journal of Foundations of Computer Science
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Forest algebras are defined for investigating languages of forests [ordered sequences] of unranked trees, where a node may have more than two [ordered] successors. They consist of two monoids, the horizontal and the vertical, with an action of the vertical monoid on the horizontal monoid, and a complementary axiom of faithfulness. A pseudovariety is a class of finite algebras of a given signature, closed under the taking of homomorphic images, subalgebras and finitary direct products. By looking at the syntactic congruence for monoids and as the natural extension in the case of forest algebras, we could define a version of syntactic congruence of a subset of the free forest algebra, not just a forest language. Let [Formula: see text] be a finite alphabet and [Formula: see text] be a pseudovariety of finite forest algebras. A language [Formula: see text] is [Formula: see text]-recognizable if its syntactic forest algebra belongs to [Formula: see text]. Separation is a classical problem in mathematics and computer science. It asks whether, given two sets belonging to some class, it is possible to separate them by another set of a smaller class. Suppose that a forest language [Formula: see text] and a forest [Formula: see text] are given. We want to find if there exists any proof for that [Formula: see text] does not belong to [Formula: see text] just by using [Formula: see text]-recognizable languages, i.e. given such [Formula: see text] and [Formula: see text], if there exists a [Formula: see text]-recognizable language [Formula: see text] which contains [Formula: see text] and does not contain [Formula: see text]. In this paper, we present how one can use profinite forest algebra to separate a forest language and a forest term and also to separate two forest languages.

ACS Style

Saeid Alirezazadeh; Khadijeh Alibabaei. Weak Separation Problem for Tree Languages. International Journal of Foundations of Computer Science 2020, 31, 583 -593.

AMA Style

Saeid Alirezazadeh, Khadijeh Alibabaei. Weak Separation Problem for Tree Languages. International Journal of Foundations of Computer Science. 2020; 31 (5):583-593.

Chicago/Turabian Style

Saeid Alirezazadeh; Khadijeh Alibabaei. 2020. "Weak Separation Problem for Tree Languages." International Journal of Foundations of Computer Science 31, no. 5: 583-593.

Journal article
Published: 26 August 2019 in International Journal of Algebra and Computation
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It has been shown that for every prime number [Formula: see text], the pseudovariety [Formula: see text] of all finite [Formula: see text]-groups is tame with respect to an implicit signature containing the canonical implicit signature. In this paper, we generalize this result and we show that the pseudovariety of all finite nilpotent groups is tame but it is not completely tame.

ACS Style

Khadijeh Alibabaei. The pseudovariety of all nilpotent groups is tame. International Journal of Algebra and Computation 2019, 29, 1019 -1034.

AMA Style

Khadijeh Alibabaei. The pseudovariety of all nilpotent groups is tame. International Journal of Algebra and Computation. 2019; 29 (6):1019-1034.

Chicago/Turabian Style

Khadijeh Alibabaei. 2019. "The pseudovariety of all nilpotent groups is tame." International Journal of Algebra and Computation 29, no. 6: 1019-1034.

Research article
Published: 06 May 2019 in Semigroup Forum
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It has been shown that the proper, non-locally finite pseudovarieties of abelian groups are not tame with respect to the canonical signature. In this paper, we show that every decidable, proper, non-locally finite pseudovariety of abelian groups is completely tame with respect to a further enlarged implicit signature. This theorem yields as a corollary that a pseudovariety of abelian groups is decidable if and only if it is completely tame.

ACS Style

Khadijeh Alibabaei. Every decidable pseudovariety of abelian groups is completely tame. Semigroup Forum 2019, 99, 106 -125.

AMA Style

Khadijeh Alibabaei. Every decidable pseudovariety of abelian groups is completely tame. Semigroup Forum. 2019; 99 (1):106-125.

Chicago/Turabian Style

Khadijeh Alibabaei. 2019. "Every decidable pseudovariety of abelian groups is completely tame." Semigroup Forum 99, no. 1: 106-125.

Journal article
Published: 11 October 2016 in Journal of Group Theory
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We show that the wreath product of a finitely generated abelian group with a polycyclic group is a LERF group. This theorem yields as a corollary that finitely generated free metabelian groups are LERF, a result due to Coulbois. We also show that a free solvable group of class 3 and rank at least 2 does not contain a strictly ascending HNN-extension of a finitely generated group. Since such groups are known not to be LERF, this settles, in the negative, a question of J. O. Button.

ACS Style

Khadijeh Alibabaei. On the profinite topology on solvable groups. Journal of Group Theory 2016, 20, 1 .

AMA Style

Khadijeh Alibabaei. On the profinite topology on solvable groups. Journal of Group Theory. 2016; 20 (4):1.

Chicago/Turabian Style

Khadijeh Alibabaei. 2016. "On the profinite topology on solvable groups." Journal of Group Theory 20, no. 4: 1.