@article{
 title = {GHG Global Emission Prediction of Synthetic N Fertilizers Using Expectile Regression Techniques},
 type = {article},
 year = {2023},
 pages = {1-19},
 volume = {14},
 websites = {https://www.mdpi.com/2073-4433/14/2/283},
 id = {d3755c1e-33c6-3175-be0a-e7bc9eaf2391},
 created = {2023-02-14T12:07:24.836Z},
 file_attached = {true},
 profile_id = {668cf8d1-9efc-3c39-9e3c-0445aa0d2cd9},
 last_modified = {2023-02-14T12:07:25.889Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {Agriculture accounts for a large percentage of nitrous oxide (N2O) emissions, mainly due to the misapplication of nitrogen-based fertilizers, leading to an increase in the greenhouse gas (GHG) footprint. These emissions are of a direct nature, released straight into the atmosphere through nitrification and denitrification, or of an indirect nature, mainly through nitrate leaching, runoff, and N2O volatilization processes. N2O emissions are largely ascribed to the agricultural sector, which represents a threat to sustainability and food production, subsequent to the radical contribution to climate change. In this connection, it is crucial to unveil the relationship between synthetic N fertilizer global use and N2O emissions. To this end, we worked on a dataset drawn from a recent study, which estimates direct and indirect N2O emissions according to each country, by the Intergovernmental Panel on Climate Change (IPCC) guidelines. Machine learning tools are considered great explainable techniques when dealing with air quality problems. Hence, our work focuses on expectile regression (ER) based-approaches to predict N2O emissions based on N fertilizer use. In contrast to classical linear regression (LR), this method allows for heteroscedasticity and omits a parametric specification of the underlying distribution. ER provides a complete picture of the target variable’s distribution, especially when the tails are of interest, or in dealing with heavy-tailed distributions. In this work, we applied expectile regression and the kernel expectile regression estimator (KERE) to predict direct and indirect N2O emissions. The results outline both the flexibility and competitiveness of ER-based techniques in regard to the state-of-the-art regression approaches.},
 bibtype = {article},
 author = {Benghzial, Kaoutar and Raki, Hind and Bamansour, Sami and Elhamdi, Mouad and Aalaila, Yahya and Peluffo-Ordóñez, Diego H},
 doi = {https://doi.org/10.3390/atmos14020283},
 journal = {Atmosphere},
 number = {2}
}

@inproceedings{
 title = {Crop Classification Using Deep Learning: A Quick Comparative Study of Modern Approaches},
 type = {inproceedings},
 year = {2022},
 pages = {31-44},
 publisher = {Springer International Publishing},
 city = {Cham},
 id = {a160d50c-78a1-356f-998a-33cb9701cec3},
 created = {2022-11-05T13:37:48.828Z},
 file_attached = {false},
 profile_id = {668cf8d1-9efc-3c39-9e3c-0445aa0d2cd9},
 last_modified = {2022-11-05T14:54:47.495Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {false},
 hidden = {false},
 source_type = {InProceedings},
 private_publication = {false},
 abstract = {Automatic crop classification using new technologies is recognized as one of the most important assets in today's smart farming improvement. Investments in technology and innovation are key issues for shaping agricultural productivity as well as the inclusiveness and sustainability of the global agricultural transformation. Digital image processing (DIP) has been widely adopted in this field, by merging Unmanned Aerial Vehicle (UAV) based remote sensing and deep learning (DL) as a powerful tool for crop classification. Despite the wide range of alternatives, the proper selection of a DL approach is still an open and challenging issue. In this work, we carry out an exhaustive performance evaluation of three remarkable and lightweight DL approaches, namely: Visual Geometry Group (VGG), Residual Neural Network (ResNet) and Inception V3, tested on high resolution agriculture crop images dataset. Experimental results show that InceptionV3 outperforms VGG and ResNet in terms of precision (0,92), accuracy (0,97), recall (0,91), AUC (0,98), PCR (0,97), and F1 (0,91).},
 bibtype = {inproceedings},
 author = {Raki Hind
and González-Vergara, Juan
and Aalaila Yahya
and Elhamdi Mouad
and Bamansour Sami
and Guachi-Guachi Lorena
and Peluffo-Ordoñez Diego H},
 editor = {Florez Hector
and Gomez, Henry},
 booktitle = {Applied Informatics}
}

@article{
 title = {Modelling of Proton Exchange Membrane Fuel Cells with Sinusoidal Approach},
 type = {article},
 year = {2022},
 pages = {1056},
 volume = {12},
 id = {84953db2-579a-3b5b-af31-13f8fd055dd2},
 created = {2022-11-05T14:21:34.531Z},
 file_attached = {false},
 profile_id = {668cf8d1-9efc-3c39-9e3c-0445aa0d2cd9},
 last_modified = {2022-11-05T14:54:47.107Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {false},
 hidden = {false},
 private_publication = {false},
 abstract = {This paper validates a sinusoidal approach for the proton-exchange membrane fuel cell (PEMFC) model as a supplement to experimental studies. An FC simulation or hardware emulation is necessary for prototype design, testing, and fault diagnosis to reduce the overall cost. For this objective, a sinusoidal model that is capable of accurately estimating the voltage behavior from the operating current value of the DC was developed. The model was tested using experimental data from the Ballard Nexa 1.2 kW fuel cell (FC). This methodology offers a promising approach for static and current-voltage, characteristic of the three regions of operation. A study was carried out to evaluate the effectiveness and superiority of the proposed FC Sinusoidal model compared with the Diffusive Global model and the Evolution Strategy.},
 bibtype = {article},
 author = {González-Castaño, Catalina and Aalaila, Yahya and Restrepo, C and Revelo-Fuelagán, Javier and Peluffo-Ordóñez, Diego Hernán},
 journal = {Membranes},
 number = {11}
}

@book{
 title = {Developments on Support Vector Machines for Multiple-Expert Learning},
 type = {book},
 year = {2021},
 source = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
 keywords = {Multiple expert learning,Supervised learning,Support vector machines},
 volume = {13113 LNCS},
 id = {f1a1d846-cc54-38c4-bea3-be2f754b8434},
 created = {2022-11-05T13:37:32.222Z},
 file_attached = {false},
 profile_id = {668cf8d1-9efc-3c39-9e3c-0445aa0d2cd9},
 last_modified = {2022-11-05T14:54:47.158Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 private_publication = {true},
 abstract = {In supervised learning scenarios, some applications require solve a classification problem wherein labels are not given as a single ground truth. Instead, the criteria of a set of experts is used to provide labels aimed at compensating for the erroneous influence with respect to a single labeler as well as the error bias (excellent or lousy) due to the level of perception and experience of each expert. This paper aims to briefly outline mathematical developments on support vector machines (SVM), and overview SVM-based approaches for multiple expert learning (MEL). Such MEL approaches are posed by modifying the formulation of a least-squares SVM, which enables to obtain a set of reliable, objective labels while penalizing the evaluation quality of each expert. Particularly, this work studies both two-class (binary) MEL classifier (BMLC) and its extension to multiclass through one-against all (OaA-MLC) including penalization of each expert’s influence. Formal mathematical developments are stated, as well as remarkable discussion on key aspects about the least-squares SVM formulation and penalty factors are provided.},
 bibtype = {book},
 author = {Umaquinga-Criollo, A.C. and Tamayo-Quintero, J.D. and Moreno-García, M.N. and Aalaila, Y. and Peluffo-Ordóñez, D.H.},
 doi = {10.1007/978-3-030-91608-4_57}
}