@article {Khaldi20221377,
	title = {TimeSpec4LULC: a global multispectral time series database for training LULC mapping models with machine learning},
	journal = {Earth System Science Data},
	volume = {14},
	number = {3},
	year = {2022},
	note = {cited By 0},
	pages = {1377-1411},
	abstract = {Land use and land cover (LULC) mapping are of paramount importance to monitor and understand the structure and dynamics of the Earth system. One of the most promising ways to create accurate global LULC maps is by building good quality state-of-the-art machine learning models. Building such models requires large and global datasets of annotated time series of satellite images, which are not available yet. This paper presents TimeSpec4LULC 10.5281/zenodo.5913554;Currency sign, a smart open-source global dataset of multispectral time series for 29 LULC classes ready to train machine learning models. TimeSpec4LULC was built based on the seven spectral bands of the MODIS sensors at 500gh{\texteuro}{\textasciimacron}m resolution, from 2000 to 2021, and was annotated using spatial-temporal agreement across the 15 global LULC products available in Google Earth Engine (GEE). The 22-year monthly time series of the seven bands were created globally by (1) applying different spatial-temporal quality assessment filters on MODIS Terra and Aqua satellites; (2) aggregating their original 8gh{\texteuro}{\textasciimacron}d temporal granularity into monthly composites; (3) merging Terragh{\texteuro}{\textasciimacron}+gh{\texteuro}{\textasciimacron}Aqua data into a combined time series; and (4) extracting, at the pixel level, 6gh{\texteuro}{\textasciimacron}076gh{\texteuro}{\textasciimacron}531 time series of size 262 for the seven bands along with a set of metadata: geographic coordinates, country and departmental divisions, spatial-temporal consistency across LULC products, temporal data availability, and the global human modification index. A balanced subset of the original dataset was also provided by selecting 1000 evenly distributed samples from each class such that they are representative of the entire globe. To assess the annotation quality of the dataset, a sample of pixels, evenly distributed around the world from each LULC class, was selected and validated by experts using very high resolution images from both Google Earth and Bing Maps imagery. This smartly, pre-processed, and annotated dataset is targeted towards scientific users interested in developing various machine learning models, including deep learning networks, to perform global LULC mapping. {\textcopyright} 2022 Rohaifa Khaldi et al.},
	keywords = {Land cover, Land use, Machine learning, MODIS, multispectral image, spatiotemporal analysis, Time series analysis},
	doi = {10.5194/essd-14-1377-2022},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85127624906\&doi=10.5194\%2fessd-14-1377-2022\&partnerID=40\&md5=90f9e45c7b39e149ab387a54f4392d6a},
	author = {Khaldi, R. and Alcaraz-Segura, D. and Guirado, E. and Benhammou, Y. and El Afia, A. and Herrera, F. and Tabik, S.}
}
@conference {Khaldi2019532,
	title = {Forecasting of weekly patient visits to emergency department: Real case study},
	booktitle = {Procedia Computer Science},
	volume = {148},
	year = {2019},
	note = {cited By 0},
	pages = {532-541},
	doi = {10.1016/j.procs.2019.01.026},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85062671151\&doi=10.1016\%2fj.procs.2019.01.026\&partnerID=40\&md5=2d1d746e66e3334ab894f34b3566ecf8},
	author = {Khaldi, R. and Afia, A.E. and Chiheb, R.}
}
@conference {Khaldi2018,
	title = {Feedforward and recurrent neural networks for time series forecasting: Comparative study},
	booktitle = {ACM International Conference Proceeding Series},
	year = {2018},
	doi = {10.1145/3230905.3230946},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85053485247\&doi=10.1145\%2f3230905.3230946\&partnerID=40\&md5=6fb59f2f8ddf603c98e96d68b1a47ee4},
	author = {Khaldi, R. and Chiheb, R. and Afia, A.E.}
}
@conference {Khaldi2018,
	title = {Forecasting of bitcoin daily returns with eemd-elman based model},
	booktitle = {ACM International Conference Proceeding Series},
	year = {2018},
	doi = {10.1145/3230905.3230948},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85053484904\&doi=10.1145\%2f3230905.3230948\&partnerID=40\&md5=0f2e4191d42a3eaf7857e8a6821b5f27},
	author = {Khaldi, R. and Afia, A.E. and Chiheb, R. and Faizi, R.}
}
@conference {Khaldi2017,
	title = {Artificial neural network based approach for blood demand forecasting: Fez transfusion blood center case study},
	booktitle = {ACM International Conference Proceeding Series},
	volume = {Part F129474},
	year = {2017},
	note = {cited By 0},
	abstract = {Blood demand and supply management are considered one of the major components of a healthcare supply chain, since blood is a vital element in preserving patient{\textquoteright}s life. However, forecasting it faces several challenges including frequent shortages, and possible expiration caused by demand uncertainty of hospitals. This uncertainty is mainly due to high variability in the number of emergency cases. Thereupon, this investigation presents a real case study of forecasting monthly demand of three blood components, using Artificial Neural Networks (ANNs). The demand of the three blood components (red blood cells (RBC), plasma (CP) and platelets (PFC)) and other observations are obtained from a central transfusion blood center and a University Hospital. Experiments are carried out using three networks to forecast each blood component separately. Last, the presented model is compared with ARIMA to evaluate its performance in prediction. The results of this study depict that ANN models overcomes ARIMA models in demand forecasting. Thus high ANN models can be considered as a promising approach in forecasting monthly blood demand. {\textcopyright} 2017 Association for Computing Machinery.},
	doi = {10.1145/3090354.3090415},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85028449243\&doi=10.1145\%2f3090354.3090415\&partnerID=40\&md5=7ab92ea6ba7c2f5837ee88fcb2a91ffc},
	author = {Khaldi, R. and El Afia, A. and Chiheb, R. and Faizi, R.}
}
@conference {Khaldi2017,
	title = {Prediction of supplier performance: A novel DEA-ANFIS based approach},
	booktitle = {ACM International Conference Proceeding Series},
	volume = {Part F129474},
	year = {2017},
	note = {cited By 0},
	abstract = {The focus of this paper is on investigating the feasibility of using ANFIS combined with DEA for supplier{\textquoteright}s post-evaluation. The proposed framework aims at modeling performance measurement, and forecasting of a selected hospital{\textquoteright}s drug suppliers. Even though it is broadly employed as a benchmarking tool to evaluate DMUs efficiency, DEA can hardly be used to predict the performance of unseen DMUs. For this reason, ANFIS model has been integrated to DEA due to its nonlinear mapping, strong generalization capabilities and pattern prediction functionalities. DEA based BCC model is used to evaluate the efficiency scores of a set of suppliers, then ANFIS intervenes to learn DEA patterns and to forecast the performance of new suppliers. The results of this research highlight the prediction power of the proposed model in a new scope. They present it as an efficient benchmarking tool and a promising decision support system applied at the operational level. {\textcopyright} 2017 Association for Computing Machinery.},
	doi = {10.1145/3090354.3090416},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85028459962\&doi=10.1145\%2f3090354.3090416\&partnerID=40\&md5=da01d9720d77f4831205623526bfc9d9},
	author = {Khaldi, R. and Chiheb, R. and El Afia, A. and Akaaboune, A. and Faizi, R.}
}
