@article {Alimoussa2022,
	title = {Compact Hybrid Multi-Color Space Descriptor Using Clustering-Based Feature Selection for Texture Classification},
	journal = {Journal of Imaging},
	volume = {8},
	number = {8},
	year = {2022},
	note = {cited By 0},
	abstract = {Color texture classification aims to recognize patterns by the analysis of their colors and their textures. This process requires using descriptors to represent and discriminate the different texture classes. In most traditional approaches, these descriptors are used with a predefined setting of their parameters and computed from images coded in a chosen color space. The prior choice of a color space, a descriptor and its setting suited to a given application is a crucial but difficult problem that strongly impacts the classification results. To overcome this problem, this paper proposes a color texture representation that simultaneously takes into account the properties of several settings from different descriptors computed from images coded in multiple color spaces. Since the number of color texture features generated from this representation is high, a dimensionality reduction scheme by clustering-based sequential feature selection is applied to provide a compact hybrid multi-color space (CHMCS) descriptor. The experimental results carried out on five benchmark color texture databases with five color spaces and manifold settings of two texture descriptors show that combining different configurations always improves the accuracy compared to a predetermined configuration. On average, the CHMCS representation achieves 94.16\% accuracy and outperforms deep learning networks and handcrafted color texture descriptors by over 5\%, especially when the dataset is small. {\textcopyright} 2022 by the authors.},
	doi = {10.3390/jimaging8080217},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85136887352\&doi=10.3390\%2fjimaging8080217\&partnerID=40\&md5=22239b1c87f0cb6fec0b966689303d2b},
	author = {Alimoussa, M. and Porebski, A. and Vandenbroucke, N. and El Fkihi, S. and Oulad Haj Thami, R.}
}
@article {ElKarfi2022561,
	title = {An Ensemble of Arabic Transformer-based Models for Arabic Sentiment Analysis},
	journal = {International Journal of Advanced Computer Science and Applications},
	volume = {13},
	number = {8},
	year = {2022},
	note = {cited By 0},
	pages = {561-567},
	abstract = {In recent years, sentiment analysis has gained momentum as a research area. This task aims at identifying the opinion that is expressed in a subjective statement. An opinion is a subjective expression describing personal thoughts and feelings. These thoughts and feelings can be assigned with a certain sentiment. The most studied sentiments are positive, negative, and neutral. Since the introduction of attention mechanism in machine learning, sentiment analysis techniques have evolved from recurrent neural networks to transformer models. Transformer-based models are encoder-decoder systems with attention. Attention mechanism has permitted models to consider only relevant parts of a given sequence. Making use of this feature in encoder-decoder architecture has impacted the performance of transformer models in several natural language processing tasks, including sentiment analysis. A significant number of Arabic transformer-based models have been pre-trained recently to perform Arabic sentiment analysis tasks. Most of these models are implemented based on Bidirectional Encoder Representations from Transformers (BERT) such as AraBERT, CAMeLBERT, Arabic ALBERT and GigaBERT. Recent studies have confirmed the effectiveness of this type of models in Arabic sentiment analysis. Thus, in this work, two transformer-based models, namely AraBERT and CAMeLBERT have been experimented. Furthermore, an ensemble model has been implemented to achieve more reasonable performance {\textcopyright} 2022, International Journal of Advanced Computer Science and Applications.All Rights Reserved.},
	keywords = {Arabic sentiment analyse, Attention mechanisms, Bert, Decoding, Ensemble learning, Learning systems, Performance, Positive/negative, Recurrent neural networks, Research areas, Sentiment analysis, Signal encoding, Transformer, Transformer modeling},
	doi = {10.14569/IJACSA.2022.0130865},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85137156665\&doi=10.14569\%2fIJACSA.2022.0130865\&partnerID=40\&md5=927293fb0ea8e16df6981eecff917dd6},
	author = {El Karfi, I. and El Fkihi, S.}
}
@conference {Clero2022,
	title = {Monitoring Of Geotechnical Instabilities at Deep Underground Mines, Case of Draa Sfar Mine, Morocco},
	booktitle = {World Congress on Civil, Structural, and Environmental Engineering},
	year = {2022},
	note = {cited By 0},
	abstract = {The scope of this paper concerns the identification of underground geotechnical instabilities and the analysis of the rock mass monitoring data at deep underground mines, taking as a study case Draa Sfar mine in Morocco. In fact, geotechnical conditions complexity increases in the mine{\textquoteright}s deeper levels. Therefore, rock mass behaviour analysis, on-site geotechnical instrumentation and continuous monitoring of the medium are strongly required to avoid geotechnical risks. In this context, this paper focuses on presenting, first, a general state of the art about the most frequent rock mass instabilities encountered in the underground media, their classification, and their monitoring methods. In the following section, this paper provides general information about Draa Sfar mine and presents its geotechnical monitoring data analysis. At the end, conclusions about the current monitoring results and perspectives about innovative instrumentation solutions are discussed. {\textcopyright} 2022, Avestia Publishing. All rights reserved.},
	doi = {10.11159/icgre22.212},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85139066071\&doi=10.11159\%2ficgre22.212\&partnerID=40\&md5=58c00b8254f4f94cb74bf2d21ae22607},
	author = {Clero, K. and Ed-Diny, S. and Soror, T. and Rziki, S. and Achalhi, M. and El Fkihi, S. and Boanarijesy, A.}
}
@article {Faizi202252,
	title = {A Sentiment Analysis Based Approach for Exploring Student Feedback},
	journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
	volume = {13449 LNCS},
	year = {2022},
	note = {cited By 0},
	pages = {52-59},
	abstract = {Student feedback is commonly used as a reliable source of information to evaluate learning outcomes and teaching quality. This feedback has proven to provide faculty not only with valuable insights into how students are learning, but also with an ideal opportunity to reflect on teaching resources and instructional strategies. However, given the increasing massive amounts of feedback that is available online, collecting and analyzing this data manually is not usually an easy task. The aim of this work is, therefore, to put forward a sentiment analysis classifier that is capable of categorizing student feedback as being either positive or negative. To this end, students{\textquoteright} reviews posted about online courses were automatically extracted, preprocessed and then fed into various machine learning algorithms. The findings of this analysis revealed that the Support Vector Machines (SVM) algorithm achieves the highest accuracy score (93.35\%) and, thus, outperforms other implemented models. {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.},
	keywords = {Analysis-based approaches, Education computing, Learning algorithms, Learning outcome, Machine-learning, Outcome quality, Quality control, Sentiment analysis, Sources of informations, Student feedback, Students, Support vector machines, Support vectors machine, Teaching quality, Teaching resources},
	doi = {10.1007/978-3-031-15273-3_6},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85137997385\&doi=10.1007\%2f978-3-031-15273-3_6\&partnerID=40\&md5=feac3688f39fab9ad1f85464953459ec},
	author = {Faizi, R. and El Fkihi, S.}
}
@conference {Walid2021,
	title = {Modeling and optimization of anaerobic digestion: A review},
	booktitle = {E3S Web of Conferences},
	volume = {229},
	year = {2021},
	note = {cited By 2},
	abstract = {Anaerobic digestion is recognized as being an advantageous waste management technique representing a source of clean and renewable energy. However, biogas production through such practice is complex and it relies on the interaction of several factors including changes in operating and monitoring parameters. Enormous researchers have focused and gave their full attention to mathematical modeling of anaerobic digestion to get good insights about process dynamics, aiming to optimize its efficiency. This paper gives an overview of the different approaches applied to tackle this challenge including mechanistic and data-driven models. This review has led us to conclude that neural networks combined with metaheuristic techniques has the potential to outperform mechanistic and classical machine learning models. {\textcopyright} The Authors, published by EDP Sciences, 2021.},
	keywords = {Anaerobic digestion, Biogas production, Data-driven model, Machine learning models, Management techniques, Meta-heuristic techniques, Modeling and optimization, Monitoring parameters, Optimization, Renewable energies},
	doi = {10.1051/e3sconf/202122901022},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100132508\&doi=10.1051\%2fe3sconf\%2f202122901022\&partnerID=40\&md5=5d2367868bbe9ebe8e867ca9622eda34},
	author = {Walid, F. and El Fkihi, S. and Benbrahim, H. and Tagemouati, H.}
}
@article {Achakir2021314,
	title = {Non-Model-Based approach for complete digitization by TLS or mobile scanner},
	journal = {ISPRS Journal of Photogrammetry and Remote Sensing},
	volume = {178},
	year = {2021},
	note = {cited By 2},
	pages = {314-327},
	abstract = {This paper investigates automatic digitization with complete coverage of large and complex environments using a TLS or a mobile scanner. We propose an adaptive multi-objective view-planner that can operate in an unknown environment to provide guidance for the human operator and ease the scanning task or by a mobile robot for an automatic exploration of the environment. The proposed view-planner is adapted to environments where the sensor is operating on a flat surface such as office spaces, urban areas, open fields or in some cultural heritage applications. First, we propose an adaptive gap-based method to extract occluded areas in a point cloud, which is completely automated and does not require extensive computations in a large environment such as ray-tracing or level-set methods. Then, we introduce a novel exploration strategy that uses specific regions of the environment called {\textquotedblleft}Conservative-Cells{\textquotedblright} to drastically reduce the number of sensing positions to achieve complete digitization of the environment. Both methods were validated with simulated and real point clouds. The proposed approach has been applied to a scanner carried by a mobile robot, then to data acquired by a TLS used by a human operator in a large, complex environment. Experimental results on both TLS and mobile robot show that our view-planning approach is effective in finding a sequence of positions that leads to a complete reconstruction of the environment. Moreover, the proposed approach shows efficient performance in terms of coverage rate and computational time compared to others view-planning approaches as well as the results of an experienced human operator in a large, complex environment. {\textcopyright} 2021 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS)},
	keywords = {automation, Complex environments, cultural heritage, Digitisation, digitization, Human operator, instrumentation, Mobile robots, Mobile scanner, Model based approach, multiobjective programming, Next best view, Non-model-based, Numerical methods, Office buildings, Performance assessment, Personnel, Point-clouds, ray tracing, Remote sensing, Robot programming, robotics, Scanning, Seebeck effect, Simulation, urban area, Urban planning, View planning, Visibility analysis},
	doi = {10.1016/j.isprsjprs.2021.06.014},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85109077721\&doi=10.1016\%2fj.isprsjprs.2021.06.014\&partnerID=40\&md5=32e85ee602b523c75ec0a6725ad46f50},
	author = {Achakir, F. and El Fkihi, S. and Mouaddib, E.M.}
}
@article {Moussaid2021,
	title = {Tree crowns segmentation and classification in overlapping orchards based on satellite images and unsupervised learning algorithms},
	journal = {Journal of Imaging},
	volume = {7},
	number = {11},
	year = {2021},
	note = {cited By 2},
	abstract = {Smart agriculture is a new concept that combines agriculture and new technologies to improve the yield{\textquoteright}s quality and quantity as well as facilitate many tasks for farmers in managing orchards. An essential factor in smart agriculture is tree crown segmentation, which helps farmers automatically monitor their orchards and get information about each tree. However, one of the main problems, in this case, is when the trees are close to each other, which means that it would be difficult for the algorithm to delineate the crowns correctly. This paper used satellite images and machine learning algorithms to segment and classify trees in overlapping orchards. The data used are images from the Moroccan Mohammed VI satellite, and the study region is the OUARGHA citrus orchard located in Morocco. Our approach starts by segmenting the rows inside the parcel and finding all the trees there, getting their canopies, and classifying them by size. In general, the model inputs the parcel{\textquoteright}s image and other field measurements to classify the trees into three classes: missing/weak, normal, or big. Finally, the results are visualized in a map containing all the trees with their classes. For the results, we obtained a score of 0.93 F-measure in rows segmentation. Additionally, several field comparisons were performed to validate the classification, dozens of trees were compared and the results were very good. This paper aims to help farmers to quickly and automatically classify trees by crown size, even if there are overlapping orchards, in order to easily monitor each tree{\textquoteright}s health and understand the tree{\textquoteright}s distribution in the field. {\textcopyright} 2021 by the authors. Licensee MDPI, Basel, Switzerland.},
	doi = {10.3390/jimaging7110241},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85119836347\&doi=10.3390\%2fjimaging7110241\&partnerID=40\&md5=28a65e4b68b078f94a4b0144d630f8d8},
	author = {Moussaid, A. and El Fkihi, S. and Zennayi, Y.}
}
@article {Elmouhtadi2018435,
	title = {Fingerprint identification based on hierarchical triangulation},
	journal = {Journal of Information Processing Systems},
	volume = {14},
	number = {2},
	year = {2018},
	pages = {435-447},
	doi = {10.3745/JIPS.02.0084},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85046883308\&doi=10.3745\%2fJIPS.02.0084\&partnerID=40\&md5=98878a4e67240c4ddadb744f90dab887},
	author = {Elmouhtadi, M. and El Fkihi, S. and Aboutajdine, D.}
}
@article {Elmouhtadi2018393,
	title = {Fingerprint identification using hierarchical matching and topological structures},
	journal = {Studies in Computational Intelligence},
	volume = {730},
	year = {2018},
	pages = {393-408},
	doi = {10.1007/978-3-319-63754-9_18},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85032002757\&doi=10.1007\%2f978-3-319-63754-9_18\&partnerID=40\&md5=e4d45320fd26c5982556a97c07bc754d},
	author = {Elmouhtadi, M. and El Fkihi, S. and Aboutajdine, D.}
}
@article {Elmouhtadi2018393,
	title = {Fingerprint identification using hierarchical matching and topological structures},
	journal = {Studies in Computational Intelligence},
	volume = {730},
	year = {2018},
	note = {cited By 0},
	pages = {393-408},
	abstract = {Fingerprint identification is one of the most popular and efficient biometric techniques used for improving automatic personal identification. In this paper, we will present a new indexing method, based on estimation of singular point considered as an important feature in the fingerprint by using the directional file. On the other hand, a hierarchical Delaunay triangulation is applied on the minutiae around the extracted singular point. Two fingerprints calculated by introducing the barycenter notion to ensure the exact location of the similar triangles is compared. We have performed extensive experiments and comparisons to demonstrate the effectiveness of the proposed approach using a challenging public database (i.e., FVC2000), which contains small area and low-quality fingerprints. {\textcopyright} Springer International Publishing AG 2018.},
	doi = {10.1007/978-3-319-63754-9_18},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85032002757\&doi=10.1007\%2f978-3-319-63754-9_18\&partnerID=40\&md5=e4d45320fd26c5982556a97c07bc754d},
	author = {Elmouhtadi, M. and El Fkihi, S. and Aboutajdine, D.}
}
@conference {ElKarfi201859,
	title = {A spectral clustering-based approach for sentiment classification in modern standard Arabic},
	booktitle = {MCCSIS 2018 - Multi Conference on Computer Science and Information Systems; Proceedings of the International Conferences on Big Data Analytics, Data Mining and Computational Intelligence 2018, Theory and Practice in Modern Computing 2018 and Connected Sma},
	year = {2018},
	pages = {59-65},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85054167622\&partnerID=40\&md5=101982324dd788664f052d2919012106},
	author = {El Karfi, I. and El Fkihi, S. and Faizi, R.}
}
@conference {ElBouziady20181,
	title = {Vehicle speed estimation using extracted SURF features from stereo images},
	booktitle = {2018 International Conference on Intelligent Systems and Computer Vision, ISCV 2018},
	volume = {2018-May},
	year = {2018},
	pages = {1-6},
	doi = {10.1109/ISACV.2018.8354040},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85050956891\&doi=10.1109\%2fISACV.2018.8354040\&partnerID=40\&md5=53370a3035d03bc86531c0e0e81069f5},
	author = {El Bouziady, A. and Oulad Haj Thami, R. and Ghogho, M. and Bourja, O. and El Fkihi, S.}
}
@conference {Faizi2017997,
	title = {Extracting business value from big data},
	booktitle = {Proceedings of the 29th International Business Information Management Association Conference - Education Excellence and Innovation Management through Vision 2020: From Regional Development Sustainability to Global Economic Growth},
	year = {2017},
	note = {cited By 0},
	pages = {997-1002},
	abstract = {The exponential growth of structured and unstructured data in the last few years has resulted in the emergence of {\textquoteright}big data{\textquoteright} as one of the hottest technology trends. With data of all kinds being generated in record amounts every minute, big data has become a buzzword across various fields. The objective of this paper is, therefore, to explore the various ways through which businesses can derive value from both internal and external data. In this respect, it was found out that companies, both big and small - across all industries - can use big data to increase customers{\textquoteright} retention and to meet the need of every individual customer. This huge amount of data can also help a business identify the strengths and weaknesses of its competitors and, thus, stay ahead of them. Finally, big data can provide businesses with real-time insights that would enable them to spot or predict problems, and solve them before they happen. Given all these benefits, we strongly believe that the collection and processing of the massive amounts of data, generated by customers or smart devices, can help any enterprise make more powerful and accurate decisions and, thus, significantly improve business operations and organizational performance.},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85029742611\&partnerID=40\&md5=bfa9daca776ba7dea239abddfc70501a},
	author = {Faizi, R. and El Fkihi, S. and El Afia, A. and Chiheb, R.}
}
@conference {Abouyahya201746,
	title = {Features extraction for facial expressions recognition},
	booktitle = {International Conference on Multimedia Computing and Systems -Proceedings},
	year = {2017},
	note = {cited By 0},
	pages = {46-49},
	abstract = {The recognition of an expression seems obvious and easy when classified by the human brain. However, it is clearly difficult for a computer to detect human face, extract all of the components characterizing the facial expression and then determine its classification from a single image. Moreover, based on videos, the process becomes even more complex because it must take simultaneously into account the temporal and spatial information available. Also, It should be noted that facial features have an important fact to developing a robust face representation because it aims to select the best of features and reduce dimensionality of features set by finding a new set which contains most of the face features information. For those reasons, this paper present several features extraction approaches for facial expressions recognition as state-of-the-art review. {\textcopyright} 2016 IEEE.},
	doi = {10.1109/ICMCS.2016.7905642},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85019146665\&doi=10.1109\%2fICMCS.2016.7905642\&partnerID=40\&md5=9a7be9980a63d7021cf8136fdf05dfd5},
	author = {Abouyahya, A. and El Fkihi, S. and Thami, R.O.H. and Aboutajdine, D.}
}
@article {Elmouhtadi2017714,
	title = {Fingerprint identification using hierarchical matching and topological structures},
	journal = {Advances in Intelligent Systems and Computing},
	volume = {533},
	year = {2017},
	note = {cited By 1},
	pages = {714-722},
	abstract = {Fingerprint identification is one of the most popular and efficient biometric technique used for improving automatic personal identification. In this paper, we will present a new indexing based method, in the first step, on estimation of singular point considered as an important feature in the fingerprint using a directional file. In the second step, a hierarchical Delaunay triangulation was applied on the minutiae around the singular point extracted. Comparison of two fingerprints was calculated by introducing the barycenter notion so as to ensure the exact location of the similar triangles. We have performed extensive experiments and comparisons to demonstrate the effectiveness of the proposed approach using a challenging public database (i.e., FVC2000) which contains small area and low quality fingerprints. {\textcopyright} Springer International Publishing AG 2017.},
	doi = {10.1007/978-3-319-48308-5_68},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84994504482\&doi=10.1007\%2f978-3-319-48308-5_68\&partnerID=40\&md5=8c568e5df1645e23782fc831e2182b69},
	author = {Elmouhtadi, M. and El Fkihi, S. and Aboutajdine, D.}
}
@conference {Elmouhtadi2017434,
	title = {Fingerprints indexing algorithms based on multiple characteristics},
	booktitle = {Colloquium in Information Science and Technology, CIST},
	year = {2017},
	note = {cited By 0},
	pages = {434-437},
	abstract = {Fingerprint identification in large databases is a leading concern preoccupation problem since biometrics still the most reliable and secure means of identification systems. Hence the need to find an accurate and relevant system of research and identification. In this paper we present the different approaches of fingerprints indexing algorithms that have proven satisfactory results in the field, evaluated on large databases provided by FVC. {\textcopyright} 2016 IEEE.},
	doi = {10.1109/CIST.2016.7805086},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85010223948\&doi=10.1109\%2fCIST.2016.7805086\&partnerID=40\&md5=d8555e034c9089955faeed9880ac4dda},
	author = {Elmouhtadi, M. and El Fkihi, S. and Aboutajdine, D.}
}
@conference { ISI:000351597600002,
	title = {GRADIENT-BASED TIME TO CONTACT ON PARACATADIOPTRIC CAMERA},
	booktitle = {2013 20TH IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP 2013)},
	series = {IEEE International Conference on Image Processing ICIP},
	year = {2013},
	note = {20th IEEE International Conference on Image Processing (ICIP), Melbourne, AUSTRALIA, SEP 15-18, 2013},
	pages = {5-9},
	publisher = {Inst Elect \& Elect Engineers; IEEE Signal Proc Soc},
	organization = {Inst Elect \& Elect Engineers; IEEE Signal Proc Soc},
	abstract = {The problem of time to contact or time to collision (TTC) estimation is largely discussed in perspective images. However, a few works have dealt with images of catadioptric sensors despite of their utility in robotics applications. The objective of this paper is to develop a novel model for estimating TTC with catadioptric images relative to a planar surface, and to demonstrate that TTC can be estimated only with derivative brightness and image coordinates. This model, called {\textquoteleft}{\textquoteleft}gradient based time to contact{{\textquoteright}{\textquoteright}}, does not need high processing such as explicit estimation of optical flow and feature detection and/or tracking. The proposed method allows to estimate TTC and gives additional information about the orientation of planar surface. It was tested on simulated and real datasets.},
	isbn = {978-1-4799-2341-0},
	issn = {1522-4880},
	author = {Benamar, F. and El Fkihi, S. and Demonceaux, C. and Mouaddib, E. and Aboutajdine, D.}
}
@conference {Bouirouga2010,
	title = {A comparison of skin detection techniques for objectionable videos},
	booktitle = {2010 5th International Symposium on I/V Communications and Mobile Networks, ISIVC 2010},
	year = {2010},
	note = {cited By 0},
	doi = {10.1109/ISVC.2010.5656278},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-78651520179\&doi=10.1109\%2fISVC.2010.5656278\&partnerID=40\&md5=cbf0bdbe507be5b0297aaf80e2b30d25},
	author = {Bouirouga, H. and El Fkihi, S. and Jilbab, A. and Bakrim, M.}
}
@conference {Elbhiri2010,
	title = {Developed Distributed Energy-Efficient Clustering (DDEEC) for heterogeneous wireless sensor networks},
	booktitle = {2010 5th International Symposium on I/V Communications and Mobile Networks, ISIVC 2010},
	year = {2010},
	note = {cited By 2},
	doi = {10.1109/ISVC.2010.5656252},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-78651495359\&doi=10.1109\%2fISVC.2010.5656252\&partnerID=40\&md5=04c6f29644071a785006aab6b93c9c27},
	author = {Elbhiri, B. and Rachid, S. and El Fkihi, S. and Aboutajdine, D.}
}
@article {Anoual2010157,
	title = {New approach based on texture and geometric features for text detection},
	journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
	volume = {6134 LNCS},
	year = {2010},
	note = {cited By 0},
	pages = {157-164},
	doi = {10.1007/978-3-642-13681-8_19},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-79956278929\&doi=10.1007\%2f978-3-642-13681-8_19\&partnerID=40\&md5=c16c63472198a6944f7f472697a0cc7c},
	author = {Anoual, H. and El Fkihi, S. and Jilbab, A. and Aboutajdine, D.}
}
