@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.a and El Fkihi, S.b and Aboutajdine, D.a}
}
@article {Jorio2015,
	title = {An energy-efficient clustering routing algorithm based on geographic position and residual energy for wireless sensor network},
	journal = {Journal of Computer Networks and Communications},
	volume = {2015},
	year = {2015},
	note = {cited By 4},
	abstract = {Recently wireless sensor network (WSN) has become one of the most interesting networking technologies, since it can be deployed without communication infrastructures. A sensor network is composed of a large number of sensor nodes; these nodes are responsible for supervision of the physical phenomenon and transmission of the periodical results to the base station. Therefore, improving the energy efficiency and maximizing the networking lifetime are the major challenges in this kind of networks. To deal with this, a hierarchical clustering scheme, called Location-Energy Spectral Cluster Algorithm (LESCA), is proposed in this paper. LESCA determines automatically the number of clusters in a network. It is based on spectral classification and considers both the residual energy and some properties of nodes. In fact, our approach uses the K-ways algorithm and proposes new features of the network nodes such as average energy, distance to BS, and distance to clusters centers in order to determine the clusters and to elect the cluster{\textquoteright}s heads of a WSN. The simulation results show that if the clusters are not constructed in an optimal way and/or the number of the clusters is greater or less than the optimal number of clusters, the total consumed energy of the sensor network per round is increased exponentially. {\textcopyright} 2015 Ali Jorio et al.},
	doi = {10.1155/2015/170138},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84924190502\&doi=10.1155\%2f2015\%2f170138\&partnerID=40\&md5=52a0eb2501890a83a608347ccdb9e158},
	author = {Jorio, A.a and El Fkihi, S.b and Elbhiri, B.c and Aboutajdine, D.a}
}
@conference {Elmouhtadi2015,
	title = {Fingerprint indexing based barycenter triangulation},
	booktitle = {Proceedings of 2015 IEEE World Conference on Complex Systems, WCCS 2015},
	year = {2015},
	note = {cited By 0},
	abstract = {Fingerprint indexing is an efficient technique for improving automatic fingerprint identification systems. In this paper we will present a new index method based on a hierarchical Delaunay triangulation of the minutiae points. The 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 Db1) which contains small area, low quality fingerprints. {\textcopyright} 2015 IEEE.},
	doi = {10.1109/ICoCS.2015.7483263},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84978427437\&doi=10.1109\%2fICoCS.2015.7483263\&partnerID=40\&md5=58fb933699f1e58a80089aca6885a7c3},
	author = {Elmouhtadi, M.a and Aboutajdine, D.a and El Fkihi, S.b}
}
@conference {Jorio2014861,
	title = {A hierarchical clustering algorithm based on spectral classification for Wireless Sensor Networks},
	booktitle = {International Conference on Multimedia Computing and Systems -Proceedings},
	year = {2014},
	note = {cited By 1},
	pages = {861-866},
	abstract = {A Wireless Sensor Network (WSN) is composed of a large number of autonomous and compact devices called sensor nodes. This network can be an effective tool for gathering data in a variety of enviornments. However, These sensor nodes have some constraints due to their limited energy, storage capacity and computing power. Therefor, saving energy and, thus extending the WSN lifetime entails great challenges. In order to prolong the lifetime of WSN, this study presents a hierarchical clustering algorithm based on spectral classification (HCA-SC). First, to overcome the ideal distribution of clusters, HCA-SC partition the network by spectral classification algorithm. Second, for each cluster, HCA-SC selects a node as a cluster head with regard residual energy and distance from base station. Simulation results showed that our algorithm performs better in reducing the energy consumption of sensor nodes and effectively improves the lifetime of wireless sensor networks. {\textcopyright} 2014 IEEE.},
	doi = {10.1109/ICMCS.2014.6911354},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84928732445\&doi=10.1109\%2fICMCS.2014.6911354\&partnerID=40\&md5=2070584e9f01d9b450a2a4a005ba6a08},
	author = {Jorio, A.a and El Fkihi, S.b and Elbhiri, B.c and Aboutajdine, D.a}
}
