@article {Bahri2016848,
	title = {BLB tree: An efficient index structure for fast search},
	journal = {International Review on Computers and Software},
	volume = {11},
	number = {10},
	year = {2016},
	note = {cited By 0},
	pages = {848-858},
	abstract = {Due of its interest in different areas, similarity search in high-dimensional spaces is one of the principal research axes today for CBIR Systems. The properties for high-dimensional data demand some adequate search methods to have a research in optimal time. Due to the curse of dimensionality, search time in the index structure exponentially increases, according the dimension of the descriptor. However, the performances of classical index structures become less than the sequential scan of data in search time when answering exact nearest neighbor queries. To overcome this problematic, two major approaches for high-dimensional data in CBIR systems have been proposed: The first approach permits to speed up the search by using multiple levels of lower bound distances, the second approach exploits the BTree index structure to speed up the search. We propose to combine both techniques to search for large nearest neighbors in a high-dimensional space. We develop a new multidimensional index structure, called BLBTree. In BLB Tree, instead of computing the distances in the high-dimensional original space to find the nearest neighbor, lot of candidates are to be rejected based on the lower bound distances: the research in BLB Tree does not calculate the distances in the high-dimensional original space to find the nearest neighbor if the lower bounding distance exceeds the threshold, because the distances in the high-dimensional cannot be lower than the distances in the low-dimensional. We note that each object in BLB Tree is described by a pyramid structure of histograms, with each histogram representing the lower resolution of the other histogram; due to this property, our index structure is well-suited for high-dimensional and large-scale problems. We have shown that our approach provides interesting and powerful experimental results. {\textcopyright} 2016 Praise Worthy Prize S.r.l. - All rights reserved.},
	doi = {10.15866/irecos.v11i10.9711},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85011708729\&doi=10.15866\%2firecos.v11i10.9711\&partnerID=40\&md5=db12b847849b16d2e0b98f325a4022c5},
	author = {Bahri, A.a and Zouaki, H.a and Thami, R.O.H.b}
}
@conference {Aylaj2016557,
	title = {Degeneration simulated annealing algorithm for combinatorial optimization problems},
	booktitle = {International Conference on Intelligent Systems Design and Applications, ISDA},
	volume = {2016-June},
	year = {2016},
	note = {cited By 0},
	pages = {557-562},
	abstract = {In this paper, we use the physical aspect of the simulated annealing method in order to propose a modified simulated annealing algorithm. The main idea of the algorithm is based to find the optimal solution of a combinatorial optimization problem by switching between two different subsystems of treatment; using so called degeneration of atoms energy. This new algorithm is named Degeneration Simulated Annealing (DSA) algorithm. To illustrate the effectiveness of DSA, it is applied to solve the problems of the minimum distance and the decoding, in coding theory. The computational experiment results obtained by DSA are very interesting. {\textcopyright} 2015 IEEE.},
	doi = {10.1109/ISDA.2015.7489177},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84978430456\&doi=10.1109\%2fISDA.2015.7489177\&partnerID=40\&md5=0471da6bb951204df77f8f7a184b034d},
	author = {Aylaj, B.a and Belkasmi, M.b and Zouaki, H.a and Berkani, A.b}
}
