@article {93553,
	title = {A Robust Iris Recognition Approach Based on Transfer Learning},
	journal = {International Journal of Computing and Digital Systems},
	volume = {137},
	number = {1},
	year = {2023},
	abstract = {
	Iris texture is one of the most secure biometric characteristics used for person recognition, where the most significant step in the iris identification process is effective features extraction. Deep Convolutional Neural network models have been achieved massive success in the features extraction field in recent years, but several of these models have tens to hundreds of millions of parameters, which affect the computational time and resources. A lot of systems proposed in the iris recognition field extract features from normalized iris images after applying many pre-processing steps. These steps affect the quality and computational efficiency of these systems; also, occlusion, reflections, blur, and illumination variation affect the quality of features extracted. This paper proposed a new robust approach for iris recognition that locates the iris region based on the YOLOv4-tiny, then it extracts features without using iris images{\textquoteright} pre-processing, which is a delicate task. In addition, we have proposed an effective model that accelerated the feature extraction process by reducing the architecture of the Inception-v3 model. The obtained results on four benchmark datasets validate the robustness of our approach, where we achieved average accuracy rates of 99.91\%, 99.60\%, 99.91\%, and 99.19\% on the IITD, CASIA-Iris-V1, CASIA-Iris-Interval, and CASIA-Iris-Thousand datasets, respectively.

},
	url = { http://dx.doi.org/10.12785/ijcds/130186},
	author = {Hattab,  Abdessalam and Behloul, Ali}
}
@article {62494,
	title = {Tree trunk texture classification using multi-scale statistical macro binary patterns and CNN},
	journal = {Applied Soft Computing},
	volume = {118},
	year = {2022},
	month = {2022},
	pages = {108473},
	abstract = {
	Automated plant\&nbsp;classification\&nbsp;using tree trunk has attracted increasing interest in the\&nbsp;computer vision\&nbsp;community as a contributed solution for the management of biodiversity. It is based on the description of the texture information of the bark surface. The multi-scale variants of the\&nbsp;local binary patterns\&nbsp;have achieved prominent performance in bark texture description. However, these approaches encode the scale levels of the macrostructure separately from each other. In this paper, a novel handcrafted\&nbsp;texture descriptor\&nbsp;termed multi-scale Statistical Macro Binary Patterns (ms-SMBP) is proposed to encode the characterizing macro pattern of different bark species. The proposed approach consists of defining a sampling scheme at high scale levels and summarizing the intensity distribution using statistical measures. The characterizing macro pattern is encoded by an in-depth gradient that describes the relationship between the scale levels and their adaptive statistical prototype. Besides this handcrafted feature descriptor, a learning-based description is performed with the ResNet34 model for bark classification. Extensive and comprehensive experiments on challenging and large-scale bark datasets demonstrate the effectiveness of ms-SMBP to identify bark species and outperforming different multi-scale LBP approaches. The tree trunk classification with ResNet34 shows interesting results on a very large-scale dataset.

},
	isbn = {1568-4946},
	url = {https://doi.org/10.1016/j.asoc.2022.108473},
	author = {Boudra, Safia and Yahiaoui, Itheri and Behloul, Ali}
}
@proceedings {62732,
	title = {Fine-tuned Xception for Image Classification on Tiny ImageNet},
	journal = {2021 International Conference on Artificial Intelligence for Cyber Security Systems and Privacy (AI-CSP)},
	year = {2021},
	month = {2021},
	pages = {1-4},
	publisher = {IEEE},
	address = {El Oued, Algeria},
	abstract = {Image classification has been one of the most widely topic in artificial intelligence, deep models need larger datasets and powerful hardware to improve the highperformance classification. ImageNet Challenge was started in 2010 to classify 100,000 test images into 1000 different classes. Tiny ImageNet challenge is similar to ImageNet challenge, where images are taken from the standard ImageNet and resized to be 64x64. In this paper a fine-tuned Xception to classify images into the 200 classes is presented using the standard Tiny ImageNet dataset, the down-sampling (64x64) of images and the low similarity inter-class makes feature extraction and classification difficult and more challenging. We used a transfer learning algorithm to fine-tune the Xception architecture using the Extreme version of the Inception module to achieve a high validation accuracy of 65.14\%.},
	isbn = {1-66546-714-2},
	url = {Fine-tuned Xception for Image Classification on Tiny ImageNet},
	author = {Benbrahim, Hayet and Behloul, Ali}
}
@article {62565,
	title = {A parallel content-based image retrieval system using spark and tachyon frameworks},
	journal = {Journal of King Saud University - Computer and Information SciencesJournal of King Saud University - Computer and Information Sciences},
	year = {2021},
	month = {2021},
	abstract = {With the huge increase of large-scale multimedia over Internet, especially images, building Content-Based Image Retrieval (CBIR) systems for large-scale images has become a big challenge. One of the drawbacks associated with CBIR is the very long execution time. In this article, we propose a fast Content-Based Image Retrieval system using Spark (CBIR-S) targeting large-scale images. Our system is composed of two steps.\&nbsp;(i) image indexation step, in which we use\&nbsp;MapReduce\&nbsp;distributed model on Spark in order to speed up the indexation process. We also use a memory-centric\&nbsp;distributed storage system, called Tachyon, to enhance the write operation\&nbsp;(ii) image retrieving step\&nbsp;which we speed up by using a parallel k-Nearest Neighbors (k-NN) search method based on\&nbsp;MapReduce model\&nbsp;implemented under\&nbsp;Apache Spark, in addition to exploiting the cache method of spark framework. We have showed, through a wide set of experiments, the effectiveness of our approach in terms of processing time.},
	author = {Mezzoudj, Saliha and Behloul, Ali and Seghir, Rachid and Saadna, Yassmina}
}
@article {62698,
	title = {A set of statistical radial binary patterns for tree species identification based on bark images},
	journal = {Multimedia Tools and ApplicationsMultimedia Tools and Applications},
	volume = {80},
	year = {2021},
	month = {2021},
	pages = {22373-22404},
	isbn = {1573-7721},
	author = {Boudra, Safia and Yahiaoui, Itheri and Behloul, Ali}
}
@proceedings {62893,
	title = {Face Recognition Based on Harris Detector and Convolutional Neural Networks},
	journal = {International Symposium on Modelling and Implementation of Complex Systems},
	year = {2020},
	month = {2020},
	pages = {163-171},
	publisher = {Springer},
	author = {Zeghina, Assaad Oussama and Zoubia, Oussama and Behloul, Ali}
}
@article {63323,
	title = {Computer and information sciences},
	journal = {J. King Saud UniversityJ. King Saud University},
	year = {2019},
	month = {2019},
	author = {Mezzoudj, Saliha and Behloul, Ali and Rachid, S and Saadna, Yassmina}
}
@article {63322,
	title = {A parallel content-based image retrieval system using spark and tachyon frameworks},
	journal = {Journal of King Saud University - Computer and Information SciencesJournal of King Saud University - Computer and Information Sciences},
	year = {2019},
	month = {2019},
	abstract = {With the huge increase of large-scale multimedia over Internet, especially images, building Content-Based Image Retrieval (CBIR) systems for large-scale images has become a big challenge. One of the drawbacks associated with CBIR is the very long execution time. In this article, we propose a fast Content-Based Image Retrieval system using Spark (CBIR-S) targeting large-scale images. Our system is composed of two steps.\&nbsp;(i) image indexation step, in which we use\&nbsp;MapReduce\&nbsp;distributed model on Spark in order to speed up the indexation process. We also use a memory-centric\&nbsp;distributed storage system, called Tachyon, to enhance the write operation\&nbsp;(ii) image retrieving step\&nbsp;which we speed up by using a parallel k-Nearest Neighbors (k-NN) search method based on\&nbsp;MapReduce model\&nbsp;implemented under\&nbsp;Apache Spark, in addition to exploiting the cache method of spark framework. We have showed, through a wide set of experiments, the effectiveness of our approach in terms of processing time.},
	author = {Mezzoudj, Saliha and Behloul, Ali and Seghir, Rachid and Saadna, Yassmina}
}
@article {63267,
	title = {Speed limit sign detection and recognition system using SVM and MNIST datasets},
	journal = {Neural Computing and ApplicationsNeural Computing and Applications},
	volume = {31},
	year = {2019},
	month = {2019},
	pages = {5005{\textendash}5015},
	abstract = {This article presents a computer vision system for real-time detection and robust recognition of speed limit signs, specially designed for intelligent vehicles. First, a new segmentation method is proposed to segment the image, and the CHT transformation (circle hog transform) is used to detect circles. Then, a new method based on local binary patterns is proposed to filter segmented images in order to reduce false alarms. In the classification phase, a cascading architecture of two linear support vector machines is proposed. The first is trained on the GTSRB dataset to decide whether the detected region is a speed limit sign or not, and the second is trained on the MNIST dataset to recognize the sign numbers. The system achieves a classification recall of 99.81\% with a precision of 99.08\% on the GTSRB dataset; in addition, the system is also tested on the BTSD and STS datasets, and it achieves a classification recall of 99.39\% and 98.82\% with a precision of 99.05\% and 98.78\%, respectively, within a processing time of 11.22\&nbsp;ms.},
	author = {Saadna, Yassmina and Behloul, Ali and Mezzoudj, Saliha}
}
@article {61634,
	title = {
	A Bayer pattern-based fragile watermarking scheme for color image tamper detection and restoration


 

},
	journal = {Multidim Syst Sign Process},
	volume = {30},
	year = {2019},
	pages = {1093{\textendash}1112},
	author = {Noui, Lemnouar and Belferdi, Wassila and Behloul, Ali}
}
@proceedings {65043,
	title = {Bark identification using improved statistical radial binary patterns},
	journal = {2018 International conference on content-based multimedia indexing (CBMI)},
	year = {2018},
	month = {2018},
	pages = {1-6},
	publisher = {IEEE},
	isbn = {1-5386-7021-6},
	author = {Boudra, Safia and Yahiaoui, Itheri and Behloul, Ali}
}
@article {65104,
	title = {A Bayer pattern-based fragile watermarking scheme for color image tamper detection and restoration},
	journal = {Multidimensional Systems and Signal ProcessingMultidimensional Systems and Signal Processing},
	volume = {30},
	year = {2018},
	month = {2018},
	pages = {1093{\textendash}1112},
	abstract = {The security of multimedia documents becomes an urgent need, especially with the increasing image falsifications provided by the easy access and use of image manipulation tools. Hence, usage of image authentication techniques fulfills this need. In this paper, we propose an effective self-embedding fragile watermarking scheme for color images tamper detection and restoration. To decrease the capacity of insertion, a Bayer pattern is used to reduce the color host image into a gray-level watermark, to further improve the security Torus Automorphism permutation is used to scramble the gray-level watermark. In our algorithm, three copies of the watermark are inserted over three components (R, G, and B channels) of the color host image, providing a high probability of detection accuracy and recovery if one copy is destroyed. In the tamper detection process, a majority voting technique is used to determine the legitimacy of the image and recover the tampered regions after interpolating the extracted gray-level watermark. Using our proposed method, tampering rate can achieve 25\% with a high visual quality of recovered image and PSNR values greater than 34 (dB). Experimental results demonstrate that the proposed method affords three major properties: the high quality of watermarked image, the sensitive tamper detection and high localization accuracy besides the high-quality of recovered image.},
	author = {Belferdi, Wassila and Behloul, Ali and Noui, Lemnouar}
}
@article {64998,
	title = {The Impact of Online Indexing in Improving Arabic Information Retrieval Systems},
	journal = {InformaticaInformatica},
	volume = {42},
	year = {2018},
	month = {2018},
	isbn = {1854-3871},
	author = {Dilekh, Tahar and BENHARZALLAH, Saber and Behloul, Ali}
}
@proceedings {65044,
	title = {Plant identification from bark: A texture description based on Statistical Macro Binary Pattern},
	journal = {2018 24th International Conference on Pattern Recognition (ICPR)},
	year = {2018},
	month = {2018},
	pages = {1530-1535},
	publisher = {IEEE},
	isbn = {1-5386-3788-X},
	author = {Boudra, Safia and Yahiaoui, Itheri and Behloul, Ali}
}
@proceedings {64473,
	title = {Statistical radial binary patterns (srbp) for bark texture identification},
	journal = {International conference on advanced concepts for intelligent vision systems},
	year = {2017},
	month = {2017},
	pages = {101-113},
	publisher = {Springer},
	author = {Boudra, Safia and Yahiaoui, Itheri and Behloul, Ali}
}
@proceedings {65602,
	title = {A Novel Cholesky Decomposition-based Scheme for Strict Image Authentication},
	journal = {2nd international Conference on Pattern Analysis and Intelligent Systems},
	year = {2016},
	month = {2016},
	author = {Belferdi, Wassila and Noui, Lemnouar and Behloul, Ali}
}
@proceedings {65976,
	title = {A comparison of multi-scale local binary pattern variants for bark image retrieval},
	journal = {International conference on advanced concepts for intelligent vision systems},
	year = {2015},
	month = {2015},
	pages = {764-775},
	publisher = {Springer},
	author = {Boudra, Safia and Yahiaoui, Itheri and Behloul, Ali}
}
@article {66019,
	title = {Plants leaves images segmentation based on pseudo Zernike moments},
	journal = {International Journal of Image, Graphics and Signal ProcessingInternational Journal of Image, Graphics and Signal Processing},
	volume = {7},
	year = {2015},
	month = {2015},
	pages = {17},
	isbn = {2074-9074},
	author = {Behloul, Ali and Belkacemi, Soundous}
}
@proceedings {66410,
	title = {A blind dual color images watermarking based on IWT and sub-sampling},
	journal = {3rd international Conference on Complex Systems CISC{\textquoteright}2014},
	year = {2014},
	month = {2014},
	author = {Belferdi, Wassila and Behloul, Ali and Naoui, Lemnaouar}
}
@proceedings {66415,
	title = {A blind robust image watermarking using interest points and IWT},
	journal = {Proceedings of the 6th International Conference on Management of Emergent Digital EcoSystems},
	year = {2014},
	month = {2014},
	pages = {139-145},
	author = {Behloul, Ali}
}
@article {66414,
	title = {A fast and robust traffic sign recognition},
	journal = {International Journal of Innovation and Applied StudiesInternational Journal of Innovation and Applied Studies},
	volume = {5},
	year = {2014},
	month = {2014},
	pages = {139},
	isbn = {2028-9324},
	author = {Behloul, Ali and Saadna, Yassmina}
}
@article {67031,
	title = {Implementation of a new hybrid method for stemming of Arabic text},
	journal = {International Journal of Computer ApplicationsInternational Journal of Computer Applications},
	volume = {46},
	year = {2012},
	month = {2012},
	pages = {14-19},
	author = {Dilekh, Tahar and Behloul, Ali}
}
@proceedings {67105,
	title = {Protection of digital watermarking based on SVD against false positive detection vulnerability},
	journal = {1st international Conference on Advanced Communication and Information Systems ICASIS{\textquoteright}2012},
	year = {2012},
	month = {2012},
	author = {Belferdi, Wassila and Behloul, Ali}
}
@article {62112,
	title = {Conception d{\textquoteright}une plate-forme multi-agent orient{\'e}e caract{\'e}risation de documents visuels},
	year = {2007},
	month = {2007},
	author = {Behloul, Ali}
}
