<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bibi, Somia</style></author><author><style face="normal" font="default" size="100%">Titouna, Chafiq</style></author><author><style face="normal" font="default" size="100%">TITOUNA, Faiza</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A Bayesian-optimized 1D CNN-based outlier detection approach for wireless sensor networks</style></title><secondary-title><style face="normal" font="default" size="100%">Transactions of the Institute of Measurement and Control</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2025</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://doi.org/10.1177/01423312241309428</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p style=&quot;text-align: justify;&quot;&gt;
	Wireless sensor networks (WSNs) have recently emerged as a critical technology in various applications, including industrial automation, building monitoring, and military. However, the data generated by these networks are often prone to outliers, which can compromise sensor data quality and reliability. Detecting outliers is paramount to ensure proper network functioning. Traditional detection techniques pose several challenges, such as weak adaptability to the increasing complexity and dynamic environmental changes, limited accuracy, and higher computation costs. To address these challenges, this paper proposes an optimized one-dimensional convolutional neural networks (1D CNN)-based outlier detection approach for WSNs. This approach comprises two key modules: a predictor module and an outlier detector. The predictor module employs a 1D CNN model to forecast forthcoming sensor measurements based on historical data. Bayesian optimization is used to enhance the 1D CNN model’s accuracy. The outlier detector identifies outliers based on the Euclidean distance between the predicted measurements and their corresponding actual values. Experiments on synthetic and real-world datasets reveal that our proposed approach outperforms other existing deep learning-based frameworks in terms of accuracy, F1 score, and false alarm rates.
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</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Benoughidene, Abdelhalim</style></author><author><style face="normal" font="default" size="100%">TITOUNA, Faiza</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A novel method for video shot boundary detection using CNN-LSTM approach</style></title><secondary-title><style face="normal" font="default" size="100%"> International Journal of Multimedia Information Retrieval </style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2022</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://link.springer.com/article/10.1007/s13735-022-00251-8</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">11</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p style=&quot;text-align: justify;&quot;&gt;
	Due to the rapid growth of digital videos and the massive increase in video content, there is an urgent need to develop efficient automatic video content analysis mechanisms for different tasks, namely summarization, retrieval, and classification. In all these applications, one needs to identify shot boundary detection. This paper proposes a novel dual-stage approach for cut transition detection that can withstand certain illumination and motion effects. Firstly, we present a deep neural network model using the pre-trained model combined with long short-term memory LSTM network and the euclidean distance metric. Two parallel pre-trained models sharing the same weights extract the spatial features. Then, these features are fed to the LSTM and the euclidean distance metric to classify the frames into specific categories (similar or not similar). To train the model, we generated a new database containing 5000 frame pairs with two labels (similar, dissimilar) for training and 1000 frame pairs for testing from online videos. Secondly, we adopt the segment selection process to predict the shot boundaries. This preprocessing method can help improve the accuracy and speed of the VSBD algorithm. Then, cut transition detection based on the similarity model is conducted to identify the shot boundaries in the candidate segments. Experimental results on standard databases TRECVid 2001, 2007, and RAI show that the proposed approach achieves better detection rates over the state-of-the-art SBD methods in terms of the F1 score criterion.
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</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Hamza, Rafik</style></author><author><style face="normal" font="default" size="100%">Yan, Zheng</style></author><author><style face="normal" font="default" size="100%">Muhammad, Khan</style></author><author><style face="normal" font="default" size="100%">Bellavista, Paolo</style></author><author><style face="normal" font="default" size="100%">TITOUNA, Faiza</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A privacy-preserving cryptosystem for IoT E-healthcare</style></title><secondary-title><style face="normal" font="default" size="100%">Information SciencesInformation Sciences</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2020</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2020</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">527</style></volume><pages><style face="normal" font="default" size="100%">493-510</style></pages><isbn><style face="normal" font="default" size="100%">0020-0255</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Titouna, Chafiq</style></author><author><style face="normal" font="default" size="100%">TITOUNA, Faiza</style></author><author><style face="normal" font="default" size="100%">Ari, Ado Adamou Abba</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Outlier detection algorithm based on mahalanobis distance for wireless sensor networks</style></title><secondary-title><style face="normal" font="default" size="100%">2019 International Conference on Computer Communication and Informatics (ICCCI)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2019</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2019</style></date></pub-dates></dates><publisher><style face="normal" font="default" size="100%">IEEE</style></publisher><pages><style face="normal" font="default" size="100%">1-6</style></pages><isbn><style face="normal" font="default" size="100%">1-5386-8260-5</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Hamza, Rafik</style></author><author><style face="normal" font="default" size="100%">Hewage, Chaminda</style></author><author><style face="normal" font="default" size="100%">TITOUNA, Faiza</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">INVESTIGATION OF THREE-DIMENSIONAL IMAGE SECURITY BASED ON IMPROVED IMAGE RANDOMISED ENCRYPTION METHOD.</style></title><secondary-title><style face="normal" font="default" size="100%">NED University Journal of ResearchNED University Journal of Research</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2018</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2018</style></date></pub-dates></dates><isbn><style face="normal" font="default" size="100%">1023-3873</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Hamza, Rafik</style></author><author><style face="normal" font="default" size="100%">Muhammad, Khan</style></author><author><style face="normal" font="default" size="100%">Lv, Zhihan</style></author><author><style face="normal" font="default" size="100%">TITOUNA, Faiza</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Secure video summarization framework for personalized wireless capsule endoscopy</style></title><secondary-title><style face="normal" font="default" size="100%">Pervasive and Mobile ComputingPervasive and Mobile Computing</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2017</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2017</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">41</style></volume><pages><style face="normal" font="default" size="100%">436-450</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Wireless capsule&amp;nbsp;endoscopy&amp;nbsp;(WCE) has several benefits over traditional endoscopy such as its portability and ease of usage, particularly for remote&amp;nbsp;internet of things&amp;nbsp;(IoT)-assisted&amp;nbsp;healthcare services. During the WCE procedure, a significant amount of redundant video data is generated, the transmission of which to healthcare centers and gastroenterologists securely for analysis is challenging as well as wastage of several resources including energy, memory, computation, and bandwidth. In addition to this, it is inherently difficult and time consuming for gastroenterologists to analyze this huge volume of gastrointestinal video data for desired contents. To surmount these issues, we propose a secure video summarization framework for outdoor patients going through WCE procedure. In the proposed system,&amp;nbsp;keyframes&amp;nbsp;are extracted using a light-weighted video summarization scheme, making it more suitable for WCE. Next, a&amp;nbsp;cryptosystem&amp;nbsp;is presented for security of extracted keyframes based on 2D Zaslavsky chaotic map. Experimental results validate the performance of the proposed cryptosystem in terms of robustness and high-level security compared to other recent image encryption schemes during dissemination of important keyframes to healthcare centers and gastroenterologists for personalized WCE.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">AKSA, Karima</style></author><author><style face="normal" font="default" size="100%">TITOUNA, Faiza</style></author><author><style face="normal" font="default" size="100%">BENALI, Bilal</style></author><author><style face="normal" font="default" size="100%">DJETTAOU, Bilel</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Gestion Dynamique des Carrefours à Feux</style></title><secondary-title><style face="normal" font="default" size="100%">IJMS - The International Journal of Multi-disciplinary Sciences </style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2017</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://docplayer.fr/176311359-Gestion-dynamique-des-carrefours-a-feux.html</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">1</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p style=&quot;text-align: justify;&quot;&gt;
	Les technologies utilisées dans les systèmes de transport intelligents varient, allant
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&lt;p style=&quot;text-align: justify;&quot;&gt;
	de systèmes de gestion basiques
&lt;/p&gt;

&lt;p style=&quot;text-align: justify;&quot;&gt;
	comme les systèmes de gestion des carrefours à
&lt;/p&gt;

&lt;p style=&quot;text-align: justify;&quot;&gt;
	feux, les systèmes de gestion des conteneurs, les panneaux à messages variables,
&lt;/p&gt;

&lt;p style=&quot;text-align: justify;&quot;&gt;
	radars automatiques ou la vidéosurveillance aux applications plus avancées qui
&lt;/p&gt;

&lt;p style=&quot;text-align: justify;&quot;&gt;
	intègrent des données en temps
&lt;/p&gt;

&lt;p style=&quot;text-align: justify;&quot;&gt;
	-
&lt;/p&gt;

&lt;p style=&quot;text-align: justify;&quot;&gt;
	réel avec ret
&lt;/p&gt;

&lt;p style=&quot;text-align: justify;&quot;&gt;
	ours d'informations de nombreuses
&lt;/p&gt;

&lt;p style=&quot;text-align: justify;&quot;&gt;
	sources, comme les informations météorologiques, ...etc.
&lt;/p&gt;

&lt;p style=&quot;text-align: justify;&quot;&gt;
	Cet article donne un
&lt;/p&gt;

&lt;p style=&quot;text-align: justify;&quot;&gt;
	bref
&lt;/p&gt;

&lt;p style=&quot;text-align: justify;&quot;&gt;
	aperçu sur
&lt;/p&gt;

&lt;p style=&quot;text-align: justify;&quot;&gt;
	une
&lt;/p&gt;

&lt;p style=&quot;text-align: justify;&quot;&gt;
	gestion intelligente des carrefours à feux
&lt;/p&gt;

&lt;p style=&quot;text-align: justify;&quot;&gt;
	utilisant des capteurs sans fils
&lt;/p&gt;
</style></abstract><issue><style face="normal" font="default" size="100%">17</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Hamza, Rafik</style></author><author><style face="normal" font="default" size="100%">TITOUNA, Faiza</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A novel sensitive image encryption algorithm based on the Zaslavsky chaotic map</style></title><secondary-title><style face="normal" font="default" size="100%">Information Security Journal: A Global PerspectiveInformation Security Journal: A Global Perspective</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2016</style></date></pub-dates></dates><number><style face="normal" font="default" size="100%">4</style></number><volume><style face="normal" font="default" size="100%">25</style></volume><pages><style face="normal" font="default" size="100%">6</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">In this article, a novel sensitive encryption scheme to secure the digital images based on the Zaslavsky chaotic map is proposed. We employ the Zaslavsky chaotic map as a pseudo-random generator to produce the key encryption of the proposed image cryptosystem. The cipher structure has been chosen based on permutation-diffusion processes, where we adopt the classic permutation substitution network, which ensures both confusion and diffusion properties for the encrypted image. Our proposed algorithm has high sensitivity in plain image and the secret key. Moreover, the results show that the characteristics of our approach have excellent performance, with high scores (NPRC = 99.61%, UACI = 33.47%, entropy (CipherImage)&amp;nbsp;&amp;nbsp;8, and correlation coefficient&amp;nbsp;&amp;nbsp;0). Experimental results have been studied and analyzed in detail with various types of security analysis. These results demonstrate that our proposed cryptosystem has highly satisfactory security performance and can withstand various attacks compared to state-of-the-art methods.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">TITOUNA, Faiza</style></author><author><style face="normal" font="default" size="100%">Benferhat, Salem</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Lukasiewicz-based merging possibilistic networks</style></title><secondary-title><style face="normal" font="default" size="100%">International journal of approximate reasoningInternational journal of approximate reasoning</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2014</style></date></pub-dates></dates><number><style face="normal" font="default" size="100%">8</style></number><volume><style face="normal" font="default" size="100%">55</style></volume><pages><style face="normal" font="default" size="100%">1747-1763</style></pages><isbn><style face="normal" font="default" size="100%">0888-613X</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">TITOUNA, Faiza</style></author><author><style face="normal" font="default" size="100%">Benferhat, Salem</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Merging possibilistic networks through a disjunctive mode</style></title><secondary-title><style face="normal" font="default" size="100%">International Conference on Belief Functions</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2014</style></date></pub-dates></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><pages><style face="normal" font="default" size="100%">265-274</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">TITOUNA, Faiza</style></author><author><style face="normal" font="default" size="100%">Benferhat, Salem</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Qualitative fusion-based traffic signal preemption</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the 16th International Conference on Information Fusion</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2013</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2013</style></date></pub-dates></dates><publisher><style face="normal" font="default" size="100%">IEEE</style></publisher><pages><style face="normal" font="default" size="100%">1926-1933</style></pages><isbn><style face="normal" font="default" size="100%">1-4799-0284-5</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">TITOUNA, Faiza</style></author><author><style face="normal" font="default" size="100%">Titouna, Chafiq</style></author><author><style face="normal" font="default" size="100%">Benferhat, Salem</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Probabilistic Checkpointing Protocol to Sensor Network Fault-Tolerant</style></title><secondary-title><style face="normal" font="default" size="100%">International Journal of Computer Science Issues (IJCSI)International Journal of Computer Science Issues (IJCSI)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2012</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2012</style></date></pub-dates></dates><number><style face="normal" font="default" size="100%">5</style></number><volume><style face="normal" font="default" size="100%">9</style></volume><pages><style face="normal" font="default" size="100%">112</style></pages><isbn><style face="normal" font="default" size="100%">1694-0814</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Benferhat, Salem</style></author><author><style face="normal" font="default" size="100%">TITOUNA, Faiza</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">On the fusion of probabilistic networks</style></title><secondary-title><style face="normal" font="default" size="100%">International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2011</style></date></pub-dates></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><pages><style face="normal" font="default" size="100%">49-58</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Benferhat, Salem</style></author><author><style face="normal" font="default" size="100%">TITOUNA, Faiza</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Fusion and normalization of quantitative possibilistic networks</style></title><secondary-title><style face="normal" font="default" size="100%">Applied IntelligenceApplied Intelligence</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2009</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2009</style></date></pub-dates></dates><number><style face="normal" font="default" size="100%">2</style></number><volume><style face="normal" font="default" size="100%">31</style></volume><pages><style face="normal" font="default" size="100%">135-160</style></pages><isbn><style face="normal" font="default" size="100%">1573-7497</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">TITOUNA, Faiza</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Fusion de réseaux causaux possibilistes</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2009</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2009</style></date></pub-dates></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Benferhat, Salem</style></author><author><style face="normal" font="default" size="100%">TITOUNA, Faiza</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Agregating Quantitative Possibilistic Networks.</style></title><secondary-title><style face="normal" font="default" size="100%">FLAIRS Conference</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2006</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2006</style></date></pub-dates></dates><pages><style face="normal" font="default" size="100%">800-805</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Benferhat, Salem</style></author><author><style face="normal" font="default" size="100%">TITOUNA, Faiza</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Min-based fusion of possibilistic networks</style></title><secondary-title><style face="normal" font="default" size="100%">EUSFLAT Conf.</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2005</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2005</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.univ-valenciennes.fr/congres/JFRB06/docJFRBaccesWeb/benferhatExpose.pdf</style></url></web-urls></urls><pages><style face="normal" font="default" size="100%">553-558</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record></records></xml>