@article {Ounasser202245,
	title = {Generative and Autoencoder Models for Large-Scale Mutivariate Unsupervised Anomaly Detection},
	journal = {Smart Innovation, Systems and Technologies},
	volume = {237},
	year = {2022},
	note = {cited By 0},
	pages = {45-58},
	abstract = {Anomaly detection is a major problem that has been well studied in various fields of research and fields of application. In this paper, we present several methods that can be built on existing deep learning solutions for unsupervised anomaly detection, so that outliers can be separated from normal data in an efficient manner. We focus on approaches that use generative adversarial networks (GAN) and autoencoders for anomaly detection. By using these deep anomaly detection techniques, we can overcome the problem that we need to have a large-scale anomaly data in the learning phase of a detection system. So, we compared various methods of machine based and deep learning anomaly detection with its application in various fields. This article used seven available datasets. We report the results on anomaly detection datasets, using performance metrics, and discuss their performance on finding clustered and low density anomalies. {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.},
	keywords = {Anomaly detection, Auto encoders, Deep learning, Detection system, Generative adversarial networks, ITS applications, Large-scales, Learning phasis, Performance, Performance metrices, Unsupervised anomaly detection},
	doi = {10.1007/978-981-16-3637-0_4},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85116885193\&doi=10.1007\%2f978-981-16-3637-0_4\&partnerID=40\&md5=d381663c0ba073f5139a00cbfe2819c8},
	author = {Ounasser, N. and Rhanoui, M. and Mikram, M. and Asri, B.E.}
}
@article {Mikram2022167,
	title = {Hybrid Deep Learning Models for Diabetic Retinopathy Classification},
	journal = {Lecture Notes in Networks and Systems},
	volume = {489 LNNS},
	year = {2022},
	note = {cited By 0},
	pages = {167-178},
	abstract = {Diabetic retinopathy is a complication of diabetes in the eye. This disease is caused by the damage of the blood vessels of the back of eye (i.e., retina). Unfortunately, diabetic retinopathy can cause several symptoms, the most serious of which is complete vision loss. Indeed, the detection of diabetic retinopathy is a time-consuming manual process that requires a qualified clinician to examine and evaluate digital color photographs of the retina{\textquoteright}s fundus. Currently, several researches are looking to employ artificial intelligence techniques, especially the Deep Learning, to deal with this issue. In this paper, we study some hybrid models for diabetic retinopathy severity classification in distributed and non-distributed environments. The studied models perform two main tasks: deep feature extraction and then classification of diabetic retinopathy according to its severity. The models were trained and validated on a publicly available dataset of 80,000 images and they achieved an accuracy of 80.7\%. {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.},
	doi = {10.1007/978-3-031-07969-6_13},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85135078332\&doi=10.1007\%2f978-3-031-07969-6_13\&partnerID=40\&md5=bf1f98b5263465c335e38e77dda6ea10},
	author = {Mikram, M. and Moujahdi, C. and Rhanoui, M. and Meddad, M. and Khallout, A.}
}
@article {Rhanoui20222809,
	title = {A hybrid recommender system for patron driven library acquisition and weeding},
	journal = {Journal of King Saud University - Computer and Information Sciences},
	volume = {34},
	number = {6},
	year = {2022},
	note = {cited By 2},
	pages = {2809-2819},
	abstract = {Nowadays the explosion of information sources has shaped how library users access information and provide feedback on their preferences. Therefore, faced with this explosion and the blossoming of digital libraries, modern libraries must take up the challenge of meeting the needs of their users and considering their opinions and preferences in order to offer them adequate resources and henceforth eliminate those that are unsatisfactory. Almost all library recommender systems aim to provide users with items of interest to them. However, despite its definite interest, the staff-oriented adaptation and application of this revolutionary technique to the collection development process is still in an embryonic stage. We propose a patron-driven hybrid library recommender system that uses machine learning techniques to recommend and assist in the acquisition and weeding decision-making operations by extracting and analyzing users{\textquoteright} opinions and ratings. The recommender system is applied and validated in a real national library case using Amazon{\textquoteright}s digital library and the library{\textquoteright}s catalog as a data source. {\textcopyright} 2020 The Authors},
	doi = {10.1016/j.jksuci.2020.10.017},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096428211\&doi=10.1016\%2fj.jksuci.2020.10.017\&partnerID=40\&md5=49af7074b1295a09ce952c01fd4c7f92},
	author = {Rhanoui, M. and Mikram, M. and Yousfi, S. and Kasmi, A. and Zoubeidi, N.}
}
@conference {Sabiri2022428,
	title = {Impact of Hyperparameters on the Generative Adversarial Networks Behavior},
	booktitle = {International Conference on Enterprise Information Systems, ICEIS - Proceedings},
	volume = {1},
	year = {2022},
	note = {cited By 0},
	pages = {428-438},
	abstract = {Generative adversarial networks (GANs) have become a full-fledged branch of the most important neural network models for unsupervised machine learning. A multitude of loss functions have been developed to train the GAN discriminators and they all have a common structure: a sum of real and false losses which depend only on the real losses and generated data respectively. A challenge associated with an equally weighted sum of two losses is that the formation can benefit one loss but harm the other, which we show causes instability and mode collapse. In this article, we introduce a new family of discriminant loss functions which adopts a weighted sum of real and false parts. With the use the gradients of the real and false parts of the loss, we can adaptively choose weights to train the discriminator in the sense that benefits the stability of the GAN model. Our method can potentially be applied to any discriminator model with a loss which is a sum of the real and fake parts. Our method consists in adjusting the hyper-parameters appropriately in order to improve the training of the two antagonistic models Experiences validated the effectiveness of our loss functions on image generation tasks, improving the base results by a significant margin on dataset Celebdata. Copyright {\textcopyright} 2022 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved.},
	keywords = {Common structures, Computer vision, Deep learning, Generative adversarial networks, Hyper-parameter, Image enhancement, Learning systems, Loss functions, Machine-learning, Network behaviors, Neural network model, Neural-networks, Unsupervised machine learning, Weighted Sum},
	doi = {10.5220/0011115100003179},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85140905402\&doi=10.5220\%2f0011115100003179\&partnerID=40\&md5=a465ab3cd8d0650f9e310edf8a94d422},
	author = {Sabiri, B. and Asri, B. and Rhanoui, M.}
}
@conference {Sabiri2022418,
	title = {Mechanism of Overfitting Avoidance Techniques for Training Deep Neural Networks},
	booktitle = {International Conference on Enterprise Information Systems, ICEIS - Proceedings},
	volume = {1},
	year = {2022},
	note = {cited By 0},
	pages = {418-427},
	abstract = {The objective of a deep learning neural network is to have a final model that performs well both on the data used to train it and the new data on which the model will be used to make predictions. Overfitting refers to the fact that the predictive model produced by the machine learning algorithm adapts well to the training set. In this case, the predictive model will capture the generalizable correlations and the noise produced by the data and will be able to give very good predictions on the data of the training set, but it will predict badly on the data that it has not yet seen during his learning phase. This paper proposes two techniques among many others to reduce or prevent overfitting. Furthermore, by analyzing dynamics during training, we propose a consensus classification algorithm that avoids overfitting, we investigate the performance of these two types of techniques in convolutional neural network. Early stopping allowing to save the hyper-parameters of a model at the right time. And the dropout making the learning of the model harder allowing to gain up to more than 50\% by decreasing the loss rate of the model. Copyright {\textcopyright} 2022 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved.},
	keywords = {Computer vision, Convolution, Convolutional neural network, Convolutional neural networks, Data overfitting, Deep learning, Deep neural networks, Dropout, Early stopping, Forecasting, Learning algorithms, Learning systems, Machine-learning, Max-pooling, Overfitting, Predictive models, Training sets},
	doi = {10.5220/0011114900003179},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85140895255\&doi=10.5220\%2f0011114900003179\&partnerID=40\&md5=286b687a1791aefc7394085755e790c8},
	author = {Sabiri, B. and Asri, B.E. and Rhanoui, M.}
}
@article {Yousfi2022,
	title = {Smart big data framework for insight discovery},
	journal = {Journal of King Saud University - Computer and Information Sciences},
	year = {2022},
	note = {cited By 2},
	abstract = {Big Data deal with new challenges such as data variety, data veracity (correct, incorrect, misleading, etc.) and data completeness (provide a single part of the overall information.). In fact, the knowledge discovered from a single source that can offer incorrect or incomplete data, may have a negative impact on the quality of decisions based on it. Therefore, integrating data coming from multiple sources allows verifying the veracity and ensuring the completeness of the results and thus improving the quality of analysis and enhancing business decisions. In this paper, we present a smart framework that falls within the Big Data value chain process and aims to improve the quality of analytical results by focusing two main concerns regarding Big Data Integration; data completeness and data veracity. The framework integrates Big Data in order to build a complete global and correct insight from heterogeneous sources. The paper presents two implementations of the framework in the context of urban and highway traffic management systems. {\textcopyright} 2021},
	doi = {10.1016/j.jksuci.2021.12.009},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85122932927\&doi=10.1016\%2fj.jksuci.2021.12.009\&partnerID=40\&md5=16742da4bfac0205224464c7c43d3855},
	author = {Yousfi, S. and Chiadmi, D. and Rhanoui, M.}
}
@conference {Ouardi2021790,
	title = {Hybrid Prototypical Networks Augmented by a Non-linear Classifier},
	booktitle = {International Conference on Enterprise Information Systems, ICEIS - Proceedings},
	volume = {1},
	year = {2021},
	note = {cited By 0},
	pages = {790-794},
	abstract = {Text classification is one of the most prolific domains in machine learning. Present in a raw format all around us in our daily life Starting from human to human communication mainly by the social networks apps, arriving at the human-machine interaction especially with chatbots, text is a rich source of information. However, despite the remarkable performances that deep learning achieves in this field, the cost in therm of the amount of data needed to train this model still considerably high, adding to that the need of retraining this model to learn every new task. Nevertheless, a new sub-field of machine learning has emerged, named meta-learning it targets the overcoming of those limitations, widely used for image-related tasks, it can also bring solutions to tasks associated with text. Starting from this perspective we proposed a hybrid architecture based on well-known prototypical networks consisting of adapting this model to text classification and augmenting it with a non-linear classifier. Copyright {\textcopyright} 2021 by SCITEPRESS {\textendash} Science and Technology Publications, Lda. All rights reserved.},
	keywords = {Chatbots, Classification (of information), Daily lives, Deep learning, Few-shot learning, Human communications, Human machine interaction, Learning systems, Machine-learning, Metalearning, Nonlinear classifiers, Sources of informations, Text classification, Text processing},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85137962135\&partnerID=40\&md5=8d0e3e181cd871cde23a9553ea783fb2},
	author = {Ouardi, A.E. and Rhanoui, M. and Benlarabi, A. and Asri, B.E.}
}
@conference {Lahrichi2021322,
	title = {Toward a multimodal multitask model for neurodegenerative diseases diagnosis and progression prediction},
	booktitle = {Proceedings of the 10th International Conference on Data Science, Technology and Applications, DATA 2021},
	year = {2021},
	note = {cited By 1},
	pages = {322-328},
	abstract = {Recent studies on modelling the progression of Alzheimer{\textquoteright}s disease use a single modality for their predictions while ignoring the time dimension. However, the nature of patient data is heterogeneous and time dependent which requires models that value these factors in order to achieve a reliable diagnosis, as well as making it possible to track and detect changes in the progression of patients{\textquoteright} condition at an early stage. This article overviews various categories of models used for Alzheimer{\textquoteright}s disease prediction with their respective learning methods, by establishing a comparative study of early prediction and detection Alzheimer{\textquoteright}s disease progression. Finally, a robust and precise detection model is proposed. Copyright {\textcopyright} 2021 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved},
	keywords = {Alzheimer{\textquoteright}s disease, Comparative studies, Data Science, Detection models, Diagnosis, Early prediction, Forecasting, Hospital data processing, Learning methods, Learning systems, Multi-task model, Neurodegenerative diseases, Patients{\textquoteright} conditions, Time dependent},
	doi = {10.5220/0010600003220328},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85111735908\&doi=10.5220\%2f0010600003220328\&partnerID=40\&md5=1f255250606020cc06dfdc4c73cc29c0},
	author = {Lahrichi, S. and Rhanoui, M. and Mikram, M. and Asri, B.E.}
}
@article {Yousfi202050,
	title = {Comparative study of cnn and lstm for opinion mining in long text},
	journal = {Journal of Automation, Mobile Robotics and Intelligent Systems},
	volume = {14},
	number = {3},
	year = {2020},
	note = {cited By 0},
	pages = {50-55},
	abstract = {The digital revolution has encouraged many companies to set up new strategic and operational mechanisms to supervise the flow of information published about them on the Web. Press coverage analysis is a part of sentiment analysis that allows companies to discover the opinion of the media concerning their activities, products and services. It is an important research area, since it involves the opinion of informed public such as journalists, who may influence the opinion of their readers. However, from an implementation perspective, the analysis of the opinion from media coverage encounters many challenges. In fact, unlike social networks, the Media coverage is a set of large textual documents written in natural language. The training base being huge, it is necessary to adopt large-scale processing techniques like Deep Learning to analyze their content. To guide researchers to choose between one of the most commonly used models CNN and LSTM, we compare and apply both models for opinion mining from long text documents using real datasets. {\textcopyright} 2020, Industrial Research Institute for Automation and Measurements. All rights reserved.},
	doi = {10.14313/JAMRIS/3-2020/34},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100520263\&doi=10.14313\%2fJAMRIS\%2f3-2020\%2f34\&partnerID=40\&md5=fc9c41966d823ca1951c9f01e3fa341c},
	author = {Yousfi, S. and Rhanoui, M. and Mikram, M.}
}
@article {Mikram202071,
	title = {Unit load devices (Uld) demand forecasting in the air cargo fooptimal cost management},
	journal = {Journal of Automation, Mobile Robotics and Intelligent Systems},
	volume = {14},
	number = {3},
	year = {2020},
	note = {cited By 0},
	pages = {71-80},
	abstract = {In recent decades, the airline industry has become very competitive. With the advent of large aircraft in service, unit load devices (ULD) have become an essential element for efficient air transport. They can load a large amount of baggage, cargo or mail using only one unit. Since this results in fewer units to load, saving time and efforts of ground crews and helping to avoid delayed flights. However, a deficient loading of the units causes operating irregularities, costing the company and contributing to the dissatisfaction of the customers. In contrast, an excess load of containers is at the expense of cargo. In this paper we propose an approach to predict the demand for baggage in order to optimize the management of its ULD flow. Specifically, we build prediction models: ARIMA following the BOX-JENKINS approach and exponential smoothing methods, in order to obtain more accurate forecasts. The approach is tested using the operational data of flight processing and the results are compared with four benchmark method (SES, DES, Holt-Winters and Naive prediction) using different performance indicators: MAE, MSE, MAPE, WAPE, RMSE, SMPE. The results obtained with the exponential smoothing methods surpass the benchmarks by providing more accurate forecasts. {\textcopyright} 2020, Industrial Research Institute for Automation and Measurements. All rights reserved.},
	doi = {10.14313/JAMRIS/3-2020/37},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100524740\&doi=10.14313\%2fJAMRIS\%2f3-2020\%2f37\&partnerID=40\&md5=2a7b355fdf9b83161cf3d7a9c85f19b8},
	author = {Mikram, M. and Rhanoui, M. and Yousfi, S. and Briwa, H.}
}
@article {Yousfi201979,
	title = {Mixed-Profiling Recommender Systems for Big Data Environment},
	journal = {Advances in Intelligent Systems and Computing},
	volume = {756},
	year = {2019},
	note = {cited By 0},
	pages = {79-89},
	doi = {10.1007/978-3-319-91337-7_8},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85047301457\&doi=10.1007\%2f978-3-319-91337-7_8\&partnerID=40\&md5=95ad0c9dc701ce47a8fefa5d66452387},
	author = {Yousfi, S. and Rhanoui, M. and Chiadmi, D.}
}
@article {Yousfi2019207,
	title = {Towards a generic multimodal architecture for batch and streaming Big Data integration},
	journal = {Journal of Computer Science},
	volume = {15},
	number = {1},
	year = {2019},
	note = {cited By 0},
	pages = {207-220},
	doi = {10.3844/jcssp.2019.207.220},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85061562274\&doi=10.3844\%2fjcssp.2019.207.220\&partnerID=40\&md5=5e332b31353f824de4847e4a59a0d84a},
	author = {Yousfi, S. and Rhanoui, M. and Chiadmi, D.}
}
@conference {Houari20181,
	title = {Hybrid big data warehouse for on-demand decision needs},
	booktitle = {Proceedings of 2017 International Conference on Electrical and Information Technologies, ICEIT 2017},
	volume = {2018-January},
	year = {2018},
	pages = {1-6},
	doi = {10.1109/EITech.2017.8255261},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85047477758\&doi=10.1109\%2fEITech.2017.8255261\&partnerID=40\&md5=f88360d6e054f43cc33c4593431fa891},
	author = {Houari, M.E. and Rhanoui, M. and Asri, B.E.}
}
@conference {Bahadi201842,
	title = {Towards efficient big data: Hadoop data placing and processing},
	booktitle = {ACM International Conference Proceeding Series},
	year = {2018},
	pages = {42-47},
	doi = {10.1145/3289100.3289108},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85058656669\&doi=10.1145\%2f3289100.3289108\&partnerID=40\&md5=52e6fda5215333977a9413c6cb1ff547},
	author = {Bahadi, J. and Asri, B.E. and Courtine, M. and Rhanoui, M. and Kergosien, Y.}
}
@conference {Khtira2017,
	title = {From data to big data: Moroccan public sector},
	booktitle = {ACM International Conference Proceeding Series},
	volume = {Part F129474},
	year = {2017},
	note = {cited By 0},
	abstract = {Digitalization, interconnection, open data, and the use of internet and social media by governments and citizens have consequently leaded to an enormous growth of data in the public sector. As government data available increases, many big data initiatives have been launched by governments in order to derive insights and create new value in many areas. This paper details some concepts related to government data specifically open data and big data in order to draw the relationships between them and show its potential value. The article also brings into light the impact of digital progress, made so far in the Moroccan Administration, on the growth of data in the public sector, and presents first steps taken by the government toward big data era. In particular, we focus on social media, open government data and e-government initiatives. In addition, the paper showcases examples of applying advanced analytics in the public finances specifically in Tax Administration to uncover insights and make better decisions from large datasets. This article also overviews some challenges facing future big data initiatives in the Moroccan public sector and proposes recommendations to address them. {\textcopyright} 2017 Association for Computing Machinery.},
	doi = {10.1145/3090354.3090401},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85028471176\&doi=10.1145\%2f3090354.3090401\&partnerID=40\&md5=2637a68edcc8418376431b149ac707fe},
	author = {Khtira, R. and Elasri, B. and Rhanoui, M.}
}
@conference {ElHouari2015,
	title = {From Big Data to Big Knowledge: The art of making Big Data alive},
	booktitle = {Proceedings of 2015 International Conference on Cloud Computing Technologies and Applications, CloudTech 2015},
	year = {2015},
	note = {cited By 0},
	abstract = {Nowadays Big Data becomes one of the biggest buzz concepts in IT world especially with the vertiginous development driving the increase of data encouraged by the emergence of high technologies of storage like cloud computing. Big Data can create efficient challenging solutions in health, security, government and more; and usher in a new era of analytics and decisions. Knowledge Management comprises a set of strategies and practices used to identify, create, represent, distribute, and enable creating experience that can constitute a real immaterial capital. However, to bring significant meaning to the perpetual tsunami of data and manage them, Big Data needs Knowledge Management. In the same way, to broaden the scope of its targeted analyzes, Knowledge Management requires Big Data. Thus, there is a complementary relation between these two major concepts. This paper presents a state of art where we try to explore Big Data within the context of Knowledge Management. We discuss the bi-directional relationship linking this two fundamental concepts and their strategic utility in making analytics valuable especially with the combination of their interactions which create an effective Big Knowledge to build experience. {\textcopyright} 2015 IEEE.},
	doi = {10.1109/CloudTech.2015.7337001},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84962850767\&doi=10.1109\%2fCloudTech.2015.7337001\&partnerID=40\&md5=44ddf094080e112ff052b4be166062e8},
	author = {El Houari, M. and Rhanoui, M. and El Asri, B.}
}
@conference {Rhanoui2014,
	title = {A multilevel contract model for dependable Feature-Oriented components},
	booktitle = {2014 9th International Conference on Intelligent Systems: Theories and Applications, SITA 2014},
	year = {2014},
	note = {cited By 0},
	abstract = {Feature-Oriented System Development is a promising and recent approach for developing domain-specific component-based systems. To be adopted in a safety-critical environment it must handle dependability requirements and offer mechanisms to ensure the reliability level of the components and the system. For this purpose, the contract-based approach is a lightweight formal method for designing and specifying systems{\textquoteright} requirements, it can be introduced in an early stage during the design phase. In this paper, we present a multilevel contract model and a domain-specific modeling language that aims to address reliability and quality issues for component-based systems by expressing and specifying a set of its properties and constraints. {\textcopyright} 2014 IEEE.},
	doi = {10.1109/SITA.2014.6847278},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84904596650\&doi=10.1109\%2fSITA.2014.6847278\&partnerID=40\&md5=6f574de296778d6b1ed4ff898fc66eb1},
	author = {Rhanoui, M. and El Asri, B.}
}
@conference {Hammani2014146,
	title = {Towards a variable non-functional requirements integration for component-based product line: A generic approach},
	booktitle = {2014 2nd World Conference on Complex Systems, WCCS 2014},
	year = {2014},
	note = {cited By 0},
	pages = {146-151},
	abstract = {Software Product Line and Component-Based Software Engineering are two expanding paradigms for developing critical and complex systems. Therefore, these two paradigms seem have complementary strengths. So, significant benefits can be gained from their integration. Despite the fact that Non-Functional Requirements (such as security, performance and reliability) are critical to software systems, and they must be taken into account at early stage of software development life cycle and should be explicitly specified as well as functional requirements; these NFR are overlooked in Software Product line and Component-Based Software Development Processes. So, our main objective is to discuss some recent approaches in the field and to propose a new extension to Software Product Line processes that supports Component-Based Approaches, and integrates Non-Functional Requirement in domain requirements stage. {\textcopyright} 2014 IEEE.},
	doi = {10.1109/ICoCS.2014.7060992},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84988286060\&doi=10.1109\%2fICoCS.2014.7060992\&partnerID=40\&md5=3c43beb6d08dd3ee03dc69958f9d5ee7},
	author = {Hammani, F.Z. and Rhanoui, M. and El Asri, B.}
}
@conference {Rhanoui2012377,
	title = {Towards contract-based approach for quality-driven Service Component Architecture},
	booktitle = {2nd International Conference on Innovative Computing Technology, INTECH 2012},
	year = {2012},
	note = {cited By 0},
	pages = {377-384},
	doi = {10.1109/INTECH.2012.6457754},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84874497855\&doi=10.1109\%2fINTECH.2012.6457754\&partnerID=40\&md5=b34258865186cc7e811056d11b5ead41},
	author = {Rhanoui, M. and Asri, B.E.}
}
