@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.}
}
@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.}
}
@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.}
}
@conference {Remli2020249,
	title = {Computer integrated manufacturing architecture: A literature review},
	booktitle = {IC3K 2020 - Proceedings of the 12th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management},
	volume = {3},
	year = {2020},
	note = {cited By 0},
	pages = {249-256},
	abstract = {The exponential technological revolution has had a positive impact on industrial companies, providing them with plenty of opportunities to improve their production flows and optimize their costs. This revolution has led to contemporary computer integrated manufacturing (CIM) that consists of linking the shop floor systems to the high business layer. And in order to do that, there has been some research to define a reference architecture to cover all the use cases. This paper presents a literature review of CIM architectures. The purpose of this review is to enumerate the different aspects covered by the different architectures in the literature and the approaches proposed to handle them. Copyright {\textcopyright} 2020 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved.},
	keywords = {Computer architecture, Computer integrated manufacturing, Industrial companies, Knowledge management, Literature reviews, Production flows, Reference architecture, Search engines, Shop floor, Technological revolution},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85107133186\&partnerID=40\&md5=5b2fbe31955f56e4cfd019808278476c},
	author = {Remli, A. and Khtira, A. and Asri, B.E.}
}
@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 {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.}
}
@conference {Hafiddi2011,
	title = {A model driven approach for context-aware services development},
	booktitle = {International Conference on Multimedia Computing and Systems -Proceedings},
	year = {2011},
	note = {cited By 6},
	doi = {10.1109/ICMCS.2011.5945628},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-79961237316\&doi=10.1109\%2fICMCS.2011.5945628\&partnerID=40\&md5=b09e1287fcbf49b9b8cc1fb6ff702c95},
	author = {Hafiddi, H. and Baidouri, H. and Nassar, M. and Asri, B.E. and Kriouile, A.}
}
@conference {Kenzi200838,
	title = {The multiview service : A new concept for the development of adaptable Service Oriented systems},
	booktitle = {Proceedings of the 4th IEEE International Symposium on Service-Oriented System Engineering, SOSE 2008},
	year = {2008},
	note = {cited By 2},
	pages = {38-43},
	doi = {10.1109/SOSE.2008.31},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-62249157099\&doi=10.1109\%2fSOSE.2008.31\&partnerID=40\&md5=1e64b5ddf0ad5ddb3c8491f8544409a9},
	author = {Kenzi, A. and Asri, B.E. and Nassar, M. and Kriouile, A.}
}
