@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 {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.}
}
@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.}
}
