@article {Zerouaoui2022,
	title = {Deep hybrid architectures for binary classification of medical breast cancer images},
	journal = {Biomedical Signal Processing and Control},
	volume = {71},
	year = {2022},
	note = {cited By 16},
	abstract = {The diagnosis of breast cancer in the early stages significantly decreases the mortality rate by allowing the choice of adequate treatment. This study developed and evaluated twenty-eight hybrid architectures combining seven recent deep learning techniques for feature extraction (DenseNet 201, Inception V3, Inception ReseNet V2, MobileNet V2, ResNet 50, VGG16, and VGG19), and four classifiers (MLP, SVM, DT, and KNN) for a binary classification of breast pathological images over the BreakHis and FNAC datasets. The designed architectures were evaluated using: (1) four classification performance criteria (accuracy, precision, recall, and F1-score), (2) Scott Knott (SK) statistical test to cluster the proposed architectures and identify the best cluster of the outperforming architectures, and (3) the Borda Count voting method to rank the best performing architectures. The results showed the potential of combining deep learning techniques for feature extraction and classical classifiers to classify breast cancer in malignant and benign tumors. The hybrid architecture using the MLP classifier and DenseNet 201 for feature extraction (MDEN) was the top performing architecture with higher accuracy values reaching 99\% over the FNAC dataset, 92.61\%, 92\%, 93.93\%, and 91.73\% over the four magnification factor values of the BreakHis dataset: 40X, 100X, 200X, and 400X, respectively. The results of this study recommend the use of hybrid architectures using DenseNet 201 for the feature extraction of the breast cancer histological images because it gave the best results for both datasets BreakHis and FNAC, especially when combined with the MLP classifier. {\textcopyright} 2021 Elsevier Ltd},
	keywords = {accuracy, algorithm, Article, augmentation index, Binary classification, biopsy technique, Breast Cancer, Breast Cancer Histopathological Image Classification, Classification (of information), Classification algorithm, classifier, Cluster computing, clustering algorithm, Clustering algorithms, colloid carcinoma, Computer aided diagnosis, Computer architecture, construct validity, contrast limited adaptive histogram equalization, Convolutional neural network, Convolutional neural networks, deep hybrid architecture, Deep learning, Deep neural networks, Diseases, ductal carcinoma, external validity, Extraction, F1 score, Feature extraction, Features extraction, feed forward neural network, fibroadenoma, fine needle aspiration biopsy, histogram, Histological images, histology, Hybrid architectures, Image classification, image processing, Images processing, internal validity, learning algorithm, Learning algorithms, Learning techniques, lobular carcinoma, Machine learning, measurement precision, Medical imaging, MLP classifiers, Mortality rate, Network architecture, papillary carcinoma, Pathological images, phyllodes tumor, recall, residual neural network, scoring system, Scott Knott, Support vector machines},
	doi = {10.1016/j.bspc.2021.103226},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85125746862\&doi=10.1016\%2fj.bspc.2021.103226\&partnerID=40\&md5=b9b74d0dcb135861bc2e3d820f836efa},
	author = {Zerouaoui, H. and Idri, A.}
}
@article {Zerouaoui2021186,
	title = {Breast Fine Needle Cytological Classification Using Deep Hybrid Architectures},
	journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
	volume = {12950 LNCS},
	year = {2021},
	note = {cited By 6},
	pages = {186-202},
	abstract = {Diagnosis of breast cancer in the early stages allows to significantly decrease the mortality rate by allowing to choose the adequate treatment. This paper develops and evaluates twenty-eight hybrid architectures combining seven recent deep learning techniques for feature extraction (DenseNet 201, Inception V3, Inception ReseNet V2, MobileNet V2, ResNet 50, VGG16 and VGG19), and four classifiers (MLP, SVM, DT and KNN) for binary classification of breast cytological images over the FNAC dataset. To evaluate the designed architectures, we used: (1) four classification performance criterias (accuracy, precision, recall and F1-score), (1) Scott Knott (SK) statistical test to cluster the developed architectures and identify the best cluster of the outperforming architectures, and (2) Borda Count voting method to rank the best performing architectures. Results showed the potential of combining deep learning techniques for feature extraction and classical classifiers to classify breast cancer in malignant and benign tumors. The hybrid architectures using MLP classifier and DenseNet 201 for feature extraction were the top performing architectures with a higher accuracy value reaching 99\% over the FNAC dataset. As results, the findings of this study recommend the use of the hybrid architectures using DenseNet 201 for the feature extraction of the breast cancer cytological images since it gave the best results for the FNAC data images, especially if combined with the MLP classifier. {\textcopyright} 2021, Springer Nature Switzerland AG.},
	keywords = {Binary classification, Breast Cancer, Classification (of information), Classification performance, Cluster computing, Clustering algorithms, Computer aided diagnosis, Computer architecture, Convolutional neural networks, Deep neural networks, Diseases, Extraction, Feature extraction, Features extraction, Histological images, Hybrid architectures, Image classification, Images processing, Learning algorithms, Learning techniques, Medical imaging, MLP classifiers, Mortality rate, Network architecture, Support vector machines},
	doi = {10.1007/978-3-030-86960-1_14},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85125258661\&doi=10.1007\%2f978-3-030-86960-1_14\&partnerID=40\&md5=a4535504f03d3143ae1648f7edeb152c},
	author = {Zerouaoui, H. and Idri, A. and Nakach, F.Z. and Hadri, R.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.}
}
@article {4562825420091002,
	title = {Resource Optimization of Mobile Intelligent System With Heart MPLS Network.},
	journal = {International Journal of Interactive Mobile Technologies},
	volume = {3},
	number = {S2},
	year = {2009},
	pages = {58 - 64},
	abstract = {In this paper, we introduce the original Mobile Intelligent System (MIS) in embedded FPGA architecture. This node will allow the construction of autonomous mobile network units which can move in unknowns, inaccessible or hostile environment for human beings, in order to collect data by various sensors and transmits them by routing to a unit of distant process. For the sake of improving the performance of transmission, we propose a global schema of QoS management using DiffServ/MPLS backbones. We provide an evaluation of several scenarios for combining QoS IP networks with MIS access network. We conclude with a study on interoperability between QoS patterns in access and backbone networks. [ABSTRACT FROM AUTHOR]},
	keywords = {Artificial intelligence, Computer architecture, DiffServ and MPLS, Information networks, intelligent sensors, Mobile communication systems, Quality of service, Routing (Computer network management) {\textendash} Equipment \& supplies, wireless sensor network},
	issn = {18657923},
	url = {http://search.ebscohost.com/login.aspx?direct=true\&db=iih\&AN=45628254\&site=ehost-live},
	author = {Oubaha, Jawad and Habbani, Ahmed and Elkoutbi, Mohammed}
}
@article {4562825020091002,
	title = {Using Mobility to Enhance Routing Process in MIS System.},
	journal = {International Journal of Interactive Mobile Technologies},
	volume = {3},
	number = {S2},
	year = {2009},
	pages = {24 - 32},
	abstract = {This paper introduces the original Mobile Intelligent System (MIS) in an embedded Field-Programmable Gate Array (FPGA) architecture. This would allow the construction of autonomous mobile network units which can move in environments that are unknown, inaccessible or hostile for human beings, in order to collect data by various sensors and route it to a distant processing unit. To have a better performing routing process, we propose a new mobility measure. Each node measures its own mobility in the network, based on its neighbors{\textquoteright} information. This measure has no unit and is calculated by quantification in regular time intervals. [ABSTRACT FROM AUTHOR]},
	keywords = {ad hoc networks, Computer architecture, Information systems embedded application, Intelligent agents (Computer software), intelligent sensors, Mobile communication systems, multipoint relays, node mobility and mobility quantification, OLSR protocol, Routers (Computer networks), Routing (Computer network management) {\textendash} Equipment \& supplies, wireless sensor network},
	issn = {18657923},
	url = {http://search.ebscohost.com/login.aspx?direct=true\&db=iih\&AN=45628250\&site=ehost-live},
	author = {Oudidi, K. and Habbani, A. and Elkoutbi, M.}
}
