@article {Bourja2022994,
	title = {End-to-End Car Make and Model Classification using Compound Scaling and Transfer Learning},
	journal = {International Journal of Advanced Computer Science and Applications},
	volume = {13},
	number = {5},
	year = {2022},
	note = {cited By 0},
	pages = {994-1001},
	abstract = {Recently, Morocco has started to invest in IoT systems to transform our cities into smart cities that will promote economic growth and make life easier for citizens. One of the most vital addition is intelligent transportation systems which represent the foundation of a smart city. However, the problem often faced in such systems is the recognition of entities, in our case, car and model makes. This paper proposes an approach that identifies makes and models for cars using transfer learning and a workflow that first enhances image quality and quantity by data augmentation and then feeds the newly generated data into a deep learning model with a scaling feature{\textendash}that is, compound scaling. In addition, we developed a web interface using the FLASK API to make real-time predictions. The results obtained were 80\% accuracy, fine-tuning it to an accuracy rate of 90\% on unseen data. Our framework is trained on the commonly used Stanford Cars dataset. {\textcopyright} 2022. International Journal of Advanced Computer Science and Applications. All Rights Reserved.},
	keywords = {Application programming interfaces (API), Compound scaling, Deep learning, Economic growths, Economics, End to end, Image enhancement, Intelligent systems, Intelligent transportation systems, Internet of things, IOT, Model classification, Scalings, Smart city, Transfer learning, Vehicle classification},
	doi = {10.14569/IJACSA.2022.01305111},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85131410221\&doi=10.14569\%2fIJACSA.2022.01305111\&partnerID=40\&md5=aeb1c5a894ab70005066f491ebf3623c},
	author = {Bourja, O. and Maach, A. and Zannouti, Z. and Derrouz, H. and Mekhzoum, H. and Abdelali, H.A. and Thami, R.O.H. and Bourzeix, F.}
}
@article {Nakach20223,
	title = {Random Forest Based Deep Hybrid Architecture for Histopathological Breast Cancer Images Classification},
	journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
	volume = {13376 LNCS},
	year = {2022},
	note = {cited By 0},
	pages = {3-18},
	abstract = {Breast cancer is the most common cancer in women worldwide. While the early diagnosis and treatment can significantly reduce the mortality rate, it is a challenging task for pathologists to accurately estimate the cancerous cells and tissues. Therefore, machine learning techniques are playing a significant role in assisting pathologists and improving the diagnosis results. This paper proposes a hybrid architecture that combines: three of the most recent deep learning techniques for feature extraction (DenseNet_201, Inception_V3, and MobileNet_V2) and random forest to classify breast cancer histological images over the BreakHis dataset with its four magnification factors: 40X, 100X, 200X and 400X. The study evaluated and compared: (1) the developed random forest models with their base learners, (2) the designed random forest models with the same architecture but with a different number of trees, (3) the decision tree classifiers with the best random forest models and (4) the best random forest models of each feature extractor. The empirical evaluations used: four classification performance criteria (accuracy, sensitivity, precision and F1-score), 5-fold cross-validation, Scott Knott statistical test, and Borda Count voting method. The best random forest model achieved an accuracy mean value of 85.88\%, and was constructed using 9 trees, 200X as a magnification factor, and Inception_V3 as a feature extractor. The experimental results demonstrated that combining random forest with deep learning models is effective for the automatic classification of malignant and benign tumors using histopathological images of breast cancer. {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.},
	keywords = {Breast Cancer, Classification (of information), Decision trees, Deep learning, Diagnosis, Diseases, early diagnosis, Feature extractor, hybrid, Hybrid architectures, Image classification, Images classification, Learning systems, Magnification factors, Medical imaging, Random forest modeling, Random forests, Transfer learning},
	doi = {10.1007/978-3-031-10450-3_1},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85135002675\&doi=10.1007\%2f978-3-031-10450-3_1\&partnerID=40\&md5=892757314e050f81e9b0dba117f2d64a},
	author = {Nakach, F.-Z. and Zerouaoui, H. and Idri, A.}
}
@article {Benbriqa2021588,
	title = {Deep and Ensemble Learning Based Land Use and Land Cover Classification},
	journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
	volume = {12951 LNCS},
	year = {2021},
	note = {cited By 1},
	pages = {588-604},
	abstract = {Monitoring of Land use and Land cover (LULC) changes is a highly encumbering task for humans. Therefore, machine learning based classification systems can help to deal with this challenge. In this context, this study evaluates and compares the performance of two Single Learning (SL) techniques and one Ensemble Learning (EL) technique. All the empirical evaluations were over the open source LULC dataset proposed by the German Center for Artificial Intelligence (EuroSAT), and used the performance criteria -accuracy, precision, recall, F1 score and change in accuracy for the EL classifiers-. We firstly evaluate the performance of SL techniques: Building and optimizing a Convolutional Neural Network architecture, implementing Transfer learning, and training Machine learning algorithms on visual features extracted by Deep Feature Extractors. Second, we assess EL techniques and compare them with SL classifiers. Finally, we compare the capability of EL and hyperparameter tuning to improve the performance of the Deep Learning models we built. These experiments showed that Transfer learning is the SL technique that achieves the highest accuracy and that EL can indeed outperform the SL classifiers. {\textcopyright} 2021, Springer Nature Switzerland AG.},
	keywords = {Classification (of information), Convolutional neural networks, Deep feature extraction, Deep learning, Ensemble learning, Features extraction, Hyper-parameter optimizations, Land cover, Land use, Learning algorithms, Learning classifiers, Learning techniques, Network architecture, Performance, Transfer learning},
	doi = {10.1007/978-3-030-86970-0_41},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115689890\&doi=10.1007\%2f978-3-030-86970-0_41\&partnerID=40\&md5=910871e0f58b4f00460e5e2509929a23},
	author = {Benbriqa, H. and Abnane, I. and Idri, A. and Tabiti, K.}
}
