@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 {Lahmar2022,
	title = {Deep hybrid architectures for diabetic retinopathy classification},
	journal = {Computer Methods in Biomechanics and Biomedical Engineering: Imaging and Visualization},
	year = {2022},
	note = {cited By 0},
	abstract = {Diabetic retinopathy (DR) is the most severe ocular complication of diabetes. It leads to serious eye complications such as vision impairment and blindness. A computer-aided diagnosis may help in the early detection of this disease, which increases the chances of treating it efficiently. This paper carried out an empirical evaluation of the performances of 28 deep hybrid architectures for an automatic binary classification of the referable diabetic retinopathy, and compared them to seven end-to-end deep learning (DL) architectures. For the hybrid architectures, we combined seven DL techniques for feature extraction (DenseNet201, VGG16, VGG19, MobileNet_V2, Inception_V3, Inception_ResNet_V2 and ResNet50) and four classifiers (SVM, MLP, DT and KNN). For the end-to-end DL architectures, we used the same techniques used for the feature extraction in the hybrid architectures. The architectures were compared in terms of accuracy, sensitivity, precision and F1-score using the Scott Knott test and the Borda count voting method. All the empirical evaluations were over three datasets: APTOS, Kaggle DR and Messidor-2, using a k-fold cross validation method. The results showed the potential of combining deep learning techniques for feature extraction and classical machine learning techniques to classify referable diabetic retinopathy. The hybrid architecture using the SVM classifier and MobileNet_V2 for feature extraction was the top performing architecture and it was classified with the best performing end-to-end architectures in the best clusters of APTOS, Kaggle DR and Messidor-2 datasets with an accuracy equal to 88.80\%, 84.01\% and 84.05\% respectively. Note that the two end-to-end architectures DenseNet201 and MobileNet_V2 outperformed all the hybrid architectures over the three datasets. However, we recommend the use of the hybrid architecture designed with SVM and MobileNet_V2 since it is promising and less time consuming, and requires less parameter tuning compared to the end-to-end techniques. {\textcopyright} 2022 Informa UK Limited, trading as Taylor \& Francis Group.},
	keywords = {Classification (of information), Computer aided diagnosis, Deep learning, Diabetic retinopathy, Empirical evaluations, End to end, Extraction, Eye protection, Feature extraction, Features extraction, Hybrid architectures, Image classification, Learning algorithms, Learning architectures, Learning techniques, Medical image, Medical imaging, Performance, Support vector machines, Vision impairments},
	doi = {10.1080/21681163.2022.2060864},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85129262042\&doi=10.1080\%2f21681163.2022.2060864\&partnerID=40\&md5=35c1d7e5976fbdb00e23b4aa456eadc9},
	author = {Lahmar, C. and Idri, A.}
}
@article {Ahajjam2022,
	title = {Experimental investigation of variational mode decomposition and deep learning for short-term multi-horizon residential electric load forecasting},
	journal = {Applied Energy},
	volume = {326},
	year = {2022},
	note = {cited By 0},
	abstract = {With the booming growth of advanced digital technologies, it has become possible for users as well as distributors of energy to obtain detailed and timely information about the electricity consumption of households. These technologies can also be used to forecast the household{\textquoteright}s electricity consumption (a.k.a. the load). In this paper, Variational Mode Decomposition and deep learning techniques are investigated as a way to improve the accuracy of the load forecasting problem. Although this problem has been studied in the literature, selecting an appropriate decomposition level and a deep learning technique providing better forecasting performance have garnered comparatively less attention. This study bridges this gap by studying the effect of six decomposition levels and five distinct deep learning networks. The raw load profiles are first decomposed into intrinsic mode functions using the Variational Mode Decomposition in order to mitigate their non-stationary aspect. Then, day, hour, and past electricity consumption data are fed as a three-dimensional input sequence to a four-level Wavelet Decomposition Network model. Finally, the forecast sequences related to the different intrinsic mode functions are combined to form the aggregate forecast sequence. The proposed method was assessed using load profiles of five Moroccan households from the Moroccan buildings{\textquoteright} electricity consumption dataset (MORED) and was benchmarked against state-of-the-art time-series models and a baseline persistence model. {\textcopyright} 2022 Elsevier Ltd},
	keywords = {data set, decomposition, Decomposition level, Deep learning, Electric load forecasting, Electric power plant loads, Electric power utilization, electricity, electricity supply, Electricity-consumption, Experimental investigations, forecasting method, Housing, Intrinsic mode functions, learning, Learning algorithms, Learning systems, Learning techniques, Load profiles, Multi-horizon forecasting, Residential load forecasting, Short-term residential load forecasting, Variational mode decomposition, Wavelet decomposition},
	doi = {10.1016/j.apenergy.2022.119963},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85138343844\&doi=10.1016\%2fj.apenergy.2022.119963\&partnerID=40\&md5=322ce14ee40d2de7bbdfa72a4aabc718},
	author = {Ahajjam, M.A. and Bonilla Licea, D. and Ghogho, M. and Kobbane, 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.}
}
@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.}
}
@article {ElIdrissi2021347,
	title = {Evaluating a Comparing Deep Learning Architectures for Blood Glucose Prediction},
	journal = {Communications in Computer and Information Science},
	volume = {1400 CCIS},
	year = {2021},
	note = {cited By 0},
	pages = {347-365},
	abstract = {To manage their disease, diabetic patients need to control the blood glucose level (BGL) by monitoring it and predicting its future values. This allows to avoid high or low BGL by taking recommended actions in advance. In this paper, we conduct a comparative study of two emerging deep learning techniques: Long-Short-Term Memory (LSTM) and Convolutional Neural Networks (CNN) for one-step and multi-steps-ahead forecasting of the BGL based on Continuous Glucose Monitoring (CGM) data. The objectives are twofold: 1) Determining the best strategies of multi-steps-ahead forecasting (MSF) to fit the CNN and LSTM models respectively, and 2) Comparing the performances of the CNN and LSTM models for one-step and multi-steps prediction. Toward these objectives, we firstly conducted series of experiments of a CNN model through parameters selection to determine its best configuration. The LSTM model we used in the present study was developed and evaluated in an earlier work. Thereafter, five MSF strategies were developed and evaluated for the CNN and LSTM models using the Root-Mean-Square Error (RMSE) with an horizon of 30~min. To statistically assess the differences between the performances of CNN and LSTM models, we used the Wilcoxon statistical test. The results showed that: 1) no MSF strategy outperformed the others for both CNN and LSTM models, and 2) the proposed CNN model significantly outperformed the LSTM model for both one-step and multi-steps prediction. {\textcopyright} 2021, Springer Nature Switzerland AG.},
	keywords = {Biomedical engineering, Blood, Blood glucose level, Comparative studies, Continuous glucosemonitoring (CGM), Convolutional neural networks, Deep learning, Diabetic patient, Disease control, Forecasting, Glucose, Learning architectures, Learning techniques, Long short-term memory, Mean square error, Parameters selection, Root mean square errors},
	doi = {10.1007/978-3-030-72379-8_17},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85107268637\&doi=10.1007\%2f978-3-030-72379-8_17\&partnerID=40\&md5=2b36d3e75ce8d317c8b80f3272f4d956},
	author = {El Idrissi, T. and Idri, A.}
}
@conference {Berradi2020367,
	title = {A comprehensive review of artificial intelligence techniques in financial market},
	booktitle = {Colloquium in Information Science and Technology, CIST},
	volume = {2020-June},
	year = {2020},
	note = {cited By 0},
	pages = {367-371},
	abstract = {Artificial intelligence is a vast and promising domain that helps improving people{\textquoteright}s lives in different areas such as, medicine, education, telecommunication, finance, and economy. The financial market is an important aspect of the economics of any country, by having a clear idea about how it functions, it helps to improve the economy of the country radically and, therefore, the people{\textquoteright}s lives. In this paper, we suggest giving the latest research of deep learning techniques applied on the financial market field that can help investors to make an accurate decision. This paper gathered all the recent articles related to deep learning techniques applied on forecasting the financial market, which includes stock market, stock index, commodity forecasting and Forex. The main goal is to find the most models used recently to solve the prediction problem using RNN, their characteristic and novelty. We will give all aspects that involve the process of forecasting beginning with preprocessing, the input features, the deep learning techniques, and the evaluation metrics employed. {\textcopyright} 2021 IEEE.},
	keywords = {Artificial intelligence techniques, Commerce, Deep learning, Electronic trading, Evaluation metrics, Financial markets, Fintech, Forecasting, Input features, Investments, It functions, Learning systems, Learning techniques, Prediction problem, Stock indices},
	doi = {10.1109/CiSt49399.2021.9357175},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85103852221\&doi=10.1109\%2fCiSt49399.2021.9357175\&partnerID=40\&md5=b8ae02858f3f66114efcfced0ae9f173},
	author = {Berradi, Z. and Lazaar, M. and Mahboub, O. and Omara, H.}
}
@article {Maryame2020441,
	title = {State of the Art of Deep Learning Applications in Sentiment Analysis: Psychological Behavior Prediction},
	journal = {Advances in Intelligent Systems and Computing},
	volume = {1076},
	year = {2020},
	note = {cited By 3},
	pages = {441-451},
	abstract = {With the explosion of web 2.0, we are witnessing a sharp increase in Internet users such as a vertiginous evolution of social media. These media constitute a source of rich and varied information for researchers in sentiment analyses. This paper describes a tool for sifting through and synthesizing reviews by identifying the main deep learning techniques applied for sentiment analysis especially when it comes to psychological behavior prediction. {\textcopyright} Springer Nature Singapore Pte Ltd. 2020.},
	keywords = {Behavior prediction, Behavioral research, Deep learning, Embedded systems, Internet users, Learning techniques, Sentiment analysis, Sharp increase, social media, State of the art, Web 2.0},
	doi = {10.1007/978-981-15-0947-6_42},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85085169691\&doi=10.1007\%2f978-981-15-0947-6_42\&partnerID=40\&md5=dcac33ec68694f08b6258ea4a477f755},
	author = {Maryame, N. and Najima, D. and Hasnae, R. and Rachida, A.}
}
