@article {Porebski2022115,
	title = {Comparison of color imaging vs. hyperspectral imaging for texture classification},
	journal = {Pattern Recognition Letters},
	volume = {161},
	year = {2022},
	note = {cited By 0},
	pages = {115-121},
	abstract = {Many approaches of texture analysis by color or hyperspectral imaging are based on the assumption that the image of a texture can be viewed as a multi-component image, where spatial interactions within and between components are jointly considered (opponent component approach) or not (marginal approach). When color images are coded in multiple color spaces, texture descriptors are based on Multi Color Channel (MCC) representations. By extension, a Multi Spectral Band (MSB) representation can be used to characterize the texture of material surfaces in hyperspectral images. MSB and MCC representations are compared in this paper for texture classification issues. The contribution of each representation is investigated with marginal and/or opponent component strategies. For this purpose, several relevant texture descriptors are considered. Since MSB and MCC representations generate high-dimensional feature spaces, a dimensionality reduction is applied to avoid the curse of dimensionality. Experimental results carried out on three hyperspectral texture databases (HyTexiLa, SpecTex and an original dataset extracted from the Timbers database) show that considering between component interactions in addition to the within ones significantly improves the classification accuracies. The proposed approaches allow also to outperform state of the art hand-designed descriptors and color texture descriptors based on deep learning networks. This study highlights the contribution of hyperspectral imaging compared to color imaging for texture classification purposes but also the advantages of color imaging depending on the considered texture representation. {\textcopyright} 2022},
	keywords = {Channel representation, Classification (of information), Color, Color channels, Color imaging, Deep learning, Features selection, Hyperspectral imaging, Image texture, Multi-colors, Multi-spectral, Spectral band, Spectroscopy, Texture classification, Texture descriptors, Texture representation, Textures},
	doi = {10.1016/j.patrec.2022.08.001},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85135883794\&doi=10.1016\%2fj.patrec.2022.08.001\&partnerID=40\&md5=e0ae6847684c79691a71e29e7b2c273b},
	author = {Porebski, A. and Alimoussa, M. and Vandenbroucke, N.}
}
@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 {AlAfandy2022739,
	title = {Efficient Classification of Remote Sensing Images Using Two Convolution Channels and SVM},
	journal = {Computers, Materials and Continua},
	volume = {72},
	number = {1},
	year = {2022},
	note = {cited By 0},
	pages = {739-753},
	abstract = {Remote sensing image processing engaged researchers{\textquoteright} attentiveness in recent years, especially classification. The main problem in classification is the ratio of the correct predictions after training. Feature extraction is the foremost important step to build high-performance image classifiers. The convolution neural networks can extract images{\textquoteright} features that significantly improve the image classifiers{\textquoteright} accuracy. This paper proposes two efficient approaches for remote sensing images classification that utilizes the concatenation of two convolution channels{\textquoteright} outputs as a features extraction using two classic convolution models; these convolution models are the ResNet 50 and the DenseNet 169. These elicited features have been used by the fully connected neural network classifier and support vector machine classifier as input features. The results of the proposed methods are compared with other antecedent approaches in the same experimental environments. Evaluation is based on learning curves plotted during the training of the proposed classifier that is based on a fully connected neural network and measuring the overall accuracy for the both proposed classifiers. The proposed classifiers are used with their trained weights to predict a big remote sensing scene{\textquoteright}s classes for a developed test. Experimental results ensure that, compared with the other traditional classifiers, the proposed classifiers are further accurate. {\textcopyright} 2022 Tech Science Press. All rights reserved.},
	keywords = {Classification (of information), Classification of remote sensing image, Convolution, Convolution model, Deep learning, Densenet, Extraction, Features extraction, Fully connected neural network, Image classification, Image Classifiers, Image enhancement, Remote sensing, Remote sensing images, Resnet, Support vector machines, SVM},
	doi = {10.32604/cmc.2022.022457},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85125387732\&doi=10.32604\%2fcmc.2022.022457\&partnerID=40\&md5=d49f68f82164e31e29c0276c95a3c3ca},
	author = {AlAfandy, K.A. and Omara, H. and El-Sayed, H.S. and Baz, M. and Lazaar, M. and Faragallah, O.S. and Al Achhab, M.}
}
@article {Derrouz20225561,
	title = {End-to-end quantum-inspired method for vehicle classification based on video stream},
	journal = {Neural Computing and Applications},
	volume = {34},
	number = {7},
	year = {2022},
	note = {cited By 1},
	pages = {5561-5576},
	abstract = {Intelligent Transportation Systems (ITS) are the most widely used systems for road traffic management. The vehicle type classification (VTC) is a crucial ITS task due to its capability to gather valuable traffic information. However, designing a performant VTC method is challenging due to the considerable intra-class variation of vehicles. This paper presents a new quantum decision-based method for VTC applied to video streaming. This method allows for earlier decision-making by considering a few stream{\textquoteright}s images. Our method is threefold. First, the video stream is acquired and preprocessed following a specific pipeline. Second, we aim to detect and track vehicles. Therefore, we apply a deep learning-based model to detect vehicles, and then a vehicle tracking algorithm is used to track each detected vehicle. Third, we seek to classify the tracked vehicle according to six defined classes. Furthermore, we transform the tracked vehicles according to a pipeline, consisting of the histogram of oriented gradients (HOG), and principal component analysis (PCA) methods. Then, we estimate the vehicles{\textquoteright} probabilities of belonging to each class by training multilayer perceptron (MLP) classifier with the resulting features. To assign a class to a vehicle, we apply a quantum-inspired probability integrator that handles each frame{\textquoteright}s information flow. The unique characteristics of the work we propose, compared to the existing ones, are expressed in the decision-making process, since the former requires a sequence of frames of different sizes, compared to the image-based-decision made by the other methods. Our method outperformed the baseline methods with an accuracy up to 96\%. {\textcopyright} 2021, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.},
	keywords = {Classification (of information), Decision making, Decisions makings, Deep learning, Early decision, Early decision making, End to end, Intelligent systems, Intelligent transportation systems, Intelligent vehicle highway systems, Pipelines, Principal component analysis, Quantum inspired algorithm, Tracked vehicles, Type classifications, Vehicle classification, Vehicle type classification, Vehicle types, Video streaming},
	doi = {10.1007/s00521-021-06718-9},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85122251939\&doi=10.1007\%2fs00521-021-06718-9\&partnerID=40\&md5=8e1d19359f8245b2f82d8558670d4ba1},
	author = {Derrouz, H. and Cabri, A. and Ait Abdelali, H. and Oulad Haj Thami, R. and Bourzeix, F. and Rovetta, S. and Masulli, F.}
}
@conference {Rachidi202213,
	title = {Network intrusion detection using Machine Learning approach},
	booktitle = {ACM International Conference Proceeding Series},
	year = {2022},
	note = {cited By 0},
	pages = {13-17},
	abstract = {Today, intrusion detection has become an active research area. Due to the rapidly increasing number of intrusion variants, intrusion detection system analyses and notifies the activities of users as normal (or) anomaly. In our paper, we built a model of intrusion detection system applied to the NSL-KDD data set using different supervised classifiers such as KNN and Na{\"\i}ve Bayes. We also proposed two algorithms for multi-classification based on the Random Forest (RF) which is an ensemble classifier and KNN. Then we used the K-folds method to evaluate and validate our model. To evaluate the performances, we realized experiments on NSL-KDD data set. The result shows that the second proposed algorithm is efficient with high accuracy and time optimization. {\textcopyright} 2022 ACM.},
	keywords = {Barium compounds, Classification (of information), Computer crime, data set, Decision trees, Intrusion detection, Intrusion Detection Systems, Intrusion-Detection, Machine learning, Machine learning approaches, Multi-classification, Network intrusion detection, NSL-KDD, Random forests, Research areas, Sodium compounds, Supervised classifiers},
	doi = {10.1145/3551690.3551693},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85139141127\&doi=10.1145\%2f3551690.3551693\&partnerID=40\&md5=e47a5f8038487b706673543f7fdf2de4},
	author = {Rachidi, Z. and Chougdali, K. and Kobbane, A. and Ben-Othman, J.}
}
@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 {Benhar2022791,
	title = {Univariate and Multivariate Filter Feature Selection for Heart Disease Classification},
	journal = {Journal of Information Science and Engineering},
	volume = {38},
	number = {4},
	year = {2022},
	note = {cited By 0},
	pages = {791-803},
	abstract = {Feature selection (FS) is a data preprocessing task that can be applied before the classification phase, and aims at improving the performance and interpretability of classifiers by finding only a few highly informative features. The present study aims at evaluating and comparing the performances of six univariate and two multivariate filter FS techniques for heart disease classification. The FS techniques were evaluated with two white-box and two black-box classification techniques using five heart disease datasets. Furthermore, this study deals with the setting of the hyperparameters{\textquoteright} values of the four classifiers. This study evaluates 600 variants of classifiers. Results show that white-box classification techniques such as K-Nearest Neighbors and Decision Trees can be very competitive with black-box ones when hyperparameters{\textquoteright} optimization and feature selection were applied. {\textcopyright} 2022 Institute of Information Science. All rights reserved.},
	keywords = {Black boxes, Cardiology, Classification (of information), Data preprocessing, Decision trees, disease classification, Diseases, feature selection, Features selection, Filter, Heart, heart disease, Nearest neighbor search, Performance, Selection techniques, Univariate, White box},
	doi = {10.6688/JISE.202207_38(4).0006},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85137170374\&doi=10.6688\%2fJISE.202207_38\%284\%29.0006\&partnerID=40\&md5=f75bca8ff78e6782d4c11e88d338784f},
	author = {Benhar, H. and Hosni, M. 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.}
}
@article {ElOuassif202150,
	title = {Classification techniques in breast cancer diagnosis: A systematic literature review},
	journal = {Computer Methods in Biomechanics and Biomedical Engineering: Imaging and Visualization},
	volume = {9},
	number = {1},
	year = {2021},
	note = {cited By 12},
	pages = {50-77},
	abstract = {Data mining (DM) consists in analysing a~set of observations to find unsuspected relationships and then summarising the data in new ways that are both understandable and useful. It has become widely used in various medical fields including breast cancer (BC), which is the most common cancer and the leading cause of death among women~worldwide.~BC diagnosis is a~challenging medical task and many studies have attempted to apply classification techniques to it. The objective of the present study is to identify studies on classification techniques~in~BC diagnosis and to analyse them from~three perspectives: classification techniques used, accuracy of the classifiers, and comparison of performance. We performed a~systematic literature review (SLR) of 176 selected studies published between January~2000 and November~2018. The results show that, of the nine classification techniques investigated, artificial neural networks, support vector machines and decision trees were the most frequently used. Moreover, artificial neural networks, support vector machines and ensemble classifiers performed better than the other techniques, with median accuracy values of 95\%, 95\% and 96\% respectively. Most of the selected studies (57.4\%) used datasets containing different types of images such as mammographic, ultrasound, and microarray images. {\textcopyright} 2021 Informa UK Limited, trading as Taylor \& Francis Group.},
	keywords = {Article, Artificial intelligence, artificial neural network, Breast Cancer, Breast cancer diagnosis, cancer diagnosis, cause of death, Causes of death, Classification (of information), Classification technique, Comparison of performance, Computer aided diagnosis, data extraction, Data mining, data synthesis, decision tree, Decision trees, Diseases, human, k nearest neighbor, Machine learning, Medical fields, Medical informatics, Network support, Neural networks, publication, qualitative research, Quality control, support vector machine, Support vector machine classifiers, Support vector machines, Support vectors machine, Systematic literature review, Systematic Review, validity},
	doi = {10.1080/21681163.2020.1811159},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85098842973\&doi=10.1080\%2f21681163.2020.1811159\&partnerID=40\&md5=9a48998b1c44d263863efcfb25f9920f},
	author = {ElOuassif, B. and Idri, A. and Hosni, M. and Abran, 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.}
}
@article {CHLIOUI20211039,
	title = {Ensemble case based reasoning imputation in breast cancer classification},
	journal = {Journal of Information Science and Engineering},
	volume = {37},
	number = {5},
	year = {2021},
	note = {cited By 1},
	pages = {1039-1051},
	abstract = {Missing Data (MD) is a common drawback that affects breast cancer classification. Thus, handling missing data is primordial before building any breast cancer classifier. This paper presents the impact of using ensemble Case-Based Reasoning (CBR) imputation on breast cancer classification. Thereafter, we evaluated the influence of CBR using parameter tuning and ensemble CBR (E-CBR) with three missingness mechanisms (MCAR: Missing completely at random, MAR: Missing at random and NMAR: not missing at random) and nine percentages (10\% to 90\%) on the accuracy rates of five classifiers: Decision trees, Random forest, K-nearest neighbor, Support vector machine and Multi-layer perceptron over two Wisconsin breast cancer datasets. All experiments were implemented using Weka JAVA API code 3.8; SPSS v20 was used for statistical tests. The findings confirmed that E-CBR yields to better results compared to CBR for the five classifiers. The MD percentage affects negatively the classifier performance: As the MD percentage increases, the accuracy rates of the classifier decrease regardless the MD mechanism and technique. RF with E-CBR outperformed all the other combinations (MD technique, classifier) with 89.72\% for MCAR, 87.08\% for MAR and 86.84\% for NMAR. {\textcopyright} 2021 Institute of Information Science. All rights reserved.},
	keywords = {Accuracy rate, Breast Cancer, Breast cancer classifications, Cancer classifier, Case based reasoning, Case-based reasoning imputation, Casebased reasonings (CBR), Classification (of information), Data handling, Decision trees, Diseases, Ensemble, Missing at randoms, Missing data, Nearest neighbor search, Parameters tuning, Support vector machines},
	doi = {10.6688/JISE.202109_37(5).0004},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115966179\&doi=10.6688\%2fJISE.202109_37\%285\%29.0004\&partnerID=40\&md5=97c15046a8900f9df38ec3430801c844},
	author = {Chlioui, I. and Idri, A. and Abnane, I. and EZZAT, M.}
}
@article {Iazzi2021,
	title = {Fall detection system-based posture-recognition for indoor environments},
	journal = {Journal of Imaging},
	volume = {7},
	number = {3},
	year = {2021},
	note = {cited By 7},
	abstract = {The majority of the senior population lives alone at home. Falls can cause serious injuries, such as fractures or head injuries. These injuries can be an obstacle for a person to move around and normally practice his daily activities. Some of these injuries can lead to a risk of death if not handled urgently. In this paper, we propose a fall detection system for elderly people based on their postures. The postures are recognized from the human silhouette which is an advantage to preserve the privacy of the elderly. The effectiveness of our approach is demonstrated on two well-known datasets for human posture classification and three public datasets for fall detection, using a Support-Vector Machine (SVM) classifier. The experimental results show that our method can not only achieves a high fall detection rate but also a low false detection. Copyright: {\textcopyright} 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/ 4.0/).},
	keywords = {Background subtraction, Classification (of information), Detection system, Fall detection, Features extraction, Head injuries, Human posture recognition, Indoor environment, Posture recognition, Serious injuries, Support vector machines, Video surveillance},
	doi = {10.3390/jimaging7030042},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85104580792\&doi=10.3390\%2fjimaging7030042\&partnerID=40\&md5=cb6ec76b0560734c5d22b49662b3876b},
	author = {Iazzi, A. and Rziza, M. and Haj Thami, R.O.}
}
@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 {Ahajjam2021,
	title = {On Multi-Label Classification for Non-Intrusive Load Identification using Low Sampling Frequency Datasets},
	booktitle = {Conference Record - IEEE Instrumentation and Measurement Technology Conference},
	volume = {2021-May},
	year = {2021},
	note = {cited By 0},
	abstract = {Non-intrusive load monitoring (NILM) aims to infer information about the electric consumption of individual loads using the premises{\textquoteright} aggregate consumption. In this work, we target supervised multi-label classification for non-intrusive load identification. We describe how we have created a new dataset from Moroccan households using a low sampling frequency. Then, we analyze the performance of three machine learning models for NILM, and investigate the impact of signal input length on performance. {\textcopyright} 2021 IEEE.},
	keywords = {Aggregate consumption, Classification (of information), Electric consumption, Load identification, Machine learning models, Measurement, Multi label classification, Non-intrusive, Nonintrusive load monitoring, Sampling frequencies},
	doi = {10.1109/I2MTC50364.2021.9460059},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85113709883\&doi=10.1109\%2fI2MTC50364.2021.9460059\&partnerID=40\&md5=2f6ca1a0b8790dce2a085d0e0ff10cf4},
	author = {Ahajjam, M.A. and Essayeh, C. and Ghogho, M. and Kobbane, A.}
}
@conference {Btissam202116,
	title = {Partially Connected Neural Networks for an Efficient Classification of Traffic Signs},
	booktitle = {Conference of Open Innovation Association, FRUCT},
	volume = {2021-October},
	year = {2021},
	note = {cited By 0},
	pages = {16-23},
	abstract = {Road signs recognition plays an important role in improving traffic safety for both drivers and pedestrians. To ensure this recognition, many approaches are proposed by researchers. To overcome the limitations of the existing methods, Deep Learning approaches are used. This type of approaches achieves high recognition performances, and is also less sensitive to real world adverse conditions. However, they are in contrast very computationally expensive due essentially to three main factors, which are more precisely, the size of input images, the type of used layers, and the number of used parameters. From this perspective, the objective of this work is to adopt an approach that aims to reduce this computational complexity, in order to ensure a fast and efficient classification of traffic signs, especially for low and limited resources environments. The adopted approach reaches good classification accuracies, and that by using BTSCD dataset. {\textcopyright} 2021 FRUCT.},
	keywords = {Classification (of information), Classification of traffic signs, Condition, Deep learning, Input image, Learning approach, Partially connected neural network, Pedestrian safety, Performance, Real-world, Resources environments, Road signs recognition, Traffic safety, Traffic signs},
	doi = {10.23919/FRUCT53335.2021.9599985},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85122993528\&doi=10.23919\%2fFRUCT53335.2021.9599985\&partnerID=40\&md5=7d1a5ba2a41b8715c681e1ec9bd10687},
	author = {Btissam, B. and Driss, B.}
}
@article {Karbal2020157,
	title = {A Comparison of Different Machine Learning Algorithms for Intrusion Detection},
	journal = {Communications in Computer and Information Science},
	volume = {1264},
	year = {2020},
	note = {cited By 0},
	pages = {157-169},
	abstract = {With the rapid development of the internet, intrusion detection became one of the major research problems in computer security. Many Intrusion Detection Systems (IDS) use data mining algorithms for classifying network traffic data and detecting different security violations. In this paper, we present some of the datasets and methods employed with the focus on network anomaly detection. We compare different machine learning techniques used in the latest research carried out for developing network intrusion detection systems. We also present an overview of some deep learning methodologies and their application for IDS purposes. The primary objective of this survey is to provide with a researcher, the state of the artwork already performed in this field of research. {\textcopyright} 2020, Springer Nature Switzerland AG.},
	keywords = {Anomaly detection, Classification (of information), Computer crime, Data communication systems, Data mining, Data mining algorithm, Deep learning, Intrusion detection, Intrusion Detection Systems, Learning algorithms, Learning systems, Machine learning techniques, Network anomaly detection, Network intrusion detection systems, Network security, Primary objective, Research problems, Security violations},
	doi = {10.1007/978-3-030-61143-9_13},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097408678\&doi=10.1007\%2f978-3-030-61143-9_13\&partnerID=40\&md5=3725b891261e386c6df84eebcbc0e514},
	author = {Karbal, B. and Romadi, R.}
}
@article {Benhar2020,
	title = {Data preprocessing for heart disease classification: A systematic literature review.},
	journal = {Computer Methods and Programs in Biomedicine},
	volume = {195},
	year = {2020},
	note = {cited By 25},
	abstract = {Context: Early detection of heart disease is an important challenge since 17.3 million people yearly lose their lives due to heart diseases. Besides, any error in diagnosis of cardiac disease can be dangerous and risks an individual{\textquoteright}s life. Accurate diagnosis is therefore critical in cardiology. Data Mining (DM) classification techniques have been used to diagnosis heart diseases but still limited by some challenges of data quality such as inconsistencies, noise, missing data, outliers, high dimensionality and imbalanced data. Data preprocessing (DP) techniques were therefore used to prepare data with the goal of improving the performance of heart disease DM based prediction systems. Objective: The purpose of this study is to review and summarize the current evidence on the use of preprocessing techniques in heart disease classification as regards: (1) the DP tasks and techniques most frequently used, (2) the impact of DP tasks and techniques on the performance of classification in cardiology, (3) the overall performance of classifiers when using DP techniques, and (4) comparisons of different combinations classifier-preprocessing in terms of accuracy rate. Method: A systematic literature review is carried out, by identifying and analyzing empirical studies on the application of data preprocessing in heart disease classification published in the period between January 2000 and June 2019. A total of 49 studies were therefore selected and analyzed according to the aforementioned criteria. Results: The review results show that data reduction is the most used preprocessing task in cardiology, followed by data cleaning. In general, preprocessing either maintained or improved the performance of heart disease classifiers. Some combinations such as (ANN + PCA), (ANN + CHI) and (SVM + PCA) are promising terms of accuracy. However the deployment of these models in real-world diagnosis decision support systems is subject to several risks and limitations due to the lack of interpretation. {\textcopyright} 2020 Elsevier B.V.},
	keywords = {Cardiology, Classification (of information), Classification technique, classifier, clinical practice, clinical research, Computer aided diagnosis, data classification, Data mining, Data preprocessing, data processing, Decision support systems, Deep learning, Diagnosis decision, diagnostic accuracy, disease classification, Diseases, empiricism, evidence based practice, feature selection, Heart, heart disease, Heart Diseases, High dimensionality, human, Humans, intermethod comparison, Machine learning, Performance of classifier, prediction, Prediction systems, Preprocessing techniques, publication, Review, Support vector machines, Systematic literature review, Systematic Review, task performance},
	doi = {10.1016/j.cmpb.2020.105635},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85087500300\&doi=10.1016\%2fj.cmpb.2020.105635\&partnerID=40\&md5=cae53ce36903d5d8b817ec96deb39b45},
	author = {Benhar, H. and Idri, A. and L Fern{\'a}ndez-Alem{\'a}n, J.}
}
@article {Ezzarii2020,
	title = {Epigenetic algorithm-based detection technique for network attacks},
	journal = {IEEE Access},
	volume = {8},
	year = {2020},
	note = {cited By 2},
	abstract = {Nowadays, the cybersecurity issue involves new strategies to protect against advanced threats and unknown attacks. Intrusion detection system (IDS) is considered a robust system dealing with attacks detection, particularly unknown attacks and anomalies. Several IDS-based algorithms have been recently inspected in the literature, among them the well-known strengthen algorithms, i.e. Genetic algorithm (GA). Moreover, Epigenetic-based algorithm (EGA) is known as an improved version of GA ensuring high performance with reduced computational complexity. Its main goal is to converge within a short time towards an optimal solution by acting on genetic operators, namely mutation and crossover. In this article, we propose a new classifier based on EGA for IDS. Especially, based on a database of network traffics, EGA is applied to classify attacks. The results, performed through EGA simulation, show that the performance of the proposed technique outperforms the ones of GA classifier by obtaining a high detection rate up to 98\% and a faster processing time than that of GA and other algorithms that we have compared in this article. {\textcopyright} 2020 BMJ Publishing Group. All rights reserved.},
	keywords = {Classification (of information), Computer crime, Genetic algorithms, Genetic operators, High detection rate, Intrusion detection, Intrusion Detection Systems, Network attack, Network security, Network traffic, Optimal solutions, Processing time, Unknown attacks},
	doi = {10.1109/ACCESS.2020.3035250},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097226994\&doi=10.1109\%2fACCESS.2020.3035250\&partnerID=40\&md5=2b4d9568d49e06ee5d704619bbb792f2},
	author = {Ezzarii, M. and El Ghazi, H. and El Ghazi, H. and El Bouanani, F.}
}
@conference {Benhar2020391,
	title = {Impact of threshold values for filter-based univariate feature selection in heart disease classification},
	booktitle = {HEALTHINF 2020 - 13th International Conference on Health Informatics, Proceedings; Part of 13th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2020},
	year = {2020},
	note = {cited By 2},
	pages = {391-398},
	abstract = {In the last decade, feature selection (FS), was one of the most investigated preprocessing tasks for heart disease prediction. Determining the optimal features which contribute more towards the diagnosis of heart disease can reduce the number of clinical tests needed to be taken by a patient, decrease the model cost, reduce the storage requirements and improve the comprehensibility of the induced model. In this study a comparison of three filter feature ranking methods was carried out. Feature ranking methods need to set a threshold (i.e. the percentage of the number of relevant features to be selected) in order to select the final subset of features. Thus, the aim of this study is to investigate if there is a threshold value which is an optimal choice for three different feature ranking methods and four classifiers used for heart disease classification in four heart disease datasets. The used feature ranking methods and selection thresholds resulted in optimal classification performance for one or more classifiers over small and large heart disease datasets. The size of the dataset takes an important role in the choice of the selection threshold. {\textcopyright} 2020 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved.},
	keywords = {Biomedical engineering, Cardiology, Classification (of information), Clinical tests, Diagnosis, Diseases, Feature extraction, Feature ranking, Heart, heart disease, Large dataset, Medical informatics, Optimal choice, Optimal classification, Relevant features, Storage requirements, Threshold-value},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083712586\&partnerID=40\&md5=4656d8b952f7c60387d4495c737c5a6d},
	author = {Benhar, H. and Idri, A. and Hosni, M.}
}
@conference {Ajallouda2020169,
	title = {K-means hac and fcm which clustering approach for arabic text?},
	booktitle = {ACM International Conference Proceeding Series},
	year = {2020},
	note = {cited By 0},
	pages = {169-174},
	abstract = {Today we are witnessing rapid growth in Web resources that allow Internet users to express and share their ideas opinions and judgments on a variety of issues. Several classification approaches have been proposed to classify textual data. But all these approaches require us to label the clusters we want to obtain. Which in reality is not available because we do not know in advance the information that can be proposed through these opinions. To overcome this constraint clustering approaches such as K-mean HAC or FCM can be exploited. In this paper we present and compare these approaches. And to show the importance of exploiting clustering algorithms to classify and analyze textual data in Arabic. By applying them to a real case that has created a great debate in Morocco which is the case of teachers contracting with academies. {\textcopyright} 2020 ACM.},
	keywords = {Arabic texts, Classification (of information), Classification approach, Clustering approach, Intelligent systems, Internet users, K-means clustering, Rapid growth, Real case, Textual data, Web resources},
	doi = {10.1145/3419604.3419779},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096415898\&doi=10.1145\%2f3419604.3419779\&partnerID=40\&md5=8aca383d8f485d15b3da191f03c5bf62},
	author = {Ajallouda, L. and Fagroud, F.Z. and Zellou, A. and Benlahmar, E.H.}
}
@article {Hosni20202177,
	title = {A mapping study of ensemble classification methods in lung cancer decision support systems},
	journal = {Medical and Biological Engineering and Computing},
	volume = {58},
	number = {10},
	year = {2020},
	note = {cited By 8},
	pages = {2177-2193},
	abstract = {Achieving a high level of classification accuracy in medical datasets is a capital need for researchers to provide effective decision systems to assist doctors in work. In many domains of artificial intelligence, ensemble classification methods are able to improve the performance of single classifiers. This paper reports the state of the art of ensemble classification methods in lung cancer detection. We have performed a systematic mapping study to identify the most interesting papers concerning this topic. A total of 65 papers published between 2000 and 2018 were selected after an automatic search in four digital libraries and a careful selection process. As a result, it was observed that diagnosis was the task most commonly studied; homogeneous ensembles and decision trees were the most frequently adopted for constructing ensembles; and the majority voting rule was the predominant combination rule. Few studies considered the parameter tuning of the techniques used. These findings open several perspectives for researchers to enhance lung cancer research by addressing the identified gaps, such as investigating different classification methods, proposing other heterogeneous ensemble methods, and using new combination rules. [Figure not available: see fulltext.] {\textcopyright} 2020, International Federation for Medical and Biological Engineering.},
	keywords = {Artificial intelligence, Automatic searches, Biological organs, cancer classification, Classification (of information), Classification accuracy, Classification methods, classifier, decision support system, Decision support systems, decision tree, Decision trees, Diagnosis, Digital libraries, Diseases, Ensemble classification, Heterogeneous ensembles, human, lung cancer, Lung cancer detections, Majority voting rules, Mapping, priority journal, Review, Systematic mapping studies},
	doi = {10.1007/s11517-020-02223-8},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85087501825\&doi=10.1007\%2fs11517-020-02223-8\&partnerID=40\&md5=bdb2bd3f923da33ea3b564a0bd6e739e},
	author = {Hosni, M. and Garc{\'\i}a-Mateos, G. and Carrillo-De-Gea, J.M. and Idri, A. and Fernandez-Aleman, J.L.}
}
@conference {ElMrabtt2020,
	title = {Novel Convex Polyhedron Classifier for Sentiment Analysis},
	booktitle = {Proceedings of 2020 5th International Conference on Cloud Computing and Artificial Intelligence: Technologies and Applications, CloudTech 2020},
	year = {2020},
	note = {cited By 1},
	abstract = {In this paper, we propose a Novel Convex Polyhedron classifier (NCPC) based on the geometric concept convex hull. NCPC is basically a linear piecewise classifier (LPC). It partitions linearly non-separable data into various linearly separable subsets. For each of these subset of data, a linear hyperplane is used to classify them. We evaluate the performance of this classifier by combining it with two feature selection methods (Chi-squared and Anova F-value). Using two datasets, the results indicate that our proposed classifier outperforms other LPC-based classifiers. {\textcopyright} 2020 IEEE.},
	keywords = {Artificial intelligence, Chi-Squared, Classification (of information), Cloud computing, Convex hull, Convex polyhedrons, F values, Feature selection methods, Geometric concepts, Geometry, Linearly separable, Piece-wise, Sentiment analysis},
	doi = {10.1109/CloudTech49835.2020.9365906},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102620927\&doi=10.1109\%2fCloudTech49835.2020.9365906\&partnerID=40\&md5=143fd1c523fe8ae24563a9b0482f632c},
	author = {El Mrabtt, S. and Lazaar, M. and Al Achhab, M. and Omara, H.}
}
@article {Aissaoui2020117,
	title = {Toward a hybrid machine learning approach for extracting and clustering learners{\textquoteright} behaviours in adaptive educational system},
	journal = {International Journal of Computing Science and Mathematics},
	volume = {12},
	number = {2},
	year = {2020},
	note = {cited By 0},
	pages = {117-131},
	abstract = {The learning style is a vital learner{\textquoteright}s characteristic that must be considered while recommending learning materials. In this paper we have proposed an approach to identify learning styles automatically. The first step of the proposed approach aims to preprocess the data extracted from the log file of the E-learning environment and capture the learners{\textquoteright} sequences. The captured learners{\textquoteright} sequences were given as an input to the K-means clustering algorithm to group them into sixteen clusters according to the FSLSM. Then the naive Bayes classifier was used to predict the learning style of a student in real time. To perform our approach, we used a real dataset extracted from an e-learning system{\textquoteright}s log file, and in order to evaluate the performance of the used classifier, the confusion matrix method was used. The obtained results demonstrate that our approach yields excellent results. Copyright {\textcopyright} 2020 Inderscience Enterprises Ltd.},
	keywords = {Adaptive systems, Classification (of information), Classifiers, Computer aided instruction, Confusion matrices, e-learning, E-learning environment, Educational systems, Hybrid machine learning, K-means clustering, Learning materials, Learning Style, Machine learning, Naive Bayes classifiers, Preprocess},
	doi = {10.1504/IJCSM.2020.111113},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096034864\&doi=10.1504\%2fIJCSM.2020.111113\&partnerID=40\&md5=e39786511c32be21cc0fef5be68a0822},
	author = {Aissaoui, O.E. and El Alami El Madani, Y. and Oughdir, L. and Dakkak, A. and El Allioui, Y.}
}
