@article {Wadghiri2022,
	title = {Ensemble blood glucose prediction in diabetes mellitus: A review},
	journal = {Computers in Biology and Medicine},
	volume = {147},
	year = {2022},
	note = {cited By 0},
	abstract = {Considering the complexity of blood glucose dynamics, the adoption of a single model to predict blood glucose level does not always capture the inter- and intra-patients{\textquoteright} context changes. Ensembles are a set of machine learning techniques combining multiple single learners to find a better variance/bias trade-off and hence improve the prediction accuracy. The present paper aims to review the state of the art in predicting blood glucose using ensemble methods with regard to 8 criteria: publication year and sources, datasets used to train/evaluate the models, types of ensembles used, single learners involved to construct ensembles, combination schemes used to aggregate the base learners, metrics and validation methods adopted to assess the performance of ensembles, reported overall performance of the predictors and accuracy comparison of ensemble techniques with single models. A systematic literature review has been conducted in order to analyze and synthetize primary studies published between 2000 and 2020 in six digital libraries. A total of 32 primary papers were selected and reviewed with regard to eight review questions. The results show that ensembles have gained wider interest during the last years and improved in general the performance compared with other single models. However, multiple gaps have been identified concerning the ensembles construction process and the performance metrics used. Several recommendations have been made in this regard to design accurate ensembles for blood glucose level prediction. {\textcopyright} 2022 Elsevier Ltd},
	keywords = {algorithm, Algorithms, Blood, Blood glucose, Blood glucose level, Data mining, Diabetes mellitus, Digital libraries, Economic and social effects, Ensemble methods, Forecasting, Glucose, glucose blood level, Glucose dynamics, human, Humans, Machine learning, Machine learning techniques, Machine-learning, Performance, Single models, Trade off},
	doi = {10.1016/j.compbiomed.2022.105674},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85132341147\&doi=10.1016\%2fj.compbiomed.2022.105674\&partnerID=40\&md5=05bd59c3726654494a4be346e4e6a682},
	author = {Wadghiri, M.Z. and Idri, A. and El Idrissi, T. and Hakkoum, H.}
}
@article {L{\'o}pez-Jim{\'e}nez2021,
	title = {Effects of gamification on the benefits of student response systems in learning of human anatomy: Three experimental studies},
	journal = {International Journal of Environmental Research and Public Health},
	volume = {18},
	number = {24},
	year = {2021},
	note = {cited By 2},
	abstract = {{This paper presents three experiments to assess the impact of gamifying an audience response system on the perceptions and educational performance of students. An audience response system called SIDRA (Immediate Audience Response System in Spanish) and two audience response systems with gamification features, R-G-SIDRA (gamified SIDRA with ranking) and RB-G-SIDRA (gamified SIDRA with ranking and badges), were used in a General and Descriptive Human Anatomy course. Students participated in an empirical study. In the academic year 2019{\textendash}2020, a total of 90 students used RB-G-SIDRA, 90 students employed R-G-SIDRA in the academic year 2018{\textendash}2019, and 92 students used SIDRA in the academic year 2017{\textendash}2018. Statistically significant differences were found between final exam grades obtained by using RB-G-SIDRA and SIDRA},
	keywords = {academic achievement, academic performance, anatomy, experiment, Gamification, human, Humans, learning, motivation, perception, student, Students},
	doi = {10.3390/ijerph182413210},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85121118668\&doi=10.3390\%2fijerph182413210\&partnerID=40\&md5=29b9263d7b8db439936394c1f2826a10},
	author = {L{\'o}pez-Jim{\'e}nez, J.J. and Fernandez-Aleman, J.L. and Garc{\'\i}a-Bern{\'a}, J.A. and Gonz{\'a}lez, L.L. and Sequeros, O.G. and Ros, J.N. and Carrillo de Gea, J.M. and Idri, A. and Toval, A.}
}
@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 {Wadghiri2021544,
	title = {Ensemble Regression for Blood Glucose Prediction},
	journal = {Advances in Intelligent Systems and Computing},
	volume = {1365 AIST},
	year = {2021},
	note = {cited By 1},
	pages = {544-554},
	abstract = {Background: Predicting blood glucose present commonly many challenges when the designed models are tested under different contexts. Ensemble methods are a set of learning algorithms that have been successfully used in many medical fields to improve the prediction accuracy. This paper aims to review the typology of ensembles used in literature to predict blood glucose. Methods: 32 papers published between 2000 and 2020 in 6 digital libraries were selected and reviewed with regard to: years and publication sources, integrated factors and data sources used to collect the data and types of ensembles. Results: This review results found that this research topic is still recent but is gaining a growing interest in the last years. Ensemble models used often blood glucose, insulin, diet and exercise as input to predict blood glucose. Moreover, both homogeneous and heterogeneous ensembles have been investigated. Conclusions: An increasing interest have been devoted to blood glucose prediction using ensemble methods during the last decade. Several gaps regarding the design of the reviewed ensembles and the data collection process have been identified and recommendations have been formulated in this direction. {\textcopyright} 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.},
	keywords = {Blood, Data acquisition, Data collection process, Designed models, Digital libraries, Ensemble methods, Ensemble models, Forecasting, Glucose, Heterogeneous ensembles, Information systems, Information use, Learning algorithms, Medical fields, Prediction accuracy, Research topics},
	doi = {10.1007/978-3-030-72657-7_52},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85105963359\&doi=10.1007\%2f978-3-030-72657-7_52\&partnerID=40\&md5=4ba83ca8e7ba5bae5fdcd172b58fb8e9},
	author = {Wadghiri, M.Z. and Idri, A. and Idrissi, T.E.}
}
@article {Amazal2021,
	title = {Estimating software development effort using fuzzy clustering-based analogy},
	journal = {Journal of Software: Evolution and Process},
	volume = {33},
	number = {4},
	year = {2021},
	note = {cited By 3},
	abstract = {During the past decades, many studies have been carried out in an attempt to build accurate software development effort estimation techniques. However, none of the techniques proposed has proven to be successful at predicting software effort in all circumstances. Among these techniques, analogy-based estimation has gained significant popularity within software engineering community because of its outstanding performance and ability to mimic the human problem solving approach. One of the challenges facing analogy-based effort estimation is how to predict effort when software projects are described by a mixture of continuous and categorical features. To address this issue, the present study proposes an improvement of our former 2FA-kprototypes technique referred to as 2FA-cmeans. 2FA-cmeans uses a clustering technique, called general fuzzy c-means, which is a generalization of the fuzzy c-means clustering technique to cluster objects with mixed attributes. The performance of 2FA-cmeans was evaluated and compared with that of our former 2FA-kprototypes technique as well as classical analogy over six datasets that are quite diverse and have different sizes. Empirical results showed that 2FA-cmeans outperforms the two other analogy techniques using both all-in and jackknife evaluation methods. This was also confirmed by the win{\textendash}tie{\textendash}loss statistics and the Scott{\textendash}Knott test. {\textcopyright} 2020 John Wiley \& Sons, Ltd.},
	keywords = {Analogy based estimation, Categorical features, Cluster analysis, Clustering techniques, Engineering community, Estimating software, Fuzzy C means clustering, Fuzzy clustering, Fuzzy systems, Human problem solving, Software design, Software development effort},
	doi = {10.1002/smr.2324},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85092446413\&doi=10.1002\%2fsmr.2324\&partnerID=40\&md5=76da5a67bd24c945c3aa985d13dad134},
	author = {Amazal, F.A. and Idri, A.}
}
@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.}
}
@article {ElKoutbi2019,
	title = {Empirical evaluation of an entropy-based approach to estimation variation of software development effort},
	journal = {Journal of Software: Evolution and Process},
	volume = {31},
	number = {3},
	year = {2019},
	note = {cited By 0},
	doi = {10.1002/smr.2149},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85063377815\&doi=10.1002\%2fsmr.2149\&partnerID=40\&md5=f8f11d4ac305f27600a3f8dec5af170c},
	author = {El Koutbi, S. and Idri, A. and Abran, A.}
}
@article {Hosni2019,
	title = {Evaluating filter fuzzy analogy homogenous ensembles for software development effort estimation},
	journal = {Journal of Software: Evolution and Process},
	volume = {31},
	number = {2},
	year = {2019},
	note = {cited By 0},
	doi = {10.1002/smr.2117},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85061585023\&doi=10.1002\%2fsmr.2117\&partnerID=40\&md5=72983b70d540ccb14da59885e96cc4ab},
	author = {Hosni, M. and Idri, A. and Abran, A.}
}
@article {Idri2018,
	title = {Evaluating Pred(p) and standardized accuracy criteria in software development effort estimation},
	journal = {Journal of Software: Evolution and Process},
	volume = {30},
	number = {4},
	year = {2018},
	doi = {10.1002/smr.1925},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85045686530\&doi=10.1002\%2fsmr.1925\&partnerID=40\&md5=3a3d72a1a9b32d7a365db2168b5e3a45},
	author = {Idri, A. and Abnane, I. and Abran, A.}
}
@article {Bachiri2018,
	title = {Evaluating the Privacy Policies of Mobile Personal Health Records for Pregnancy Monitoring},
	journal = {Journal of Medical Systems},
	volume = {42},
	number = {8},
	year = {2018},
	doi = {10.1007/s10916-018-1002-x},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85049316852\&doi=10.1007\%2fs10916-018-1002-x\&partnerID=40\&md5=39f7d1b8a34c6a17937d6935246cf95b},
	author = {Bachiri, M. and Idri, A. and Fernandez-Aleman, J.L. and Toval, A.}
}
@article {Ouhbi201749,
	title = {E-health internationalization requirements for audit purposes},
	journal = {Computer Methods and Programs in Biomedicine},
	volume = {144},
	year = {2017},
	note = {cited By 1},
	pages = {49-60},
	abstract = {Background and objective: In the 21st century, e-health is proving to be one of the strongest drivers for the global transformation of the health care industry. Health information is currently truly ubiquitous and widespread, but in order to guarantee that everyone can appropriately access and understand this information, regardless of their origin, it is essential to bridge the international gap. The diversity of health information seekers languages and cultures signifies that e-health applications must be adapted to satisfy their needs. Methods: In order to achieve this objective, current and future e-health programs should take into account the internationalization aspects. This paper presents an internationalization requirements specification in the form of a reusable requirements catalog, obtained from the principal related standards, and describes the key methodological elements needed to perform an e-health software audit by using the internationalization knowledge previously gathered. Results: S Health, a relevant, well-known Android application that has more than 150 million users in over 130 countries, was selected as a target for the e-health internationalization audit method and requirements specification presented above. This application example helped us to put into practice the proposal and show that the procedure is realistic and effective. Conclusions: The approach presented in this study is subject to continuous improvement through the incorporation of new knowledge originating from additional information sources, such as other standards or stakeholders. The application example is useful for early evaluation and serves to assess the applicability of the internationalization catalog and audit methodology, and to improve them. It would be advisable to develop of an automated tool with which to carry out the audit method. {\textcopyright} 2017 Elsevier B.V.},
	doi = {10.1016/j.cmpb.2017.03.014},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85016069397\&doi=10.1016\%2fj.cmpb.2017.03.014\&partnerID=40\&md5=4cdb24852f0764dc64c9ed73f5614eda},
	author = {Ouhbi, S. and Fernandez-Aleman, J.L. and Carrillo-De-Gea, J.M. and Toval, A. and Idri, A.}
}
@conference {Abnane20171302,
	title = {Empirical evaluation of fuzzy analogy for Software Development Effort Estimation},
	booktitle = {Proceedings of the ACM Symposium on Applied Computing},
	volume = {Part F128005},
	year = {2017},
	note = {cited By 0},
	pages = {1302-1304},
	abstract = {Software Development Effort Estimation (SDEE) plays a primary role in software project management. Among several techniques suggested for estimating software development effort, analogybased software effort estimation approaches stand out as promising techniques. In this paper, the performance of Fuzzy Analogy is compared with that of six other SDEE techniques (Linear Regression, Support Vector Regression, Multi-Layer Perceptron, M5P and Classical Analogy). The first step of the evaluation aimed to ensure that the SDEE techniques outperformed random guessing by using the Standardized Accuracy (SA). Then, we used a set of reliable performance measures and Borda count to rank them and identify which techniques are the most accurate. The results suggest that Fuzzy Analogy statistically outperformed the other SDEE techniques regardless of the dataset used. {\textcopyright} 2017 ACM.},
	doi = {10.1145/3019612.3019905},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85020898807\&doi=10.1145\%2f3019612.3019905\&partnerID=40\&md5=df809b8bee59594505af4961af714d5e},
	author = {Abnane, I. and Idri, A. and Abran, A.}
}
@conference {Koutbi2017195,
	title = {Entropy-based framework dealing with error in software development effort estimation},
	booktitle = {ENASE 2017 - Proceedings of the 12th International Conference on Evaluation of Novel Approaches to Software Engineering},
	year = {2017},
	note = {cited By 0},
	pages = {195-202},
	abstract = {Software engineering community often investigates the error concerning software development effort estimation as a part, and sometimes, as an improvement of an effort estimation technique. The aim of this paper is to propose an approach dealing with both model and attributes measurement error sources whatever the effort estimation technique used. To do that, we explore the concepts of entropy and fuzzy clustering to propose a new framework to cope with both error sources. The proposed framework has been evaluated with the COCOMO{\textquoteright}81 dataset and the Fuzzy Analogy effort estimation technique. The results are promising since the actual confidence interval percentages are closer to those proposed by the framework. {\textcopyright} 2017 by SCITEPRESS - Science and Technology Publications, Lda. All Rights Reserved.},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85025448467\&partnerID=40\&md5=59f6479705cc067ea76a34a1d85422dd},
	author = {Koutbi, S.E. and Idri, A.}
}
@conference {Abnane2017,
	title = {Evaluating Fuzzy Analogy on incomplete software projects data},
	booktitle = {2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016},
	year = {2017},
	note = {cited By 0},
	abstract = {Missing Data (MD) is a widespread problem that can affect the ability to use data to construct effective software development effort prediction systems. This paper investigates the use of missing data (MD) techniques with Fuzzy Analogy. More specifically, this study analyze the predictive performance of this analogy-based technique when using toleration, deletion or k-nearest neighbors (KNN) imputation techniques using the Pred(0.25) accuracy criterion and thereafter compares the results with the findings when using the Standardized Accuracy (SA) measure. A total of 756 experiments were conducted involving seven data sets, three MD techniques (toleration, deletion and KNN imputation), three missingness mechanisms (MCAR: missing completely at random, MAR: missing at random, NIM: non-ignorable missing), and MD percentages from 10 percent to 90 percent. The results of accuracy measured in terms of Pred(0.25) confirm the findings of a study which used the SA measure. Moreover, we found that SA and Pred(0.25) measure different aspects of technique performance. Hence, SA is not sufficient to conclude about the technique accuracy and it should be used with other metrics, especially Pred(0.25). {\textcopyright} 2016 IEEE.},
	doi = {10.1109/SSCI.2016.7849922},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85016044171\&doi=10.1109\%2fSSCI.2016.7849922\&partnerID=40\&md5=9ece9d331d7bf0bbef07ed39c97ca2d1},
	author = {Abnane, I. and Idri, A.}
}
@article {Idri20161,
	title = {Evaluating a decision making system for cardiovascular dysautonomias diagnosis},
	journal = {SpringerPlus},
	volume = {5},
	number = {1},
	year = {2016},
	note = {cited By 1},
	pages = {1-17},
	abstract = {Autonomic nervous system (ANS) is the part of the nervous system that is involved in homeostasis of the whole body functions. A malfunction in this system can lead to a cardiovascular dysautonomias. Hence, a set of dynamic tests are adopted in ANS units to diagnose and treat patients with cardiovascular dysautonomias. The purpose of this study is to develop and evaluate a decision tree based cardiovascular dysautonomias prediction system on a dataset collected from the ANS unit of the Moroccan university hospital Avicenne. We collected a dataset of 263 records from the ANS unit of the Avicenne hospital. This dataset was split into three subsets: training set (123 records), test set (55 records) and validation set (85 records). C4.5 decision tree algorithm was used in this study to develop the prediction system. Moreover, Java Enterprise Edition platform was used to implement a prototype of the developed system which was deployed in the Avicenne ANS unit so as to be clinically validated. The performance of the decision tree-based prediction system was evaluated by means of the error rate criterion. The error rates were measured for each classifier and have achieved an average value of 1.46, 2.24 and 0.89~\% in training, test, and validation sets respectively. The results obtained were encouraging but further replicated studies are still needed to be performed in order to confirm the findings of this study. {\textcopyright} 2016, Idri and Kadi.},
	doi = {10.1186/s40064-016-1730-7},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84955586605\&doi=10.1186\%2fs40064-016-1730-7\&partnerID=40\&md5=0c9525bf9e5c6b03de358df01c579172},
	author = {Idri, A. and Kadi, I.}
}
@conference {Saissi2014178,
	title = {Extraction of relational schema from deep web sources: A form driven approach},
	booktitle = {2014 2nd World Conference on Complex Systems, WCCS 2014},
	year = {2014},
	note = {cited By 5},
	pages = {178-182},
	abstract = {The deep web is the biggest unexplored part of the web and we need to access directly to its entire data web sources without using any crawling or surfacing method. For this, we choose to use a virtual web integration system. However, the deep web virtual integration methods existing today, focuses only on the integration of the query interfaces giving access to the deep web. These query interfaces are integrated to build a global query interface able to query all the deep web sources. The objective of our work is to propose another vision of a deep web virtual integration system that uses a mediated schema built with a relational schema describing each deep web source. This paper proposes our approach to extract a relational schema describing a deep web source. The key idea underlying our approach is to analyze two structured information: the HTML Form and the HTML Table extracted from the deep web source to discover its data structure and to allow us to build a relational schema describing it. We use also a knowledge table to take profit of our learning experience on extracting relational schema from deep web source. {\textcopyright} 2014 IEEE.},
	doi = {10.1109/ICoCS.2014.7060888},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84929193260\&doi=10.1109\%2fICoCS.2014.7060888\&partnerID=40\&md5=19d19e00ed72fa7b4c9eeae768e3b02e},
	author = {Saissi, Y. and Zellou, A. and Idri, A.}
}
