@article {Miloudi2022191,
	title = {The Impact of Instance Selection Algorithms on Maintenance Effort Estimation for Open-Source Software},
	journal = {Lecture Notes in Networks and Systems},
	volume = {470 LNNS},
	year = {2022},
	note = {cited By 0},
	pages = {191-201},
	abstract = {Open-source software are very used nowadays in the industry, and the performance of the estimation of their maintenance effort becomes an interesting research topic. In this context, researchers have conducted many open-source software maintenance effort estimation (O-MEE) studies based on statistical and machine learning (ML) techniques for better estimation. This study focuses on the impact of instance selection on the performance of ML techniques in O-MEE, mainly for bug resolution. An empirical study was conducted using three techniques: K-nearest neighbor (kNN), support vector machine (SVM), and multinomial na{\"\i}ve Bayes (MNB) using all-kNN instance selection algorithms on three datasets: Eclipse JDT, Eclipse Platform, and Mozilla Thunderbird datasets. This study reports on a set of 18 experiments and a comparison of the results. The results of this study show that instance selection helped make ML techniques more performant. {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.},
	doi = {10.1007/978-3-031-04829-6_17},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85130234489\&doi=10.1007\%2f978-3-031-04829-6_17\&partnerID=40\&md5=7ac0a2d5915327c057f3ec68815ba211},
	author = {Miloudi, C. and Cheikhi, L. and Idri, A. and Abran, A.}
}
@article {Hakkoum2022,
	title = {Interpretability in the medical field: A systematic mapping and review study},
	journal = {Applied Soft Computing},
	volume = {117},
	year = {2022},
	note = {cited By 4},
	abstract = {Context: Recently, the machine learning (ML) field has been rapidly growing, mainly owing to the availability of historical datasets and advanced computational power. This growth is still facing a set of challenges, such as the interpretability of ML models. In particular, in the medical field, interpretability is a real bottleneck to the use of ML by physicians. Therefore, numerous interpretability techniques have been proposed and evaluated to help ML gain the trust of its users. Methods: This review was carried out according to the well-known systematic map and review process to analyze the literature on interpretability techniques when applied in the medical field with regard to different aspects: publication venues and publication year, contribution and empirical types, medical and ML disciplines and objectives, ML black-box techniques interpreted, interpretability techniques investigated, their performance and the best performing techniques, and lastly, the datasets used when evaluating interpretability techniques. Results: A total of 179 articles (1994{\textendash}2020) were selected from six digital libraries: ScienceDirect, IEEE Xplore, ACM Digital Library, SpringerLink, Wiley, and Google Scholar. The results showed that the number of studies dealing with interpretability increased over the years with a dominance of solution proposals and experiment-based empirical type. Diagnosis, oncology, and classification were the most frequent medical task, discipline, and ML objective studied, respectively. Artificial neural networks were the most widely used ML black-box techniques investigated for interpretability. Additionally, global interpretability techniques focusing on a specific black-box model, such as rules, were the dominant explanation types, and most of the metrics used to evaluate interpretability were accuracy, fidelity, and number of rules. Moreover, the variety of the techniques used by the selected papers did not allow categorization at the technique level, and the high number of the sum of evaluations (671) of the articles raised a suspicion of subjectivity. Datasets that contained numerical and categorical attributes were the most frequently used in the selected studies. Conclusions: Further effort is needed in disciplines other than diagnosis and classification. Global techniques such as rules are the most used because of their comprehensibility to doctors, but new local techniques should be explored more in the medical field to gain more insights into the model{\textquoteright}s behavior. More experiments and comparisons against existing techniques are encouraged to determine the best performing techniques. Lastly, quantitative evaluation of interpretability and physicians{\textquoteright} implications in interpretability techniques evaluation is highly recommended to evaluate how the techniques will perform in real-world scenarios. It can ensure the soundness of the techniques and help gain trust in black-box models in medical environments. {\textcopyright} 2022 Elsevier B.V.},
	keywords = {Black box modelling, Black boxes, Computational power, Computer aided diagnosis, Digital libraries, Explainability, Historical dataset, Interpretability, Machine learning, Medical fields, Neural networks, Systematic mapping, Systematic Review, XAI},
	doi = {10.1016/j.asoc.2021.108391},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85122624142\&doi=10.1016\%2fj.asoc.2021.108391\&partnerID=40\&md5=38db4d1f5c417a07d0a3204639e157a2},
	author = {Hakkoum, H. and Abnane, I. and Idri, A.}
}
@article {ElOuassif2021263,
	title = {Investigating Accuracy and Diversity in Heterogeneous Ensembles for Breast Cancer Classification},
	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 2},
	pages = {263-281},
	abstract = {Breast Cancer (BC) is one of the most common forms of cancer among women. Detecting and accurately diagnosing breast cancer at an early phase increase the chances of women{\textquoteright}s survival. For this purpose, various single classification techniques have been investigated to diagnosis BC. Nevertheless, none of them proved to be accurate in all circumstances. Recently, a promising approach called ensemble classifiers have been widely used to assist physicians accurately diagnose BC. Ensemble classifiers consist on combining a set of single classifiers by means of an aggregation layer. The literature in general shows that ensemble techniques outperformed single ones when ensemble members are accurate (i.e. have the lowest percentage error) and diverse (i.e. the single classifiers make uncorrelated errors on new~instances). Hence, selecting ensemble members is often a crucial task since it can lead to the opposite: single techniques outperformed their ensemble. This paper evaluates and compares ensemble members{\textquoteright} selection based on accuracy and diversity with ensemble members{\textquoteright} selection based on accuracy only. A comparison with ensembles without member selection was also performed. Ensemble performance was assessed in terms of accuracy, F1-score. Q statistics diversity measure was used to calculate the classifiers diversity. The experiments were carried out on three well-known BC datasets available from online repositories. Seven single classifiers were used in our experiments. Skott Knott test and Borda Count voting system were used to assess the significance of the performance differences and rank ensembles according to theirs performances. The findings of this study suggest that: (1) Investigating both accuracy and diversity to select ensemble members often led to better performance, and (2) In general, selecting ensemble members using accuracy and/or diversity led to better ensemble performance than constructing ensembles without members{\textquoteright} selection. {\textcopyright} 2021, Springer Nature Switzerland AG.},
	keywords = {Breast Cancer, Combining classifiers, Computer aided diagnosis, Diseases, Diversity measure, Ensemble members, Ensemble-classifier, Heterogeneous ensembles, Member selection, Performance, Selection based, Voting, Voting machines},
	doi = {10.1007/978-3-030-86960-1_19},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85125278232\&doi=10.1007\%2f978-3-030-86960-1_19\&partnerID=40\&md5=58285ac3fe1b29564700bef136a94771},
	author = {El Ouassif, B. and Idri, A. and Hosni, M.}
}
@conference {ElBajta2020239,
	title = {Identifying software cost attributes of software project management in global software development: An integrative framework},
	booktitle = {ACM International Conference Proceeding Series},
	year = {2020},
	note = {cited By 0},
	pages = {239-244},
	abstract = {The management of global and distributed software projects is a very difficult task further complicated by the emergence of new challenges inherent in stakeholder dispersion. Software cost estimation plays a central role to face challenges in the context of Global Software Development (GSD). The objective of this study is to identify software cost attributes related to GSD context to present an integrative framework encompassing these attributes. Thirty cost attributes were identified using a Systematic Literature Review (SLR) and later compiled into a framework inspired by the Software Engineering Institute (SEI) taxonomy. {\textcopyright} 2020 ACM.},
	keywords = {Cost engineering, Cost estimating, Distributed software, global software development, Integrative framework, Intelligent systems, Project management, Software cost, Software cost estimations, Software design, Software engineering institutes, Software project management, Systematic literature review (SLR)},
	doi = {10.1145/3419604.3419780},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096416219\&doi=10.1145\%2f3419604.3419780\&partnerID=40\&md5=54b147fb2d591d8cef6a98304201c1e5},
	author = {El Bajta, M. and Idri, A.}
}
@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.}
}
@article {Rachad2020,
	title = {Intelligent Mobile Applications: A Systematic Mapping Study},
	journal = {Mobile Information Systems},
	volume = {2020},
	year = {2020},
	note = {cited By 3},
	abstract = {Smart mobiles as the most affordable and practical ubiquitous devices participate heavily in the enhancement of our daily life by the use of many convenient applications. However, the significant number of mobile users in addition to their heterogeneity (different profiles and contexts) obligates developers to enhance the quality of their apps by making them more intelligent and more flexible. This is realized mainly by analyzing mobile user{\textquoteright}s data. Machine learning (ML) technology provides the methodology and techniques needed to extract knowledge from data to facilitate decision-making. Therefore, both developers and researchers affirm the benefits of combining ML techniques and mobile technology in several application fields as e-health, e-learning, e-commerce, and e-coaching. Thus, the purpose of this paper is to have an overview of the use of ML techniques in the design and development of mobile applications. Therefore, we performed a systematic mapping study of papers published on this subject in the period between 1 January 2007 and 31 December 2019. A total number of 71 papers were selected, studied, and analyzed according to the following criteria, year, sources and channel of publication, research type, and methods, kind of collected data, and finally adopted ML models, tasks, and techniques. {\textcopyright} 2020 Taoufik Rachad and Ali Idri.},
	keywords = {Application fields, Decision making, Design and Development, Mapping, Methodology and techniques, Mobile applications, Mobile commerce, Mobile computing, Mobile Technology, Mobile users, Systematic mapping studies, Ubiquitous devices},
	doi = {10.1155/2020/6715363},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083095554\&doi=10.1155\%2f2020\%2f6715363\&partnerID=40\&md5=d474ab3cece2179d34f54fa7087a4cfb},
	author = {Rachad, T. and Idri, A.}
}
@conference {Mimouni20181,
	title = {The impact of query precision on returned data precision criterion},
	booktitle = {2017 International Conference on Electrical and Computing Technologies and Applications, ICECTA 2017},
	volume = {2018-January},
	year = {2018},
	pages = {1-6},
	doi = {10.1109/ICECTA.2017.8252061},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85045990243\&doi=10.1109\%2fICECTA.2017.8252061\&partnerID=40\&md5=553344ed3926f1d6f3b01b121907b491},
	author = {Mimouni, L. and Zellou, A. and Idri, A.}
}
@conference {Abnane20181015,
	title = {Improved analogy-based effort estimation with incomplete mixed data},
	booktitle = {Proceedings of the 2018 Federated Conference on Computer Science and Information Systems, FedCSIS 2018},
	year = {2018},
	pages = {1015-1024},
	doi = {10.15439/2018F95},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85057257109\&doi=10.15439\%2f2018F95\&partnerID=40\&md5=04ce7873db15a5eed3fd50a320b73e4f},
	author = {Abnane, I. and Idri, A.}
}
@conference {Chadli201712,
	title = {Identifying and mitigating risks of software project management in global software development},
	booktitle = {ACM International Conference Proceeding Series},
	volume = {Part F131936},
	year = {2017},
	note = {cited By 0},
	pages = {12-22},
	abstract = {Managing global software projects is a difficult task further complicated by the emergence of new risks inherent to the dispersion of stakeholders. Project managers of Global Software Development (GSD) projects deal with challenges related to geographical, temporal and socio-cultural distance. The aim of this paper is to identify mitigation strategies intended to counter partially or fully the effects of risk factors related to the management of GSD projects that are available in literature and update the list of risk factors proposed in a previous research. This study proposes a framework for the Software Risk Management (SRM) of GSD projects designed to help practitioners identify risk factors and alleviate their effects through a list of recommended mitigation strategies. Using a systematic literature review (SLR), 39 risk factors and 58 mitigation strategies were identified and classified using a framework inspired from Leavitt{\textquoteright}s model of organizational change. Results show that the mitigation strategies identified in this SLR target 38 out of 39 risk factors, indicating a high academic interest in resolving the challenges of managing GSD projects. Results also reveal that the list of risk factors submitted in this paper and compiled using a different set of selected studies, concurs with the list introduced in a previous research. {\textcopyright} 2017 Association for Computing Machinery.},
	doi = {10.1145/3143434.3143453},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85038388343\&doi=10.1145\%2f3143434.3143453\&partnerID=40\&md5=4d1ca1bb448c86a4078da3a7727dc70c},
	author = {Chadli, S.Y. and Idri, A.}
}
@conference {Idri2017262,
	title = {ISO/IEC 25010 Based Evaluation of Free Mobile Personal Health Records for Pregnancy Monitoring},
	booktitle = {Proceedings - International Computer Software and Applications Conference},
	volume = {1},
	year = {2017},
	note = {cited By 0},
	pages = {262-267},
	abstract = {Background: Mobile Personal Health Records (mPHRs) are mobile applications that allow organizing and saving the patients{\textquoteright} health data in order to ease their management and access for both patients and doctors. Objective: This study aims to evaluate, based on ISO/IEC 25010 quality standard, the software quality of mPHRs for pregnancy monitoring. Method: An External quality evaluation was carried out on 17 pregnancy monitoring mPHRs for iOS and Android. The evaluation consisted of performing a set of tasks and filling in a quality assessment questionnaire that covers the four External quality characteristics: Functional suitability, Operability, Performance efficiency and Reliability. Moreover, 14 mPHRs for pregnancy monitoring were used to study the relationship between the External quality and the Quality in-use by means of four classification techniques: Iterative Dichotomiser 3 (ID3), C4.5, K-nearest neighbors (K-NN) and Na{\"\i}ve Bayes. Results: Functional suitability and Reliability are the most covered quality characteristics by the 17 apps compared to Operability and Performance efficiency. Furthermore, K-NN and C4.5 obtained the highest accuracy rates, as evaluating the accuracy rate of each classification technique can determine if a classifier can be designed to predict the Quality in-use from the External Quality for the apps used in this study. Conclusion: The need of studying the relationship between fulfilling the External quality and the Quality in-use for pregnancy monitoring mPHRs, is of great importance for the developers of these apps for future improvements. {\textcopyright} 2017 IEEE.},
	doi = {10.1109/COMPSAC.2017.159},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85031943834\&doi=10.1109\%2fCOMPSAC.2017.159\&partnerID=40\&md5=33e58a2940bb96d4a2c3c7a272a519e5},
	author = {Idri, A. and Bachiri, M. and Fernandez-Aleman, J.L. and Toval, A.}
}
