@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.}
}
@conference {Sardi2022435,
	title = {A Reusable Catalog of Requirements for Gamified Mobile Health Applications},
	booktitle = {International Conference on Evaluation of Novel Approaches to Software Engineering, ENASE - Proceedings},
	year = {2022},
	note = {cited By 0},
	pages = {435-442},
	abstract = {The new era of mobile technologies has revolutionized all fundamental human pursuits in that many sectors have been reshaped and tangibly improved, such as education, finance, and healthcare. Referred to as mHealth, the use of mobile technologies to track and improve health outcomes is a rapidly expanding trend. A plethora of mobile health applications (mHealth apps) are being constantly developed thanks to the rapid adoption of mobile devices. From basic exercise trackers to powerful self-management tools, mHealth apps play several roles towards the provision of better healthcare. To induce playful experiences and boost engagement, gamification has proven to be extremely effective with mHealth apps. This paper presents a catalog of reusable requirements for gamified mHealth apps in terms of gamification, functional suitability, usability, and security, Further improvements are intended to be made continuously to the catalog by adding additional requirements scattered from other standards and information sources. Copyright {\textcopyright} 2022 by SCITEPRESS {\textendash} Science and Technology Publications, Lda. All rights reserved.},
	keywords = {Biomedical engineering, Catalog, Gamification, Health outcomes, ISO/IEC, ISO/IEC 25010, Management tool, mHealth, Mobile health application, Mobile Technology, Requirement, Self management},
	doi = {10.5220/0011071700003176},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85140984297\&doi=10.5220\%2f0011071700003176\&partnerID=40\&md5=dc18bd51fe840506f2a31af133df9fb2},
	author = {Sardi, L. and Idri, A. and Redman, L.M. and Alami, H. and Fernandez-Aleman, J.L.}
}
@article {Kharbouch2022,
	title = {Reviewing the features and functionalities of contraception mPHRs},
	journal = {Health Policy and Technology},
	volume = {11},
	number = {3},
	year = {2022},
	note = {cited By 0},
	abstract = {Background and objective. Women{\textquoteright}s circumstances and preferences tend to change over times signifying that accessing and choosing a contraceptive method during their reproductive life is crucial. By preventing unintended pregnancies and reducing the need for abortion, effective contraception ensures women{\textquoteright}s well-being and autonomy, improves their social and economic role, and enables them to fully participate in society while supporting the health and development of their communities. The use of mobile applications (apps) for contraception is considered a promising approach by which to facilitate access to a reproductive health service of this nature and spread awareness about contraception. The aim of this paper is, therefore, to assess the features and functionalities of the contraception mPHRs (Mobile Personal Health Records) that are available on both the Android and iOS platforms. Methods. In order to select, extract data from and evaluate the features and functionalities of existing contraception apps, an analysis process based on the well-known Systematic Literature Review (SLR) protocol was conducted for the contraception apps that are available on both the Android and iOS app platforms. The analysis and assessment of selected apps{\textquoteright} features were performed in accordance with a twenty-seven item assessment questionnaire. The latter was developed on the basis of the scientific literature concerning contraception, and a preliminary analysis of the contraception apps currently available. Results. A total of 54 contraception apps were selected, including 38 for Android, 4 for iOS, and 12 for crossed platform apps. The results showed that only 11\% of the apps selected do support the World Health Organization{\textquoteright}s (WHO) medical eligibility criteria for contraception (MEC), and that barely 43\% provide relevant information about contraceptive methods, their efficiency, risks, and contraindications. Conclusion. This study will help users, eHealth apps providers, and developers. Users on the one hand will be able to better select the most appropriate contraceptive apps. On the other hand, both eHealth application providers and developers can thereby: (1) identify the relevant features and functionalities implemented by the top-scoring contraceptive apps, and (2) distinguish contraception-related aspects that require further efforts to be properly addressed and improved. {\textcopyright} 2022},
	keywords = {awareness, contraception, Data availability, female, human, information security, medical record, Monitoring, Preferred Reporting Items for Systematic Reviews and Meta-Analyses, questionnaire, recording, reminder system, reproductive health, Review, social support, Systematic Review},
	doi = {10.1016/j.hlpt.2022.100633},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85132651990\&doi=10.1016\%2fj.hlpt.2022.100633\&partnerID=40\&md5=b101e386be125c92bddf8bf212de3a39},
	author = {Kharbouch, M. and Idri, A. and Fernandez-Aleman, J.L. and Redman, L. and Toval, A. and Stelate, Y.}
}
@article {ElFhel2021324,
	title = {A Requirements Catalog of Mobile Geographic Information System for Data Collection},
	journal = {Advances in Intelligent Systems and Computing},
	volume = {1366 AISC},
	year = {2021},
	note = {cited By 2},
	pages = {324-336},
	abstract = {Mobile Geographic Information System (mGIS) for data collection are designed to capture, analyze and manage geographical data. The aim of this paper is to define a requirements catalog for mGIS for data collection based on the main software engineering standards, GIS standards and literature. The catalog contains functional requirements in addition to requirements about usability, internationalization (i18n), performance efficiency, reliability and sustainability. This catalog can be useful for stakeholders and developers to evaluate their apps and/or identify potential requirements for new mGIS apps for data collection. {\textcopyright} 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.},
	keywords = {Data acquisition, Data collection, Functional requirement, Geographic information systems, Geographical data, Information management, Information systems, Information use, Mobile geographic information systems, Performance efficiency, Software engineering, Software engineering standards},
	doi = {10.1007/978-3-030-72651-5_32},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85107324425\&doi=10.1007\%2f978-3-030-72651-5_32\&partnerID=40\&md5=16bcf6a546df5b7bf9d750897b7ad643},
	author = {El Fhel, B. and Sardi, L. and Idri, A.}
}
@article {Zerouaoui2021,
	title = {Reviewing Machine Learning and Image Processing Based Decision-Making Systems for Breast Cancer Imaging},
	journal = {Journal of Medical Systems},
	volume = {45},
	number = {1},
	year = {2021},
	note = {cited By 19},
	abstract = {Breast cancer (BC) is the leading cause of death among women worldwide. It affects in general women older than 40~years old. Medical images analysis is one of the most promising research areas since it provides facilities for diagnosis and decision-making of several diseases such as BC. This paper conducts a Structured Literature Review (SLR) of the use of Machine Learning (ML) and Image Processing (IP) techniques to deal with BC imaging. A set of 530 papers published between 2000 and August 2019 were selected and analyzed according to ten criteria: year and publication channel, empirical type, research type, medical task, machine learning techniques, datasets used, validation methods, performance measures and image processing techniques which include image pre-processing, segmentation, feature extraction and feature selection. Results showed that diagnosis was the most used medical task and that Deep Learning techniques (DL) were largely used to perform classification. Furthermore, we found out that classification was the most ML objective investigated followed by prediction and clustering. Most of the selected studies used Mammograms as imaging modalities rather than Ultrasound or Magnetic Resonance Imaging with the use of public or private datasets with MIAS as the most frequently investigated public dataset. As for image processing techniques, the majority of the selected studies pre-process their input images by reducing the noise and normalizing the colors, and some of them use segmentation to extract the region of interest with the thresholding method. For feature extraction, we note that researchers extracted the relevant features using classical feature extraction techniques (e.g. Texture features, Shape features, etc.) or DL techniques (e. g. VGG16, VGG19, ResNet, etc.), and finally few papers used feature selection techniques in particular the filter methods. {\textcopyright} 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature.},
	keywords = {Article, breast, Breast Cancer, cancer diagnosis, classifier, Computer-Assisted, Convolutional neural network, Decision making, Deep learning, deep neural network, diagnostic accuracy, diagnostic imaging, echography, Feature extraction, feature selection, female, human, Humans, image processing, image segmentation, Machine learning, Magnetic Resonance Imaging, mammography, multilayer perceptron, nuclear magnetic resonance imaging},
	doi = {10.1007/s10916-020-01689-1},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100125842\&doi=10.1007\%2fs10916-020-01689-1\&partnerID=40\&md5=3074b3d443373f75f5a3c1c6134512ee},
	author = {Zerouaoui, H. and Idri, A.}
}
@conference {Miloudi2020251,
	title = {A Review of Open Source Software Maintenance Effort Estimation},
	booktitle = {ACM International Conference Proceeding Series},
	year = {2020},
	note = {cited By 0},
	pages = {251-256},
	abstract = {Open Source Software (OSS) is gaining interests of software engineering community as well as practitioners from industry with the growth of the internet. Studies in estimating maintenance effort (MEE) of such software product have been published in the literature in order to provide better estimation. The aim of this study is to provide a review of studies related to maintenance effort estimation for open source software (OSSMEE). To this end, a set of 60 primary empirical studies are selected from six electronic databases and a discussion is provided according to eight research questions (RQs) related to: publication year, publication source, datasets (OSS projects), metrics (independent variables), techniques, maintenance effort (dependent variable), validation methods, and accuracy criteria used in the empirical validation. This study has found that popular OSS projects have been used, Linear Regression, Na{\"\i}ve Bayes and k Nearest Neighbors were frequently used, and bug resolution was the most used regarding the estimation of maintenance effort for the future releases. A set of gaps are identified and recommendations for researchers are also provided. {\textcopyright} 2020 ACM.},
	keywords = {Computer software maintenance, Dependent variables, Electronic database, Empirical validation, Engineering community, Independent variables, Intelligent systems, K-nearest neighbors, Maintenance efforts, Nearest neighbor search, Open source software, Open systems, Research questions, Sodium compounds},
	doi = {10.1145/3419604.3419809},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096413214\&doi=10.1145\%2f3419604.3419809\&partnerID=40\&md5=7ad5b9eacb605d7875e2123cfec6ddeb},
	author = {Miloudi, C. and Cheikhi, L. and Idri, A.}
}
@article {Ezzat202065,
	title = {Reviewing Data Analytics Techniques in Breast Cancer Treatment},
	journal = {Advances in Intelligent Systems and Computing},
	volume = {1161 AISC},
	year = {2020},
	note = {cited By 1},
	pages = {65-75},
	abstract = {Data mining (DM) or Data Analytics is the process of extracting new valuable information from large quantities of data; it is reshaping many industries including the medical one. Its contribution to medicine is very important particularly in oncology. Breast cancer is the most common type of cancer in the world and it occurs almost entirely in women, but men can get attacked too. Researchers over the world are trying every day to improve, prevention, detection and treatment of Breast Cancer (BC) in order to provide more effective treatments to patients. In this vein, the present paper carried out a systematic map of the use of data mining technique in breast cancer treatment. The aim was to analyse and synthetize studies on DM applied to breast cancer treatment. In this regard, 44 relevant articles published between 1991 and 2019 were selected and classified according to three criteria: year and channel of publication, research type through DM contribution in BC treatment and DM techniques. Of course, there are not many articles for treatment, because the researchers have been interested in the diagnosis with the different classification techniques, and it may be because of the importance of early diagnosis to avoid danger. Results show that papers were published in different channels (especially journals or conferences), researchers follow the DM pipeline to deal with a BC treatment, the challenge is to reduce the number of non-classified patients, and affect them in the most appropriate group to follow the suitable treatment, and classification was the most used task of DM applied to BC treatment. {\textcopyright} 2020, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.},
	keywords = {Breast Cancer, Breast cancer treatment, Classification technique, Computer aided diagnosis, Data Analytics, Data mining, Diseases, early diagnosis, Information systems, Information use, Medical computing, Patient treatment, Systematic maps},
	doi = {10.1007/978-3-030-45697-9_7},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85085470786\&doi=10.1007\%2f978-3-030-45697-9_7\&partnerID=40\&md5=cc91fa4ca2e05ed50ac292abaf968494},
	author = {EZZAT, M. and Idri, A.}
}
@article {CruzZapata2018,
	title = {Reusable Software Usability Specifications for mHealth Applications},
	journal = {Journal of Medical Systems},
	volume = {42},
	number = {3},
	year = {2018},
	note = {cited By 0},
	abstract = {One of the key factors for the adoption of mobile technologies, and in particular of mobile health applications, is usability. A usable application will be easier to use and understand by users, and will improve user{\textquoteright}s interaction with it. This paper proposes a software requirements catalog for usable mobile health applications, which can be used for the development of new applications, or the evaluation of existing ones. The catalog is based on the main identified sources in literature on usability and mobile health applications. Our catalog was organized according to the ISO/IEC/IEEE 29148:2011 standard and follows the SIREN methodology to create reusable catalogs. The applicability of the catalog was verified by the creation of an audit method, which was used to perform the evaluation of a real app, S Health, application created by Samsung Electronics Co. The usability requirements catalog, along with the audit method, identified several usability flaws on the evaluated app, which scored 83\%. Some flaws were detected in the app related to the navigation pattern. Some more issues related to the startup experience, empty screens or writing style were also found. The way a user navigates through an application improves or deteriorates user{\textquoteright}s experience with the application. We proposed a reusable usability catalog and an audit method. This proposal was used to evaluate a mobile health application. An audit report was created with the usability issues identified on the evaluated application. {\textcopyright} 2018, Springer Science+Business Media, LLC, part of Springer Nature.},
	doi = {10.1007/s10916-018-0902-0},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85040984885\&doi=10.1007\%2fs10916-018-0902-0\&partnerID=40\&md5=32ddf999ef891e8986a8a19c7271249a},
	author = {Cruz Zapata, B. and Fernandez-Aleman, J.L. and Toval, A. and Idri, A.}
}
@conference {Ouhbi2017557,
	title = {Requirements for a mobile personal health record to improve cardiovascular healthcare services},
	booktitle = {Proceedings of the ACM Symposium on Applied Computing},
	volume = {Part F128005},
	year = {2017},
	note = {cited By 1},
	pages = {557-562},
	abstract = {Analyzing health records of patients suffering from chronic diseases such as cardiovascular disease can help understanding their conditions and therefore improving healthcare services offered to them. To improve the cardiology healthcare quality and efficiency in the Autonomic Nervous System centre in Avicenne Hospital in Rabat in Morocco, a mobile personal health record (mPHR) along with an electronic health record have been developed. This paper presents our mPHR{\textquoteright}s functional requirements used in the product development. It also presents some aspects of quality evaluation of the mPHR. An experiment has been conducted to assess and improve the mPHR{\textquoteright}s quality using the ISO/IEC 25010 standard. Copyright 2017 ACM.},
	doi = {10.1145/3019612.3019725},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85020871426\&doi=10.1145\%2f3019612.3019725\&partnerID=40\&md5=6cf797c1b1a37c7309de64935405f2e8},
	author = {Ouhbi, S. and Idri, A. and Hakmi, R. and Benjelloun, H. and Fernandez-Aleman, J.L. and Toval, A.}
}
@conference {Ouhbi2017285,
	title = {A reusable requirements catalog for internationalized and sustainable blood donation apps},
	booktitle = {ENASE 2017 - Proceedings of the 12th International Conference on Evaluation of Novel Approaches to Software Engineering},
	year = {2017},
	note = {cited By 0},
	pages = {285-292},
	abstract = {Blood donation mobile applications are efficient tools to increase awareness about the importance of blood donation acts and to attract blood donors. The aim of this paper is to define a reusable requirements repository (catalog) for blood donation applications based on the main related software engineering standards, e-health technology standards and literature. The catalog contains requirements regarding internationalization to bridge the cultural and language barriers among blood donors. It includes also requirements for sustainable blood donation applications which cover the individual, social, environmental, and technical dimensions of sustainability. This catalog can be very useful to develop, evaluate and audit blood donation applications and it can be adapted to other m-health applications. {\textcopyright} 2017 by SCITEPRESS - Science and Technology Publications, Lda. All Rights Reserved.},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85025445810\&partnerID=40\&md5=b3c2b8763e5f5a729522529e5ca3e148},
	author = {Ouhbi, S. and Fernandez-Aleman, J.L. and Idri, A. and Toval, A. and Pozo, J.R. and Bajta, M.E.}
}
@article {Anter20162128,
	title = {Retrieving and materializing data in hybrid mediators},
	journal = {International Journal of Applied Engineering Research},
	volume = {11},
	number = {3},
	year = {2016},
	note = {cited By 0},
	pages = {2128-2134},
	abstract = {With the emergence of the new generation of information technologies and telecommunications, the mass of information produced by individuals and enterprises has increased in a considerable manner. Thus, and in order to manage this diversity of information, the integration systems were proposed. Among these, we find the hybrid information integration systems. They allow materializing a part of data in a local database, while integrating virtually the other part. As these materialized one is organized as views, it becomes necessary to propose algorithms for this objective. Among the most interesting ones, there is k-schema. It allows organize the attributes into a set of views, while affecting an attribute to a single view. This choice causes the not loading some of data that are highly requested by users, and at the same time, it loads other that are rarely requested. In this paper, we propose a new algorithm in this end. In this latter, it is authorized to assign a same attribute to several views. We also proposed new functions for calculating the dependencies between attributes. {\textcopyright} Research India Publications.},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84973470278\&partnerID=40\&md5=140f0f35ca53260f11d97b5958f6ee8c},
	author = {Anter, S. and Zellou, A. and Idri, A.}
}
