@article {El-Ateif2022, title = {Single-modality and joint fusion deep learning for diabetic retinopathy diagnosis}, journal = {Scientific African}, volume = {17}, year = {2022}, note = {cited By 0}, abstract = {The current study evaluated and compared single-modality and joint fusion deep learning approaches for automatic binary classification of diabetic retinopathy (DR) using seven convolutional neural network models (VGG19, ResNet50V2, DenseNet121, InceptionV3, InceptionResNetV2, Xception, and MobileNetV2) over two datasets: APTOS 2019 blindness detection and Messidor-2. The empirical evaluations used (1) six performance metrics (accuracy, sensitivity, specificity, precision, F1-score, and area under the curve), (2) the Scott-Knott Effect Size difference (SK ESD) statistical test to rank and cluster the models based on accuracy, and (3) the Borda count voting method to rank the best models figuring in the first SK ESD cluster, based on sensitivity, specificity, precision, F1-score, and area under the curve. Results showed that the single-modality DenseNet121 and InceptionV3 were the top-performing and less sensitive approaches, with an accuracy of 90.63\% and 75.25\%, respectively. The joint fusion strategy outperformed single-modality techniques across the two datasets, regardless of the modality used, because of the additional information provided by the preprocessed modality to the Fundus. The Fundus modality was the most favorable modality for DR diagnosis using the seven models. Furthermore, the joint fusion VGG19 model performed best with an accuracy of 97.49\% and 91.20\% over APTOS19 and Messidor-2, respectively; as the VGG19 model was fine-tuned in comparison to the remaining six models. In comparison with state-of-the-art models, Attention Fusion, and Cascaded Framework, joint fusion VGG19 ranks below the Attention Fusion network and outperforms the Cascaded Framework on the Messidor dataset by 5.6\% and 8\%, respectively. {\textcopyright} 2022 The Authors}, doi = {10.1016/j.sciaf.2022.e01280}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85134929351\&doi=10.1016\%2fj.sciaf.2022.e01280\&partnerID=40\&md5=9519f7afe80a2afb76cceae701940911}, author = {El-Ateif, S. and Idri, A.} } @article {Idlahcen2022466, title = {Systematic Map of Data Mining for Gynecologic Oncology}, journal = {Lecture Notes in Networks and Systems}, volume = {468 LNNS}, year = {2022}, note = {cited By 0}, pages = {466-475}, abstract = {Gynecologic cancers are a significant cause of morbidity and mortality among women both in developed and low- middle- income countries. To alleviate the burden, the application of Data Mining (DM) in gynecologic oncology is needed in clinical environments. This study presents a systematic mapping to explore in detail the breadth of the available literature on the use of DM in gynecologic oncology. The mapping questions and the PICO framework served to determine the search string of this systematic map. The resultant was conducted on five well-known databases, PubMed, IEEE Xplore, ScienceDirect, Springer Link, and Google Scholar, to catch relevant articles published between 2011, and mid of 2021. Of the 2,807 potential records, 169 studies fulfilled the inclusion/exclusion criteria and were in-depth analyzed. The findings revealed that DM efforts peaked considerably from 2019 in terms of cervical cancer screening and diagnosis. Further studies are needed to investigate a wider range of research questions as gynecologic oncology is a very rich field with a collection of distinct features cancers which, in turn, allow Machine Learning (ML) opportunities. {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.}, doi = {10.1007/978-3-031-04826-5_47}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85130252805\&doi=10.1007\%2f978-3-031-04826-5_47\&partnerID=40\&md5=62c013d4962d32086190ef98c3ae36e7}, author = {Idlahcen, F. and Idri, A.} } @article {Zizaan2022425, title = {Systematic Map of Machine Learning Based Breast Cancer Screening}, journal = {Lecture Notes in Networks and Systems}, volume = {468 LNNS}, year = {2022}, note = {cited By 0}, pages = {425-434}, abstract = {Although Breast Cancer (BC) deaths have decreased over time, it is still the second largest cause of cancer death among women. With the technical revolution of Artificial Intelligence (AI), and the big healthcare data that is becoming more of a reality, many researchers have attempted to employ Machine Learning (ML) techniques to gain a better understanding of this disease. The present paper is a systematic mapping study of the application of ML techniques in Breast Cancer Screening (BCS) between the years 2011 and early 2021. Out of 129 candidate papers we retrieved from six digital libraries, a total of 66 papers were selected according to 5 criteria: year and publication venue, paper type, BCS modality, and empirical type. The results show that classification was the most used ML objective, and that mammography was the most frequent BCS modality used. {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.}, doi = {10.1007/978-3-031-04826-5_43}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85130248734\&doi=10.1007\%2f978-3-031-04826-5_43\&partnerID=40\&md5=4017a93b47d63e929eb5d2e71e97f5f0}, author = {Zizaan, A. and Idri, A.} } @article {Hosni20212827, title = {A systematic mapping study for ensemble classification methods in cardiovascular disease}, journal = {Artificial Intelligence Review}, volume = {54}, number = {4}, year = {2021}, note = {cited By 5}, pages = {2827-2861}, abstract = {Ensemble methods overcome the limitations of single machine learning techniques by combining different techniques, and are employed in the quest to achieve a high level of accuracy. This approach has been investigated in various fields, one of them being that of bioinformatics. One of the most frequent applications of ensemble techniques involves research into cardiovascular diseases, which are considered the leading cause of death worldwide. The purpose of this research work is to identify the papers that investigate ensemble classification techniques applied to cardiology diseases, and to analyse them according to nine aspects: their publication venues, the medical tasks tackled, the empirical and research types adopted, the types of ensembles proposed, the single techniques used to construct the ensembles, the validation frameworks adopted to evaluate the proposed ensembles, the tools used to build the ensembles, and the optimization methods employed for the single techniques. This paper reports the carrying out of a systematic mapping study. An extensive automatic search in four digital libraries: IEEE Xplore, ACM Digital Library, PubMed, and Scopus, followed by a study selection process, resulted in the identification of 351 papers that were used to address our mapping questions. This study found that the papers selected had been published in a large number of different resources. The medical task addressed most frequently by the selected studies was diagnosis. In addition, the experiment-based empirical type and evaluation-based research type were the most dominant approaches adopted by the selected studies. Homogeneous ensembles were the ensemble type that was developed most often in literature, while decision trees, artificial neural networks and Bayesian classifiers were the single techniques used most frequently to develop ensemble classification methods. The weighted majority and majority voting rules were adopted to obtain the final decision of the ensembles developed. With regard to evaluation frameworks, the datasets obtained from the UCI and PhysioBank repositories were those used most often to evaluate the ensemble methods, while the k-fold cross-validation method was the most frequently-employed validation technique. Several tools with which to build ensemble classifiers were identified, and the type of software adopted with the greatest frequency was open source. Finally, only a few researchers took into account the optimization of the parameter settings of either single or meta ensemble classifiers. This mapping study attempts to provide a greater insight into the application of ensemble classification methods in cardiovascular diseases. The majority of the selected papers reported positive feedback as regards the ability of ensemble methods to perform better than single methods. Further analysis is required to aggregate the evidence reported in literature. {\textcopyright} 2020, Springer Nature B.V.}, keywords = {Bayesian networks, Cardio-vascular disease, Cardiology, Decision trees, Diagnosis, Digital libraries, Diseases, Ensemble classification, Ensemble classifiers, Evaluation framework, K fold cross validations, Learning systems, Majority voting rules, Mapping, Open source software, Open systems, Optimization method, Systematic mapping studies}, doi = {10.1007/s10462-020-09914-6}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85091735819\&doi=10.1007\%2fs10462-020-09914-6\&partnerID=40\&md5=69ea4b02de420c3ec6a85e1f3c7dddaf}, author = {Hosni, M. and Carrillo de Gea, J.M. and Idri, A. and El Bajta, M. and Fern{\'a}ndez Alem{\'a}n, J.L. and Garc{\'\i}a-Mateos, G. and Abnane, I.} } @article {Zakrani202072, title = {Software Effort Estimation Using an Optimal Trees Ensemble: An Empirical Comparative Study}, journal = {Smart Innovation, Systems and Technologies}, volume = {146}, year = {2020}, note = {cited By 0}, pages = {72-82}, abstract = {Since information systems have become the heartbeat of many organizations, the investment in software is growing rapidly and consuming then a significant portion of the company budget. In this context, both the software engineering practitioners and researchers are more interested than ever about accurately estimating the effort and the quality of software product under development. Accurate estimates are desirable but no technique has demonstrated to be successful at effectively and reliably estimating software development effort. In this paper, we propose the use of an optimal trees ensemble (OTE) to predict the software development effort. The ensemble employed is built by combining only the top ranked trees, one by one, from a set of random forests. Each included tree must decrease the unexplained variance of the ensemble for software development effort estimation (SDEE). The effectiveness of the OTE model is compared with other techniques such as regression trees, random forest, RBF neural networks, support vector regression and multiple linear regression in terms of the mean magnitude relative error (MMRE), MdMRE and Pred(l) obtained on five well known datasets namely: ISBSG R8, COCOMO, Tukutuku, Desharnais and Albrecht. According to the results obtained from the experiments, it is shown that the proposed ensemble of optimal trees outperformed almost all the other techniques. Also, OTE model outperformed statistically the other techniques at least in one dataset. {\textcopyright} 2020, Springer Nature Switzerland AG.}, keywords = {Accuracy evaluation, Budget control, Decision trees, Forestry, Linear regression, Multiple linear regressions, Optimal trees ensemble, Radial basis function networks, Random forests, RBF Neural Network, Regression trees, Software design, Software development effort, Support vector regression (SVR)}, doi = {10.1007/978-3-030-21005-2_7}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85069482179\&doi=10.1007\%2f978-3-030-21005-2_7\&partnerID=40\&md5=8d9d107d9b52c20f6cd93bb86477a6a0}, author = {Zakrani, A. and Idri, A. and Hain, M.} } @article {Kharbouch2020894, title = {Software Requirement Catalog on Acceptability, Usability, Internationalization and Sustainability for Contraception mPHRs}, journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}, volume = {12252 LNCS}, year = {2020}, note = {cited By 0}, pages = {894-905}, abstract = {Contraception Mobile Personal Health Records (mPHRs) are efficient mobile health applications (apps) to increase awareness about fertility and contraception and to allow women to access, track, manage, and share their health data with healthcare providers. This paper aims to develop a requirements catalog, according to standards, guidelines, and relevant literature to e-health technology and psychology. The requirements covered by this catalog are Acceptability, Usability, Sustainability, and Internationalization (i18n). This catalog can be very useful for developing, evaluating, and auditing contraceptive apps, as well as helping stakeholders and developers identify potential requirements for their mPHRs to improve them. {\textcopyright} 2020, Springer Nature Switzerland AG.}, keywords = {Health, Health care providers, Health data, Mobile health application, Personal health record, Software requirements, Sustainable development}, doi = {10.1007/978-3-030-58811-3_63}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85092222585\&doi=10.1007\%2f978-3-030-58811-3_63\&partnerID=40\&md5=119f051c4582d8079abdb813399d89d1}, author = {Kharbouch, M. and Idri, A. and Redman, L. and Alami, H. and Fernandez-Aleman, J.L. and Toval, A.} } @conference {ElIdrissi2020337, title = {Strategies of multi-step-ahead forecasting for blood glucose level using LSTM neural networks: A comparative study}, 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 3}, pages = {337-344}, abstract = {Predicting the blood glucose level (BGL) is crucial for self-management of Diabetes. In general, a BGL prediction is done based on the previous measurements of BGL, which can be taken either (manually) by using sticks or (automatically) by using continuous glucose monitoring (CGM) devices. To allow the diabetic patients to take appropriate actions, the BGL predictions should be done ahead of time; thus a multi-step ahead prediction is suitable. Therefore, many Multi-Step-ahead Forecasting (MSF) strategies have been developed and evaluated, and can be categorized in five types: Recursive, Direct, MIMO (for Multiple Input Multiple Output), DirMO (combining Direct and MIMO) and DirRec (combining Direct and Recursive). However, none of them is known to be the best strategy in all contexts. The present study aims at: 1) reviewing the MSF strategies, and 2) determining the best strategy to fit with a LSTM Neural Network model. Hence, we evaluated and compared in terms of two performance criteria: Root-Mean-Square Error (RMSE) and Mean Absolute Error (MAE), the five MSF strategies using a LSTM Neural Network with an horizon of 30 minutes. The results show that there is no strategy that significantly outperformed others when using the Wilcoxon statistical test. However, when using the Sum Ranking Differences method, MIMO is the best strategy for both RMSE and MAE criteria. {\textcopyright} 2020 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved.}, keywords = {Biomedical engineering, Blood, Blood glucose level, Comparative studies, Continuous glucosemonitoring (CGM), Forecasting, Glucose, Long short-term memory, Mean absolute error, Mean square error, Medical informatics, MIMO systems, Multi-step-ahead predictions, Neural network model, Performance criterion, Root mean square errors}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083722214\&partnerID=40\&md5=bd9e45a8538559492b02432e5567d10e}, author = {El Idrissi, T. and Idri, A. and Kadi, I. and Bakkoury, Z.} } @article {Nassif2019, title = {Software development effort estimation using regression fuzzy models}, journal = {Computational Intelligence and Neuroscience}, volume = {2019}, year = {2019}, note = {cited By 0}, doi = {10.1155/2019/8367214}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85062599935\&doi=10.1155\%2f2019\%2f8367214\&partnerID=40\&md5=efd8130615f3d9857ff368174c7a2db1}, author = {Nassif, A.B. and Azzeh, M. and Idri, A. and Abran, A.} } @article {Benhar2019, title = {A Systematic Mapping Study of Data Preparation in Heart Disease Knowledge Discovery}, journal = {Journal of Medical Systems}, volume = {43}, number = {1}, year = {2019}, note = {cited By 0}, doi = {10.1007/s10916-018-1134-z}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85058606352\&doi=10.1007\%2fs10916-018-1134-z\&partnerID=40\&md5=f92d6f6b431ad7798a78fb05c7a5624d}, author = {Benhar, H. and Idri, A. and Fernandez-Aleman, J.L.} } @article {Bajta2018690, title = {Software project management approaches for global software development: A systematic mapping study}, journal = {Tsinghua Science and Technology}, volume = {23}, number = {6}, year = {2018}, pages = {690-714}, doi = {10.26599/TST.2018.9010029}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85055661732\&doi=10.26599\%2fTST.2018.9010029\&partnerID=40\&md5=7601769b8094dafca96fea06d11fd3db}, author = {Bajta, M.E. and Idri, A. and Ros, J.N. and Fernandez-Aleman, J.L. and de Gea, J.M.C. and Garc{\'\i}a, F. and Toval, A.} } @article {Idri2018, title = {Support vector regression-based imputation in analogy-based software development effort estimation}, journal = {Journal of Software: Evolution and Process}, volume = {30}, number = {12}, year = {2018}, doi = {10.1002/smr.2114}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85058493764\&doi=10.1002\%2fsmr.2114\&partnerID=40\&md5=09059bd4e9087fda5b7225f1c8aeea9d}, author = {Idri, A. and Abnane, I. and Abran, A.} } @article {Chadli2018408, title = {A survey on the impact of risk factors and mitigation strategies in global software development}, journal = {Advances in Intelligent Systems and Computing}, volume = {746}, year = {2018}, pages = {408-417}, doi = {10.1007/978-3-319-77712-2_39}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85045296980\&doi=10.1007\%2f978-3-319-77712-2_39\&partnerID=40\&md5=9b40aafda48fa8eff4f79973caf81f38}, author = {Chadli, S.Y. and Idri, A.} } @article {Ouhbi2018, title = {Sustainability requirements for connected health applications}, journal = {Journal of Software: Evolution and Process}, volume = {30}, number = {7}, year = {2018}, doi = {10.1002/smr.1922}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85050354987\&doi=10.1002\%2fsmr.1922\&partnerID=40\&md5=70c90bb949fb18d52004aec275734df0}, author = {Ouhbi, S. and Fernandez-Aleman, J.L. and Toval, A. and Rivera~Pozo, J. and Idri, A.} } @conference {Idri2018, title = {A systematic map of data analytics in breast cancer}, booktitle = {ACM International Conference Proceeding Series}, year = {2018}, doi = {10.1145/3167918.3167930}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85044739616\&doi=10.1145\%2f3167918.3167930\&partnerID=40\&md5=4cfbf5c917bfa294988a996c72ff2234}, author = {Idri, A. and Chlioui, I. and El Ouassif, B.} } @article {Idri201869, title = {A systematic map of medical data preprocessing in knowledge discovery}, journal = {Computer Methods and Programs in Biomedicine}, volume = {162}, year = {2018}, pages = {69-85}, doi = {10.1016/j.cmpb.2018.05.007}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85046706780\&doi=10.1016\%2fj.cmpb.2018.05.007\&partnerID=40\&md5=30324a8f7b2702347d51c7c56b55b294}, author = {Idri, A. and Benhar, H. and Fernandez-Aleman, J.L. and Kadi, I.} } @article {Moumane201858, title = {A systematic map of mobile software usability evaluation}, journal = {Advances in Intelligent Systems and Computing}, volume = {746}, year = {2018}, pages = {58-67}, doi = {10.1007/978-3-319-77712-2_6}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85045321817\&doi=10.1007\%2f978-3-319-77712-2_6\&partnerID=40\&md5=c77c64a151faac6ec168b3914e6eb0dc}, author = {Moumane, K. and Idri, A.} } @conference {Hosni20171251, title = {Software effort estimation using classical analogy ensembles based on random subspace}, booktitle = {Proceedings of the ACM Symposium on Applied Computing}, volume = {Part F128005}, year = {2017}, note = {cited By 1}, pages = {1251-1258}, abstract = {Software effort estimation is one of the important and complex tasks in software project management. It influences almost all the process of software development such as: bidding, planning, and budgeting. Hence, estimating the software project effort in early stages of the software life cycle is considered the key of success of any project. To this goal, many techniques have been proposed to predict the effort required to develop a software system. Unfortunately, there is no consensus about the single best technique. Recently, Ensemble Effort Estimation has been investigated to estimate software effort and consists on generating the software effort by combining more than one solo estimation technique by means of a combination rule. In this paper, we have developed different homogeneous ensembles based on combination of Random Subspace method and Classical Analogy technique using two linear rules over seven datasets. The results confirm that the Random Space ensembles outperform the solo Classical Analogy regardless of the dataset used and that the median rule generates better estimation than the average one. {\textcopyright} 2017 ACM.}, doi = {10.1145/3019612.3019784}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85020917571\&doi=10.1145\%2f3019612.3019784\&partnerID=40\&md5=8b1b6f9ac3cb26187929f17a78dd5669}, author = {Hosni, M. and Idri, A.} } @conference {Idri2017, title = {A survey of secondary studies in software process improvement}, booktitle = {Proceedings of IEEE/ACS International Conference on Computer Systems and Applications, AICCSA}, year = {2017}, note = {cited By 0}, abstract = {Software Process Improvement (SPI) has become one of the main strategic objectives in software industry. Companies make more investments in implementing software quality standards and models that focus on process assessment to improve their performance and productivity. To achieve these goals, companies focus on improving their process by means of improvement initiatives which may be implemented. To help practitioners find more innovative ways to manage and implement software process improvement initiatives efficiently, an important number of studies related to this topic have been emerged in recent years. Some of them, referred to as secondary studies, focused on the interpretation and synthesis of available published research works by giving an up to date state of art about SPI. This state of the art is provided in a form of literature surveys or in a methodological form using well established approaches such as systematic reviews or systematic mappings or tertiary studies. The objective of this paper is to identify and present the current secondary studies on SPI. The purpose is to discuss methods that these literature reviews of SPI use, their quality, and specific subjects that they cover. A set of survey research questions have been proposed and discussed through the investigation of 70 selected secondary studies collected from different digital libraries. The results show that success factors and issues related to implementation of SPI initiatives are the most studied, and there is a need to address in depth the measurement aspects in SPI. {\textcopyright} 2016 IEEE.}, doi = {10.1109/AICCSA.2016.7945655}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85021871351\&doi=10.1109\%2fAICCSA.2016.7945655\&partnerID=40\&md5=e6b8548942846520db1fe2f2d41c4ecc}, author = {Idri, A. and Cheikhi, L.} } @article {Sardi201731, title = {A systematic review of gamification in e-Health}, journal = {Journal of Biomedical Informatics}, volume = {71}, year = {2017}, note = {cited By 5}, pages = {31-48}, abstract = {Gamification is a relatively new trend that focuses on applying game mechanics to non-game contexts in order to engage audiences and to inject a little fun into mundane activities besides generating motivational and cognitive benefits. While many fields such as Business, Marketing and e-Learning have taken advantage of the potential of gamification, the digital healthcare domain has also started to exploit this emerging trend. This paper aims to summarize the current knowledge regarding gamified e-Health applications. A systematic literature review was therefore conducted to explore the various gamification strategies employed in e-Health and to address the benefits and the pitfalls of this emerging discipline. A total of 46 studies from multiple sources were then considered and thoroughly investigated. The results show that the majority of the papers selected reported gamification and serious gaming in health and wellness contexts related specifically to chronic disease rehabilitation, physical activity and mental health. Although gamification in e-Health has attracted a great deal of attention during the last few years, there is still a dearth of valid empirical evidence in this field. Moreover, most of the e-Health applications and serious games investigated have been proven to yield solely short-term engagement through extrinsic rewards. For gamification to reach its full potential, it is therefore necessary to build e-Health solutions on well-founded theories that exploit the core experience and psychological effects of game mechanics. {\textcopyright} 2017 Elsevier Inc.}, doi = {10.1016/j.jbi.2017.05.011}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85019895406\&doi=10.1016\%2fj.jbi.2017.05.011\&partnerID=40\&md5=9da7af1cff144bf7fab5532c2a4aea66}, author = {Sardi, L. and Idri, A. and Fernandez-Aleman, J.L.} } @conference {Elmidaoui2016, title = {A survey of empirical studies in software product maintainability prediction models}, booktitle = {SITA 2016 - 11th International Conference on Intelligent Systems: Theories and Applications}, year = {2016}, note = {cited By 0}, abstract = {Software product maintainability is critical to the achievement of the software product quality. In order to keep the software useful as long as possible, software product maintainability prediction (SPMP) has become an important endeavor. The objective of this paper is to identify and present the current research on SPMP. The search was conducted using digital libraries to And as much research papers as possible. Selected papers are classified according to the following survey classification criteria (SCs): research type, empirical type, publication year and channel. Based on the results of the survey, we provide a discussion of the current state of the art in software maintainability prediction models or techniques. We believe that this study will be a reliable basis for further research in software maintainability studies. {\textcopyright} 2016 IEEE.}, doi = {10.1109/SITA.2016.7772267}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85010468522\&doi=10.1109\%2fSITA.2016.7772267\&partnerID=40\&md5=8184d51d9fbbba990544f8dc158d2375}, author = {Elmidaoui, S. and Cheikhi, L. and Idri, A.} } @conference {Idri2013207, title = {Software cost estimation by classical and Fuzzy Analogy for Web Hypermedia Applications: A replicated study}, booktitle = {Proceedings of the 2013 IEEE Symposium on Computational Intelligence and Data Mining, CIDM 2013 - 2013 IEEE Symposium Series on Computational Intelligence, SSCI 2013}, year = {2013}, note = {cited By 1}, pages = {207-213}, abstract = {The aim of this paper is to evaluate and to compare the Classical Analogy and Fuzzy Analogy for software cost estimation on a Web software dataset. Hence, the paper aims to replicate the results of our precedent experiments on this dataset. Moreover, questions regarding the estimates accuracy, the tolerance of imprecision and uncertainty of cost drivers, and the favorable context to use estimation by analogy are discussed. This study approved the usefulness of Fuzzy Analogy for software cost estimation. {\textcopyright} 2013 IEEE.}, doi = {10.1109/CIDM.2013.6597238}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84885622172\&doi=10.1109\%2fCIDM.2013.6597238\&partnerID=40\&md5=a7c6b5315989675548f9310b89df6f32}, author = {Idri, A. and Zahi, A.} } @conference {Idri2012863, title = {Software cost estimation by fuzzy analogy for ISBSG repository}, booktitle = {World Scientific Proc. Series on Computer Engineering and Information Science 7; Uncertainty Modeling in Knowledge Engineering and Decision Making - Proceedings of the 10th International FLINS Conf.}, volume = {7}, year = {2012}, note = {cited By 0}, pages = {863-868}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84892659716\&partnerID=40\&md5=2001b51ce67b133f56f1ca779fc56611}, author = {Idri, A. and Amazal, F.A.} } @article {Cheikhi2012462, title = {Software productivity: Harmonization in ISO/IEEE software engineering standards}, journal = {Journal of Software}, volume = {7}, number = {2}, year = {2012}, note = {cited By 2}, pages = {462-470}, doi = {10.4304/jsw.7.2.462-470}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84857929017\&doi=10.4304\%2fjsw.7.2.462-470\&partnerID=40\&md5=55088a24a8af596776fe0e2ddf74892c}, author = {Cheikhi, L. and Al-Qutaish, R.E. and Idri, A.} } @article {Idri200821, title = {Software cost estimation models using radial basis function neural networks}, journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}, volume = {4895 LNCS}, year = {2008}, note = {cited By 4}, pages = {21-31}, doi = {10.1007/978-3-540-85553-8_2}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-54249149525\&doi=10.1007\%2f978-3-540-85553-8_2\&partnerID=40\&md5=483abae3a29ef61079aab5da6a389786}, author = {Idri, A. and Zahi, A. and Mendes, E. and Zakrani, A.} }