@article {Kerzazi2020472,
	title = {Teaching Pedigree Analysis and Risk Calculation for Diagnosis Purposes of Genetic Disease},
	journal = {Advances in Intelligent Systems and Computing},
	volume = {1159 AISC},
	year = {2020},
	note = {cited By 0},
	pages = {472-485},
	abstract = {A Faculty of Medicine needs to respond appropriately to the rapid changes in medical education, to ensure that future geneticists are well trained to meet the challenges of medical practice. This paper presents five core requirements for a tool integrating new methodologies for teaching graduate-level a course of medical genetics. Our methodology presented here focuses on exploiting pedigree analysis in the field of medical genetics, particularly to explore them in the diagnosis of genetic diseases. Because of its important relevance as one of the skills that future medical practitioners must have, we designed this approach as a learning process supported by a tool. We also discuss an ongoing supported effort at utilizing common tools and IT resources to make it easier for the learners to reach their expected skill levels and provide them with a rich learning experience. We find that not only do our tool prototype has a positive impact on the learning process, practitioners also have expectations to feed a bio-bank of medical cases as inputs for future empirical studies. {\textcopyright} 2020, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.},
	keywords = {Chromosomes, Diagnosis, Empirical studies, Information systems, Information use, Learning process, Learning systems, Medical education, Medical genetics, Medical practice, Medical practitioner, Pathology, Pedigree analysis, Rich learning experiences, Risk analysis, Risk assessment, Risk calculation},
	doi = {10.1007/978-3-030-45688-7_48},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85085529134\&doi=10.1007\%2f978-3-030-45688-7_48\&partnerID=40\&md5=7e1e7a85185ad292f5e21b992eefe113},
	author = {Kerzazi, N. and Tajir, M. and Boulouiz, R. and Bellaoui, M. and Azizi, M.}
}
@conference {Ouatiti20207,
	title = {Towards Amazon Fake Reviewers Detection: The Effect of Bulk Users},
	booktitle = {ACM International Conference Proceeding Series},
	year = {2020},
	note = {cited By 0},
	pages = {7-12},
	abstract = {Online marketplaces such as Amazon allow people to share their experiences about purchased products using textual comments known as product reviews. These reviews have become a common tool that users rely on to get insights on the quality and functionality of products and services from online consumers. However, like any other online information, reviewers raise serious questions concerning the credibility and reliability, since anyone can post reviews, which might impact the reliability of the information. This paper tackles the phenomenon of Bulk reviewers. We first analyze a large dataset of reviews from Amazon aiming to spot bulk reviewers according to their behavior. We then apply a what-if analysis to assess the effect of bulk reviews on the online marketplaces using a metric called Net Promoter Score to measure the willingness of users to recommend products. Our Results reveal that bulk users (i.e., users that review multiple times) have same distribution of ratings as non-bulk users indicating that a bulk reviewer is not automatically a fake reviewer. Yet, we discover that bulk users do inflate NPS metric and thus contribute to overestimate the level of customer satisfaction. {\textcopyright} 2020 ACM.},
	keywords = {Electronic assessment, Electronic commerce, Intelligent systems, Large dataset, On-line information, On-line marketplaces, Online consumers, Product reviews, Products and services, User experience, What-if Analysis},
	doi = {10.1145/3419604.3419800},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096423765\&doi=10.1145\%2f3419604.3419800\&partnerID=40\&md5=1a79dcff298ad489f414ee1d7743d93c},
	author = {Ouatiti, Y.E. and Kerzazi, N.}
}
