Abstract-WebMeasuring the similarity between the web services interfaces is the most suitable solution for this kind of problems, it will classify available web services so as to know those that best match the searched profile and those that do not match. Thus, the main goal of this work is to study the degree of similarity between any two web services by offering a new method that is more effective than existing works.

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’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.

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Many research works and official reports approve that irresponsible driving behavior on the road is the main cause of accidents. Consequently, responsible driving behavior can significantly reduce accidents’ number and severity. Therefore, in the research area as well as in the industrial area, mobile technologies are widely exploited in assisting drivers in reducing accident rates and preventing accidents. For instance, several mobile apps are provided to assist drivers in improving their driving behavior. Recently and thanks to mobile cloud computing, smartphones can benefit from the computing power of servers in the cloud for executing machine learning algorithms. Therefore, many mobile applications of driving assistance and control are based on machine learning techniques to adjust their functioning automatically to driver history, context, and profile. Additionally, gamification is a key element in the design of these mobile applications that allow drivers to develop their engagement and motivation to improve their driving behavior. To have an overview concerning existing mobile apps that improve driving behavior, we have chosen to conduct a systematic mapping study about driving behavior mobile apps that exist in the most common mobile apps repositories or that were published as research works in digital libraries. In particular, we should explore their functionalities, the kinds of collected data, the used gamification elements, and the used machine learning techniques and algorithms. We have successfully identified 220 mobile apps that help to improve driving behavior. In this work, we will extract all the data that seem to be useful for the classification and analysis of the functionalities offered by these applications.

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Every year, many lives are lost around the world owing to road accidents that are caused in most cases by irresponsible driving behavior. Thus, the main goal of this study was to provide an overview of aberrant driving behavior and its relation to the occurrence of accidents in Morocco. The study was conducted through a survey based on the most used version of the Driver Behavior Questionnaire (DBQ). The adopted DBQ structure was validated using exploratory factor analysis. Subsequently, several regressions were applied to the collected data to discover the factors that influence driver behavior, leading to the occurrence of accidents. We conclude that relatively young age, low educational level, and lack of driving experience are the main features that characterize all types of aberrant driving behavior. In addition, we found that, except for lapses, all other types of aberrant driving behavior contribute to the occurrence of accidents, and we have found that accidents are closely related to driving experience and slightly related to concentration while driving. These results can be exploited in the development of intervention frameworks to improve driving behavior.

The postnatal period is a critical phase in both the lives of the mothers and the newborns. Due to all the inherent changes that occur during this period, quality care is crucial during this period to enhance the wellbeing of the mothers and the newborns. In Morocco, the neglection of postnatal care services are often associated to poor communication, financial difficulties and cultural barriers. Mobile technology constitutes therefore a promising approach to bridge this gap and promote postnatal care. In order to improve the effectiveness of mobile technology, gamification has become a powerful feature to boost motivation and induce fun and interactivity into the mobile solutions’ tasks. Based on a previous review on mobile applications for postnatal care available in app repositories, a set of requirements have been identified to build a comprehensive mobile solution that cater the needs of both the mothers and the newborns. These requirements have, then, been enriched with real needs elicited at maternity Les orangers that belongs to the University Hospital Avicenne of Rabat. Along with the functional and non-functional requirements, gamification aspects have been also analyzed. After the analysis and design phases, a pilot version of the solution called ‘Mamma&Baby’ has been implemented using android framework. ‘Mamma&Baby’ is a mobile solution dedicated to assist new mothers during their postnatal period. As future work, it is expected to fully integrate the gamification elements into the solution and conduct an empirical evaluation of the overall quality and the potential of the solution with real puerperal women.