Machine Learning (ML) is a science that forces computers to learn and behave like humans. As these systems interact with data, networks, and people, they automatically become smarter so that they can eventually solve or predict a practical issue in the world for us. The use of ML can be a giant leap for cannot simply be integrated as the top layer. This requires redefining workflow, architecture, data collection and storage, analytics, and other modules. This paper aims to discuss the issue of machine learning technique for analysis data of mobile user. First, we identified the machine learning benefits and drawbacks, challenges, advantages of using Machine Learning. Then, we propose a generic model of analytic mobile user data using ML, the model is centered on the machine learning component, which interacts with two other components, including mobile user data, and system. The interactions go in both directions. For instance, mobile user data serves as inputs to the learning component and the latter generates outputs; system architecture has impact on how learning algorithms should run and how efficient it is to run them, and simultaneously meeting. Mobile user data goes through several stages: prepossessing which includes the steps we need to follow to transform or encode the data so that it can be easily analyzed by the machine. Then, modelling in this step we will be clustering and classification the data obtained. Finally, evaluation, various measures of performance, accuracy, recall, precision, and F-measure were used to analyze the results of the naive Bayes, SVM, and K-nearest neighbor classification algorithms.

Privacy and Security in big data and smart cities play a major role to ensure better quality of citizens life. Privacy emphasizes on the data being collected, shared, and used in the right manner, and security focuses on protecting the data from intruders’ attack, and exploitation of data for other purposes such as criminal behavior. This paper aims to discuss the issue of privacy and security of mobile users in big data and smart cities. First, based on big data privacy and security challenges, classification, and models, we identify the privacy and security requirements for a mobile user. Then, we propose a generic architecture and an algorithm for the management of privacy and security of a mobile user in a smart environment. This architecture groups the main components required for implementing the proposed algorithm that ensure the privacy and security of mobile users in smart cities.

Expert finding and expert profiling are two important tasks for organizations, researchers, and work seekers. This importance can also be seen in online communities especially with the explosion of social networks. Expert finding on one hand addresses the task of finding the right person with the appropriate knowledge or skills. Expert profiling on the other hand gives a concise and meaningful description of a candidate expert. This paper focuses on what social tagging can bring to improve expert finding and profiling. A novel expertise indicator that models and assesses an expert based on the expert's tagging activities is proposed. First, tags are used as interest indicator to build candidate's profiles; then, Latent Dirichlet Allocation algorithm (LDA) is used to construct the tags distribution over topics by exploiting the tag's semantic characteristics. Topics of interest are then filtered using tag's depth. The latter is finally used as the expertise indicator. Experiments performed on the stack overflow dataset show the accuracy of the proposed approach.

Recommendation predicts which items the user might be interested in, and aims to help users finding the adequate element. Such as movies, music and commercial products, persons may be also recommended. In the case of handicraft women, we propose a recommendation approach based on extracted user's interest using his/her social tagging operations to improve business activities of the handicrafts women, the approach is applied with preliminary tests.