Wafa Mribah

Work place: MARS Research Laboratory LR17ES05, University of Sousse, Tunisia

E-mail: wafa.meribah@gmail.com


Research Interests:


Wafa Mribah did her higher studies at the National Engineering School of Sousse in Tunisia where she obtained her engineering degree in Computer Science option Applied Computer Science. Currently she is an engineer in Product Manager at LeasePlan in Netherlands.

Author Articles
Towards an Intelligent Machine Learning-based Business Approach

By Mohamed Nazih Omri Wafa Mribah

DOI: https://doi.org/10.5815/ijisa.2022.01.01, Pub. Date: 8 Feb. 2022

With the constant increase of data induced by stakeholders throughout a product life cycle, companies tend to rely on project management tools for guidance. Business intelligence approaches that are project-oriented will help the team communicate better, plan their next steps, have an overview of the current project state and take concrete actions prior to the provided forecasts. The spread of agile working mindsets are making these tools even more useful. It sets a basic understanding of how the project should be running so that the implementation is easy to follow on and easy to use.
In this paper, we offer a model that makes project management accessible from different software development tools and different data sources. Our model provide project data analysis to improve aspects: (i) collaboration which includes team communication, team dashboard. It also optimizes document sharing, deadlines and status updates. (ii) planning: allows the tasks described by the software to be used and made visible. It will also involve tracking task time to display any barriers to work that some members might be facing without reporting them. (iii) forecasting to predict future results from behavioral data, which will allow concrete measures to be taken. And (iv) Documentation to involve reports that summarize all relevant project information, such as time spent on tasks and charts that study the status of the project. The experimental study carried out on the various data collections on our model and on the main models that we have studied in the literature, as well as the analysis of the results, which we obtained, clearly show the limits of these studied models and confirms the performance of our model as well as efficiency in terms of precision, recall and robustness.

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