Toward new data for IT and IoT project management method prediction

: pp. 557–565
Received: February 13, 2023
Accepted: April 21, 2023
Laboratory of Information Technology and Modeling, Faculty of Sciences Ben M'Sik, Hassan II University of Casablanca
Laboratory of Sciences and Technology of Information and Education, Faculty of Sciences Ben M'Sik, Hassan II University of Casablanca
Laboratory of Information Technology and Modeling, Faculty of Sciences Ben M'Sik, Hassan II University of Casablanca
Laboratory of Information Technology and Modeling, Faculty of sciences Ben M'Sik, Hassan II University of Casablanca

Selecting the best project management method at the workplace helps to deliver a high-quality product to the customer.  Hence, the need for good knowledge of management methods, their characteristics, advantages, and disadvantages, is necessary to be able to select the best for the specific project.  However, until now, no large dataset for Machine Learning and decision-making model, model or system has been proposed to help project managers to the most efficient method adapted to the constraints of their projects.  This work develops the construction of the dataset for agile and IoT project management method based on the real experiences.  In this paper, our objective is to propose a criteria-based model that allows the choice of the best management method to adopt for such an IT or IoT project according to a set of criteria.

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Mathematical Modeling and Computing, Vol. 10, No. 2, pp. 557–565 (2023)