@conference {ElHamlaoui2022424,
	title = {A Model Based Approach for Generating Modular Manufacturing Control Systems Software},
	booktitle = {Proceedings of the International Conference on Software Engineering and Knowledge Engineering, SEKE},
	year = {2022},
	note = {cited By 0},
	pages = {424-427},
	abstract = {Digitalization is transforming manufacturing systems to become more agile and smart thanks to the integration of sensors and connection technologies that help capture data at all phases of a product{\textquoteright}s life cycle. Digitalization promises to improve manufacturing flexibility, quality, productivity, and reliability. However, there is still a significant need of effective methods to develop models that could enhance the capabilities of manufacturing systems. Formal methods and tools are becoming essential to achieve this objective. Within this context, this paper introduces a formal software solution for the automatic generation of modular manufacturing control systems software. The proposed solution leverages software Model-Based Design (MBD) techniques to reduce development effort, time, and human error by automating several manual steps. {\textcopyright} 2022 Knowledge Systems Institute Graduate School. All rights reserved.},
	keywords = {All phase, Component, Control system software, Control systems, Cybe-physical manufacturing, Cyber Physical System, Cyber physicals, Digital subscriber lines, Embedded systems, formal methods, Grafcet, Industry 4.0, Life cycle, Manufacturing control systems, MDE, Metadata, Model based approach, Modulars},
	doi = {10.18293/SEKE2022-151},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85137161158\&doi=10.18293\%2fSEKE2022-151\&partnerID=40\&md5=3784648e72d7591498aa74fb97896480},
	author = {El Hamlaoui, M. and Qamsane, Y. and Laghouaouta, Y. and Mishra, A.}
}
@article {Fissaa2021151,
	title = {Combining planning and learning for context aware service composition},
	journal = {International Journal of Data Analysis Techniques and Strategies},
	volume = {13},
	number = {1-2},
	year = {2021},
	note = {cited By 0},
	pages = {151-169},
	abstract = {Computing vision introduced by Mark Weiser in the early {\textquoteright}90s has defined the basis of what is called now ubiquitous computing. This new discipline results from the convergence of powerful, small and affordable computing devices with networking technologies that connect them all together. Thus, ubiquitous computing has brought a new generation of service-oriented architectures (SOA) based on context-aware services. These architectures provide users with personalised and adapted behaviours by composing multiple services according to their contexts. In this context, the objective of this paper is to propose an approach for context-aware semantic-based services composition. Our contributions are built around following axes: 1) a semantic-based context modelling and context-aware semantic composite service specification; 2) an architecture for context-aware semantic-based services composition using artificial intelligence planning; 3) an intelligent mechanism based on reinforcement learning for context-aware selection in order to deal with dynamicity and uncertain character of modern ubiquitous environment. Copyright {\textcopyright} 2021 Inderscience Enterprises Ltd.},
	doi = {10.1504/IJDATS.2021.114673},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85105363779\&doi=10.1504\%2fIJDATS.2021.114673\&partnerID=40\&md5=a71041d2e85a5cf3407180fb5cff29b2},
	author = {Fissaa, T. and El Hamlaoui, M. and Guermah, H. and Hafiddi, H. and Nassar, M.}
}
@conference {Ba{\"\i}na2020215,
	title = {Business Process Modelling Augmented: Model Driven transformation of User Stories to Processes},
	booktitle = {ACM International Conference Proceeding Series},
	year = {2020},
	note = {cited By 0},
	pages = {215-220},
	abstract = {The Purpose of our paper is to present a lightweight efficient approach for analysing user stories backlog in order to generate a business process model. A review of literature has been conducted to study contributions in the domain of automatic business process extraction from textual requirements. We found that most of interesting approaches analysing user stories use natural language processing techniques for software projects requirements understanding and none of them target business process modeling automation. The Originality of our contribution is the proposition of a model driven based parsing of user stories backlog and transformations to generate a process model. This work thus contributes with a novel agile iterative methodology augmenting business process design phase with automation assistant transforming user stories textual requirements into a business process model. {\textcopyright} 2020 ACM.},
	keywords = {Business Process, Business process design, Business process model, Business process modelling, Intelligent systems, Iterative methodology, Iterative methods, Modeling languages, NAtural language processing, Natural language processing systems, Process design, Process Modeling, Software project},
	doi = {10.1145/3419604.3419793},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096411195\&doi=10.1145\%2f3419604.3419793\&partnerID=40\&md5=9c2213523602ada2a3463369b32c3d10},
	author = {Baina, K. and El Hamlaoui, M. and Kabbaj, H.}
}
@conference {Boumahdi2020603,
	title = {Crisis management systems: Big data and machine learning approach},
	booktitle = {ENASE 2020 - Proceedings of the 15th International Conference on Evaluation of Novel Approaches to Software Engineering},
	year = {2020},
	note = {cited By 0},
	pages = {603-610},
	abstract = {A crisis is defined as an event that, by its nature or its consequences, poses a threat to the vital national interests or the basic needs of the population, encourages rapid decision making, and requires coordination between the various departments and agencies. Hence the need and importance of crisis and disaster management systems. These crisis and disaster management systems have several phases and techniques, and require many resources and tactics and needs. Among the needs of these systems are useful and necessary information that can be used to improve the making of good decisions, such as data on past and current crises. The application of machine learning and big data technologies in data processing of crises and disasters can yield important results in this area. In this document, we address in the first part the crisis management systems, and the tools of big data and machine learning that can be used. Then in the second part, we have established a literature review that includes a state of the art, and a discussion. Then we established a machine learning and big data approach for crisis management systems, with a description and experimentation, as well as a discussion of results and future work. {\textcopyright} Copyright 2020 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved.},
	keywords = {Big data, Crisis management systems, Data handling, Data technologies, Decision making, Disaster management, Disaster prevention, Disasters, Information management, Literature reviews, Machine learning, Machine learning approaches, Software engineering, State of the art},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85088381343\&partnerID=40\&md5=b90b38f94e34455a12781472d1aba3ab},
	author = {Boumahdi, A. and El Hamlaoui, M. and Nassar, M.}
}
@article {ElHamlaoui2019167,
	title = {Support cloud SLA establishment using MDE},
	journal = {Lecture Notes in Networks and Systems},
	volume = {49},
	year = {2019},
	note = {cited By 0},
	pages = {167-184},
	doi = {10.1007/978-3-319-97719-5_12},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85063299209\&doi=10.1007\%2f978-3-319-97719-5_12\&partnerID=40\&md5=78747c9999533ac370999a8301a4f99f},
	author = {El Hamlaoui, M. and Fissaa, T. and Laghouaouta, Y. and Nassar, M.}
}
@conference {Bennani2018152,
	title = {Collaborative model-based matching of heterogeneous models},
	booktitle = {Proceedings of the 2018 IEEE 22nd International Conference on Computer Supported Cooperative Work in Design, CSCWD 2018},
	year = {2018},
	pages = {152-157},
	doi = {10.1109/CSCWD.2018.8465228},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85054369642\&doi=10.1109\%2fCSCWD.2018.8465228\&partnerID=40\&md5=5c36a33efe1da810c568cd8ac7f96749},
	author = {Bennani, S. and El Hamlaoui, M. and Nassar, M. and Ebersold, S. and Coulette, B.}
}
@conference {Salah-Eddine2018130,
	title = {Computerised method of coping with technostress in organizational situation},
	booktitle = {2018 International Conference on Information Management and Processing, ICIMP 2018},
	volume = {2018-January},
	year = {2018},
	pages = {130-134},
	doi = {10.1109/ICIMP1.2018.8325854},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85051020956\&doi=10.1109\%2fICIMP1.2018.8325854\&partnerID=40\&md5=edfd6f6dbd98a85dea77c6b85c7adbe3},
	author = {Salah-Eddine, M. and El Hamlaoui, M. and Belaissaoui, M.}
}
@conference {Saidi2018,
	title = {Translation of heterogeneous requirements meta-models through a pivot meta-model},
	booktitle = {ENASE 2018 - Proceedings of the 13th International Conference on Evaluation of Novel Approaches to Software Engineering},
	volume = {2018-January},
	year = {2018},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85056807026\&partnerID=40\&md5=f8a470ca9cd9874928c91f4110c79259},
	author = {Saidi, I.E. and El Hamlaoui, M. and Dkaki, T. and Zarour, N.E. and Charrel, P.-J.}
}
