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Special Session 5:
Machine Learning for Predictive Models in Engineering Applications (MLPMEA 2023)

The MLPMEA 2023 special session provides an international forum for sharing knowledge and results on the theory, methodology, and applications of Machine Learning for developing predictive models for different engineering applications. Various Machine Learning methods can efficiently handle complex prediction problems and have abilities to handle large-scale datasets with uniform characteristics and noisy data. This MLPMEA 2023 special session covers topics related to building predictive models using different Machine Learning methods to solve specific problems that arise in the engineering domain. The MLPMEA encourages researchers to publicly share their data, to promote interdisciplinary research between the machine learning and engineering communities and conduct verifiable, repeatable experiments that practitioners can use.

Scope and topics:

Topics relevant to this session include, but are not limited to:

• Predictive modeling applications and pressing challenges/opportunities in engineering.
• Using predictive modeling for accelerating materials design.
• Developing predictive modeling tools that can assist engineers in defining, exploring, and evaluating alternative systems or designs.
• Using predictive modeling in additive manufacturing.
• Applications of artificial neural networks and other machine learning methods to solve engineering problems.

Chairs: Ali Bou Nassif, Mohammad Azzeh, Shadi Banitaan

Bio: Ali Bou Nassif, Department of Computer Engineering, University of Sharjah, UAE Email: anassifsharjah.ac.ae Mohammad Azzeh, Department of Data Science, Princess Sumaya University for Technology, Jordan Email: m.azzehpsut.edu.jo Shadi Banitaan, Department of Electrical and Computer Engineering and Computer Science, University of Detroit Mercy, USA Email: banitashudmercy.edu

Technical Committee

• Abdallah Moubayed, IBM, USA
• Ahmet Samet, INSA, USA
• Akbar Siami Namin, Texas Tech University, USA
• Ali Bou Nassif, University of Sharjah, UAE
• Celso Lopes Junior, Universidade de Pernambuco, Brazil
• Cherif Salama , The American University in Cairo, Egypt
• Cuauhtemoc Lopez-Martin, Universidad De Guadalajara, Mexico
• Ghaith Alrefai, Oakland University, USA
• Hui Li, Nankai University, China
• Kamran Kowsari, University of Virginia, USA
• KatarinaGrolinger, The University of Western Ontario, Canada
• Lukasz Radlinski, West Pomeranian University of Technology
• Szczecin, Poland Mahmoud Elmezain, Tanta University, Egypt
• Man Leung Wong, Lingnan University, China
• Mina Maleki, University of Detroit Mercy, USA
• Ming Zhang, Utah State University, USA
• Miriam Faied, University of Detroit Mercy, USA
• Mohammad Azzeh, Applied Science University, Jordan
• MohammadNoor Injadat, University of Western Ontario, Canada
• Nandakumar Selvaraj, Biofourmis, USA
• Raed Seetan, Slippery Rock University, USA
• Shadi Banitaan, University of Detroit Mercy, USA
• Sotiris Kotsiantis, University of Patras, Greece
• Teng-Sheng Moh, San Jose State University, USA

Paper Submission Instructions

All papers will be double-blind reviewed and must present original work.

• CMT Submission Site
• Select the track: Special Session 5: Machine Learning for Predictive Models in Engineering Applications

Papers submitted for reviewing should conform to IEEE specifications. Manuscript templates can be downloaded from:

• IEEE website

Keydates

• Submission due date: September 5, 2023
• Notification of Acceptance: September 25, 2023
• Camera Ready Papers: October 5, 2023
• Pre-registration: October 15, 2023
• Conference: December 15-17, 2023

Registration

In order for your paper to be presented in the virtual session and published in the proceedings you must register to the conference.

Paper Presentation Instructions

The papers submitted to this track will be presented in person as part of the conference. There is no virtual presentation for this session.