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PPMSA@IDEAL 2024 : Special Session on Predictive and Prescriptive Models for Smart Cities’ Applications at the 25th International Conference on Intelligent Data Engineering and Automated Learning | |||||||||||||||
Link: https://ideal2024.webs.upv.es/special-sessions/predictive-and-prescriptive-models-for-smart-cities-applications/ | |||||||||||||||
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Call For Papers | |||||||||||||||
Urbanization and the life in large cities have brought about unprecedented challenges, from resource scarcity and environmental degradation to social inequality and infrastructural strain. Moreover, the imperative to build sustainable, resilient cities capable of withstanding the impacts of climate change looms large on the global agenda. In an era where urbanization is rapidly expanding, the concepts of smart city technologies and artificial intelligence have become instrumental in addressing the complex challenges faced by modern cities. These technologies play a pivotal role in optimizing scarce resources, making equitable decisions, mitigating environmental risks, reducing carbon footprints, and promoting sustainable cities.
This special session aims to explore innovative applications of artificial intelligence in enhancing urban living standards and optimizing city operations. More specifically, the special session focuses on both predictive and prescriptive models. The former enable actionable information on the near or farther future of a city’s operations. The latter employ all the available information, both factual or predicted, to automate or support effective decision-making. Both types of models are necessary and complement each other to tackle the important challenges faced by cities. The special session is interested on predictive and prescriptive models focusing on solving problems for smart cities that may be based on: - Machine Learning & Data mining - Natural Language Processing - Statistical Learning & Statistics - Multi-agent Systems - Reinforcement Learning - Logics - Robotics - Optimization & Operations Research - Game Theory - Simulation - Mathematical Modeling INFORMATION FOR AUTHORS Authors are invited to submit their manuscripts (in pdf format) written in English by the deadline via the conference online submission system (see conference website). All submissions will be peer-reviewed by experts in the field based on originality, significance, quality and clarity. All contributions must be original, must not have been published elsewhere, and must not have been submitted elsewhere during the review period. Papers should normally be within 9 pages (including references) but must not exceed 12 pages, and must comply with the format of Springer LNCS/LNAI Proceedings (see www.springer.com/lncs). To encourage emerging results and novel initial developments, especially from PhD students and young researchers, we accept short papers (within 6 LNCS pages). Short papers can be submitted a week after the deadline. Such papers will also go through our rigorous peer-review process for their novelty and soundness with a quicker turn-around time. The submission system will treat any paper within 6 pages as short papers and Special Session chairs will ensure a speedy review of them. As part of IDEAL 2024, authors of selected accepted papers may be invited to submit an extended and improved version for consideration for Special Issues in the following journals: - Expert Systems (Wiley). JCR Q2. https://onlinelibrary.wiley.com/journal/14680394 - Iberoamerican Journal of Artificial Intelligence (Iberamia). JCR rank to appear 2024. https://journal.iberamia.org/index.php/intartif - International Journal of Interactive Multimedia and Artificial Intelligence. JCR Q3. https://www.ijimai.org/journal/ IMPORTANT DATES - Submission deadline: 26th June, 2024 - Notification: September 2nd, 2024 - Camera-ready submission: 9th September, 2024 - Conference dates: November 20-22, 2024 ORGANIZERS Vicente Botti, Universitat Politècnica de València, vbotti@dsic.upv.es Víctor Sánchez-Anguix, Universitat Politècnica de València, vicsana1@upv.es Jose Machado – Universidade do Minho, jmac@di.uminho.pt |
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