Submit your paper here: Openreview.net.
Detailed instructions can be found in the guidelines available at the following link: — follow the Guidelines.
Please download and sign the copyright form from the following link: — follow the Cooperate.
Posters should be submitted via this e-mail: posters@sadasc.com — follow the Template.
Conference poster: Download the Affiche.
Co-organized by CCPS laboratory and Hassan II University of Casablanca
will be held on June 3–4, 2027 in Córdoba, Spain. SADASC'27 will bring together researchers and industry professionals contributing towards different phases of designing, exploiting and maintaining Smart Cyber Physical Systems and their Applications. These phases include requirements engineering, data acquisition/cleaning, storage, deployment, exploitation, and visualization. Designing these systems also also has to consider issues such as ethics, security and privacy. CCPS'2027 follows the success of the Agadir (2016), Casablanca (2018), Marrakech (2020), Marrakech (2022), Tangier (2024) and Marrakech (2026)
Highlights from previous SADASC conferences
All accepted papers will be published in CCIS, Springer and indexed in Scopus.
Session chairs: Mohamed Bousmah & Zakaria Hamidi Alaoui
Session chairs: N. Rabbah & M. Ouartassi
Session chairs: B. Kissi & H. Khatib
Session chairs: A. Touati & A. El Afia
Session chair: Mohamed Tabaa
Session chairs: Haddou El Ghazi & Mohamed Moutchou
Distinguished experts sharing their insights
Research Professor at Ikerbasque, Bilbao, Spain, and the University of the Basque Country UPV/EHU, San Sebastian, Spain.
Talk: Advances in Graph-Based Multi-view Learning and Medical Image Analysis.
Abstract: In this talk, I will present two key research topics from my recent work: Graph Neural Networks (GNNs) for semi-supervised and unsupervised learning and advanced deep learning techniques for medical image analysis. Multi-view learning has emerged as a powerful paradigm for enhancing machine learning models by leveraging complementary information from multiple data views, leading to improved performance and robustness. Within this framework, I will discuss several graph-based solutions, including both shallow and deep learning models, designed to tackle key challenges in multi-view learning. In the second part of the talk, I will highlight some recent contributions to medical image analysis, focusing on: 1. A plug-and-play, model-agnostic data augmentation strategy to enhance medical image segmentation. 2. Angular margin-based softmax loss functions for improving classification performance in medical imaging. 3. Deep learning approaches for infection severity assessment in lung X-ray and CT scans. Biography Fadi Dornaika, an Ikerbasque Research Professor, has a distinguished academic and research career. He earned his Ph.D. from INRIA, France, in 1995, focusing on geometric modeling for vision and robotics. Before joining IKERBASQUE, he held various research positions in Europe, China, and Canada. His expertise spans computer vision, image processing, pattern recognition, and machine learning, with a focus on Multiview Clustering, Structured Semi-Supervised Learning, and Deep Learning for medical image analysis. He ranks in the top 2% of scholars in Stanford University's 2024 ranking (DOI:10.17632/btchxktzyw.7). He has published over 400 papers, including 190 indexed journal articles, in computer vision, pattern recognition, and machine learning. He has supervised many graduate students in computer science and information technology. He has supervised 15 PhD students.
Professor in Computer Science at the University of Toulouse and researcher at IRIT
Talk: LLM applications and low resource languages.
LLM applications and low resource languages Bio: Josiane Mothe is a Professor in Computer Science at the University of Toulouse and a researcher at IRIT (Institut de Recherche en Informatique de Toulouse). Her expertise includes Information Retrieval, Query Performance Prediction, and data driven artificial intelligence. She has coordinated and participated in multiple interdisciplinary research projects, including the IA4Agri and O3T projects, focusing on integrating AI solutions into domains such as sustainable agriculture and multimodal information processing. Her recent research emphasizes Query Performance Prediction as well as low resource languages in the era of Large Language Models, combining theoretical insights with practical applications in information systems. Josiane collaborates extensively with international partners and private companies of different sectors, and contributes actively to the development of innovative methodologies bridging computer science and other disciplines.
The people behind SADASC'27
Córdoba, Spain — June 3–4, 2027
City listed as a UNESCO World Heritage Site
SADASC'27 will be held in Córdoba, Andalusia, a city of art and history famous for its Mosque-Cathedral, Roman Bridge, and flower-filled patios. A crossroads of cultures and an ideal setting for scientific exchange, Córdoba will welcome participants at the heart of an exceptional heritage.
Exact venue to be confirmed: the conference site and partner hotel will be announced soon on this page.
Seville (~45 min by AVE train)
Málaga (~1 h by AVE train)
High-speed AVE hub
Direct trains to Madrid & Barcelona
Historic center walkable
City buses & taxis
Organizations supporting SADASC'27
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