Dr Yousra Chabchoub
LISITE
ISEP: Institut Supérieur d’Electronique de Paris, France
E-mail: yousra.chabchoub@isep.fr


Dr Maurras Togbe
LISITE
ISEP: Institut Supérieur d’Electronique de Paris, France
E-mail: maurras.togbe@isep.fr

Anomalies detection is a crucial aspect of machine learning and deep learning that focuses on identifying patterns or instances that deviate significantly from the normal behavior within a given dataset. Deep learning-based anomalies methods outperform traditional approaches due to their ability to model complex relationships within data. They can be applied to various domains, including cybersecurity, fraud detection, industrial equipment monitoring and healthcare. The ADMDL 2024 Special Session at the 16th International Conference on Computational Collective Intelligence (ICCCI 2024) aims to address the challenges and specific issues of the application and machine and deep learning models to anomalies detection field. The scope of the ADMDL 2024 includes, but is not limited to the following topics:

• Explainability and interpretability in anomalies detection models
• Online anomalies detection in data streams
• Deep learning architectures for anomalies detection
• Multimodal anomalies detection
• Adversarial attacks on anomalies detection systems
• Anomalies detection in Healthcare
• Anomalies detection in Cybersecurity
• Transfer Learning for anomalies detection
• Incremental Learning for anomalies detection
• Scalability and Efficiency in anomalies detection
• Semi-Supervised anomalies detection
• Unsupervised anomalies detection in high-dimensional data
• Anomalies detection in NLP
• Privacy-Preserving anomalies detection
• Graph based anomalies detection