Fadi Dornaika – författare

This two-volume set CCIS 2167-2168 constitutes the proceedings of the 5th International Conference on Smart Applications and Data Analysis, SADASC 2024, held in Tangier, Morocco, in April 2024. The 30 full papers presented together with 10 short papers were carefully reviewed and selected from 91 submissions. They cover the following topics: designing and modeling; data management; tinyML and anomaly detection; network technologies and IOT; control, dynamic systems and optimisation; and exploitation and exploration.

This two-volume  set CCIS 2167-2168 constitutes the proceedings of the 5th International Conference on Smart Applications and Data Analysis, SADASC 2024, held in Tangier, Morocco, in April 2024.  The 30 full papers  presented together with 10 short papers were carefully reviewed and selected from 91 submissions. They cover the following topics: designing and modeling; data management; tinyML and anomaly detection; network technologies and IOT; control, dynamic systems and optimisation; and exploitation and exploration.

This book presents selected papers from the International Conference on Explainable Intelligence in Digital Twins (EIDT 2025) held during November 12–14, at Ho Chi Minh City, Vietnam. The integration of explainable intelligence in digital twins offers significant advantages, enhancing transparency, trust, and informed decision-making across various industries, including manufacturing, healthcare, transportation, and smart cities. By combining real-time data with AI-driven analytics, digital twins enable predictive maintenance, process optimization, transparent communication, and system automation. While these technologies have immense potential to revolutionize digital ecosystems, they also pose significant challenges, such as bias in AI models, scalability of explainable methods, and trade-offs between performance and interpretability. To address these critical issues, the International Conference on Explainable Intelligence in Digital Twins (EIDT) was established as a dedicated platform for research scholars and engineers to discuss, innovate, and collaborate on advancing transparent, interpretable, and trustworthy AI-driven digital twins. EIDT fosters cutting-edge research and real-world applications that drive the next generation of explainable intelligence in digital twin networks and systems.

This book presents selected papers from the International Conference on Explainable Intelligence in Digital Twins (EIDT 2025) held during November 12–14, at Ho Chi Minh City, Vietnam. The integration of explainable intelligence in digital twins offers significant advantages, enhancing transparency, trust, and informed decision-making across various industries, including manufacturing, healthcare, transportation, and smart cities. By combining real-time data with AI-driven analytics, digital twins enable predictive maintenance, process optimization, transparent communication, and system automation. While these technologies have immense potential to revolutionize digital ecosystems, they also pose significant challenges, such as bias in AI models, scalability of explainable methods, and trade-offs between performance and interpretability. To address these critical issues, the International Conference on Explainable Intelligence in Digital Twins (EIDT) was established as a dedicated platform for research scholars and engineers to discuss, innovate, and collaborate on advancing transparent, interpretable, and trustworthy AI-driven digital twins. EIDT fosters cutting-edge research and real-world applications that drive the next generation of explainable intelligence in digital twin networks and systems.

Clustering, a foundational technique in data analytics, finds diverse applications across scientific, technical, and business domains. Within the theme of “Data Clustering,” this book assumes substantial importance due to its indispensable clustering role in various contexts.As the era of online media facilitates the rapid generation of large datasets, clustering emerges as a pivotal player in data mining and machine learning. At its core, clustering seeks to unveil heterogeneous groups within unlabeled data, representing a crucial unsupervised task in machine learning. The objective is to automatically assign labels to each unlabeled datum with minimal human intervention. Analyzing this data allows for categorization and drawing conclusions applicable across diverse application domains. The challenge with unlabeled data lies in defining a quantifiable goal to guide the model-building process, constituting the central theme of clustering.This book presents concepts and different methodologies of data clustering. For example, deep clustering of images, semi-supervised deep clustering, deep multi-view clustering, etc. This book can be used as a reference for researchers and postgraduate students in related research background.