Complex, Intelligent and Software Intensive Systems Proceedings of the 19th International Conference on Complex, Intelligent, and Software Intensive Systems (CISIS-2025), Volume 2

Software intensive systems are systems which heavily interact with other systems, sensors, actuators, devices, other software systems and users. More and more domains are involved with software intensive systems, e.g., automotive systems, telecommunication systems, embedded systems in general, industrial automation systems and business applications. Moreover, the outcome of web services delivers a new platform for enabling software intensive systems. 
Complex systems research is focused on the overall understanding of systems rather than their components. Complex systems are very much characterized by the changing environments in which they act by their multiple internal and external interactions. They evolve and adapt through internal and external dynamic interactions.
The development of intelligent systems and agents, which can be characterized by ontologies and their logical foundations builds a fruitful impulse for both software intensive systems and complex systems. Recent researches in the field of intelligent systems, robotics, neuroscience, artificial intelligence, and cognitive sciences are very important factors for the future development and innovation of software intensive and complex systems.
The aim of the book is to deliver a platform of scientific interaction between the three interwoven challenging areas of research and development of future ICT-enabled applications: software intensive systems, complex systems, and intelligent systems.

Federated, Adaptive and Human-in-the-Loop Sensor Data Fusion for Industry 5.0: A Multi-Criteria Decision-Making Framework.- Disaster Response Knowledge-Sharing System for Local Governments Using Retrieval-Augmented Generation Technology.- MARLPpo: Multi-Agent Drone Surveillance Using Deep Reinforcement Learning in Urban Aerial Mobility.- Anomaly Detection System in Environment Sensor Data Using Modality Statistical Tool and Deep Learning.- BigSEIZ: Combining BigBird with Epidemic Models to analyze spreading Fake News on Twitter Temporal Network.- Digital Twin-Based Optimization of Soil Moisture Classification Using CMUW LED Illumination.- Analysis and Prediction of At-Risk Students using Machine Learning Algorithms.- PLAN SMART: A Tourism Plan with Efficient Minimal Cost.- Implementation of the Machine Learning for the Detection of Human Postures and Positions.- Enhancing AR Virtual Makeup Trials with Machine Learning: Emotional and Behavioral Insights for Personalized Consumer Experiences.