‘Complex Intelligent Systems and Applications’ presents the most up-to-date advances in complex, software intensive and intelligent systems. Each self-contained chapter is the contribution of distinguished experts in areas of research relevant to the study of complex, intelligent, and software intensive systems. These contributions focus on the resolution of complex problems from areas of networking, optimization and artificial intelligence. The book is divided into three parts focusing on complex intelligent network systems, efficient resource management in complex systems, and artificial data mining systems. Through the presentation of these diverse areas of application, the volume provides insights into the multidisciplinary nature of complex problems. Throughout the entire book, special emphasis is placed on optimization and efficiency in resource management, network interaction, and intelligent system design. This book presents the most recent interdisciplinary results in this area of research and can serve as a valuable tool for researchers interested in defining and resolving the types of complex problems that arise in networking, optimization, and artificial intelligence.
Cuprins
Efficient Integration of Complex Information Systems in the ATM Domain with Explicit Expert Knowledge Models.- An Ontology-Based Approach for Supporting Business-IT Alignment.- EPCIS-Based Supply Chain Event Management.- Cost-Benefit Analysis to Hedge with Third-Party Producers in Demand-Driven Production.- A Security Assurance Model to Holistically Assess the Information Security Posture.- Risk-Aware Business Process Management—Establishing the Link Between Business and Security.- Self-Optimised Tree Overlays Using Proximity-Driven Self-Organised Agents.- Filtering Order Adaptation Based on Attractor Selection for Data Broadcasting System.- Stream APAS: Query Language and Data Model.- Agent-Supported Programming of Multicore Computing Systems.- Multimodal and Agent-Based Human–Computer Interaction in Cultural Heritage Applications: an Overview.- Reinforced Operators in Fuzzy Clustering Systems.
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