Modelling And Implementation Of Complex Systems

Robert Siegfried presents a framework for efficient agent-based modeling and simulation of complex systems. He compares different approaches for describing structure and dynamics of agent-based models in detail. Based on this evaluation the author introduces the “General Reference Model for Agent-based Modeling and Simulation” (GRAMS). Furthermore he presents parallel and distributed simulation approaches for execution of agent-based models –from small scale to very large scale. The author shows how agent-based models may be executed by different simulation engines that utilize underlying hardware resources in an optimized fashion.

The breakthrough of AI in the design of complex systems in our daily lives has prompted researchers around the world to integrate this paradigm into their work. From simple citizens to large industrialists, including academics and politicians, there is a great enthusiasm for making all objects intelligent. This has led the scientific community to develop complex intelligent systems adaptable to different real-life systems. This book, which is a selective collection of research papers accepted by the international program committee of the 7th international symposium on Modeling and Implementation of Complex Systems (MISC 2022), makes its contribution in this vast field and addresses subjects that are as interesting as they are useful to the citizen life, namely In healthcare: health monitoring systems for heart patients, a model for cardiovascular disease prediction, early diabetic detection, COVID-19 screening from cough sound, and detection in epidemiological diseases. In natural language processing: summarization of major Arabic machine translation corpora, impact of normalization, and data augmentation on named entity recognition (NER) task on Algerian text. In Agriculture 5.0: schedule of the most widely used IoT architectures and plant recognition. In robotics: visually real-time control of a mobile EV3 robot in an indoor environment. In social media: the identification of rumors on social networks. In computer vision and biometrics: illumination-robust face recognition system. In IoT ecosystem, networks and cloud computing: technologies and protocols, architectures and modeling IoT applications, Named Data Networking (NDN) for the emergent IoT, unmanned aerial vehicle carried base stations (UAV-BSs) placement problem in 5G networks, assignment of the submitted tasks to the available resources in a cloud computing environment, providing routes in the presence of obstacles, and security aspects. Finally, the reader finds how to approach problems even if they have no algorithmic or no exact solution by using the following techniques developed in the different chapters of this book: deep CNN models and dense CNN models, voluntary simulation, hybrid gray wolf optimizer (GWO), multi-verse optimizer (MVO), coronavirus herd immunity optimizer (CHIO) algorithm, multi-population differential evolution, graphical formalism with machine learning and Color Petri Nets, and extension of BPMN 2.0.

This proceedings book gives a new vision and real progress towards more difficult problems resolution. In trying to solve the problems we face every day in the complex world we are living, we are constantly developing artificial systems and increasingly complex middleware. Indeed, the research works contained in this book address a large spread of nowadays topics like IoT architectures, communication and routing protocols, smart systems, software defined networks (SDNs), natural language processing (NLP), social media, health systems, machine intelligence and data science, soft computing and optimization, and software technology. This book, which is a selective collection of research papers accepted by the international program committee of the 6th International Symposium on Modelling and Implementation of Complex Systems (MISC 2020), considers intelligence (CI) more as a way of thinking about problems. It includes a mix of old efficient (Fuzzy, NN, GA) and modern AI techniques (deep learning and CNN). The whole complex systems research community finds in this book an appropriate way to approach problems that have no algorithmic solution and finds many well-formulated technical challenges.

This illuminating text/reference presents a review of the key aspects of the modeling and simulation (M&S) life cycle, and examines the challenges of M&S in different application areas. The authoritative work offers valuable perspectives on the future of research in M&S, and its role in engineering complex systems. Topics and features: reviews the challenges of M&S for urban infrastructure, healthcare delivery, automated vehicle manufacturing, deep space missions, and acquisitions enterprise; outlines research issues relating to conceptual modeling, covering the development of explicit and unambiguous models, communication and decision-making, and architecture and services; considers key computational challenges in the execution of simulation models, in order to best exploit emerging computing platforms and technologies; examines efforts to understand and manage uncertainty inherent in M&S processes, and how these can be unified under a consistent theoretical and philosophical foundation; discusses the reuse of models and simulations to accelerate the simulation model development process. This thought-provoking volume offers important insights for all researchers involved in modeling and simulation across the full spectrum of disciplines and applications, defining a common research agenda to support the entire M&S research community.

In this book you'll learn how to make the right selection of alternative designs or architectures; produce executable structure models that can be transformed into alternative views and verified by computer; formulate a design that meets all functional and performance requirements and is both feasible and optimal for the marketplace; accurately assess information made available to the systems engineering team; perform trade-off analyses that enhance decision making; create, build, and test a plan; and tailor the six-step engineering process to fit specific business environments. This scalable systems engineering approach can be easily adapted to model products, services, businesses, processes, and plans. Guidelines are included on how to improve training capability in your company ... select and develop tools for automation ... build highly efficient infrastructure ... and customize the system engineering process to commercial or aerospace projects. In addition, the authors clearly distinguish management tasks from the technical tasks of systems engineers.

This book illustrates how models of complex systems are built up and provides indispensable mathematical tools for studying their dynamics. This second edition includes more recent research results and many new and improved worked out examples and exercises.

Model integration – the process by which different modelling efforts can be brought together to simulate the target system – is a core technology in the field of Systems Biology. In the work presented here model integration was addressed directly taking cancer systems as an example. An in-depth literature review was carried out to survey the model forms and types currently being utilised. This was used to formalise the main challenges that model integration poses, namely that of paradigm (the formalism on which a model is based), focus (the real-world system the model represents) and scale. A two-tier model integration strategy, including a knowledge-driven approach to address model semantics, was developed to tackle these challenges. In the first step a novel description of models at the level of behaviour, rather than the precise mathematical or computational basis of the model, is developed by distilling a set of abstract classes and properties. These can accurately describe model behaviour and hence describe focus in a way that can be integrated with behavioural descriptions of other models. In the second step this behaviour is decomposed into an agent-based system by translating the models into local interaction rules. The book provides a detailed and highly integrated presentation of the method, encompassing both its novel theoretical and practical aspects, which will enable the reader to practically apply it to their model integration needs in academic research and professional settings. The text is self-supporting. It also includes an in-depth current bibliography to relevant research papers and literature. The review of the current state of the art in tumour modelling provides added value.

This book presents the proceedings of the fifth International Symposium on Modelling and Implementation of Complex Systems (MISC 2018). The event was held in Laghouat, Algeria, on December 16-18, 2018. The 25 papers gathered here have been selected from 109 submissions using a strict peer-review process, and address a range of topics concerning the theory and applications of networking and distributed computing, including: cloud computing and the IoT, metaheuristics and optimization, computational intelligence, software engineering and formal methods.