Yu Lei – författare

Combinatorial testing of software analyzes interactions among variables using a very small number of tests. This advanced approach has demonstrated success in providing strong, low-cost testing in real-world situations. Introduction to Combinatorial Testing presents a complete self-contained tutorial on advanced combinatorial testing methods for real-world software.The book introduces key concepts and procedures of combinatorial testing, explains how to use software tools for generating combinatorial tests, and shows how this approach can be integrated with existing practice. Detailed explanations and examples clarify how and why to use various techniques. Sections on cost and practical considerations describe tradeoffs and limitations that may impact resources or funding. While the authors introduce some of the theory and mathematics of combinatorial methods, readers can use the methods without in-depth knowledge of the underlying mathematics.Accessible to undergraduate students and researchers in computer science and engineering, this book illustrates the practical application of combinatorial methods in software testing. Giving pointers to freely available tools and offering resources on a supplementary website, the book encourages readers to apply these methods in their own testing projects.

This book presents the latest research advances in complex network structure analytics based on computational intelligence (CI) approaches, particularly evolutionary optimization. Most if not all network issues are actually optimization problems, which are mostly NP-hard and challenge conventional optimization techniques. To effectively and efficiently solve these hard optimization problems, CI based network structure analytics offer significant advantages over conventional network analytics techniques.  Meanwhile, using CI techniques may facilitate smart decision making by providing multiple options to choose from, while conventional methods can only offer a decision maker a single suggestion. In addition, CI based network structure analytics can greatly facilitate network modeling and analysis. And employing CI techniques to resolve network issues is likely to inspire other fields of study such as recommender systems, system biology, etc., which will in turn expand CI’s scope and applications.As a comprehensive text, the book covers a range of key topics, including network community discovery, evolutionary optimization, network structure balance analytics, network robustness analytics, community-based personalized recommendation, influence maximization, and biological network alignment.Offering a rich blend of theory and practice, the book is suitable for students, researchers and practitioners interested in network analytics and computational intelligence, both as a textbook and as a reference work.

This book presents the latest research advances in complex network structure analytics based on computational intelligence (CI) approaches, particularly evolutionary optimization. Most if not all network issues are actually optimization problems, which are mostly NP-hard and challenge conventional optimization techniques. To effectively and efficiently solve these hard optimization problems, CI based network structure analytics offer significant advantages over conventional network analytics techniques.  Meanwhile, using CI techniques may facilitate smart decision making by providing multiple options to choose from, while conventional methods can only offer a decision maker a single suggestion. In addition, CI based network structure analytics can greatly facilitate network modeling and analysis. And employing CI techniques to resolve network issues is likely to inspire other fields of study such as recommender systems, system biology, etc., which will in turn expand CI’s scope and applications.As a comprehensive text, the book covers a range of key topics, including network community discovery, evolutionary optimization, network structure balance analytics, network robustness analytics, community-based personalized recommendation, influence maximization, and biological network alignment.Offering a rich blend of theory and practice, the book is suitable for students, researchers and practitioners interested in network analytics and computational intelligence, both as a textbook and as a reference work.

This book vividly shows the fostering environment, activity characteristics, distribution pattern, economic losses, casualties, disaster risks and management of earthquakes, mass movement, drought, floods, and marine disasters in the Silk Road region.

This book vividly shows the fostering environment, activity characteristics, distribution pattern, economic losses, casualties, disaster risks and management of earthquakes, mass movement, drought, floods, and marine disasters in the Silk Road region.

Nowadays, remote sensing systems and technologies have been widely studied and applied in environmental monitoring, land survey, and disaster management. As a pivotal remote sensing task, change detection aims to identify and quantify spatio-temporal changes using multi-temporal imagery, supporting timely decision-making and sustainable resource planning. Nevertheless, conventional change detection approaches remain limited in addressing challenges including sensitivity to noise, discrepancies in spatial resolution, sensor misalignment, and the fusion of multi-source heterogeneous data. To address these issues, advanced computational intelligence (CI) techniques, particularly deep learning and evolutionary computation, are being increasingly adopted, offering improved robustness and adaptability for modern change detection tasks.This book establishes the first systematic framework of CI-driven methodologies in remote sensing change detection, providing a comprehensive exposition spanning theoretical foundations, algorithmic innovation, and empirical validation. Opening with the research principles of remote sensing change detection and core CI theories, it  covers  CI-driven methodologies tailored to homogeneous (e.g., single-sensor time series) and heterogeneous (e.g., cross-sensor) paradigms. These methodologies address domain-critical challenges such as noise robustness, feature space alignment, and multi-source fusion through rigorously designed technical workflows that cover data preprocessing, adaptive model learning, and task-specific network architecture.  Extensive validation across diverse remote sensing data types—including synthetic aperture radar, optical, multispectral, and hyperspectral imagery—empirically confirms the operational efficacy of these methodologies in delivering accurate and robust change monitoring. By bridging theory and practice, this book empowers readers to formulate complex problems, develop robust models, and apply cutting-edge CI techniques to remote sensing change detection tasks. It is ideal for researchers and engineers working at the intersection of remote sensing, machine learning, and computational intelligence who seek practical and scalable solutions for change detection in evolving environments.