Bansal J., Fung L., Simic M., Ghosh A. (eds.) Advances in Applications of Data-Driven Computing

Springer, 2021. — 187 p. — (Advances in Intelligent Systems and Computing, 1319). — ISBN: 978-9813369184.This book aims to foster machine and deep learning approaches to data-driven applications, in which data governs the behavior of applications. Applications of Artificial intelligence (AI)-based systems play a significant role in today’s software industry. The sensor data from hardware-based systems make a mammoth database, increasing day by day. Recent advances in big data generation and management have created an avenue for decision-makers to utilize these huge volumes of data for different purposes and analyses. AI-based application developers have long utilized conventional machine learning techniques to design better user interfaces and vulnerability predictions. However, with the advancement of deep learning-based and neural-based networks and algorithms, researchers can explore and learn more about data and their exposed relationships or hidden features. This new trend of developing data-driven application systems seeks the adaptation of computational neural network algorithms and techniques in many application domains, including software systems, cyber security, human activity recognition, and behavioral modeling. As such, computational neural network algorithms can be refined to address problems in data-driven applications. Original research and review work with models and building data-driven applications using computational algorithms are included as chapters in this book.