We have witnessed an explosion of research activity around nature-inspired computing and bio-inspired optimization techniques, which can provide powerful tools for solving learning problems and data analysis in very large data sets. To design and implement optimization algorithms, several methods are used that bring superior performance. However, in some applications, the search space increases exponentially with the problem size. To overcome these limitations and to solve efficiently large scale combinatorial and highly nonlinear optimization problems, more flexible and adaptable algorithms are necessary.

Nature-inspired computing is oriented towards the application of outstanding information-processing aptitudes of the natural realm to the computational domain. The discipline of nature-inspired optimization algorithms is a major field of computational intelligence, soft computing and optimization. Metaheuristic search algorithms with population-based frameworks are capable of handling optimization in high-dimensional real-world problems for several domains including imaging, IoT, smart manufacturing, and healthcare. The integration of intelligence with smart technology enhances accuracy and efficiency. Smart devices and systems are revolutionizing the world by linking innovative thinking with innovative action and innovative implementation.

The aim of this edited book is to review the intertwining disciplines of nature-inspired computing and bio-inspired soft-computing (BISC) and their applications to real world challenges. The contributors cover the interaction between metaheuristics, such as evolutionary algorithms and swarm intelligence, with complex systems. They explain how to better handle different kinds of uncertainties in real-life problems using state-of-art of machine learning algorithms. They also explore future research perspectives to bridge the gap between theory and real-life day-to-day challenges for diverse domains of engineering.

The book will offer valuable insights to researchers and scientists from academia and industry in ICTs, IT and computer science, data science, AI and machine learning, swarm intelligence and complex systems. It is also a useful resource for professionals in related fields, and for advanced students with an interest in optimization and IoT applications.

Blockchain is a digital ledger of transactions duplicated and distributed across an entire network of computer systems on the blockchain which makes it more difficult to hack or tamper with. The popularity of blockchain has been increasing with the growth of social media and the internet of things (IoT). Social media are interactive digitally mediated technologies that facilitate the creation or sharing and exchange of information, ideas, opinions and interests via virtual communities and social network platforms. However, unmonitored social accounts can be the target of hackers who post fraudulent messages or virus-infected links that spread to contacts and followers, and "employee weakness" is responsible for 20% of cyberattacks in companies. So, it has become essential to secure both personal and professional social media networks, accounts and data.

Blockchain Technology for Secure Social Media Computing covers recent advances, trends and future opportunities in the security framework of social media-computing. The contributors focus on how to protect social media platforms, and present methods for making social media computing more reliable and effective to achieve trusted IoT-based social computing with blockchain technology.

The book is aimed at an advanced research audience in industry and academia focused on blockchain technology and social media computing working in cybersecurity, security data analytics, computer science, distributed computing, networking, internet of things and related fields of applications. It will be also of interest to application and software developers and project managers using blockchain technology and multiple encryptions.