Overview

This book explores deep learning architectures such as convolutional neural networks and recurrent neural networks for tasks like image analysis, speech recognition, and natural language processing within network paradigms. It uses machine learning algorithms such as neural networks, support vector machines, and decision trees for data analysis and prediction tasks.

Topics Covered

Covers a wide range of topics within network paradigms, including intelligence modeling, sustainability, quantum computing, and network security.

Utilizes various machine learning algorithms such as neural networks, support vector machines, and decision trees for data analysis, and prediction tasks.

Addresses contemporary issues like fake news detection, social media analysis, and cybersecurity.

Employs network analysis techniques to understand the structure and dynamics of complex systems, including social networks, communication networks, and biological networks.

Explores the integration of quantum computing principles and algorithms to solve computational intelligence tasks efficiently, especially in quantum-based network paradigms.

Intended Audience

It is primarily written for senior undergraduates, graduate students, and academic researchers in the fields including electrical engineering, electronics and communications engineering, computer engineering, and information technology.