Alessandro Bruno – författare

This book comprehensively discusses basic data-driven intelligent systems, the methods for processing the data, and cloud computing with artificial intelligence. It presents fundamental and advanced techniques used for handling large user data, and for the data stored in the cloud. It further covers data-driven decision-making for smart logistics and manufacturing systems, network security, and privacy issues in cloud computing.This book:Discusses intelligent systems and cloud computing with the help of artificial intelligence and machine learning. Showcases the importance of machine learning and deep learning in data-driven and cloud-based applications to improve their capabilities and intelligence. Presents the latest developments in data-driven and cloud applications with respect to their design and architecture. Covers artificial intelligence methods along with their experimental result analysis through data processing tools. Presents the advent of machine learning, deep learning, and reinforcement technique for cloud computing to provide cost-effective and efficient services. The text will be useful for senior undergraduate, graduate students, and academic researchers in diverse fields including electrical engineering, electronics and communications engineering, computer engineering, manufacturing engineering, and production engineering.

This book comprehensively discusses basic data-driven intelligent systems, the methods for processing the data, and cloud computing with artificial intelligence. It presents fundamental and advanced techniques used for handling large user data, and for the data stored in the cloud. It further covers data-driven decision-making for smart logistics and manufacturing systems, network security, and privacy issues in cloud computing.This book:Discusses intelligent systems and cloud computing with the help of artificial intelligence and machine learning. Showcases the importance of machine learning and deep learning in data-driven and cloud-based applications to improve their capabilities and intelligence. Presents the latest developments in data-driven and cloud applications with respect to their design and architecture. Covers artificial intelligence methods along with their experimental result analysis through data processing tools. Presents the advent of machine learning, deep learning, and reinforcement technique for cloud computing to provide cost-effective and efficient services. The text will be useful for senior undergraduate, graduate students, and academic researchers in diverse fields including electrical engineering, electronics and communications engineering, computer engineering, manufacturing engineering, and production engineering.

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.This book:Covers a wide range of topics within network paradigms, including intelligence modeling, sustainability, quantum computing, and network securityUtilizes various machine learning algorithms such as neural networks, support vector machines, and decision trees for data analysis, and prediction tasksAddresses contemporary issues like fake news detection, social media analysis, and cybersecurityEmploys network analysis techniques to understand the structure and dynamics of complex systems, including social networks, communication networks, and biological networksExplores the integration of quantum computing principles and algorithms to solve computational intelligence tasks efficiently, especially in quantum-based network paradigmsIt 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.

This book comprehensively discusses basic data-driven intelligent systems, the methods for processing the data, and cloud computing with artificial intelligence. It presents fundamental and advanced techniques used for handling large user data, and for the data stored in the cloud. It further covers data-driven decision-making for smart logistics and manufacturing systems, network security, and privacy issues in cloud computing.

This book:

Discusses intelligent systems and cloud computing with the help of artificial intelligence and machine learning. Showcases the importance of machine learning and deep learning in data-driven and cloud-based applications to improve their capabilities and intelligence. Presents the latest developments in data-driven and cloud applications with respect to their design and architecture. Covers artificial intelligence methods along with their experimental result analysis through data processing tools. Presents the advent of machine learning, deep learning, and reinforcement technique for cloud computing to provide cost-effective and efficient services.

The text will be useful for senior undergraduate, graduate students, and academic researchers in diverse fields including electrical engineering, electronics and communications engineering, computer engineering, manufacturing engineering, and production engineering.

This book comprehensively discusses basic data-driven intelligent systems, the methods for processing the data, and cloud computing with artificial intelligence. It presents fundamental and advanced techniques used for handling large user data, and for the data stored in the cloud. It further covers data-driven decision-making for smart logistics and manufacturing systems, network security, and privacy issues in cloud computing.

This book:

Discusses intelligent systems and cloud computing with the help of artificial intelligence and machine learning. Showcases the importance of machine learning and deep learning in data-driven and cloud-based applications to improve their capabilities and intelligence. Presents the latest developments in data-driven and cloud applications with respect to their design and architecture. Covers artificial intelligence methods along with their experimental result analysis through data processing tools. Presents the advent of machine learning, deep learning, and reinforcement technique for cloud computing to provide cost-effective and efficient services.

The text will be useful for senior undergraduate, graduate students, and academic researchers in diverse fields including electrical engineering, electronics and communications engineering, computer engineering, manufacturing engineering, and production engineering.

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.This book: 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 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.

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.This book: 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 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.

The book involves the original research and practical implementations presented at the International Conference on Smart Systems for Sustainable Development (SSSD 2025). This volume provides smart systems and data-driven inventions contributing to sustainable development across various disciplines. It offers a multidisciplinary perspective of how artificial intelligence (AI), machine learning, internet of things, and optimization techniques are being integrated to address challenging real-world problems in the areas of climate, energy, healthcare, agriculture, infrastructure, etc. The volume is interdisciplinary, illustrating knowledge from fields such as engineering, computer science, environmental science, and industrial technology, presenting researchers with the information desired to solve demanding societal issues. Contributions cover theoretical developments in deep learning, swarm intelligence, and cyber-physical systems, applied research for sensor networks, blockchain, forecasting models, etc. Integrating intelligent decision-making with issues of global accessibility and international sustainability, the book aims to contribute to the main global challenges set out in the United Nations Sustainable Development Goals (SDGs). The introduction of coverage of optimization frameworks, intelligent automation, predictive analytics, and interdisciplinary system design makes the book a value addition for the readers with everything they need to develop resilient and energy-efficient ecosystems in today's digital world. In addition, applied case studies and hybrid methods are presented. This book is an important resource for researchers, academics, graduate students, engineers, data scientists, and industrial professionals interested in using novel methods to advance sustainability, intelligent transportation, environmental science, and artificial intelligence for sustainable development.