Susheela Hooda – författare

This book examines the applications, trends and challenges of 5G Enabled technologies for Smart City and Urbanization systems. It addresses the challenges to bringing such capabilities of 5G-enabled technologies for smart cities and urbanisation into practice by presenting the theoretical as well as technical research outcomes with case studies. It covers key areas, including smart building, smart health care, smart mobility, smart living, smart surveillance, and IOT-based systems. It explains how these systems are connected using different technologies that support 5G access and control protocols.

• Offers a comprehensive understanding of the emergence of 5G technology and its integration with IoT, Big Data, and Artificial Intelligence for smart city and urbanisation

• Focuses on useful applications of Smart City and Urbanization, which can enhance different aspects of urban life

• Explores the advantages of using massive IoT and predictive analytics approaches in smart cities

• IoT, Bigdata, Deep learning and machine learning techniques are explained to fuel smart city and Urbanization system

• Addresses both theoretical and technical research outcomes related to smart city and urbanisation with 5G technology.

It serves as a valuable reference for graduate students, researchers, and m practitioners seeking to deepen their knowledge and engage with the latest advancements in the areas of Smart cities and Urbanization systems.

This book examines the applications, trends and challenges of 5G Enabled technologies for Smart City and Urbanization systems. It addresses the challenges to bringing such capabilities of 5G-enabled technologies for smart cities and urbanisation into practice by presenting the theoretical as well as technical research outcomes with case studies. It covers key areas, including smart building, smart health care, smart mobility, smart living, smart surveillance, and IOT-based systems. It explains how these systems are connected using different technologies that support 5G access and control protocols.

• Offers a comprehensive understanding of the emergence of 5G technology and its integration with IoT, Big Data, and Artificial Intelligence for smart city and urbanisation

• Focuses on useful applications of Smart City and Urbanization, which can enhance different aspects of urban life

• Explores the advantages of using massive IoT and predictive analytics approaches in smart cities

• IoT, Bigdata, Deep learning and machine learning techniques are explained to fuel smart city and Urbanization system

• Addresses both theoretical and technical research outcomes related to smart city and urbanisation with 5G technology.

It serves as a valuable reference for graduate students, researchers, and m practitioners seeking to deepen their knowledge and engage with the latest advancements in the areas of Smart cities and Urbanization systems.

A unique title that introduces the whole range of agile software development processes from the fundamental concepts to the highest levels of applications such as requirement analysis, software testing, quality assurance, and risk management.

Agile Software Development (ASD) has become a popular technology because its methods apply to any programming paradigm. It is important in the software development process because it emphasizes incremental delivery, team collaboration, continuous planning, and learning over delivering everything at once near the end. Agile has gained popularity as a result of its use of various frameworks, methods, and techniques to improve software quality. Scrum is a major agile framework that has been widely adopted by the software development community.

Metaheuristic techniques have been used in the agile software development process to improve software quality and reliability. These techniques not only improve quality and reliability but also test cases, resulting in cost-effective and time-effective software. However, many significant research challenges must be addressed to put such ASD capabilities into practice. With the use of diverse techniques, guiding principles, artificial intelligence, soft computing, and machine learning, this book seeks to study theoretical and technological research findings on all facets of ASD. Also, it sheds light on the latest trends, challenges, and applications in the area of ASD.

This book explores the theoretical as well as the technical research outcomes on all the aspects of Agile Software Development by using various methods, principles, artificial intelligence, soft computing, and machine learning.

Audience

The book is designed for computer scientists and software engineers both in research and industry. Graduate and postgraduate students will find the book accessible as well.

A unique title that introduces the whole range of agile software development processes from the fundamental concepts to the highest levels of applications such as requirement analysis, software testing, quality assurance, and risk management.

Agile Software Development (ASD) has become a popular technology because its methods apply to any programming paradigm. It is important in the software development process because it emphasizes incremental delivery, team collaboration, continuous planning, and learning over delivering everything at once near the end. Agile has gained popularity as a result of its use of various frameworks, methods, and techniques to improve software quality. Scrum is a major agile framework that has been widely adopted by the software development community.

Metaheuristic techniques have been used in the agile software development process to improve software quality and reliability. These techniques not only improve quality and reliability but also test cases, resulting in cost-effective and time-effective software. However, many significant research challenges must be addressed to put such ASD capabilities into practice. With the use of diverse techniques, guiding principles, artificial intelligence, soft computing, and machine learning, this book seeks to study theoretical and technological research findings on all facets of ASD. Also, it sheds light on the latest trends, challenges, and applications in the area of ASD.

This book explores the theoretical as well as the technical research outcomes on all the aspects of Agile Software Development by using various methods, principles, artificial intelligence, soft computing, and machine learning.

Audience

The book is designed for computer scientists and software engineers both in research and industry. Graduate and postgraduate students will find the book accessible as well.

The book is a must-have resource for anyone looking to understand the complexities of generative AI, offering comprehensive insights into LLMs, effective training strategies, and practical applications.

Textual Intelligence: Large Language Models and Their Real-World Applications provides an overview of generative AI and its multifaceted applications, as well as the significance and potential of Large Language Models (LLMs), including GPT and LLaMA. It addresses the generative AI project lifecycle, challenges in existing data architectures, proposed use case planning and scope definition, model deployment, and application integration. Training LLMs, data requirements for effective LLM training, pre-training and fine-tuning processes, and navigating computational resources and infrastructure are also discussed. The volume delves into in-context learning and prompt engineering, offering strategies for crafting effective prompts, techniques for controlling model behavior and output quality, and best practices for prompt engineering.

Textual Intelligence: Large Language Models and Their Real-World Applications also discusses cost optimization strategies for LLM training, aligning models to human values, optimizing model architectures, the power of transfer learning and fine-tuning, instruction fine-tuning for precision, and parameter-efficient fine-tuning (PEFT) with adapters such as LoRA, QLoRA, and soft prompts, making it an essential guide for both beginners and industry veterans.

Readers will find this book:

Audience

Industry professionals, academics, graduate students, and researchers seeking real-world solutions using generative AI.

The book is a must-have resource for anyone looking to understand the complexities of generative AI, offering comprehensive insights into LLMs, effective training strategies, and practical applications.

Textual Intelligence: Large Language Models and Their Real-World Applications provides an overview of generative AI and its multifaceted applications, as well as the significance and potential of Large Language Models (LLMs), including GPT and LLaMA. It addresses the generative AI project lifecycle, challenges in existing data architectures, proposed use case planning and scope definition, model deployment, and application integration. Training LLMs, data requirements for effective LLM training, pre-training and fine-tuning processes, and navigating computational resources and infrastructure are also discussed. The volume delves into in-context learning and prompt engineering, offering strategies for crafting effective prompts, techniques for controlling model behavior and output quality, and best practices for prompt engineering.

Textual Intelligence: Large Language Models and Their Real-World Applications also discusses cost optimization strategies for LLM training, aligning models to human values, optimizing model architectures, the power of transfer learning and fine-tuning, instruction fine-tuning for precision, and parameter-efficient fine-tuning (PEFT) with adapters such as LoRA, QLoRA, and soft prompts, making it an essential guide for both beginners and industry veterans.

Readers will find this book:

Audience

Industry professionals, academics, graduate students, and researchers seeking real-world solutions using generative AI.

The book is essential for anyone seeking to understand and leverage the transformative power of intelligent automation technologies, providing crucial insights into current trends, challenges, and effective solutions that can significantly enhance operational efficiency and decision-making within organizations.

Intelligent automation systems, also called cognitive automation, use automation technologies such as artificial intelligence, business process management, and robotic process automation, to streamline and scale decision-making across organizations. Intelligent automation simplifies processes, frees up resources, improves operational efficiencies, and has a variety of applications. Intelligent automation systems aim to reduce costs by augmenting the workforce and improving productivity and accuracy through consistent processes and approaches, which enhance quality, improve customer experience, and address compliance and regulations with confidence. Handbook of Intelligent Automation Systems Using Computer Vision and Artificial Intelligence explores the significant role, current trends, challenges, and potential solutions to existing challenges in the field of intelligent automation systems, making it an invaluable guide for researchers, industry professionals, and students looking to apply these innovative technologies.

Readers will find the volume:

Audience

The book is designed for AI and data scientists, software developers and engineers in industry and academia, as well as business leaders and entrepreneurs who are interested in the applications of intelligent automation systems.