Advancing Software Engineering Through Ai Federated Learning And Large Language Models

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Advancing Software Engineering Through AI, Federated Learning, and Large Language Models

The rapid evolution of software engineering demands innovative approaches to meet the growing complexity and scale of modern software systems. Traditional methods often need help to keep pace with the demands for efficiency, reliability, and scalability. Manual development, testing, and maintenance processes are time-consuming and error-prone, leading to delays and increased costs. Additionally, integrating new technologies, such as AI, ML, Federated Learning, and Large Language Models (LLM), presents unique challenges in terms of implementation and ethical considerations. Advancing Software Engineering Through AI, Federated Learning, and Large Language Models provides a compelling solution by comprehensively exploring how AI, ML, Federated Learning, and LLM intersect with software engineering. By presenting real-world case studies, practical examples, and implementation guidelines, the book ensures that readers can readily apply these concepts in their software engineering projects. Researchers, academicians, practitioners, industrialists, and students will benefit from the interdisciplinary insights provided by experts in AI, ML, software engineering, and ethics.
Redefining Security With Cyber AI

In the rapidly evolving digital landscape, the importance of cybersecurity has never been more critical. With the increasing sophistication of cyber threats, traditional security measures often fall short in providing adequate protection. Cyber artificial intelligence (AI) offers advanced capabilities to detect, prevent, and respond to attacks in real time. As cyber threats continue to grow in complexity and frequency, the integration of AI into cybersecurity frameworks is not just advantageous but essential for maintaining robust and resilient defenses. Redefining Security With Cyber AI delves into the profound transformation of security paradigms brought about by the advent of AI. This book explores the intricate dance between the ever-expanding frontiers of digital technology and the AI-driven mechanisms that aim to safeguard them. Covering topics such as artificial neural networks, intrusion detection, and large language models, this book is an excellent resource for cybersecurity professionals, AI and machine learning researchers, IT executives and managers, policy makers and regulators, postgraduate students and educators, academicians, and more.
Establishing AI-Specific Cloud Computing Infrastructure

As artificial intelligence (AI) continues to drive innovation across industries, the need for specialized cloud computing infrastructure to support AI workloads is critical. Traditional cloud platforms often struggle to meet the high computational demands and storage requirements of AI models, especially as they grow in complexity and scale. Establishing AI-specific cloud computing infrastructure involves designing systems optimized for the needs of AI, such as powerful processing capabilities, massive data storage, and real-time processing. With advancements in hardware like graphics processing units and tensor processing units, along with sophisticated data management solutions, businesses can better harness the full potential of AI technologies. This specialized infrastructure enhances the performance and scalability of AI applications while enabling faster innovation and more efficient deployment of AI-driven solutions across sectors. Establishing AI-Specific Cloud Computing Infrastructure explores how AI has evolved as a transformative new technology, capable of delivering large incremental value to a wide range of sectors. It examines recent advances in innovation, specifically how computing power, data storage, and digitized data have led to AI-based applications for business and governance. This book covers topics such as digital technology, sustainable development, and artificial intelligence, and is a useful resource for computer engineers, business owners, academicians, data scientists, and researchers.