Название: Artificial Intelligence and Optimization Techniques for Smart Information System GenerationsАвтор: Aleem Ali, Rajdeep Chakraborty, Nawaf Alharbe
Издательство: CRC Press
Год: 2025
Страниц: 319
Язык: английский
Формат: pdf (true), epub
Размер: 49.4 MB
The text comprehensively focusses on the use of Artificial Intelligence (AI) and optimization techniques for creating smart information systems.
• Focuses on extracting information from blockchain repository using Artificial Intelligence and Machine Learning algorithms.
• Presents deep learning models to identify and locate objects within images and videos, making it possible for machines to perform tasks such as self-driving cars, surveillance, and robotics.
• Discusses Artificial Intelligence and optimization techniques for geographic information system (GIS) generation such as spatial data processing.
• Covers Artificial Intelligence algorithms such as dimensionality, distance metrics, clustering, error calculation, hill climbing, and linear regression.
• Illustrates topics such as image recognition, natural language processing (NLP), fraud detection, information system security, and intrusion detection system
In the rapidly evolving landscape of technology, the convergence of Artificial Intelligence (AI) and optimization techniques has emerged as a powerful catalyst for innovation across various domains. This book, “Artificial Intelligence and Optimization Techniques for Smart Information System Generations,” seeks to explore this dynamic synergy, presenting a comprehensive guide to the theoretical foundations, advanced applications, and practical implementations of these technologies.
The impetus for this book stems from the growing need to understand how AI and optimization can be harnessed to create smarter, more efficient information systems. As researchers, practitioners, and educators, we have witnessed firsthand the transformative potential of these technologies. Our goal is to provide a resource that not only elucidates the core principles of AI and optimization but also demonstrates their applicability in solving real-world problems.
In the quickly changing field of Artificial Intelligence (AI) and Machine Learning (ML), deploying models that are not only powerful but also interpretable and transparent has become critical. Explainable AI (XAI) meets this demand by providing insights into how models make decisions, promoting confidence and accountability in AI systems. This paper presents an architectural approach for building XAI as a Service (XAIaaS) that is specifically designed for container-orchestrated environments, such as those controlled by Kubernetes. Using Kubernetes’ scalability and flexibility, this design attempts to streamline the deployment, management, and explainability of ML models in a smooth and efficient manner. As AI systems become more integrated into crucial decision-making processes across multiple industries, the demand for transparency in AI-driven choices has increased.
Quantum Machine Learning (QML) is an emerging field that integrates Machine Learning and Quantum Computing to develop novel algorithms with diverse applications, particularly in the biomedical domain. In Machine Learning, data is analyzed, patterns are found, and predictions are made using mathematical and statistical models. Using special insights to expedite computation and provide a much more effective handling of massive volumes of data is the aim of QML. In essence, QML algorithms work by first encoding data into quantum states, then manipulating those states using quantum operations, and then measuring the resultant states to extract data-related information. Quantum computing uses qubits, which have the ability to be entangled with other qubits and lie in states, such as both 0 and 1, in contrast to classical computing, which uses classical bits (either 0 or 1). The invention of novel algorithms that are not possible to execute on classical computers is made possible by these special quantum features, which also allow for more effective processing of massive volumes of data. In some respects, QML is different from traditional machine learning. Firstly, QML algorithms may theoretically do calculations tenfold faster than classical algorithms since they work on quantum states. Second, QML algorithms are typically created to make use of special facts of quantum computing, for carrying out tasks. Lastly, in order to execute calculations, QML algorithms need specific hardware, either a quantum computer or a quantum simulator.
This book is organized into three parts, each designed to cater to a specific aspect of AI and optimization:
• Foundational Concepts and Applications of AI: This section lays the groundwork by exploring fundamental AI techniques and their applications. From machine learning and digital image forgery detection to health information systems, the chapters provide a solid understanding of how AI can be leveraged to generate intelligent solutions.
• Advanced AI Applications and Techniques: Building on the foundational knowledge, this section delves into more complex and innovative applications. Topics such as smart agriculture, AI-augmented health systems, and quantum machine learning are discussed, showcasing the cutting-edge advancements in the field.
• AI in Security and Specialized Applications: The final section focuses on the integration of AI in security and specialized domains. It addresses critical areas such as information system security, medical image compression, and the classification of gravitational waves, highlighting the versatility and impact of AI across diverse sectors.
Throughout the book, we have included contributions from esteemed researchers and practitioners from around the world. Their insights and expertise have been instrumental in shaping the content, ensuring that it is both comprehensive and up-to-date. We are grateful for their collaboration and dedication to advancing the field of AI and optimization.
We also recognize the importance of bridging the gap between academia and industry. Therefore, this book is designed to be a valuable resource for students, researchers, and professionals alike. Whether you are seeking to deepen your understanding of AI and optimization or looking for practical solutions to implement in your work, we hope this book serves as a useful guide.
As we look to the future, the potential for AI and optimization to drive innovation and efficiency in information systems is immense. We hope this book inspires new ideas, fosters collaboration, and contributes to the ongoing development of smarter, more intelligent technologies.
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