Relational Nosql And Artificial Intelligence Integrated Database Architectures Foundations Cloud Platforms And Regulatory Compliant Systems
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Relational, NoSQL, and Artificial Intelligence-Integrated Database Architectures: Foundations, Cloud Platforms, and Regulatory-Compliant Systems
Author: Sibaram Prasad Panda
language: en
Publisher: Deep Science Publishing
Release Date: 2025-06-22
A modern entrance to the science of data. This textbook introduces the basic principles of the database system and guides students to advanced subjects such as distributed data processing, NOSQL model and intelligent query. Explanation, with practice on hands and real-world scenarios, prepares learners for both academic and professional activities in data management. Beyond the tradition, the book examines modern architecture including emerging patterns such as NoSQL database, Amazon RDS and Google Big Query such as cloud-country platforms and distributed and multi-model systems. We also check how artificial intelligence is changing database management through automation, discrepancy detection and future maintenance. Recognizing the increasing importance of trust and compliance, dedicated chapters focus on industries’ rules such as safety, access control, data governance and GDPR and HIPAA. The study of real-world cases from areas such as retail, healthcare and finance provides valuable insight into practical implementation, challenges and migration strategies. Whether you are a student, data engineer, software developer, or IT leader, this book serves as a complete guide to understand the developed world of database-where basic knowledge fulfils the state-of-the-art innovation.
SQL Database Mastery: Relational Architectures, Optimization Techniques,and Cloud-Based Applications
Author: Mohanraju Muppala
language: en
Publisher: Deep Science Publishing
Release Date: 2025-07-27
SQL remains at the core of modern data management, powering mission-critical systems across industries. This book, SQL Database Mastery: Architecture, Optimization, and Real-World Applications, bridges foundational concepts with advanced techniques to help readers design, optimize, and manage relational databases effectively. Drawing from years of practical experience in marine IT and enterprise systems, this book combines technical depth with hands-on relevance. Topics range from relational theory, indexing, and normalization to cloud SQL platforms, dynamic queries, and performance tuning. Real-world use cases and best practices are included to ensure practical application of each concept. Whether you're a student, developer, or database architect, this guide aims to support your journey toward mastering SQL in today’s data-driven world. I am grateful to my peers in the field of Marine IT Technology and AI-based data systems who have inspired and supported the development of this book. I hope it serves as a valuable guide in your journey toward mastering the architecture and optimization of relational databases in an era where data is more critical than ever.
Ethical Considerations and Bias Detection in Artificial Intelligence/Machine Learning Applications
Author: Jayesh Rane
language: en
Publisher: Deep Science Publishing
Release Date: 2025-07-10
At a time when artificial intelligence (AI) and machine learning (ML) are used to make sensitive societal decisions such as the ones related to criminal justice, healthcare, finance, education, employment, algorithmic fairness and bias mitigation are among the most important but challenging issues at hand. The goal of this book is to provide a holistic view across various disciplines of the ethical base, detection methods, and technical measures for trustworthy AI systems. Starting from a solid foundation of statistical bias, transparency systems and fairness-aware ML models, this book methodically looks at state-of-the-art methodologies, where we highlight their shortcomings and introduce a unified model framework for detecting bias and transparent algorithms. Moving beyond technical diagnoses, it examines key sociotechnical and policy tools that are required to implement AI responsibly, providing guidance to researchers, engineers, policy makers, and organizational leaders. Literature review has been driven following the experimental case, the fairness trade-offs, intersectional bias, explainability and regulatory compliance are discussed in depth by the authors. This work underscores that fairness in automated decision-making systems depends not only on algorithmic accuracy, but also institutional will and stakeholder engagement. The chapters in this book function as both an academic primer and a resourceful handbook, transitioning readers through an ever-growing ethical AI terrain. Whether you are a data scientist building and deploying an algorithm that encourages ethical speech, or a regulator working to create and refine guidelines around such algorithms, this book provides you with both the tools and the understanding you need for ethical technology development and deployment.