Books

Maintaining equipment is not just about fixing it when it breaks. It's about working hard to make sure it runs well all the time. This means that even when the equipment is working well, maintenance workers are still finding ways to improve it to make sure it keeps running smoothly. This book aims to explain the best ways to do work that is usually done to avoid issues with transformers. This book covers everything about choosing and storing transformers. It also talks about advanced methods for checking transformers using predictive maintenance or condition monitoring. It also includes a real example of using FMEA to make power transformers more reliable in a system or production process. The techniques in this book are not for making big changes to repair a transformer. However, many things are done as part of regular procedures. Maintenance and big transformer repair could be the same. We can do the tasks to take care of the transformer if it's not too broken. The advice in this book is similar to the suggestions that companies give for their products. If you need to know how to do something, the person in charge should check the instruction book from the company that made the product. Regularly check and fix small problems to keep transformers in good condition. Also follow special care instructions. Also, if the machine is set up and used the right way, it will keep working for a long time without any issues. This book is for everyone, including technical, engineers, managers, leaders, and quality professionals.

Different methods can be used to check how electric motors are working. Taking care of equipment is not only about repairing it when it stops working. It's important to work hard to keep it running smoothly all the time. This means that even when the machines are working well, people who take care of them are still trying to make them better so they stay working well.

Stop Drowning in Meetings, Emails, and Workplace Chaos. Take Back Control of Your Day.In today’s work culture, being busy has replaced being productive. If your calendar is packed with meetings, your inbox is overflowing, and every day ends in last-minute "fire drills," this book is your rescue plan.“Surviving the Modern Workday” is a practical guide to mastering the three biggest productivity killers: pointless meetings, endless emails, and constant interruptions. With clear strategies and actionable systems, you’ll learn how to reclaim your time and focus—without burning out or becoming the office villain.

Next-Gen Surveillance: A Practical Guide to Modern Security Systems for Home & Business provides a comprehensive, step-by-step guide for anyone looking to secure their property with modern technology. Written by an experienced engineer with hands-on experience designing personal home security systems, this book covers everything from fundamental principles to advanced AI-driven surveillance.

يطرح هذا الكتاب رؤية جديدة وجذرية لإدارة الأمن داخل المدن السكنية الحديثة، من خلال توظيف منهجيات إدارية أثبتت نجاحها، مثل:التحسين المستمرتوحيد المهام والإجراءاتالإدارة البصريةإدارة الأهداف الاستراتيجيةالتقييم الميداني المباشر من خلال الذهاب لمكان أو بيئه العملوذلك ضمن إطار متكامل لإعادة تصميم منظومة الأمن الحضري بطريقة عملية وشاملة، تُراعي الأبعاد التنظيمية والتقنية والبشرية. ✳️ لماذا هذا الكتاب مختلف؟ في ظل التحديات المتصاعدة في أمن المجتمعات السكنية، يقدم هذا الكتاب نموذجًا مبتكرًا مستلهمًا من فلسفة "التحسين المستمر" "والاداره الرشيقه"، والتي غيّرت شكل الإنتاج والتنظيم في كبرى المؤسسات. والمستوحاه من نظام انتاج تويوتا العالمى. ويُعيد المؤلف توظيف هذه المفاهيم في بيئة الأمن الحضري، من خلال أدوات عملية مثل:القضاء على الهدر في الوقت والمواردتحسين تدفق العمل الأمنيتوحيد الإجراءات وتبسيط التقاريرتعزيز مهارات الأفراد بالتدريب المستمرربط الأداء الميداني بغرف التحكم والمراقبة

Most guides teach how to publish. This one teaches how to engineer your publishing system — the Toyota way. It redefines authorship as a process of continuous learning, measurable improvement, and sustainable creativity.Dr. Soliman combines his deep background in industrial and systems engineering with his hands-on experience as a KDP author since 2013, transforming years of practical observation into a structured framework for professional writers, publishers, and educators.

Vous recherchez une approche de gouvernance d'entreprise à haute performance qui améliore la gestion, l'exécution et la livraison tout en gérant simultanément de multiples projets/produits, événements, parties prenantes et équipes ? Une approche qui vous apporte de meilleurs résultats financiers, une mise sur le marché plus rapide, une charge de travail réduite, une meilleure prédictibilité, des employés plus satisfaits et des clients ravis ? Alors découvrez TameFlow !

A crochet book for makers ready to design, not just follow — a practical guide to structure, tension, materials, finishing, proportion, and creative textile design.

Betrayed, poisoned, and robbed by the woman he trusted—Chunmun Singh faced a choice: destroy his enemies or destroy the chains binding his own soul.He chose freedom.A gripping story of betrayal, obsession, and ultimate liberation that proves the greatest revenge is to outgrow those who tried to break you.

 Why Should You Study This Book?Comprehensive Coverage: This book covers all essential C programming concepts, from the basics to intermediate topics, making it suitable for students across various academic levels, including Class 12, graduates, and MCA students.Clear Explanations: The concepts are presented in a simple, easy-to-understand manner. Whether you are a novice or have some programming experience, you’ll find the explanations clear and easy to follow.Practical Focus: It includes numerous hands-on examples and practice problems to reinforce the concepts. With each chapter, you will have the opportunity to write real C programs, which will help you solidify your understanding and develop practical programming skills.Real-World Applications: The book does not just cover theory. It guides you through creating practical programs such as a basic calculator, a library management system, and even a text editor. These projects provide a real-world context to what you’re learning, making it easier to grasp how C programming is used in various applications.Skill Development: Learning C programming will sharpen your problem-solving abilities, logical thinking, and analytical skills. These skills are essential not only for programming but also for tackling complex challenges in various fields of study and work.Error Handling and Debugging: The book emphasizes the importance of writing error-free and optimized code. It introduces common pitfalls and debugging techniques, helping students become efficient and error-resistant programmers.Preparation for Further Studies: If you're planning to learn advanced topics like data structures, algorithms, or even system programming, this book provides the foundation you'll need. Mastering C will give you a strong base for learning other programming languages and advanced computing concepts.

Why This Book Is Unique·        Focused specifically on data science applications of SQL, not just traditional database operations.·        Includes Python integration, bridging database skills with modern data analysis.·        Covers NoSQL and unstructured data, expanding student exposure beyond relational databases.·        Emphasizes real datasets, case studies, and hands-on exercises, making learning interactive and practical.·        Prepares students for academic projects, internships, and entry-level data science roles.  

Detailed Overview of the Book’s ChaptersBelow is a narrative walkthrough of the chapters, showing how each builds upon the previous ones to form a complete, advanced-level textbook. Chapter 1: Advanced Deep Learning Paradigms This chapter introduces paradigms that extend traditional feedforward and convolutional networks. It covers Capsule Networks (CapsNet), which model hierarchical relationships in images better than CNNs, and Neural Ordinary Differential Equations (Neural ODEs), which bring the power of continuous mathematics into deep learning. Readers will also learn about Graph Neural Networks (GNNs) for relational data, Hypernetworks that generate weights for other networks, and Neural Turing Machines (NTMs) that combine computation with memory.Importance: Provides readers with a toolbox of new architectures that go beyond the limits of CNNs and RNNs. Chapter 2: Hybrid and Ensemble Neural Architectures Modern AI is rarely a single architecture—it is often a hybrid system. This chapter explains how Ensemble Learning improves accuracy and robustness, how Neuro-Symbolic AI combines logic with deep learning, and how Mixture of Experts (MoE) models power large-scale language systems like Google’s Switch Transformer. Hybrid CNN-RNN-Attention architectures are also explained with real-world examples in speech and video processing.Importance: Teaches how combining models enhances performance and robustness, preparing readers for cutting-edge AI system design. Chapter 3: Advanced Optimization and Training Strategies Training deep networks is a science in itself. This chapter covers second-order optimization methods, meta-learning, continual learning, and curriculum learning. It also introduces Neural Architecture Search (NAS), which automates the design of optimal architectures.Importance: Equips students with modern training techniques needed to train extremely deep or complex networks efficiently. Chapter 4: Neural Networks for Structured and Non-Euclidean Data Many real-world problems deal with non-Euclidean data such as graphs, networks, and manifolds. This chapter explains Graph Convolutional Networks, Graph Attention Networks, and Spatio-Temporal Networks used in social networks, protein modeling, and traffic prediction.Importance: Prepares readers for the graph revolution in AI, a rapidly growing area in research and applications. Chapter 5: Neural Networks in Reinforcement Learning This chapter integrates deep learning with reinforcement learning to explain how systems like AlphaGo and autonomous vehicles are trained. It covers DQN, Policy Gradient methods, Actor-Critic models, and Multi-Agent RL.Importance: Provides the foundation for building AI systems that learn by interacting with environments, essential for robotics, games, and adaptive decision-making. Chapter 6: Advanced Generative Neural Architectures Going beyond GANs and VAEs, this chapter covers StyleGAN, Diffusion Models, Flow-based Models, and Energy-based Models. It explains how these architectures power text-to-image models, generative art, and scientific simulations.Importance: Essential for students and professionals exploring generative AI, one of the most disruptive areas today. Chapter 7: Neural Networks for Multimodal Learning This chapter explores fusion of multiple data modalities—text, vision, speech—into unified models. It introduces CLIP, Flamingo, and multimodal transformers. Applications in healthcare, AR/VR, and robotics are presented.Importance: Trains readers in building AI that integrates multiple senses, moving toward more general intelligence. Chapter 8: Quantum-Inspired and Neuromorphic Neural Networks This futuristic chapter introduces Quantum Neural Networks (QNNs), Spiking Neural Networks (SNNs), and neuromorphic hardware. It also explores memristors and analog neural computing.Importance: Prepares students for the next paradigm of AI hardware and computation, beyond GPUs and TPUs. Chapter 9: Neural Networks for Real-World Applications This chapter presents detailed applications across healthcare, finance, climate modeling, cybersecurity, and IoT. Each section shows how advanced architectures are applied to practical challenges.Importance: Bridges the gap between theory and practice, showing the impact of neural networks on society. Chapter 10: Research Frontiers in Neural Networks The final chapter summarizes Large Language Models, Scaling Laws, Trustworthy AI, Green AI, and AGI pathways. It invites readers to think critically about what comes next in AI research.Importance: Inspires advanced learners and researchers to contribute to next-generation breakthroughs in neural networks. Why This Book is Essential for Study1.     For Students:o   Provides a clear, structured, and advanced-level curriculum beyond basics.o   Helps in M.Tech, PhD, and UGC NET/AI competitive exams.o   Equips students with knowledge of cutting-edge research areas.2.     For Researchers:o   Serves as a consolidated reference for diverse advanced architectures.o   Saves time by integrating material from scattered research papers.o   Offers insights into emerging frontiers like neuromorphic and quantum AI.3.     For Industry Professionals:o   Enables professionals to adopt latest AI methods in real-world projects.o   Covers practical applications across industries.o   Provides knowledge on multimodal and generative AI, essential in today’s AI-driven world.4.     For Educators:o   Acts as a teaching resource for advanced AI courses.o   Includes examples, applications, and research trends useful for course design. ConclusionThis book is not just another deep learning textbook—it is a gateway to the future of AI. It connects foundational knowledge with cutting-edge innovations, making it indispensable for students, educators, researchers, and professionals alike. By studying this book, readers will be prepared not only to understand today’s most powerful neural architectures but also to contribute to the AI breakthroughs of tomorrow.

Pedagogical Philosophy of the BookThis book is designed with three guiding principles:1.     Clarity over Formalism While maintaining mathematical accuracy, the book avoids unnecessary formalism that can confuse beginners. Instead, it uses intuitive explanations, diagrams, and real-world analogies.2.     Integration of Computation Every mathematical concept is tied to computational practice. Readers are encouraged to implement simple code snippets (in Python, NumPy, or similar tools) to reinforce their understanding.3.     Balance Between Breadth and Depth The book covers the essential calculus concepts in sufficient depth to support AI applications, without delving into overly abstract branches that have limited relevance to machine learning. Who Should Read This Book?·        Students of Computer Science, Data Science, and AI – who want to strengthen their mathematical foundation for advanced courses and projects.·        Researchers in AI – who need a refresher or structured guide to connect calculus with modern algorithms.·        Industry Professionals and Engineers – who want to move beyond using libraries like TensorFlow or PyTorch blindly and instead gain an understanding of the mathematics behind the models.·        Educators – who seek a resource that connects abstract mathematics with practical AI examples for teaching purposes.Benefits of Studying This Book1.     Builds Mathematical Confidence – Readers who once found calculus intimidating will discover a fresh, accessible perspective tailored for AI.2.     Enables Deeper Understanding of Algorithms – Going beyond “black box” usage of AI tools, readers will understand why models work.3.     Enhances Problem-Solving Skills – By mastering calculus-driven optimization, readers can design new models and improve existing ones.4.     Supports Academic and Career Growth – Mastery of calculus strengthens research capabilities, technical interviews, and advanced study opportunities.5.     Encourages Critical Thinking – Rather than rote memorization, the book fosters curiosity about the connections between mathematics and intelligent systems. The Long-Term VisionArtificial Intelligence is not just a passing trend—it is shaping the future of science, technology, and human society. Calculus, as a timeless branch of mathematics, ensures that learners have the intellectual tools to adapt to new paradigms. As AI expands into quantum computing, neuroscience-inspired architectures, and beyond, the reliance on calculus will remain unshaken.This book provides readers not just with knowledge, but with intellectual independence—the ability to reason about algorithms, derive insights, and innovate confidently.   

The ultimate practical guide for beginners to master Cisco networks and Packet Tracer labs step-by-step.

5. How This Book is Useful for StudyThis book is carefully designed to meet the needs of students, teachers, and researchers in computer science, artificial intelligence, and applied mathematics. Here’s how it will benefit learners:1.     Conceptual Clarity: The book explains abstract concepts like fuzzy sets, fuzzy logic, and fuzzy inference in simple and student-friendly language.2.     Step-by-Step Approach: Mathematical derivations, examples, and solved problems are provided in a logical sequence.3.     Exam-Oriented Content: The text covers topics that are important for BCA, MCA, B.Tech, M.Tech, and UGC NET examinations.4.     Practical Relevance: Numerous case studies and applications in AI systems are provided, making the subject practical and engaging.5.     Interdisciplinary Approach: The book connects mathematics, computer science, and engineering, preparing students for multidisciplinary research.6.     Hands-On Learning: Exercises, numerical problems, and small projects are suggested for practice.7.     Self-Study Friendly: Designed in such a way that even students without a strong background in higher mathematics can grasp the concepts.In summary, this book transforms fuzzy mathematics from a theoretical subject into a practical skill set essential for AI study and research. 6. Why This Book is Different and ImportantWhile there are many books on fuzzy logic or AI separately, very few combine mathematical foundations with AI applications in such a comprehensive, student-oriented way.·        Balanced Coverage: Unlike purely theoretical texts, this book strikes a balance between mathematical rigor and practical applications.·        Accessible Style: Concepts are explained in simple terms, supported with examples, diagrams, and illustrations.·        Focus on Students: Written with examination and career needs in mind, making it directly useful for academic success.·        AI-Oriented Approach: Unlike classical fuzzy mathematics books, this one emphasizes how fuzzy tools integrate into AI systems.·        Future-Ready Content: Emerging areas like fuzzy deep learning, quantum fuzzy logic, and hybrid systems are included to prepare students for future research.This makes the book not just a textbook, but a career-building guide for students of AI. 7. Pedagogical FeaturesTo make learning engaging and effective, the book is structured with special features such as:·        Definitions and Key Points highlighted for quick revision.·        Illustrations and Diagrams for visual understanding.·        Solved Examples to demonstrate problem-solving techniques.·        Exercises and Practice Problems at the end of each chapter.·        Case Studies to connect theory with real-life AI applications.·        Summary Sections for quick review before exams. 8. Who Should Read This Book? (Target Audience)This book is intended for a wide range of learners:·        Undergraduate Students (BCA, B.Sc., B.Tech): To build a foundation in fuzzy mathematics and AI.·        Postgraduate Students (MCA, M.Sc., M.Tech): For advanced study and research in AI and soft computing.·        Competitive Exam Aspirants (UGC NET, GATE): As a reference guide for key topics in AI and fuzzy logic.·        Researchers and Scholars: To explore applications of fuzzy systems in machine learning, NLP, and robotics.·        Industry Professionals: To understand the practical applications of fuzzy logic in AI-based projects. 9. Practical Outcomes After Studying the BookBy the end of this book, readers will be able to:·        Understand the mathematical foundations of fuzzy sets, relations, and logic.·        Apply fuzzy reasoning to solve uncertain and imprecise problems.·        Design fuzzy inference systems and controllers for AI applications.·        Implement fuzzy clustering and decision-making models in machine learning.·        Integrate fuzzy systems with neural networks and genetic algorithms.·        Contribute to research in advanced areas like fuzzy deep learning and quantum fuzzy logic.This ensures that learners not only gain theoretical knowledge but also practical competence in applying fuzzy mathematics in AI.