Books

This textbook accompanies a year-long Discrete Mathematics course for Computer Science and AI students, covering classical topics such as combinatorics, graph theory, probability, logic, and set theory. It emphasizes applications across computer science and complements the standard curriculum with advanced topics in each chapter.

Move beyond prompts and learn the design patterns behind modern AI agents.From ReAct and RAG to planning, verification, memory, orchestration, and multi-agent collaboration, LLM Agent Patterns shows you how to build LLM systems that are not only powerful, but structured, reliable, and production-minded. If you want to understand how agentic systems actually work—and how to build them yourself—this book is your guide.

Readers will learn discrete mathematical abstracts as well as its implementation in algorithm and data structures shown in various programming languages, such as C, C++, PHP, Java, C#, Python and Dart. This book combines two major components of Mathematics and Computer Science under one roof.

Readers will learn discrete mathematical abstracts as well as its implementation in algorithm and data structures shown in various programming languages, such as C, C++, PHP, Java, C#, Python and Dart. This book combines two major components of Mathematics and Computer Science under one roof.

Mastering Algorithms: From Fundamentals to Advanced Applications for Students and Professionals provides a structured journey through algorithm fundamentals, complexity analysis, design strategies, optimization techniques, and advanced graph algorithms.Explore Divide and Conquer, Greedy Algorithms, Dynamic Programming, Backtracking, Branch and Bound, graph traversal, shortest paths.

Master Discrete Mathematics — the foundation of Computer Science. Comprehensive guide specially designed for BCA & MCA students with detailed explanations, solved examples, real-world applications, and exam-focused practice. Perfect for university exams, GATE, and interviews.

The future of Artificial Intelligence is connected.From social networks and recommendation engines to autonomous vehicles and cybersecurity systems, modern AI increasingly relies on understanding relationships rather than isolated data points.How do Graph Neural Networks learn from complex networks?How do recommendation systems predict user preferences?How can AI detect fraud, misinformation, and cyber threats using graph structures?How will future Graph Foundation Models transform machine intelligence?Graph Theory with AI Applications: Foundations, Algorithms, and Modern Neural Approaches (VOL-2) provides a comprehensive guide to the technologies driving the next generation of AI.Explore Graph Neural Networks, graph embeddings, knowledge graphs, explainable AI, distributed graph learning, and cutting-edge research topics that are reshaping artificial intelligence.Whether you are a student, researcher, educator, or AI professional, this book will help you understand how intelligent systems learn from relationships, networks, and connected data.Learn the science behind Graph AI. Build the intelligence behind tomorrow's connected world.

Graphs are everywhere.From social media networks and recommendation systems to autonomous vehicles, cybersecurity platforms, and modern artificial intelligence, graph structures have become the language of connected data.But how do machines understand relationships?How do search engines rank billions of pages?How do recommendation systems predict what users will like next?How do AI systems learn from complex networks?Graph Theory with AI Applications: Foundations, Algorithms, and Modern Neural Approaches (VOL-1) provides the answers.This book takes readers on a structured journey through graph fundamentals, graph algorithms, shortest path methods, network optimization, social network analytics, community detection, and graph mining techniques. Designed for students, researchers, educators, and professionals, it combines mathematical foundations with practical AI applications.If you want to understand the science behind connected intelligence and prepare yourself for the future of Graph Neural Networks and AI-driven graph learning, this book is your starting point.Discover the foundations. Master the algorithms. Build the future of Graph AI.

Discover the logical heart of the NFSI. This paper explains the 3-stage pipeline used to transform heterogeneous OSINT signals into a traceable and auditable geopolitical stability index. Check the live data on https://nationfiles.com

A deep dive into the NationFiles Stability Index (NFSI). Discover how 115+ real-time indicators and the Naciro Intelligence Engine redefine geopolitical risk analysis through transparent, rule-based 15-minute recalibration. Check the live data on https://nationfiles.com

Language models guess. Knowledge graphs know. **Rusty Graph** shows you how to build a local AI agent whose memory is a real knowledge graph — typed, validated, reasoned over, and queryable with SPARQL — all inside a single static Rust binary. No JVM. No Docker sidecar. No Python runtime bolted to the side. You will build **Ares**, a research-assistant agent that observes papers, forms beliefs, makes promises to other agents, and tracks the provenance of every fact it holds. Chapter by chapter, Ares grows from an empty Cargo workspace into a full pipeline: > load → reason → validate → query → answer All of it in under a thousand lines of idiomatic Rust, using three crates that actually work today: `oxigraph`, `reasonable`, and `rudof_lib`. `grapfeo` You will learn how to - Model a domain as RDF triples and load them into an embedded store. - Write RDFS and OWL 2 RL axioms that infer trust, identity, and inverse relationships — automatically. - Guard your graph with SHACL shapes that reject bad data at the boundary, not in production. - Query everything with SPARQL, from simple lookups to federated queries across named graphs. - Wire the graph into a hybrid RAG pipeline so your LLM answers are grounded in facts, not vibes. Who it is for Rust developers building agents, assistants, or any system where the answer "the model said so" is not good enough. You should be comfortable with Cargo and traits. You do **not** need any prior semantic-web background — every concept is introduced through Ares before any formal definition appears. Why Rust, why now Local agents are the next deployment target: a user's laptop, a Raspberry Pi, a WASM sandbox. Python cannot go there comfortably. Rust can. This book is the missing manual for the Rust side of the semantic web — the one that tells you exactly which crates work, where the ecosystem is thin, and how to ship anyway. Stop hoping your model tells the truth. Give it a graph that does.

Learn LangGraph the fast, practical way by building real projects from your very first chapter. This hands-on guide cuts out the fluff and walks you step-by-step from simple graphs to powerful AI agents like ReAct and RAG. If you prefer learning by doing, this book will get you up and running quickly without overwhelming you.

"How Does Computer Science Work?" answers 28 key questions every software engineer should know, unraveling the technologies that shape our digital world. From how browsers work and cryptography secures data to AI, Web3, and quantum computing, this book bridges curiosity and understanding. Perfect for students, self-taught programmers, and seasoned developers, it’s your ultimate guide to mastering foundational concepts and cutting-edge innovations in computer science.

Designed for JavaScript developers, this handbook dives into the practical implementation of advanced data structures such as B-Trees, Tries, and probabilistic models like Bloom Filters. Focused on real-world challenges, it explains how to handle large datasets, optimize caching, and implement efficient searches. With clear examples and technical depth, this book offers a realistic approach to applying advanced concepts in scalable and reliable systems.