Books

Artificial Intelligence is often described as learning from data.But beneath every learning algorithm lies something even more fundamental:Probability, randomness, and uncertainty.From Hidden Markov Models and stochastic gradient descent to Monte Carlo methods and reinforcement learning, modern AI systems depend on stochastic processes to make predictions, learn from experience, and adapt to changing environments.In Stochastic Processes in Artificial Intelligence, Anshuman Mishra provides a structured and accessible journey through the mathematical foundations that power intelligent systems.Discover how uncertainty becomes intelligence—and why stochastic thinking is essential for the future of Artificial Intelligence.

Every intelligent system faces a fundamental challenge:How do you find the best solution when there are millions—or even trillions—of possibilities?From A* search and heuristic optimization to neural architecture search, hyperparameter tuning, constraint satisfaction, and quantum optimization, modern AI depends on sophisticated strategies for navigating combinatorial search spaces.In this advanced second volume, Anshuman Mishra explores the powerful algorithms that enable intelligent systems to search smarter, optimize faster, and scale beyond brute force computation.Discover how combinatorial thinking drives machine learning, optimization, quantum AI, and the future of intelligent decision-making.The future of AI belongs to those who understand combinatorial complexity.

What if the monster didn’t escape into death… but was reborn to suffer as his victims once did? Shattered into animal forms and hunted by the souls he destroyed, Victor Kane’s eternal torment is only beginning. A brutal karmic nightmare where the apex predator becomes the ultimate prey.

Artificial Intelligence is not merely about data, learning, or neural networks—it is fundamentally about exploring vast spaces of possibilities.Every search algorithm, planning system, optimization framework, recommendation engine, and machine learning model faces a common challenge: navigating an enormous combinatorial universe of potential solutions.In Combinatorial Thinking in Artificial Intelligence, Anshuman Mishra reveals the mathematical foundations that drive modern AI systems. Through permutations, combinations, graph theory, probability, search strategies, and optimization techniques, readers learn how intelligent systems reason, search, and make decisions in complex environments.If you want to understand why AI algorithms work—not just how to implement them—this book provides the missing mathematical perspective.Discover the combinatorial engine behind intelligence.

Are you trapped in the "Paper-by-Paper" cycle of academic writing? The Research Production System™ provides the exact templates, AI-assisted workflows, and strategic frameworks required to transform sporadic research efforts into a structured, repeatable, and highly productive publication pipeline. Stop hoping for momentum and start engineering it today.

Advanced cybersecurity research based on multi-source data correlation across honeypots, IDS systems and network telemetry, analyzing unified attacker behavior and infrastructure patterns

Real-world network security research based on Suricata IDS data, analyzing malicious traffic, intrusion attempts and attack patterns observed at the network layer

Can Hidden Markov Models predict financial market regimes?How are genes discovered from DNA sequences?How do robots determine their location in uncertain environments?Can probabilistic AI still compete with Transformers and Deep Learning?Volume-3 of Hidden Markov Models and AI answers these questions through real-world applications, Python implementations, industrial case studies, advanced projects, and future AI research.Explore how Hidden Markov Models are used in bioinformatics, cybersecurity, finance, robotics, autonomous systems, anomaly detection, and scientific discovery. Learn to build HMM systems from scratch, work with professional AI libraries, and understand the evolving relationship between probabilistic models and deep learning.This volume is designed for readers who want to move beyond theory and develop practical expertise in modern sequential artificial intelligence.

How do voice assistants understand speech?How does a chatbot track conversation context?How can machines identify speakers, translate languages, recognize named entities, and process sequential information?The answer lies in sequence modeling.In Volume-2 of Hidden Markov Models and AI, readers move beyond theory into practical applications of Hidden Markov Models in speech recognition, natural language processing, machine translation, speaker verification, conversational AI, and intelligent decision-making systems.Learn how modern AI systems transform speech signals and language sequences into meaningful intelligence using probabilistic models that continue to influence today's most advanced technologies.Whether you are an AI student, NLP researcher, speech engineer, or machine learning professional, this volume provides the practical knowledge required to master sequential learning systems.

Artificial Intelligence is not only about neural networks and transformers. Behind many of the world's most influential AI systems lies a powerful probabilistic framework known as the Hidden Markov Model (HMM).From speech recognition and natural language processing to robotics, cybersecurity, finance, and bioinformatics, HMMs remain one of the most important sequence modeling techniques ever developed.This book takes readers on a complete journey through Markov Chains, probabilistic reasoning, Hidden Markov Models, Forward-Backward algorithms, Viterbi decoding, Baum-Welch training, and advanced HMM architectures.Designed for students, researchers, and AI professionals, the book combines rigorous mathematics with practical applications, making complex concepts accessible and immediately useful.If you want to truly understand how intelligent systems model uncertainty, learn from temporal patterns, and reason about hidden information, this book provides the foundation.

Build four real mobile apps from scratch — no coding experience required. Claude Code handles the code. You handle the idea.

A no-fluff guide to building real sites with HTML, CSS & JavaScript — and getting paid to do it

What if the unpredictable behavior of intelligent systems is not a flaw—but a feature?In Nonlinear Dynamics and Chaos Theory in Artificial Intelligence (VOL-II), Anshuman Mishra explores the fascinating intersection of chaos, complexity, fractals, adaptive intelligence, and machine learning.Discover how chaotic neural networks, nonlinear optimization, fractal learning architectures, autonomous robotics, reinforcement learning, and emergent intelligence are transforming the future of AI.Through mathematical rigor, practical Python implementations, real-world case studies, and cutting-edge research directions, this book reveals how chaos can become a powerful tool for building smarter, more adaptive, and more resilient intelligent systems.For researchers, students, engineers, and AI innovators, this volume opens the door to one of the most exciting frontiers of next-generation artificial intelligence.