Dr.Padmaja Pulicherla, Professor, Department of Computer Science and Engineering, Hyderabad Institute of Technology and Management, Affiliated to JNTU, Hyderabad, Telangana, India. Dr.Kasarla Satish Reddy, Professor, Department of Electronics and Communication Engineering, Hyderabad Institute of Technology and Management, Affiliated to JNTU, Hyderabad, Telangana, India. D.Satyanarayana, Assistant Professor, Department of Computer Science and Engineering(DS), Santhiram Engineering College(Autonomous), Nandyal, Andhra Pradesh, India. Dr.R.Sudheer Babu, Associate Professor, Department of Electronics and Communication Engineering, G.Pulla Reddy Engineering College (Autonomous), Kurnool, Andhra Pradesh, India. Dr.Ravi Babu Devareddi, Assistant Professor, Department of Computer Science and Engineering, SRKR Engineering College, Bhimavaram, Andhra Pradesh, India.

An intuitive approach to machine learning covering key concepts, real-world applications, and practical Python coding exercises.

In today's data-driven era, the persistent gap between theoretical understanding and practical implementation in data science poses a formidable challenge. As we navigate through the complexities of harnessing data, deciphering algorithms, and unleashing the potential of modeling techniques, the need for a comprehensive guide becomes increasingly evident. This is the landscape explored in Practical Applications of Data Processing, Algorithms, and Modeling. This book is a solution to the pervasive problem faced by aspiring data scientists, seasoned professionals, and anyone fascinated by the power of data-driven insights. From the web of algorithms to the strategic role of modeling in decision-making, this book is an effective resource in a landscape where data, without proper guidance, risks becoming an untapped resource. The objective of Practical Applications of Data Processing, Algorithms, and Modeling is to address the pressing issue at the heart of data science – the divide between theory and practice. This book seeks to examine the complexities of data processing techniques, algorithms, and modeling methodologies, offering a practical understanding of these concepts. By focusing on real-world applications, the book provides readers with the tools and knowledge needed to bridge the gap effectively, allowing them to apply these techniques across diverse industries and domains. In the face of constant technological advancements, the book highlights the latest trends and innovative approaches, fostering a deeper comprehension of how these technologies can be leveraged to solve complex problems. As a practical guide, it empowers readers with hands-on examples, case studies, and problem-solving scenarios, aiming to instill confidence in navigating data challenges and making informed decisions using data-driven insights.

Network Science Network Science offers comprehensive insight on network analysis and network optimization algorithms, with simple step-by-step guides and examples throughout, and a thorough introduction and history of network science, explaining the key concepts and the type of data needed for network analysis, ensuring a smooth learning experience for readers. It also includes a detailed introduction to multiple network optimization algorithms, including linear assignment, network flow and routing problems. The text is comprised of five chapters, focusing on subgraphs, network analysis, network optimization, and includes a list of case studies, those of which include influence factors in telecommunications, fraud detection in taxpayers, identifying the viral effect in purchasing, finding optimal routes considering public transportation systems, among many others. This insightful book shows how to apply algorithms to solve complex problems in real-life scenarios and shows the math behind these algorithms, enabling readers to learn how to develop them and scrutinize the results. Written by a highly qualified author with significant experience in the field, Network Science also includes information on: Sub-networks, covering connected components, bi-connected components, community detection, k-core decomposition, reach network, projection, nodes similarity and pattern matching Network centrality measures, covering degree, influence, clustering coefficient, closeness, betweenness, eigenvector, PageRank, hub and authority Network optimization, covering clique, cycle, linear assignment, minimum-cost network flow, maximum network flow problem, minimum cut, minimum spanning tree, path, shortest path, transitive closure, traveling salesman problem, vehicle routing problem and topological sort With in-depth and authoritative coverage of the subject and many case studies to convey concepts clearly, Network Science is a helpful training resource for professional and industry workers in, telecommunications, insurance, retail, banking, healthcare, public sector, among others, plus as a supplementary reading for an introductory Network Science course for undergraduate students.

This book consitiutes the refereed proceedings of the 4th International Conference on Deep Learning Theory and Applications, DeLTA 2023, held in Rome, Italy from 13 to 14 July 2023. The 9 full papers and 22 short papers presented were thoroughly reviewed and selected from the 42 qualified submissions. The scope of the conference includes such topics as models and algorithms; machine learning; big data analytics; computer vision applications; and natural language understanding.

**Chaos Unveiled An Expedition into the Intricate World of Chaos Theory and Complex Systems** Unlock the mysteries of the universe's most captivating phenomenon with "Chaos Unveiled," an enlightening journey through chaos theory and complex systems. Crafted to engross and educate, this book serves as your definitive guide to understanding the unpredictable nature of the world around us. Dive into **Chapter 1** and explore the humble beginnings of chaos theory and its dramatic evolution into a pivotal scientific discipline. Discover key contributors and the milestones that have defined this groundbreaking field. Move into **Chapter 2**, where the intricate mathematics behind chaos comes to life. Understand nonlinear dynamics, sensitivity to initial conditions, and the enigmatic strange attractors that lead to seemingly random behaviors out of deterministic processes. In **Chapter 3**, delve into the mesmerizing world of fractals and self-similarity. From the beauty of fractal geometry to real-world applications, this chapter is a visual and intellectual feast. **Chapter 4** takes you into fluid dynamics, demystifying the complex phenomena of turbulence, vortices, and the iconic Lorenz attractor that revolutionized weather prediction and modeling. Explore the fascinating roles chaos plays in **biological systems** in **Chapter 5**, from population dynamics and cardiac rhythms to intricate ecosystems and food webs. Unravel the secrets of chaotic behavior in chemical reactions in **Chapter 6**, and discover how these principles govern both simple reactions and the complex chemistry of living organisms. Venture into **economic chaos** in **Chapter 7**, understanding stock market fluctuations, market bubbles, and the unpredictable nature of economic forecasting. The applications of chaos theory in **engineering** are unlocked in **Chapter 8**, revealing insights into control systems, structural analysis, and the ever-evolving field of robotics and machine learning. **Chapter 9** sheds light on chaos in social systems, from human behavior and social networks to the intricacies of urban planning. Experience medical breakthroughs in **Chapter 10**, where chaos theory enhances diagnostics, epidemiology, and our understanding of brain activity. Discover chaos in the vast expanse of **environmental science** and **astronomy** in **Chapters 11 and 12**, exploring climate change, earthquake prediction, orbital mechanics, and cosmic phenomena. **Chapter 13** provides groundbreaking insights into computational approaches, from numerical simulations to data analysis and pattern recognition. Reflect on the **philosophical implications** in **Chapter 14** as you weigh determinism against randomness and ponder the ethical considerations of predictability. Finally, look towards the **future of chaos theory** in **Chapter 15**, exploring emerging research areas, interdisciplinary collaborations, and the bright horizon of unanswered questions and challenges. "Chaos Unveiled" is not merely a book—it's an invitation to explore, question, and understand the beautifully intricate world of chaos theory. Get ready to have your mind expanded and your curiosity ignited!

The two-volume set, CCIS 681 and CCIS 682, constitutes the proceedings of the 11th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2016, held in Xi'an, China, in October 2016.The 115 revised full papers presented were carefully reviewed and selected from 343 submissions. The papers of Part I are organized in topical sections on DNA Computing; Membrane Computing; Neural Computing; Machine Learning. The papers of Part II are organized in topical sections on Evolutionary Computing; Multi-objective Optimization; Pattern Recognition; Others.