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Think Python: How to Think Like a Computer Scientist
by Allen DowneyIf you want to learn how to program, working with Python is an excellent way to start. This hands-on guide takes you through the language a step at a time, beginning with basic programming concepts before moving on to functions, recursion, data structures, and object-oriented design. This second edition and its supporting code have been updated for Python 3. <p><p> Through exercises in each chapter, you’ll try out programming concepts as you learn them. Think Python is ideal for students at the high school or college level, as well as self-learners, home-schooled students, and professionals who need to learn programming basics. Beginners just getting their feet wet will learn how to start with Python in a browser. <p><p>• Start with the basics, including language syntax and semantics <p>• Get a clear definition of each programming concept <p>• Learn about values, variables, statements, functions, and data structures in a logical progression <p>• Discover how to work with files and databases <p>• Understand objects, methods, and object-oriented programming <p>• Use debugging techniques to fix syntax, runtime, and semantic errors <p>• Explore interface design, data structures, and GUI-based programs through case studies
Think Python: How To Think Like A Computer Scientist
by Allen DowneyPython is an excellent way to get started in programming, and this clear, concise guide walks you through Python a step at a time—beginning with basic programming concepts before moving on to functions, data structures, and object-oriented design. This revised third edition reflects the growing role of large language models (LLMs) in programming and includes exercises on effective LLM prompts, testing code, and debugging skills.With this popular hands-on guide at your side, you'll get:A grounding in the syntax and semantics of the Python languageA clear definition of each programming concept, with emphasis on clear vocabularyHow to work with variables, statements, functions, and data structures in a logical progressionTechniques for reading and writing files and databasesA solid understanding of objects, methods, and object-oriented programmingDebugging strategies for syntax, runtime, and semantic errorsAn introduction to recursion, interface design, data structures, and basic algorithmsHow to use LLMs—including effective prompts, testing code, and debuggingAnd more
Think Python: How To Think Like A Computer Scientist
by Allen B. DowneyIf you want to learn how to program, working with Python is an excellent way to start. This hands-on guide takes you through the language a step at a time, beginning with basic programming concepts before moving on to functions, recursion, data structures, and object-oriented design. This second edition and its supporting code have been updated for Python 3.Through exercises in each chapter, youâ??ll try out programming concepts as you learn them. Think Python is ideal for students at the high school or college level, as well as self-learners, home-schooled students, and professionals who need to learn programming basics. Beginners just getting their feet wet will learn how to start with Python in a browser.Start with the basics, including language syntax and semanticsGet a clear definition of each programming conceptLearn about values, variables, statements, functions, and data structures in a logical progressionDiscover how to work with files and databasesUnderstand objects, methods, and object-oriented programmingUse debugging techniques to fix syntax, runtime, and semantic errorsExplore interface design, data structures, and GUI-based programs through case studies
Think Stats
by Allen B. DowneyIf you know how to program, you have the skills to turn data into knowledge using the tools of probability and statistics. This concise introduction shows you how to perform statistical analysis computationally, rather than mathematically, with programs written in Python. You'll work with a case study throughout the book to help you learn the entire data analysis process--from collecting data and generating statistics to identifying patterns and testing hypotheses. Along the way, you'll become familiar with distributions, the rules of probability, visualization, and many other tools and concepts. Develop your understanding of probability and statistics by writing and testing code Run experiments to test statistical behavior, such as generating samples from several distributions Use simulations to understand concepts that are hard to grasp mathematically Learn topics not usually covered in an introductory course, such as Bayesian estimation Import data from almost any source using Python, rather than be limited to data that has been cleaned and formatted for statistics tools Use statistical inference to answer questions about real-world data
Think Stats: Exploratory Data Analysis
by Allen B. DowneyIf you know how to program, you have the skills to turn data into knowledge, using tools of probability and statistics. This concise introduction shows you how to perform statistical analysis computationally, rather than mathematically, with programs written in Python.By working with a single case study throughout this thoroughly revised book, you’ll learn the entire process of exploratory data analysis—from collecting data and generating statistics to identifying patterns and testing hypotheses. You’ll explore distributions, rules of probability, visualization, and many other tools and concepts.New chapters on regression, time series analysis, survival analysis, and analytic methods will enrich your discoveries.Develop an understanding of probability and statistics by writing and testing codeRun experiments to test statistical behavior, such as generating samples from several distributionsUse simulations to understand concepts that are hard to grasp mathematicallyImport data from most sources with Python, rather than rely on data that’s cleaned and formatted for statistics toolsUse statistical inference to answer questions about real-world data
Thinking about Video Games
by David S. HeinemanThe growth in popularity and complexity of video games has spurred new interest in how games are developed and in the research and technology behind them. David Heineman brings together some of the most iconic, influential, and interesting voices from across the gaming industry and asks them to weigh in on the past, present, and future of video games. Among them are legendary game designers Nolan Bushnell (Pong) and Eugene Jarvis (Defender), who talk about their history of innovations from the earliest days of the video game industry through to the present; contemporary trailblazers Kellee Santiago (Journey) and Casey Hudson (Mass Effect), who discuss contemporary relationships between those who create games and those who play them; and scholars Ian Bogost (How to Do Things With Videogames) and Edward Castronova (Exodus to the Virtual World), who discuss how to research and write about games in ways that engage a range of audiences. These experts and others offer fascinating perspectives on video games, game studies, gaming culture, and the game industry more broadly.
Thinking Ahead - Essays on Big Data, Digital Revolution, and Participatory Market Society
by Dirk HelbingThe rapidly progressing digital revolution is now touching the foundations of the governance of societal structures. Humans are on the verge of evolving from consumers to prosumers, and old, entrenched theories – in particular sociological and economic ones – are falling prey to these rapid developments. The original assumptions on which they are based are being questioned. Each year we produce as much data as in the entire human history - can we possibly create a global crystal ball to predict our future and to optimally govern our world? Do we need wide-scale surveillance to understand and manage the increasingly complex systems we are constructing, or would bottom-up approaches such as self-regulating systems be a better solution to creating a more innovative, more successful, more resilient, and ultimately happier society? Working at the interface of complexity theory, quantitative sociology and Big Data-driven risk and knowledge management, the author advocates the establishment of new participatory systems in our digital society to enhance coordination, reduce conflict and, above all, reduce the “tragedies of the commons,” resulting from the methods now used in political, economic and management decision-making. The authorPhysicist Dirk Helbing is Professor of Computational Social Science at the Department of Humanities, Social and Political Sciences and an affiliate of the Computer Science Department at ETH Zurich, as well as co-founder of ETH’s Risk Center. He is internationally known for the scientific coordination of the FuturICT Initiative which focuses on using smart data to understand techno-socio-economic systems. “Prof. Helbing has produced an insightful and important set of essays on the ways in which big data and complexity science are changing our understanding of ourselves and our society, and potentially allowing us to manage our societies much better than we are currently able to do. Of special note are the essays that touch on the promises of big data along with the dangers...this is material that we should all become familiar with!” Alex Pentland, MIT, author of Social Physics: How Good Ideas Spread - The Lessons From a New Science "Dirk Helbing has established his reputation as one of the leading scientific thinkers on the dramatic impacts of the digital revolution on our society and economy. Thinking Ahead is a most stimulating and provocative set of essays which deserves a wide audience.” Paul Ormerod, economist, and author of Butterfly Economics and Why Most Things Fail. "It is becoming increasingly clear that many of our institutions and social structures are in a bad way and urgently need fixing. Financial crises, international conflicts, civil wars and terrorism, inaction on climate change, problems of poverty, widening economic inequality, health epidemics, pollution and threats to digital privacy and identity are just some of the major challenges that we confront in the twenty-first century. These issues demand new and bold thinking, and that is what Dirk Helbing offers in this collection of essays. If even a fraction of these ideas pay off, the consequences for global governance could be significant. So this is a must-read book for anyone concerned about the future." Philip Ball, science writer and author of Critical Mass “This collection of papers, brought together by Dirk Helbing, is both timely and topical. It raises concerns about Big Data, which are truly frightening and disconcerting, that we do need to be aware of; while at the same time offering some hope that the technology, which has created the previously unthought-of dangers to our privacy, safety and democracy can be the means to address these dangers by enabling social, economic and political participation and coordination, not possible in the past. It makes for compelling reading and I hope for timely action.”Eve Mitleton-Kelly, LSE, author of Corporate Governance and Complexity Theory and editor of Co-evolution of Intelligent Socio-
Thinking and Learning with ICT: Raising Achievement in Primary Classrooms
by Lyn Dawes Rupert WegerifPrimary teachers need to incorporate the use of computers in their daily lesson plans, but how can this be done most effectively to promote learning skills in the classroom? In this fascinating book, Lyn Dawes and Rupert Wegerif outline a strategy for enhancing the effectiveness of computers for teaching and learning with an emphasis on: * raising pupil achievement in the core subject areas* developing collaborative learning in small groups* using group discussions as a way of improving general communication, as well as thinking and reasoning skills. The approach is to use computers as a support for collaborative learning in small groups and this book presents ways to prepare pupils for talking, learning and thinking together around computers. Excerpts from pupils' discussions illustrate the main issues and guidance on lesson planning and developing and choosing appropriate software is also provided. Thinking and Learning with ICT will be a valuable resource for primary teachers and student teachers.
Thinking as Computation: A First Course (The\mit Press Ser.)
by Hector J. LevesqueStudents explore the idea that thinking is a form of computation by learning to write simple computer programs for tasks that require thought. This book guides students through an exploration of the idea that thinking might be understood as a form of computation. Students make the connection between thinking and computing by learning to write computer programs for a variety of tasks that require thought, including solving puzzles, understanding natural language, recognizing objects in visual scenes, planning courses of action, and playing strategic games. The material is presented with minimal technicalities and is accessible to undergraduate students with no specialized knowledge or technical background beyond high school mathematics. Students use Prolog (without having to learn algorithms: “Prolog without tears!”), learning to express what they need as a Prolog program and letting Prolog search for answers.After an introduction to the basic concepts, Thinking as Computation offers three chapters on Prolog, covering back-chaining, programs and queries, and how to write the sorts of Prolog programs used in the book. The book follows this with case studies of tasks that appear to require thought, then looks beyond Prolog to consider learning, explaining, and propositional reasoning. Most of the chapters conclude with short bibliographic notes and exercises. The book is based on a popular course at the University of Toronto and can be used in a variety of classroom contexts, by students ranging from first-year liberal arts undergraduates to more technically advanced computer science students.
Thinking Better: The Art of the Shortcut in Math and Life
by Marcus du SautoyOne of the world's great mathematicians shows why math is the ultimate timesaver—and how everyone can make their lives easier with a few simple shortcuts.We are often told that hard work is the key to success. But success isn&’t about hard work – it&’s about shortcuts. Shortcuts allow us to solve one problem quickly so that we can tackle an even bigger one. They make us capable of doing great things. And according to Marcus du Sautoy, math is the very art of the shortcut.Thinking Better is a celebration of how math lets us do more with less. Du Sautoy explores how diagramming revolutionized therapy, why calculus is the greatest shortcut ever invented, whether you must really practice for ten thousand hours to become a concert violinist, and why shortcuts give us an advantage over even the most powerful AI. Throughout, we meet artists, scientists, and entrepreneurs who use mathematical shortcuts to change the world.Delightful, illuminating, and above all practical, Thinking Better is for anyone who has wondered why you should waste time climbing the mountain when you could go around it much faster.
Thinking Clearly with Data: A Guide to Quantitative Reasoning and Analysis
by Ethan Bueno de Mesquita Anthony FowlerAn engaging introduction to data science that emphasizes critical thinking over statistical techniquesAn introduction to data science or statistics shouldn’t involve proving complex theorems or memorizing obscure terms and formulas, but that is exactly what most introductory quantitative textbooks emphasize. In contrast, Thinking Clearly with Data focuses, first and foremost, on critical thinking and conceptual understanding in order to teach students how to be better consumers and analysts of the kinds of quantitative information and arguments that they will encounter throughout their lives.Among much else, the book teaches how to assess whether an observed relationship in data reflects a genuine relationship in the world and, if so, whether it is causal; how to make the most informative comparisons for answering questions; what questions to ask others who are making arguments using quantitative evidence; which statistics are particularly informative or misleading; how quantitative evidence should and shouldn’t influence decision-making; and how to make better decisions by using moral values as well as data. Filled with real-world examples, the book shows how its thinking tools apply to problems in a wide variety of subjects, including elections, civil conflict, crime, terrorism, financial crises, health care, sports, music, and space travel.Above all else, Thinking Clearly with Data demonstrates why, despite the many benefits of our data-driven age, data can never be a substitute for thinking.An ideal textbook for introductory quantitative methods courses in data science, statistics, political science, economics, psychology, sociology, public policy, and other fieldsIntroduces the basic toolkit of data analysis—including sampling, hypothesis testing, Bayesian inference, regression, experiments, instrumental variables, differences in differences, and regression discontinuityUses real-world examples and data from a wide variety of subjectsIncludes practice questions and data exercises
Thinking Collaboratively: Learning in a Community of Inquiry
by D. Randy GarrisonThinking Collaboratively is a theoretical and practical guide to thinking and learning in deep and meaningful ways within purposeful communities of inquiry. Critical thinking has long been recognized as an important educational goal but, until now, has largely been conceived and operationalized as an individual attitude and ability. Increasingly, however, a more relevant and complete cognitive construct has been emerging: thinking collaboratively. Thinking collaboratively is the means to inquire, test, and apply new understandings, and to make sense of the information that bombards us continuously. In short, thinking collaboratively is required to flourish in our highly connected world and, in this book based on more than a decade of research, Garrison provides an essential introduction to this vital concept.
Thinking Data Science: A Data Science Practitioner’s Guide (The Springer Series in Applied Machine Learning)
by Poornachandra SarangThis definitive guide to Machine Learning projects answers the problems an aspiring or experienced data scientist frequently has: Confused on what technology to use for your ML development? Should I use GOFAI, ANN/DNN or Transfer Learning? Can I rely on AutoML for model development? What if the client provides me Gig and Terabytes of data for developing analytic models? How do I handle high-frequency dynamic datasets? This book provides the practitioner with a consolidation of the entire data science process in a single “Cheat Sheet”.The challenge for a data scientist is to extract meaningful information from huge datasets that will help to create better strategies for businesses. Many Machine Learning algorithms and Neural Networks are designed to do analytics on such datasets. For a data scientist, it is a daunting decision as to which algorithm to use for a given dataset. Although there is no single answer to this question, a systematic approach to problem solving is necessary. This book describes the various ML algorithms conceptually and defines/discusses a process in the selection of ML/DL models. The consolidation of available algorithms and techniques for designing efficient ML models is the key aspect of this book. Thinking Data Science will help practising data scientists, academicians, researchers, and students who want to build ML models using the appropriate algorithms and architectures, whether the data be small or big.
Thinking, Drawing, Modelling: GEOMETRIAS 2017, Coimbra, Portugal, June 16–18 (Springer Proceedings in Mathematics & Statistics #326)
by Vera Viana Vítor Murtinho João Pedro XavierThis book presents a selection of papers from the International Conference Geometrias’17, which was hosted by the Department of Architecture at the University of Coimbra from 16 to 18 June 2017. The Geometrias conferences, organized by Aproged (the Portuguese Geometry and Drawing Teachers’ Association), foster debate and exchange on practical and theoretical research in mathematics, architecture, the arts, engineering, and related fields.Geometrias’17, with the leitmotif “Thinking, Drawing, Modelling”, brought together a group of recognized experts to discuss the importance of geometric literacy and the science of representation for the development of scientific and technological research and professional practices. The 12 peer-reviewed papers gathered here show how geometry, drawing, stereotomy, and the science of representation are still at the core of every act leading to the conception and materialization of form, and highlight their continuing relevance for scholars and professionals in the fields of architecture, engineering, and applied mathematics.
Thinking Functionally With Haskell
by Richard BirdRichard Bird is famed for the clarity and rigour of his writing. His new textbook, which introduces functional programming to students, emphasises fundamental techniques for reasoning mathematically about functional programs. By studying the underlying equational laws, the book enables students to apply calculational reasoning to their programs, both to understand their properties and to make them more efficient. The book has been designed to fit a first- or second-year undergraduate course and is a thorough overhaul and replacement of his earlier textbooks. It features case studies in Sudoku and pretty-printing, and over 100 carefully selected exercises with solutions. This engaging text will be welcomed by students and teachers alike.
Thinking in LINQ
by Sudipta MukherjeeLINQ represents a paradigm shift for developers used to an imperative/object oriented programming style, because LINQ draws on functional programming principles. Thinking in LINQ addresses the differences between these two by providing a set of succinct recipes arranged in several groups, including: Basic and extended LINQ operators Text processing Loop refactoring Monitoring code health Reactive Extensions (Rx. NET) Building domain-specific languages Using the familiar "recipes" approach, Thinking in LINQ shows you how to approach building LINQ-based solutions, how such solutions are different from what you already know, and why they're better. The recipes cover a wide range of real-world problems, from using LINQ to replace existing loops, to writing your own Swype-like keyboard entry routines, to finding duplicate files on your hard drive. The goal of these recipes is to get you "thinking in LINQ," so you can use the techniques in your own code to write more efficient and concise data-intensive applications. What you'll learn Basic and extended LINQ operators Text processing Loop refactoring Monitoring code health Reactive Extensions (Rx. NET) Building domain-specific languages Who this book is for . NET programmers who are comfortable with some high level programming language like C++/C#. Prior knowledge of LINQ is helpful but not required. Table of Contents 1. Thinking Functionally 2. Series Generation 3. Text Processing 4. Refactoring with LINQ 5. Refactoring with MoreLINQ 6. Creating DSL using LINQ 7. Static Code Analysis 8. Exploratory Data Analysis 9. Interacting with the File System Appendix A: Lean LINQ Tips Appendix B: Taming Streaming Data with Rx. NET
Thinking in Pandas: How to Use the Python Data Analysis Library the Right Way
by Hannah StepanekUnderstand and implement big data analysis solutions in pandas with an emphasis on performance. This book strengthens your intuition for working with pandas, the Python data analysis library, by exploring its underlying implementation and data structures. Thinking in Pandas introduces the topic of big data and demonstrates concepts by looking at exciting and impactful projects that pandas helped to solve. From there, you will learn to assess your own projects by size and type to see if pandas is the appropriate library for your needs. Author Hannah Stepanek explains how to load and normalize data in pandas efficiently, and reviews some of the most commonly used loaders and several of their most powerful options. You will then learn how to access and transform data efficiently, what methods to avoid, and when to employ more advanced performance techniques. You will also go over basic data access and munging in pandas and the intuitive dictionary syntax. Choosing the right DataFrame format, working with multi-level DataFrames, and how pandas might be improved upon in the future are also covered.By the end of the book, you will have a solid understanding of how the pandas library works under the hood. Get ready to make confident decisions in your own projects by utilizing pandas—the right way.What You Will LearnUnderstand the underlying data structure of pandas and why it performs the way it does under certain circumstancesDiscover how to use pandas to extract, transform, and load data correctly with an emphasis on performanceChoose the right DataFrame so that the data analysis is simple and efficient.Improve performance of pandas operations with other Python libraries Who This Book Is ForSoftware engineers with basic programming skills in Python keen on using pandas for a big data analysis project. Python software developers interested in big data.
Thinking in Promises: Designing Systems for Cooperation
by Mark BurgessImagine a set of simple principles that could help you to understand how parts combine to become a whole, and how each part sees the whole from its own perspective. If such principles were any good, it shouldn’t matter whether we’re talking about humans on a team, birds in a flock, computers in a datacenter, or cogs in a Swiss watch. A theory of cooperation ought to be pretty universal, so we should be able to apply it both to technology and to the workplace.Such principles are the subject of Promise Theory, and the focus of this insightful book. The goal of Promise Theory is to reveal the behavior of a whole from the sum of its parts, taking the viewpoint of the parts rather than the whole. In other words, it is a bottom-up, constructionist view of the world. Start Thinking in Promises and find out why this discipline works for documenting system behaviors from the bottom-up.
Thinking Like a Computer: An Introduction to Digital Reality
by George TownerThinking Like a Computer is the result of a detailed 30-year study of how computers imitate life.Although they are machines, computers are designed to act like human beings. Software is specifically created to help accomplish human-like tasks and to be understood in human terms. Yet unlike human life, computer operations can be analyzed in detail because we build the machines that accomplish them and we know the design decisions that make them work.With every choice made during the evolution of digital technology, computer architects have intuitively or consciously incorporated truths of human functioning into their designs.Thinking Like a Computer is based on these truths, assembling them into a new explanation of human knowledge. In addition, it provides insights into the foundations of theoretical science because much of digital technology is dedicated to creating new realities.
Thinking Machines: The Quest for Artificial Intelligence--and Where It's Taking Us Next
by Luke DormehlA fascinating look at Artificial Intelligence, from its humble Cold War beginnings to the dazzling future that is just around the corner.When most of us think about Artificial Intelligence, our minds go straight to cyborgs, robots, and sci-fi thrillers where machines take over the world. But the truth is that Artificial Intelligence is already among us. It exists in our smartphones, fitness trackers, and refrigerators that tell us when the milk will expire. In some ways, the future people dreamed of at the World's Fair in the 1960s is already here. We're teaching our machines how to think like humans, and they're learning at an incredible rate.In Thinking Machines, technology journalist Luke Dormehl takes you through the history of AI and how it makes up the foundations of the machines that think for us today. Furthermore, Dormehl speculates on the incredible--and possibly terrifying--future that's much closer than many would imagine. This remarkable book will invite you to marvel at what now seems commonplace and to dream about a future in which the scope of humanity may need to widen to include intelligent machines.From the Trade Paperback edition.
Thinking Skills for the Digital Generation
by Balu H. Athreya Chrystalla MouzaThis important text synthesizes the state of knowledge related to thinking and technology and provides strategies for helping young people cultivate thinking skills required to navigate the new digital landscape. The rise of technology has resulted in new ways of searching and communicating information among youth, often creating information "overload". We do not know how the new technologies will affect the ways young people learn and think. There are plenty of warnings about the dangers of information technology, but there is also enormous potential for technology to aid human thinking, which this book explores from an open-minded perspective. Coverage Includes: - An up to date review of the literature on thinking skills in general, and in relation to technology. - Practical guidelines for thinking with technology. - A scholarly review of the characteristics of the digital generation. - A discussion of the various steps involved in the thinking process. - A historical context of the Information Age and the transition from oral history, to printing press, to the Internet. Thinking Skills for the Digital Generation: The Development of Thinking and Learning in the Age of Information is an invaluable reference for educators and research professionals particularly interested in educational technology, and improving thinking and problem-solving skills.
Thinking Through Digital Media
by Dale Hudson Patricia R. ZimmermannThinking through Digital Media offers a means of conceptualizing digital media by looking at projects that think through digital media, migrating between documentary, experimental, narrative, animation, video game, and live performance. Hudson and Zimmermann analyze projects at the intersections of imbedded technologies, transitory micropublics, human-machine interface, and critical cartographies to forward a set of speculations about how things work together rather than what they represent. The book frames debates on participation/surveillance, outsourcing, global warming, migrations, GMOs, and war across some of the most dynamic, innovative sites for digital media, including Brazil, Canada, China, Germany, India, Indonesia, Italy, Kenya, Nigeria, Palestine, Saudi Arabia, Singapore, and the United States.
Thinking with Data: How to Turn Information into Insights
by Max ShronMany analysts are too concerned with tools and techniques for cleansing, modeling, and visualizing datasets and not concerned enough with asking the right questions. In this practical guide, data strategy consultant Max Shron shows you how to put the why before the how, through an often-overlooked set of analytical skills.Thinking with Data helps you learn techniques for turning data into knowledge you can use. You’ll learn a framework for defining your project, including the data you want to collect, and how you intend to approach, organize, and analyze the results. You’ll also learn patterns of reasoning that will help you unveil the real problem that needs to be solved.Learn a framework for scoping data projectsUnderstand how to pin down the details of an idea, receive feedback, and begin prototypingUse the tools of arguments to ask good questions, build projects in stages, and communicate resultsExplore data-specific patterns of reasoning and learn how to build more useful argumentsDelve into causal reasoning and learn how it permeates data workPut everything together, using extended examples to see the method of full problem thinking in action
The Third Apple: Personal Computers and the Cultural Revolution
by Jean-Louis GasséeThe title refers not to Eve's or Newton's apple, but the computer.
Third Congress on Intelligent Systems: Proceedings of CIS 2022, Volume 1 (Lecture Notes in Networks and Systems #608)
by Sandeep Kumar Harish Sharma K. Balachandran Joong Hoon Kim Jagdish Chand BansalThis book is a collection of selected papers presented at the Third Congress on Intelligent Systems (CIS 2022), organized by CHRIST (Deemed to be University), Bangalore, India, under the technical sponsorship of the Soft Computing Research Society, India, during September 5–6, 2022. It includes novel and innovative work from experts, practitioners, scientists, and decision-makers from academia and industry. It covers topics such as the Internet of Things, information security, embedded systems, real-time systems, cloud computing, big data analysis, quantum computing, automation systems, bio-inspired intelligence, cognitive systems, cyber-physical systems, data analytics, data/web mining, data science, intelligence for security, intelligent decision-making systems, intelligent information processing, intelligent transportation, artificial intelligence for machine vision, imaging sensors technology, image segmentation, convolutional neural network, image/video classification, soft computing for machine vision, pattern recognition, human-computer interaction, robotic devices and systems, autonomous vehicles, intelligent control systems, human motor control, game playing, evolutionary algorithms, swarm optimization, neural network, deep learning, supervised learning, unsupervised learning, fuzzy logic, rough sets, computational optimization, and neuro-fuzzy systems.