This book asks fundamental questions about the extent to which India is participating in the global shift towards knowledge-based forms of competitiveness. It charts Indian performance and progress using a unique framework benchmarked against fourteen other countries. In the course of the analysis, critical areas for improvement are identified, and the book provides detailed and objective insights for policy-makers and researchers to facilitate change and institutional reform in India. Readers will derive a comprehensive understanding of India’s performance and prospects as it emerges as a serious global economic player. A particular feature of the work is the development of an original knowledge footprint concept that measures the extent and impact of knowledge development and diffusion domestic and internationally.The views expressed in this book are the author’s.
Special Features: · Familiarizes the readers with the basic concepts, principles and methods associated with quality control· Helps readers understand how quality control concepts, principles and methods can be effective in a variety of situations· Illustrates the relationship between total quality principles and the theories and models studied in management courses· Conforms to the engineering and management syllabi of all Indian universities · Discusses the step-by-step evolution of Quality since Juran and Deming· Covers all essential features of Quality Control and Total Quality Management· Discusses about Six Sigma problem-solving methodology that will give readers an excellent framework to use in conducting quality improvement projects· Includes learning goals, summery, review questions and multiple-choice questions with each chaptersIncludes over:- 90 tables- 155 figures- 51 solved examples - 56 review questions- 36 multiple-choice questionsThe book conforms completely to syllabi of Quality Control subject of all universities of Maharashtra, Goa, Gujarat, Karnataka, Punjab and major universities viz. Anna University, J.N.T.U., R.G.P.V. About The Book: Quality Control is designed with an integrated approach for the interdisciplinary courses on Quality Control and Total Quality Management. The book serves as a textbook for the core course on Statistical Quality Control and is aimed at undergraduate students of engineering at all Indian universities. The text provides a comprehensive coverage of the subject from basic principles to state-of-the-art concepts and applications. With a strong engineering and management orientation, the book explores the modern use of statistical methods in quality control and improvement
In 2031, a group of scientists discovered a shocking revelation from NEBULA, their most spectacular space station orbiting outside the solar system as an independent man-made planet. An extinction-level event (ELE) was an inevitable reality in the next 6 months! How can one prevent an unknown catastrophe based on limited information? An event ten times massive and catastrophic as the asteroid collision that brought the extinction of dinosaurs! Would humans be able to survive with Noah’s Ark in the 21st century? Will Ranjan Sharma, Jovan Novsky and Arjun Bhatia, the brilliant scientists of this era, be able to solve this problem? Would ordinary people on a planet that is filled with corrupt leaders survive? The Reboot is a story of the humans of this planet and how they collaborate with a common purpose to save mother earth.
Mechanical Engineering domain problems are generally complex, consisting of different design variables and constraints. These problems may not be solved using gradient-based optimization techniques. The stochastic nature-inspired optimization techniques have been proposed in this book to efficiently handle the complex problems. The nature-inspired algorithms are classified as bio-inspired, swarm, and physics/chemical-based algorithms. Socio-inspired is one of the subdomains of bio-inspired algorithms, and Cohort Intelligence (CI) models the social tendencies of learning candidates with an inherent goal to achieve the best possible position. In this book, CI is investigated by solving ten discrete variable truss structural problems, eleven mixed variable design engineering problems, seventeen linear and nonlinear constrained test problems and two real-world applications from manufacturing domain. Static Penalty Function (SPF) is also adopted to handle the linear and nonlinear constraints, and limitations in CI and SPF approaches are examined. Constraint Handling in Cohort Intelligence Algorithm is a valuable reference to practitioners working in the industry as well as to students and researchers in the area of optimization methods.
This book studies the role of Artificial Intelligence (AI) in journalism. It traces the origin, growth and development of the media and communication industry in the globalized world and discusses the implications of technologies such as Augmented Reality, Virtual Reality and Extended Reality which have helped foster a communication revolution across the globe. The volume discusses technology-centric media theories in the context of AI and examines if AI has been a boon or bane for data journalism. It also looks at artificial intelligence in beat reporting, and citizen journalism, and analyses the social-cultural implications of artificial intelligence driven journalism and the ethical concerns arising from it. An important contribution, this book will be indispensable for students and researchers of media studies, communication studies, journalism, social media, technology studies, and digital humanities. It will also be useful for media professionals.
This book provides an emerging computational intelligence tool in the framework of collective intelligence for modeling and controlling distributed multi-agent systems referred to as Probability Collectives. In the modified Probability Collectives methodology a number of constraint handling techniques are incorporated, which also reduces the computational complexity and improved the convergence and efficiency. Numerous examples and real world problems are used for illustration, which may also allow the reader to gain further insight into the associated concepts.
This Volume discusses the underlying principles and analysis of the different concepts associated with an emerging socio-inspired optimization tool referred to as Cohort Intelligence (CI). CI algorithms have been coded in Matlab and are freely available from the link provided inside the book. The book demonstrates the ability of CI methodology for solving combinatorial problems such as Traveling Salesman Problem and Knapsack Problem in addition to real world applications from the healthcare, inventory, supply chain optimization and Cross-Border transportation. The inherent ability of handling constraints based on probability distribution is also revealed and proved using these problems.
This book discusses comprehensively the advanced manufacturing processes, including illustrative examples of the processes, mathematical modeling, and the need to optimize associated parameter problems. In addition, it describes in detail the cohort intelligence methodology and its variants along with illustrations, to help readers gain a better understanding of the framework. The theoretical and statistical rigor is validated by comparing the solutions with evolutionary algorithms, simulation annealing, response surface methodology, the firefly algorithm, and experimental work. Lastly, the book critically reviews several socio-inspired optimization methods.
This book explores the use of a socio-inspired optimization algorithm (the Cohort Intelligence algorithm), along with Cognitive Computing and a Multi-Random Start Local Search optimization algorithm. One of the most important types of media used for steganography is the JPEG image. Considering four important aspects of steganography techniques – picture quality, high data-hiding capacity, secret text security and computational time – the book provides extensive information on four novel image-based steganography approaches that employ JPEG compression. Academics, scientists and engineers engaged in research, development and application of steganography techniques, optimization and data analytics will find the book’s comprehensive coverage an invaluable resource.
Start with the basics of reinforcement learning and explore deep learning concepts such as deep Q-learning, deep recurrent Q-networks, and policy-based methods with this practical guide Key FeaturesUse TensorFlow to write reinforcement learning agents for performing challenging tasksLearn how to solve finite Markov decision problemsTrain models to understand popular video games like BreakoutBook Description Various intelligent applications such as video games, inventory management software, warehouse robots, and translation tools use reinforcement learning (RL) to make decisions and perform actions that maximize the probability of the desired outcome. This book will help you to get to grips with the techniques and the algorithms for implementing RL in your machine learning models. Starting with an introduction to RL, you’ll be guided through different RL environments and frameworks. You’ll learn how to implement your own custom environments and use OpenAI baselines to run RL algorithms. Once you’ve explored classic RL techniques such as Dynamic Programming, Monte Carlo, and TD Learning, you’ll understand when to apply the different deep learning methods in RL and advance to deep Q-learning. The book will even help you understand the different stages of machine-based problem-solving by using DARQN on a popular video game Breakout. Finally, you’ll find out when to use a policy-based method to tackle an RL problem. By the end of The Reinforcement Learning Workshop, you’ll be equipped with the knowledge and skills needed to solve challenging problems using reinforcement learning. What you will learnUse OpenAI Gym as a framework to implement RL environmentsFind out how to define and implement reward functionExplore Markov chain, Markov decision process, and the Bellman equationDistinguish between Dynamic Programming, Monte Carlo, and Temporal Difference LearningUnderstand the multi-armed bandit problem and explore various strategies to solve itBuild a deep Q model network for playing the video game BreakoutWho this book is for If you are a data scientist, machine learning enthusiast, or a Python developer who wants to learn basic to advanced deep reinforcement learning algorithms, this workshop is for you. A basic understanding of the Python language is necessary.
Mechanical Engineering domain problems are generally complex, consisting of different design variables and constraints. These problems may not be solved using gradient-based optimization techniques. The stochastic nature-inspired optimization techniques have been proposed in this book to efficiently handle the complex problems. The nature-inspired algorithms are classified as bio-inspired, swarm, and physics/chemical-based algorithms. Socio-inspired is one of the subdomains of bio-inspired algorithms, and Cohort Intelligence (CI) models the social tendencies of learning candidates with an inherent goal to achieve the best possible position. In this book, CI is investigated by solving ten discrete variable truss structural problems, eleven mixed variable design engineering problems, seventeen linear and nonlinear constrained test problems and two real-world applications from manufacturing domain. Static Penalty Function (SPF) is also adopted to handle the linear and nonlinear constraints, and limitations in CI and SPF approaches are examined. Constraint Handling in Cohort Intelligence Algorithm is a valuable reference to practitioners working in the industry as well as to students and researchers in the area of optimization methods.
This book presents the latest insights and developments in the field of socio-cultural inspired algorithms. Akin to evolutionary and swarm-based optimization algorithms, socio-cultural algorithms belong to the category of metaheuristics (problem-independent computational methods) and are inspired by natural and social tendencies observed in humans by which they learn from one another through social interactions. This book is an interesting read for engineers, scientists, and students studying/working in the optimization, evolutionary computation, artificial intelligence (AI) and computational intelligence fields.
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