The revised edition of this book offers an extended overview of quantum walks and explains their role in building quantum algorithms, in particular search algorithms. Updated throughout, the book focuses on core topics including Grover´s algorithm and the most important quantum walk models, such as the coined, continuous-time, and Szedgedy´s quantum walk models. There is a new chapter describing the staggered quantum walk model. The chapter on spatial search algorithms has been rewritten to offer a more comprehensive approach and a new chapter describing the element distinctness algorithm has been added. There is a new appendix on graph theory highlighting the importance of graph theory to quantum walks. As before, the reader will benefit from the pedagogical elements of the book, which include exercises and references to deepen the reader´s understanding, and guidelines for the use of computer programs to simulate the evolution of quantum walks. Review of the first edition: ´´The book is nicely written, the concepts are introduced naturally, and many meaningful connections between them are highlighted. The author proposes a series of exercises that help the reader get some working experience with the presented concepts, facilitating a better understanding. Each chapter ends with a discussion of further references, pointing the reader to major results on the topics presented in the respective chapter.´´ - Florin Manea, zbMATH.
With significant growth of bio-molecular sequence data in the last decade the need for algorithms to extract patterns and meaningful information from such data has been felt strongly. Alignment of sequences, in order to determine regions of common descent, has also been an important area of research as it helps scientist discover the evolution of species. Another problem that researchers are putting in a lot of effort into, is document summary. As the lower bound for computation is being met for various algorithms, to further expedite the computing on large data sets, parallelization has become imperative. New multiprocessor architectures like the Cell Broadband Engine have the potential to do extensive calculations and act as mini-supercomputers. Other applications for these include onboard aircraft fault diagnosis and prognosis. We take a peek into some existing algorithms for these problems as well as propose novel algorithms along with their implementations to address these problems in the field of bioinformatics.
This textbook explains the concepts and techniques required to write programs that can handle large amounts of data efficiently. Project-oriented and classroom-tested, the book presents a number of important algorithms supported by examples that bring meaning to the problems faced by computer programmers. The idea of computational complexity is also introduced, demonstrating what can and cannot be computed efficiently so that the programmer can make informed judgements about the algorithms they use. Features: includes both introductory and advanced data structures and algorithms topics, with suggested chapter sequences for those respective courses provided in the preface; provides learning goals, review questions and programming exercises in each chapter, as well as numerous illustrative examples; offers downloadable programs and supplementary files at an associated website, with instructor materials available from the author; presents a primer on Python for those from a different language background.
Continuing his exploration of the organization of complexity and the science of design, this new edition of Herbert Simon´s classic work on artificial intelligence adds a chapter that sorts out the current themes and tools--chaos, adaptive systems, genetic algorithms--for analyzing complexity and complex systems.
This book provides comprehensive coverage of the field of outlier analysis from a computer science point of view. It integrates methods from data mining, machine learning, and statistics within the computational framework and therefore appeals to multiple communities. The chapters of this book can be organized into three categories: Basic algorithms: Chapters 1 through 7 discuss the fundamental algorithms for outlier analysis, including probabilistic and statistical methods, linear methods, proximity-based methods, high-dimensional (subspace) methods, ensemble methods, and supervised methods. Domain-specific methods: Chapters 8 through 12 discuss outlier detection algorithms for various domains of data, such as text, categorical data, time-series data, discrete sequence data, spatial data, and network data. Applications: Chapter 13 is devoted to various applications of outlier analysis. Some guidance is also provided for the practitioner. The second edition of this book is more detailed and is written to appeal to both researchers and practitioners. Significant new material has been added on topics such as kernel methods, one-class support-vector machines, matrix factorization, neural networks, outlier ensembles, time-series methods, and subspace methods. It is written as a textbook and can be used for classroom teaching.
Machine learning methods majorly comprise image processing and soft computing methods and are mainly responsible for automation. Wheat production is influenced by assorted varying factors. A basic requisite for best production is the seed. Sorting or grading of agricultural products influenced by computer varies product wise and even product variety wise which changes region wise. Grading for new varieties released by the agricultural scientist is the major concerned as new varieties are produced by crossing existing varieties and for these proven optimized machine learning algorithms may give an adverse result. This book highlights various applications of image processing in agriculture and introduces a machine learning algorithm capable of classifying major 5 wheat cultivars (TRITICUM - DURUM: GDW 1255 (released in 2013 by ICAR) & TRITICUM - AESTVIUM: GW 273, GW 322, GW 496 & LOK 1) cultivated in Gujarat region. Experimental data consist of 11 traits comprised of shape, color & morphological characteristics. After applying feature selection algorithm,5 traits were considered & LM backpropagation was used to classify above wheat cultivars which ensued to more than 90.0% accuracy.
This book constitutes the refereed proceedings of the 13th CCF Conference on Computer Supported Cooperative Work and Social Computing, ChineseCSCW 2018, held in Guilin, China, in August 2018. The 33 revised full papers presented along with the 13 short papers were carefully reviewed and selected from 150 submissions. The papers of this volume are organized in topical sections on: collaborative models, approaches, algorithms, and systems, social computing, data analysis and machine learning for CSCW and social computing.
This book is a comprehensive introduction to the methods and algorithms of modern data analytics. It provides a sound mathematical basis, discusses advantages and drawbacks of different approaches, and enables the reader to design and implement data analytics solutions for real-world applications. This book has been used for more than ten years in the Data Mining course at the Technical University of Munich. Much of the content is based on the results of industrial research and development projects at Siemens.
Introducing the IBM SPSS Modeler, this book guides readers through data mining processes and presents relevant statistical methods. There is a special focus on step-by-step tutorials and well-documented examples that help demystify complex mathematical algorithms and computer programs. The variety of exercises and solutions as well as an accompanying website with data sets and SPSS Modeler streams are particularly valuable. While intended for students, the simplicity of the Modeler makes the book useful for anyone wishing to learn about basic and more advanced data mining, and put this knowledge into practice.
A decision procedure is an algorithm that, given a decision problem, terminates with a correct yes/no answer. Here, the authors focus on theories that are expressive enough to model real problems, but are still decidable. Specifically, the book concentrates on decision procedures for first-order theories that are commonly used in automated verification and reasoning, theorem-proving, compiler optimization and operations research. The techniques described in the book draw from fields such as graph theory and logic, and are routinely used in industry. The authors introduce the basic terminology of satisfiability modulo theories and then, in separate chapters, study decision procedures for each of the following theories: propositional logic; equalities and uninterpreted functions; linear arithmetic; bit vectors; arrays; pointer logic; and quantified formulas.