[PDF] Heuristics In Analytics eBook

Author : Carlos Andre Reis Pinheiro
Publisher : John Wiley & Sons
Page : 256 pages
File Size : 41,64 MB
Release : 2014-01-31
Category : Business & Economics
ISBN : 1118416740

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Employ heuristic adjustments for truly accurate analysis Heuristics in Analytics presents an approach to analysis that accounts for the randomness of business and the competitive marketplace, creating a model that more accurately reflects the scenario at hand. With an emphasis on the importance of proper analytical tools, the book describes the analytical process from exploratory analysis through model developments, to deployments and possible outcomes. Beginning with an introduction to heuristic concepts, readers will find heuristics applied to statistics and probability, mathematics, stochastic, and artificial intelligence models, ending with the knowledge applications that solve business problems. Case studies illustrate the everyday application and implication of the techniques presented, while the heuristic approach is integrated into analytical modeling, graph analysis, text analytics, and more. Robust analytics has become crucial in the corporate environment, and randomness plays an enormous role in business and the competitive marketplace. Failing to account for randomness can steer a model in an entirely wrong direction, negatively affecting the final outcome and potentially devastating the bottom line. Heuristics in Analytics describes how the heuristic characteristics of analysis can be overcome with problem design, math and statistics, helping readers to: Realize just how random the world is, and how unplanned events can affect analysis Integrate heuristic and analytical approaches to modeling and problem solving Discover how graph analysis is applied in real-world scenarios around the globe Apply analytical knowledge to customer behavior, insolvency prevention, fraud detection, and more Understand how text analytics can be applied to increase the business knowledge Every single factor, no matter how large or how small, must be taken into account when modeling a scenario or event—even the unknowns. The presence or absence of even a single detail can dramatically alter eventual outcomes. From raw data to final report, Heuristics in Analytics contains the information analysts need to improve accuracy, and ultimately, predictive, and descriptive power.

Author : Abraham Duarte
Publisher : Springer
Page : 142 pages
File Size : 35,41 MB
Release : 2017-11-24
Category : Business & Economics
ISBN : 3319681192

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This essential metaheuristics tutorial provides descriptions and practical applications in the area of business analytics. It addresses key problems in predictive and prescriptive analysis, while also illustrating how problems that arise in business analytics can be modelled and how metaheuristics can be used to find high-quality solutions. Readers will be introduced to decision-making problems for which metaheuristics offer the most effective solution technique. The book not only shows business problem modelling on a spreadsheet but also how to design and create a Visual Basic for Applications code. Extra Material can be downloaded at http://extras.springer.com/978-3-319-68117-7.

Author : Constantine Stephanidis
Publisher : Springer
Page : 664 pages
File Size : 37,52 MB
Release : 2014-05-16
Category : Computers
ISBN : 3319075098

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The four-volume set LNCS 8513-8516 constitutes the refereed proceedings of the 8th International Conference on Universal Access in Human-Computer Interaction, UAHCI 2014, held as part of the 16th International Conference on Human-Computer Interaction, HCII 2014, held in Heraklion, Crete, Greece in June 2014, jointly with 14 other thematically similar conferences. The total of 1476 papers and 220 posters presented at the HCII 2014 conferences was carefully reviewed and selected from 4766 submissions. These papers address the latest research and development efforts and highlight the human aspects of design and use of computing systems. The papers thoroughly cover the entire field of human-computer interaction, addressing major advances in knowledge and effective use of computers in a variety of application areas. The total of 251 contributions included in the UAHCI proceedings were carefully reviewed and selected for inclusion in this four-volume set. The 60 papers included in this volume are organized in the following topical sections: web accessibility; design for all in the built environment; global access infrastructures and user experiences in universal access.

Author : Rafael Martí
Publisher : Springer
Page : 3000 pages
File Size : 41,61 MB
Release : 2017-01-16
Category : Computers
ISBN : 9783319071237

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Heuristics are strategies using readily accessible, loosely applicable information to control problem solving. Algorithms, for example, are a type of heuristic. By contrast, Metaheuristics are methods used to design Heuristics and may coordinate the usage of several Heuristics toward the formulation of a single method. GRASP (Greedy Randomized Adaptive Search Procedures) is an example of a Metaheuristic. To the layman, heuristics may be thought of as ‘rules of thumb’ but despite its imprecision, heuristics is a very rich field that refers to experience-based techniques for problem-solving, learning, and discovery. Any given solution/heuristic is not guaranteed to be optimal but heuristic methodologies are used to speed up the process of finding satisfactory solutions where optimal solutions are impractical. The introduction to this Handbook provides an overview of the history of Heuristics along with main issues regarding the methodologies covered. This is followed by Chapters containing various examples of local searches, search strategies and Metaheuristics, leading to an analyses of Heuristics and search algorithms. The reference concludes with numerous illustrations of the highly applicable nature and implementation of Heuristics in our daily life. Each chapter of this work includes an abstract/introduction with a short description of the methodology. Key words are also necessary as part of top-matter to each chapter to enable maximum search engine optimization. Next, chapters will include discussion of the adaptation of this methodology to solve a difficult optimization problem, and experiments on a set of representative problems.

Author : Sachi Nandan Mohanty
Publisher : John Wiley & Sons
Page : 528 pages
File Size : 28,56 MB
Release : 2021-07-14
Category : Computers
ISBN : 1119785855

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Machine Learning Approach for Cloud Data Analytics in IoT The book covers the multidimensional perspective of machine learning through the perspective of cloud computing and Internet of Things ranging from fundamentals to advanced applications Sustainable computing paradigms like cloud and fog are capable of handling issues related to performance, storage and processing, maintenance, security, efficiency, integration, cost, energy and latency in an expeditious manner. In order to expedite decision-making involved in the complex computation and processing of collected data, IoT devices are connected to the cloud or fog environment. Since machine learning as a service provides the best support in business intelligence, organizations have been making significant investments in this technology. Machine Learning Approach for Cloud Data Analytics in IoT elucidates some of the best practices and their respective outcomes in cloud and fog computing environments. It focuses on all the various research issues related to big data storage and analysis, large-scale data processing, knowledge discovery and knowledge management, computational intelligence, data security and privacy, data representation and visualization, and data analytics. The featured technologies presented in the book optimizes various industry processes using business intelligence in engineering and technology. Light is also shed on cloud-based embedded software development practices to integrate complex machines so as to increase productivity and reduce operational costs. The various practices of data science and analytics which are used in all sectors to understand big data and analyze massive data patterns are also detailed in the book.

Author : C.S.R. Prabhu
Publisher : Springer Nature
Page : 412 pages
File Size : 48,24 MB
Release : 2019-10-14
Category : Computers
ISBN : 9811500940

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This book provides a comprehensive survey of techniques, technologies and applications of Big Data and its analysis. The Big Data phenomenon is increasingly impacting all sectors of business and industry, producing an emerging new information ecosystem. On the applications front, the book offers detailed descriptions of various application areas for Big Data Analytics in the important domains of Social Semantic Web Mining, Banking and Financial Services, Capital Markets, Insurance, Advertisement, Recommendation Systems, Bio-Informatics, the IoT and Fog Computing, before delving into issues of security and privacy. With regard to machine learning techniques, the book presents all the standard algorithms for learning – including supervised, semi-supervised and unsupervised techniques such as clustering and reinforcement learning techniques to perform collective Deep Learning. Multi-layered and nonlinear learning for Big Data are also covered. In turn, the book highlights real-life case studies on successful implementations of Big Data Analytics at large IT companies such as Google, Facebook, LinkedIn and Microsoft. Multi-sectorial case studies on domain-based companies such as Deutsche Bank, the power provider Opower, Delta Airlines and a Chinese City Transportation application represent a valuable addition. Given its comprehensive coverage of Big Data Analytics, the book offers a unique resource for undergraduate and graduate students, researchers, educators and IT professionals alike.

Author : Steven Orla Kimbrough
Publisher : CRC Press
Page : 308 pages
File Size : 29,28 MB
Release : 2018-09-03
Category : Business & Economics
ISBN : 1315362597

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Business Analytics for Decision Making, the first complete text suitable for use in introductory Business Analytics courses, establishes a national syllabus for an emerging first course at an MBA or upper undergraduate level. This timely text is mainly about model analytics, particularly analytics for constrained optimization. It uses implementations that allow students to explore models and data for the sake of discovery, understanding, and decision making. Business analytics is about using data and models to solve various kinds of decision problems. There are three aspects for those who want to make the most of their analytics: encoding, solution design, and post-solution analysis. This textbook addresses all three. Emphasizing the use of constrained optimization models for decision making, the book concentrates on post-solution analysis of models. The text focuses on computationally challenging problems that commonly arise in business environments. Unique among business analytics texts, it emphasizes using heuristics for solving difficult optimization problems important in business practice by making best use of methods from Computer Science and Operations Research. Furthermore, case studies and examples illustrate the real-world applications of these methods. The authors supply examples in Excel®, GAMS, MATLAB®, and OPL. The metaheuristics code is also made available at the book's website in a documented library of Python modules, along with data and material for homework exercises. From the beginning, the authors emphasize analytics and de-emphasize representation and encoding so students will have plenty to sink their teeth into regardless of their computer programming experience.

Author : Jürgen Deters
Publisher : Walter de Gruyter GmbH & Co KG
Page : 606 pages
File Size : 47,69 MB
Release : 2022-11-07
Category : Business & Economics
ISBN : 3110980967

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Human decisions, especially in management and personnel selection, are based on making judgments about people analytically and intuitively. Yet in business and scientific contexts, judgments are expected to be based on a rational analysis rather than intuitions or emotions. Intuition is often seen as something mystical that should not be trusted and thus eliminated from human decision-making. Our empirical and theoretical research shows that this is impossible when people are dealing with people. Instead, intuitions and emotions have significant power in the decision-making process. Neuroscience even shows that humans are incapable of switching off their emotions or intuitions when making decisions. Therefore, intuition and emotions as evolutionary achievements of human beings should be looked at more closely to use the wisdom they offer. This book provides an insight into the current state of research on rational-analytical procedures in personnel selection and complements this with research on intuitions and emotions in personnel diagnostics. By integrating scientifically verifiable rational-analytical decision-making procedures with the inner experiential knowledge of people, this book bridges two complementary ways of recognizing and making good decisions. It demonstrates how intuitions are developed and used in different fields of practice and cultures and how scientific research results from rational-analytical and intuitive-emotional selection procedures are successfully integrated by practitioners.