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Author: Carlos Andre Reis Pinheiro Publisher: John Wiley & Sons ISBN: 1118347609 Category : Business & Economics Languages : en Pages : 256
Book Description
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: Carlos Andre Reis Pinheiro Publisher: John Wiley & Sons ISBN: 1118347609 Category : Business & Economics Languages : en Pages : 256
Book Description
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: Carlos Andre Reis Pinheiro Publisher: John Wiley & Sons ISBN: 1118416740 Category : Business & Economics Languages : en Pages : 256
Book Description
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 ISBN: 3319681192 Category : Business & Economics Languages : en Pages : 142
Book Description
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: Rafael Martí Publisher: Springer ISBN: 9783319071237 Category : Computers Languages : en Pages : 3000
Book Description
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: Anne Auger Publisher: World Scientific ISBN: 9814282669 Category : Computers Languages : en Pages : 370
Book Description
This volume covers both classical results and the most recent theoretical developments in the field of randomized search heuristics such as runtime analysis, drift analysis and convergence.
Author: Constantine Stephanidis Publisher: Springer ISBN: 3319075098 Category : Computers Languages : en Pages : 664
Book Description
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: Abbass, Hussein A. Publisher: IGI Global ISBN: 1591400112 Category : Computers Languages : en Pages : 310
Book Description
Real life problems are known to be messy, dynamic and multi-objective, and involve high levels of uncertainty and constraints. Because traditional problem-solving methods are no longer capable of handling this level of complexity, heuristic search methods have attracted increasing attention in recent years for solving such problems. Inspired by nature, biology, statistical mechanics, physics and neuroscience, heuristics techniques are used to solve many problems where traditional methods have failed. Data Mining: A Heuristic Approach will be a repository for the applications of these techniques in the area of data mining.
Author: Steven Orla Kimbrough Publisher: CRC Press ISBN: 1315362597 Category : Business & Economics Languages : en Pages : 308
Book Description
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: Ghaith Rabadi Publisher: Springer ISBN: 3319260243 Category : Business & Economics Languages : en Pages : 271
Book Description
The scope of this book is limited to heuristics, metaheuristics, and approximate methods and algorithms as applied to planning and scheduling problems. While it is not possible to give a comprehensive treatment of this topic in one book, the aim of this work is to provide the reader with a diverse set of planning and scheduling problems and different heuristic approaches to solve them. The problems range from traditional single stage and parallel machine problems to more modern settings such as robotic cells and flexible job shop networks. Furthermore, some chapters deal with deterministic problems while some others treat stochastic versions of the problems. Unlike most of the literature that deals with planning and scheduling problems in the manufacturing and production environments, in this book the environments were extended to nontraditional applications such as spatial scheduling (optimizing space over time), runway scheduling, and surgical scheduling. The solution methods used in the different chapters of the book also spread from well-established heuristics and metaheuristics such as Genetic Algorithms and Ant Colony Optimization to more recent ones such as Meta-RaPS.
Author: Sachi Nandan Mohanty Publisher: John Wiley & Sons ISBN: 1119785855 Category : Computers Languages : en Pages : 528
Book Description
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: Carlos Andre Reis Pinheiro
Author: Carlos Andre Reis Pinheiro
Author: Carlos Andre Reis Pinheiro
Author: Abraham Duarte
Author: Rafael Martí
Author: Anne Auger
Author: Constantine Stephanidis
Author: Abbass, Hussein A.
Author: Steven Orla Kimbrough
Author: Ghaith Rabadi
Author: Sachi Nandan Mohanty