Fuzzy Sets-Based Methods and Techniques for Modern Analytics, Ali Ebrahimnejad; Jos? Luis Verdegay
Автор: Ebrahimnejad Название: Fuzzy Sets-Based Methods and Techniques for Modern Analytics ISBN: 3319739026 ISBN-13(EAN): 9783319739021 Издательство: Springer Рейтинг: Цена: 13974.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The book offers a comprehensive, practice-oriented introduction to the field of fuzzy mathematical programming (FMP) as key topic of modern analytics.
Описание: This book includes the proceedings of the Intelligent and Fuzzy Techniques INFUS 2019 Conference, held in Istanbul, Turkey, on July 23–25, 2019. Big data analytics refers to the strategy of analyzing large volumes of data, or big data, gathered from a wide variety of sources, including social networks, videos, digital images, sensors, and sales transaction records. Big data analytics allows data scientists and various other users to evaluate large volumes of transaction data and other data sources that traditional business systems would be unable to tackle. Data-driven and knowledge-driven approaches and techniques have been widely used in intelligent decision-making, and they are increasingly attracting attention due to their importance and effectiveness in addressing uncertainty and incompleteness. INFUS 2019 focused on intelligent and fuzzy systems with applications in big data analytics and decision-making, providing an international forum that brought together those actively involved in areas of interest to data science and knowledge engineering. These proceeding feature about 150 peer-reviewed papers from countries such as China, Iran, Turkey, Malaysia, India, USA, Spain, France, Poland, Mexico, Bulgaria, Algeria, Pakistan, Australia, Lebanon, and Czech Republic.
Описание: Cambridge for DGB 2nd Edition is a four-level American English integrated skills series for the Upper Secondary public school market in Mexico. Its syllabus is strictly aligned to the national Direccion General del Bachillerato program. It is a series that offers teachers a hands-on and practical solution to teaching English in the classroom. It builds students` language skills from A1 to A2+ in the CEFR.
As data holdings get bigger and questions get harder, data scientists and analysts must focus on the systems, the tools and techniques, and the disciplined process to get the correct answer, quickly Whether you work within industry or government, this book will provide you with a foundation to successfully and confidently process large amounts of quantitative data.
Here are just a dozen of the many questions answered within these pages:
What does quantitative analysis of a system really mean?
What is a system?
What are big data and analystics?
How do you know your numbers are good?
What will the future data science environment look like?
How do you determine data provenance?
How do you gather and process information, and then organize, store, and synthesize it?
How does an organization implement data analytics?
Do you really need to think like a Chief Information Officer?
What is the best way to protect data?
What makes a good dashboard?
What is the relationship between eating ice cream and getting attacked by a shark?
The nine chapters in this book are arranged in three parts that address systems concepts in general, tools and techniques, and future trend topics. Systems concepts include contrasting open and closed systems, performing data mining and big data analysis, and gauging data quality. Tools and techniques include analyzing both continuous and discrete data, applying probability basics, and practicing quantitative analysis such as descriptive and inferential statistics. Future trends include leveraging the Internet of Everything, modeling Artificial Intelligence, and establishing a Data Analytics Support Office (DASO).
Many examples are included that were generated using common software, such as Excel, Minitab, Tableau, SAS, and Crystal Ball. While words are good, examples can sometimes be a better teaching tool. For each example included, data files can be found on the companion website. Many of the data sets are tied to the global economy because they use data from shipping ports, air freight hubs, largest cities, and soccer teams. The appendices contain more detailed analysis including the 10 T's for Data Mining, Million Row Data Audit (MRDA) Processes, Analysis of Rainfall, and Simulation Models for Evaluating Traffic Flow.
Автор: J. Joshua Thomas, Pinar Karagoz, B. Bazeer Ahamed, Pandian Vasant Название: Deep Learning Techniques and Optimization Strategies in Big Data Analytics ISBN: 179981193X ISBN-13(EAN): 9781799811930 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 27027.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Many approaches have sprouted from artificial intelligence (AI) and produced major breakthroughs in the computer science and engineering industries. Deep learning is a method that is transforming the world of data and analytics. Optimization of this new approach is still unclear, however, and there's a need for research on the various applications and techniques of deep learning in the field of computing. Deep Learning Techniques and Optimization Strategies in Big Data Analytics is a collection of innovative research on the methods and applications of deep learning strategies in the fields of computer science and information systems. While highlighting topics including data integration, computational modeling, and scheduling systems, this book is ideally designed for engineers, IT specialists, data analysts, data scientists, engineers, researchers, academicians, and students seeking current research on deep learning methods and its application in the digital industry.
Автор: J. Joshua Thomas, Pinar Karagoz, B. Bazeer Ahamed, Название: Deep Learning Techniques and Optimization Strategies in Big Data Analytics ISBN: 1799811921 ISBN-13(EAN): 9781799811923 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 35897.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Many approaches have sprouted from artificial intelligence (AI) and produced major breakthroughs in the computer science and engineering industries. Deep learning is a method that is transforming the world of data and analytics. Optimization of this new approach is still unclear, however, and there's a need for research on the various applications and techniques of deep learning in the field of computing. Deep Learning Techniques and Optimization Strategies in Big Data Analytics is a collection of innovative research on the methods and applications of deep learning strategies in the fields of computer science and information systems. While highlighting topics including data integration, computational modeling, and scheduling systems, this book is ideally designed for engineers, IT specialists, data analysts, data scientists, engineers, researchers, academicians, and students seeking current research on deep learning methods and its application in the digital industry.
Описание: Statistical Model Checking: Past, Present and Future.- Hypothesis testing for rare-event simulation: limitations and possibilities.- Survey of Statistical Verification of Linear Unbounded Properties: Model Checking and Distances.- Feedback Control for Statistical Model Checking of Cyber-Physical Systems.- Probabilistic Model Checking of Incomplete Models.- Plasma Lab: A Modular Statistical Model Checking Platform.- Synthesizing Energy-Optimal Controllers for Multiprocessor Dataflow Applications with UPPAAL STRATEGO.- Statistical Model Checking for Product Lines.- Towards Adaptive Scheduling of Maintenance for Cyber-Physical Systems.- Better railway engineering through statistical model checking.- On Creation and Analysis of Reliability Models by Means of Stochastic Timed Automata and Statistical Model Checking: Principle.- Automatic Synthesis of Code using Genetic Programming.- Evaluation and Reproducibility of Program Analysis and Verification (Track Introduction).- Symbolic Execution with CEGAR.- Multi-Core Model Checking of Large-Scale Reactive Systems Using Different State Representations.- Sparse Analysis of Variable Path Predicates Based Upon SSA-Form.- A Model Interpreter for Timed Automata.- ModSyn-PP: Modular Synthesis of Programs and Processes: Track Introduction.- Combinatory Process Synthesis.- Synthesis from a Practical Perspective.- A Long and Winding Road Towards Modular Synthesis.- Semantic heterogeneity in the formal development of complex systems: an introduction.- On the Use of Domain and System Knowledge Modeling in Goal-Based Event-B Specifications.- Strengthening MDE and Formal Design Models by references to Domain Ontologies. A Model Annotation Based Approach.- Towards Functional Requirements Analytics.- Heterogeneous Semantics and Unifying Theories.- Static and Runtime Verification: Competitors or Friends?.- StaRVOOrS - Episode II, Strengthen and Distribute the Force.- A Model-Based Approach to Combining Static and Dynamic Verification Techniques.- Information flow analysis for Go.- Challenges in High-Assurance Runtime Verification .- Static versus Dynamic Verification in Why3, Frama-C and SPARK 2014.- Considering Type-State Verification for Quantified Event Automata.- Combining Static and Runtime Methods to Achieve Safe Standing-Up for Humanoid Robots.- On Combinations of Static and Dynamic Analysis.- Safer Refactorings.- Rigorous Engineering of Collective Adaptive Systems.- Programming of CAS systems by relying on attribute-based communication.- Towards Static Analysis of Policy-Based Self-Adaptive Computing Systems.- A Calculus for Open Ensembles and Their Composition.- Logic Fragments: coordinating entities with logic programs.- Mixed-Critical Systems Design with Coarse-grained Multi-core Interference.- A Library and Scripting Language for Tool Independent Simulation Descriptions.- Adaptation to the unforeseen: Do we master our autonomous systems?'-- Questions to the Panel.- Smart coordination of autonomic component ensembles in the context of ad-hoc communication.- A Tool-chain for Statistical Spatio-Temporal Model Checking of Bike-sharing Systems.- Rigorous graphical modelling of movement in Collective Adaptive Systems.- Integration and Promotion of Autonomy with the ARE Framework.- Safe Artificial Intelligence and Formal Methods.- Engineering Adaptivity, Universal Autonomous Systems, Ethics and Compliance Issues.- Correctness-by-Construction and Post-hoc Verification: Friends or Foes?.- Correctness-by-Construction and Post-hoc Verification: A Marriage of Convenience?.- Deductive Verification of Legacy Code.- Correctness-by-Construction $\land$ Taxonomies $\Rightarrow$\\ Deep Comprehension of Algorithm Families.- Conditions for Compatibility of Components - The case of masters and slaves.- A Logic for Statistical Model Checking of Dynamic Software Architectures.- On two Friends for getting Correct Programs - Automatically Translating Event-B Specifications to Recursive Algorithms
Описание: The two-volume set LNCS 8802 and LNCS 8803 constitutes the refereed proceedings of the 6th International Symposium on Leveraging Applications of Formal Methods, Verification and Validation, ISoLA 2014, held in Imperial, Corfu, Greece, in October 2014.
Описание: Covers development in the field of automated deduction. This book focuses on the investigation of problems derived from realistic applications. It focuses on basic research in deduction and on the knowledge on which modern deductive systems are based. It presents techniques of implementation and details about system building.
Описание: Features the concepts and methods in automated deduction. This work focuses on basic research in deduction and on the knowledge on which modern deductive systems are based. It presents techniques of implementation and details about system building. It deals with applications of deductive techniques.
Описание: Presents the concepts and methods in automated deduction. This work focuses on basic research in deduction and on the knowledge on which modern deductive systems are based. It presents techniques of implementation and details about system building. It deals with applications of deductive techniques.
Описание: finite-difference, finite-element, and finite-volume methods), parallel methods, and adaptive methods as well as fast solvers, with particular focus on explaining the interactions of the different methods.
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