Computers

Soft Computing in Data Analytics

Author: Janmenjoy Nayak

Publisher: Springer

ISBN:

Category: Computers

Page: 859

View: 235

The volume contains original research findings, exchange of ideas and dissemination of innovative, practical development experiences in different fields of soft and advance computing. It provides insights into the International Conference on Soft Computing in Data Analytics (SCDA). It also concentrates on both theory and practices from around the world in all the areas of related disciplines of soft computing. The book provides rapid dissemination of important results in soft computing technologies, a fusion of research in fuzzy logic, evolutionary computations, neural science and neural network systems and chaos theory and chaotic systems, swarm based algorithms, etc. The book aims to cater the postgraduate students and researchers working in the discipline of computer science and engineering along with other engineering branches.
Computers

Soft Computing in Data Science

Author: Michael W. Berry

Publisher: Springer Nature

ISBN:

Category: Computers

Page: 388

View: 428

This book constitutes the refereed proceedings of the 5th International Conference on Soft Computing in Data Science, SCDS 2019, held in Iizuka, Japan, in August 2019. The 30 revised full papers presented were carefully reviewed and selected from 75 submissions. The papers are organized in topical sections on ​information and customer analytics; visual data science; machine and deep learning; big data analytics; computational and artificial intelligence; social network and media analytics.
Computers

Combining Soft Computing and Statistical Methods in Data Analysis

Author: Christian Borgelt

Publisher: Springer

ISBN:

Category: Computers

Page: 644

View: 157

Over the last forty years there has been a growing interest to extend probability theory and statistics and to allow for more flexible modelling of imprecision, uncertainty, vagueness and ignorance. The fact that in many real-life situations data uncertainty is not only present in the form of randomness (stochastic uncertainty) but also in the form of imprecision/fuzziness is but one point underlining the need for a widening of statistical tools. Most such extensions originate in a "softening" of classical methods, allowing, in particular, to work with imprecise or vague data, considering imprecise or generalized probabilities and fuzzy events, etc. About ten years ago the idea of establishing a recurrent forum for discussing new trends in the before-mentioned context was born and resulted in the first International Conference on Soft Methods in Probability and Statistics (SMPS) that was held in Warsaw in 2002. In the following years the conference took place in Oviedo (2004), in Bristol (2006) and in Toulouse (2008). In the current edition the conference returns to Oviedo. This edited volume is a collection of papers presented at the SMPS 2010 conference held in Mieres and Oviedo. It gives a comprehensive overview of current research into the fusion of soft methods with probability and statistics.
Technology & Engineering

Soft Computing for Data Analytics, Classification Model, and Control

Author: Deepak Gupta

Publisher: Springer

ISBN:

Category: Technology & Engineering

Page: 380

View: 251

This book presents a set of soft computing approaches and their application in data analytics, classification model, and control. The basics of fuzzy logic implementation for advanced hybrid fuzzy driven optimization methods has been covered in the book. The various soft computing techniques, including Fuzzy Logic, Rough Sets, Neutrosophic Sets, Type-2 Fuzzy logic, Neural Networks, Generative Adversarial Networks, and Evolutionary Computation have been discussed and they are used on variety of applications including data analytics, classification model, and control. The book is divided into two thematic parts. The first thematic section covers the various soft computing approaches for text classification and data analysis, while the second section focuses on the fuzzy driven optimization methods for the control systems. The chapters has been written and edited by active researchers, which cover hypotheses and practical considerations; provide insights into the design of hybrid algorithms for applications in data analytics, classification model, and engineering control.
Computers

Synergies of Soft Computing and Statistics for Intelligent Data Analysis

Author: Rudolf Kruse

Publisher: Springer Science & Business Media

ISBN:

Category: Computers

Page: 584

View: 871

In recent years there has been a growing interest to extend classical methods for data analysis. The aim is to allow a more flexible modeling of phenomena such as uncertainty, imprecision or ignorance. Such extensions of classical probability theory and statistics are useful in many real-life situations, since uncertainties in data are not only present in the form of randomness --- various types of incomplete or subjective information have to be handled. About twelve years ago the idea of strengthening the dialogue between the various research communities in the field of data analysis was born and resulted in the International Conference Series on Soft Methods in Probability and Statistics (SMPS). This book gathers contributions presented at the SMPS'2012 held in Konstanz, Germany. Its aim is to present recent results illustrating new trends in intelligent data analysis. It gives a comprehensive overview of current research into the fusion of soft computing methods with probability and statistics. Synergies of both fields might improve intelligent data analysis methods in terms of robustness to noise and applicability to larger datasets, while being able to efficiently obtain understandable solutions of real-world problems.
Computers

Soft Computing in Data Science

Author: Azlinah Mohamed

Publisher: Springer

ISBN:

Category: Computers

Page: 440

View: 898

This book constitutes the refereed proceedings of the 6th International Conference on Soft Computing in Data Science, SCDS 2021, which was held virtually in November 2021. The 31 revised full papers presented were carefully reviewed and selected from 79 submissions. The papers are organized in topical sections on ​​AI techniques and applications; data analytics and technologies; data mining and image processing; machine & statistical learning.
Technology & Engineering

Recent Trends in Data Science and Soft Computing

Author: Faisal Saeed

Publisher: Springer

ISBN:

Category: Technology & Engineering

Page: 1126

View: 509

This book presents the proceedings of the 3rd International Conference of Reliable Information and Communication Technology 2018 (IRICT 2018), which was held in Kuala Lumpur, Malaysia, on July 23–24, 2018. The main theme of the conference was “Data Science, AI and IoT Trends for the Fourth Industrial Revolution.” A total of 158 papers were submitted to the conference, of which 103 were accepted and considered for publication in this book. Several hot research topics are covered, including Advances in Data Science and Big Data Analytics, Artificial Intelligence and Soft Computing, Business Intelligence, Internet of Things (IoT) Technologies and Applications, Intelligent Communication Systems, Advances in Computer Vision, Health Informatics, Reliable Cloud Computing Environments, Recent Trends in Knowledge Management, Security Issues in the Cyber World, and Advances in Information Systems Research, Theories and Methods.
Technology & Engineering

Towards Advanced Data Analysis by Combining Soft Computing and Statistics

Author: Christian Borgelt

Publisher: Springer

ISBN:

Category: Technology & Engineering

Page: 378

View: 183

Soft computing, as an engineering science, and statistics, as a classical branch of mathematics, emphasize different aspects of data analysis. Soft computing focuses on obtaining working solutions quickly, accepting approximations and unconventional approaches. Its strength lies in its flexibility to create models that suit the needs arising in applications. In addition, it emphasizes the need for intuitive and interpretable models, which are tolerant to imprecision and uncertainty. Statistics is more rigorous and focuses on establishing objective conclusions based on experimental data by analyzing the possible situations and their (relative) likelihood. It emphasizes the need for mathematical methods and tools to assess solutions and guarantee performance. Combining the two fields enhances the robustness and generalizability of data analysis methods, while preserving the flexibility to solve real-world problems efficiently and intuitively.
Technology & Engineering

Soft Computing in Big Data Processing

Author: Keon Myung Lee

Publisher: Springer

ISBN:

Category: Technology & Engineering

Page: 111

View: 198

Big data is an essential key to build a smart world as a meaning of the streaming, continuous integration of large volume and high velocity data covering from all sources to final destinations. The big data range from data mining, data analysis and decision making, by drawing statistical rules and mathematical patterns through systematical or automatically reasoning. The big data helps serve our life better, clarify our future and deliver greater value. We can discover how to capture and analyze data. Readers will be guided to processing system integrity and implementing intelligent systems. With intelligent systems, we deal with the fundamental data management and visualization challenges in effective management of dynamic and large-scale data, and efficient processing of real-time and spatio-temporal data. Advanced intelligent systems have led to managing the data monitoring, data processing and decision-making in realistic and effective way. Considering a big size of data, variety of data and frequent changes of data, the intelligent systems basically challenge new data management tasks for integration, visualization, querying and analysis. Connected with powerful data analysis, the intelligent systems will provide a paradigm shift from conventional store and process systems. This book focuses on taking a full advantage of big data and intelligent systems processing. It consists of 11 contributions that feature extraction of minority opinion, method for reusing an application, assessment of scientific and innovative projects, multi-voxel pattern analysis, exploiting No-SQL DB, materialized view, TF-IDF criterion, latent Dirichlet allocation, technology forecasting, small world network, and classification & regression tree structure. This edition is published in original, peer reviewed contributions covering from initial design to final prototypes and authorization.
Business & Economics

Intelligent Systems

Author: Chiranji Lal Chowdhary

Publisher: CRC Press

ISBN:

Category: Business & Economics

Page: 294

View: 557

This volume helps to fill the gap between data analytics, image processing, and soft computing practices. Soft computing methods are used to focus on data analytics and image processing to develop good intelligent systems. To this end, readers of this volume will find quality research that presents the current trends, advanced methods, and hybridized techniques relating to data analytics and intelligent systems. The book also features case studies related to medical diagnosis with the use of image processing and soft computing algorithms in particular models. Providing extensive coverage of biometric systems, soft computing, image processing, artificial intelligence, and data analytics, the chapter authors discuss the latest research issues, present solutions to research problems, and look at comparative analysis with earlier results. Topics include some of the most important challenges and discoveries in intelligent systems today, such as computer vision concepts and image identification, data analysis and computational paradigms, deep learning techniques, face and speaker recognition systems, and more.
Computers

Soft Computing in Industrial Applications

Author: Ashraf Saad

Publisher: Springer Science & Business Media

ISBN:

Category: Computers

Page: 330

View: 882

Here is a collection of papers presented at the 11th On-line World Conference on Soft Computing in Industrial Applications, held in September-October 2006. This carefully edited book provides a comprehensive overview of recent advances in the industrial applications of soft computing and covers a wide range of application areas, including data analysis and data mining, computer graphics, intelligent control, systems, pattern recognition, classifiers, as well as modeling optimization.
Data mining

Soft Computing in Data Science

Author:

Publisher:

ISBN:

Category: Data mining

Page: 388

View: 872

This book constitutes the refereed proceedings of the 5th International Conference on Soft Computing in Data Science, SCDS 2019, held in Iizuka, Japan, in August 2019. The 30 revised full papers presented were carefully reviewed and selected from 75 submissions. The papers are organized in topical sections on information and customer analytics; visual data science; machine and deep learning; big data analytics; computational and artificial intelligence; social network and media analytics.
Technology & Engineering

Recent Advances on Soft Computing and Data Mining

Author: Tutut Herawan

Publisher: Springer

ISBN:

Category: Technology & Engineering

Page: 649

View: 421

This book provides a comprehensive introduction and practical look at the concepts and techniques readers need to get the most out of their data in real-world, large-scale data mining projects. It also guides readers through the data-analytic thinking necessary for extracting useful knowledge and business value from the data. The book is based on the Soft Computing and Data Mining (SCDM-16) conference, which was held in Bandung, Indonesia on August 18th–20th 2016 to discuss the state of the art in soft computing techniques, and offer participants sufficient knowledge to tackle a wide range of complex systems. The scope of the conference is reflected in the book, which presents a balance of soft computing techniques and data mining approaches. The two constituents are introduced to the reader systematically and brought together using different combinations of applications and practices. It offers engineers, data analysts, practitioners, scientists and managers the insights into the concepts, tools and techniques employed, and as such enables them to better understand the design choice and options of soft computing techniques and data mining approaches that are necessary to thrive in this data-driven ecosystem.
Technology & Engineering

Strengthening Links Between Data Analysis and Soft Computing

Author: Przemyslaw Grzegorzewski

Publisher: Springer

ISBN:

Category: Technology & Engineering

Page: 294

View: 888

This book gathers contributions presented at the 7th International Conference on Soft Methods in Probability and Statistics SMPS 2014, held in Warsaw (Poland) on September 22-24, 2014. Its aim is to present recent results illustrating new trends in intelligent data analysis. It gives a comprehensive overview of current research into the fusion of soft computing methods with probability and statistics. Synergies of both fields might improve intelligent data analysis methods in terms of robustness to noise and applicability to larger datasets, while being able to efficiently obtain understandable solutions of real-world problems.
Computers

Soft Computing in Data Science

Author: Michael W. Berry

Publisher: Springer

ISBN:

Category: Computers

Page: 311

View: 588

This book constitutes the refereed proceedings of the International Conference on Soft Computing in Data Science, SCDS 2016, held in Putrajaya, Malaysia, in September 2016. The 27 revised full papers presented were carefully reviewed and selected from 66 submissions. The papers are organized in topical sections on artificial neural networks; classification, clustering, visualization; fuzzy logic; information and sentiment analytics.
Computers

Rough Set Theory: A True Landmark in Data Analysis

Author: Ajith Abraham

Publisher: Springer

ISBN:

Category: Computers

Page: 324

View: 997

Along the years, rough set theory has earned a well-deserved reputation as a sound methodology for dealing with imperfect knowledge in a simple though mathematically sound way. This edited volume aims at continue stressing the benefits of applying rough sets in many real-life situations while still keeping an eye on topological aspects of the theory as well as strengthening its linkage with other soft computing paradigms. The volume comprises 11 chapters and is organized into three parts. Part 1 deals with theoretical contributions while Parts 2 and 3 focus on several real world data mining applications. Chapters authored by pioneers were selected on the basis of fundamental ideas/concepts rather than the thoroughness of techniques deployed. Academics, scientists as well as engineers working in the rough set, computational intelligence, soft computing and data mining research area will find the comprehensive coverage of this book invaluable.
Technology & Engineering

Combining Soft Computing and Statistical Methods in Data Analysis

Author: Christian Borgelt

Publisher: Springer Science & Business Media

ISBN:

Category: Technology & Engineering

Page: 644

View: 228

Over the last forty years there has been a growing interest to extend probability theory and statistics and to allow for more flexible modelling of imprecision, uncertainty, vagueness and ignorance. The fact that in many real-life situations data uncertainty is not only present in the form of randomness (stochastic uncertainty) but also in the form of imprecision/fuzziness is but one point underlining the need for a widening of statistical tools. Most such extensions originate in a "softening" of classical methods, allowing, in particular, to work with imprecise or vague data, considering imprecise or generalized probabilities and fuzzy events, etc. About ten years ago the idea of establishing a recurrent forum for discussing new trends in the before-mentioned context was born and resulted in the first International Conference on Soft Methods in Probability and Statistics (SMPS) that was held in Warsaw in 2002. In the following years the conference took place in Oviedo (2004), in Bristol (2006) and in Toulouse (2008). In the current edition the conference returns to Oviedo. This edited volume is a collection of papers presented at the SMPS 2010 conference held in Mieres and Oviedo. It gives a comprehensive overview of current research into the fusion of soft methods with probability and statistics.
Science

Big Data Analytics for Intelligent Healthcare Management

Author: Nilanjan Dey

Publisher: Academic Press

ISBN:

Category: Science

Page: 312

View: 765

Big Data Analytics for Intelligent Healthcare Management covers both the theory and application of hardware platforms and architectures, the development of software methods, techniques and tools, applications and governance, and adoption strategies for the use of big data in healthcare and clinical research. The book provides the latest research findings on the use of big data analytics with statistical and machine learning techniques that analyze huge amounts of real-time healthcare data. Examines the methodology and requirements for development of big data architecture, big data modeling, big data as a service, big data analytics, and more Discusses big data applications for intelligent healthcare management, such as revenue management and pricing, predictive analytics/forecasting, big data integration for medical data, algorithms and techniques, etc. Covers the development of big data tools, such as data, web and text mining, data mining, optimization, machine learning, cloud in big data with Hadoop, big data in IoT, and more