Soft Methods for Data Science

Download Soft Methods for Data Science PDF Online Free

Author :
Release : 2016-08-30
Genre : Technology & Engineering
Kind :
Book Rating : 728/5 ( reviews)

Soft Methods for Data Science - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Soft Methods for Data Science write by Maria Brigida Ferraro. This book was released on 2016-08-30. Soft Methods for Data Science available in PDF, EPUB and Kindle. This proceedings volume is a collection of peer reviewed papers presented at the 8th International Conference on Soft Methods in Probability and Statistics (SMPS 2016) held in Rome (Italy). The book is dedicated to Data science which aims at developing automated methods to analyze massive amounts of data and to extract knowledge from them. It shows how Data science employs various programming techniques and methods of data wrangling, data visualization, machine learning, probability and statistics. The soft methods proposed in this volume represent a collection of tools in these fields that can also be useful for data science.

Advanced Soft Computing Techniques in Data Science, IoT and Cloud Computing

Download Advanced Soft Computing Techniques in Data Science, IoT and Cloud Computing PDF Online Free

Author :
Release : 2021-11-05
Genre : Technology & Engineering
Kind :
Book Rating : 572/5 ( reviews)

Advanced Soft Computing Techniques in Data Science, IoT and Cloud Computing - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Advanced Soft Computing Techniques in Data Science, IoT and Cloud Computing write by Sujata Dash. This book was released on 2021-11-05. Advanced Soft Computing Techniques in Data Science, IoT and Cloud Computing available in PDF, EPUB and Kindle. This book plays a significant role in improvising human life to a great extent. The new applications of soft computing can be regarded as an emerging field in computer science, automatic control engineering, medicine, biology application, natural environmental engineering, and pattern recognition. Now, the exemplar model for soft computing is human brain. The use of various techniques of soft computing is nowadays successfully implemented in many domestic, commercial, and industrial applications due to the low-cost and very high-performance digital processors and also the decline price of the memory chips. This is the main reason behind the wider expansion of soft computing techniques and its application areas. These computing methods also play a significant role in the design and optimization in diverse engineering disciplines. With the influence and the development of the Internet of things (IoT) concept, the need for using soft computing techniques has become more significant than ever. In general, soft computing methods are closely similar to biological processes than traditional techniques, which are mostly based on formal logical systems, such as sentential logic and predicate logic, or rely heavily on computer-aided numerical analysis. Soft computing techniques are anticipated to complement each other. The aim of these techniques is to accept imprecision, uncertainties, and approximations to get a rapid solution. However, recent advancements in representation soft computing algorithms (fuzzy logic,evolutionary computation, machine learning, and probabilistic reasoning) generate a more intelligent and robust system providing a human interpretable, low-cost, approximate solution. Soft computing-based algorithms have demonstrated great performance to a variety of areas including multimedia retrieval, fault tolerance, system modelling, network architecture, Web semantics, big data analytics, time series, biomedical and health informatics, etc. Soft computing approaches such as genetic programming (GP), support vector machine–firefly algorithm (SVM-FFA), artificial neural network (ANN), and support vector machine–wavelet (SVM–Wavelet) have emerged as powerful computational models. These have also shown significant success in dealing with massive data analysis for large number of applications. All the researchers and practitioners will be highly benefited those who are working in field of computer engineering, medicine, biology application, signal processing, and mechanical engineering. This book is a good collection of state-of-the-art approaches for soft computing-based applications to various engineering fields. It is very beneficial for the new researchers and practitioners working in the field to quickly know the best performing methods. They would be able to compare different approaches and can carry forward their research in the most important area of research which has direct impact on betterment of the human life and health. This book is very useful because there is no book in the market which provides a good collection of state-of-the-art methods of soft computing-based models for multimedia retrieval, fault tolerance, system modelling, network architecture, Web semantics, big data analytics, time series, and biomedical and health informatics.

Soft Methods in Probability, Statistics and Data Analysis

Download Soft Methods in Probability, Statistics and Data Analysis PDF Online Free

Author :
Release : 2013-12-11
Genre : Mathematics
Kind :
Book Rating : 732/5 ( reviews)

Soft Methods in Probability, Statistics and Data Analysis - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Soft Methods in Probability, Statistics and Data Analysis write by Przemyslaw Grzegorzewski. This book was released on 2013-12-11. Soft Methods in Probability, Statistics and Data Analysis available in PDF, EPUB and Kindle. Classical probability theory and mathematical statistics appear sometimes too rigid for real life problems, especially while dealing with vague data or imprecise requirements. These problems have motivated many researchers to "soften" the classical theory. Some "softening" approaches utilize concepts and techniques developed in theories such as fuzzy sets theory, rough sets, possibility theory, theory of belief functions and imprecise probabilities, etc. Since interesting mathematical models and methods have been proposed in the frameworks of various theories, this text brings together experts representing different approaches used in soft probability, statistics and data analysis.

Soft Computing in Data Science

Download Soft Computing in Data Science PDF Online Free

Author :
Release : 2021-10-28
Genre : Computers
Kind :
Book Rating : 349/5 ( reviews)

Soft Computing in Data Science - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Soft Computing in Data Science write by Azlinah Mohamed. This book was released on 2021-10-28. Soft Computing in Data Science available in PDF, EPUB and Kindle. 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.

Building Bridges between Soft and Statistical Methodologies for Data Science

Download Building Bridges between Soft and Statistical Methodologies for Data Science PDF Online Free

Author :
Release : 2022-08-24
Genre : Computers
Kind :
Book Rating : 092/5 ( reviews)

Building Bridges between Soft and Statistical Methodologies for Data Science - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Building Bridges between Soft and Statistical Methodologies for Data Science write by Luis A. García-Escudero. This book was released on 2022-08-24. Building Bridges between Soft and Statistical Methodologies for Data Science available in PDF, EPUB and Kindle. Nowadays, data analysis is becoming an appealing topic due to the emergence of new data types, dimensions, and sources. This motivates the development of probabilistic/statistical approaches and tools to cope with these data. Different communities of experts, namely statisticians, mathematicians, computer scientists, engineers, econometricians, and psychologists are more and more interested in facing this challenge. As a consequence, there is a clear need to build bridges between all these communities for Data Science. This book contains more than fifty selected recent contributions aiming to establish the above referred bridges. These contributions address very different and relevant aspects such as imprecise probabilities, information theory, random sets and random fuzzy sets, belief functions, possibility theory, dependence modelling and copulas, clustering, depth concepts, dimensionality reduction of complex data and robustness.