Noise Filtering for Big Data Analytics / / ed. by Souvik Bhattacharyya, Koushik Ghosh.

• Superior document: Title is part of eBook package: De Gruyter DG Plus DeG Package 2022 Part 1
• MitwirkendeR: Acharjee, Santanu,; Bhattacharyya, Souvik,; Chaudhuri, Dipta,; Dawud Adebayo, Agunbiade,; Ghosh, Koushik,; Indu, Pabak,; Khan, Samarpita,; Khondekar, Mofazzal H.,; Mukherjee, Moloy,; Nureni Olawale, Adeboye,; Paul, Rimi,; Purkait, Souvik,; Saha, Gokul,; Samadder, Swetadri,; Sengupta, Anindita,; Sharma, Vivek,; Singh, Vijai,
• HerausgeberIn: Bhattacharyya, Souvik,; Ghosh, Koushik,
• Place / Publishing House: Berlin ;, Boston : : De Gruyter, , [2022]; ©2022
• Year of Publication: 2022
• Language: English
• Series: De Gruyter Series on the Applications of Mathematics in Engineering and Information Sciences , 12
• Physical Description: 1 online resource (VIII, 156 p.)

This book explains how to perform data de-noising, in large scale, with a satisfactory level of accuracy. Three main issues are considered. Firstly, how to eliminate the error propagation from one stage to next stages while developing a filtered model. Secondly, how to maintain the positional import...

Table of Contents

• Frontmatter
• Preface
• Contents
• About the Editors
• Application of discrete domain wavelet filter for signal denoising
• Secret sharing scheme in defense and big data analytics
• Recent advances in digital image smoothing: A review
• Double exponential smoothing and its tuning parameters: A re-exploration
• Effect of smoothing on big data governed by polynomial memory
• Heteroskedasticity in panel data: A big challenge to data filtering
• Importance and use of digital filters in digital image processing
• Smart filter and smoothing: A new approach of data denoising
• Acknowledgement
• Index