Image-based continental shelf habitat mapping using novel automated data extraction techniques

2012 ◽  
Vol 45 ◽  
pp. 87-97 ◽  
Author(s):  
Jan Seiler ◽  
Ariell Friedman ◽  
Daniel Steinberg ◽  
Neville Barrett ◽  
Alan Williams ◽  
...  
Keyword(s):  
Continental Shelf ◽  
Data Extraction ◽  
Habitat Mapping ◽  
Extraction Techniques

Data Extraction Techniques for Spreadsheet Records

2007 ◽  
Vol 2 (1) ◽  
pp. 119-129 ◽  
Author(s):  
Mark G. Simkin
Keyword(s):  
Data Extraction ◽  
Stratified Sampling ◽  
Specific Information ◽  
Extraction Techniques ◽  
Filtering Techniques

Abstract Many accounting applications use spreadsheets as repositories of accounting records, and a common requirement is the need to extract specific information from them. This paper describes a number of techniques that accountants can use to perform such tasks directly using common spreadsheet tools. These techniques include (1) simple and advanced filtering techniques, (2) database functions, (3) methods for both simple and stratified sampling, and, (4) tools for finding duplicate or unmatched records.

CCD Time-Series Photometry of Faint Astronomical Sources

1993 ◽  
Vol 136 ◽  
pp. 318-324 ◽  
Author(s):  
Steve B. Howell
Keyword(s):  
Time Series ◽  
Data Collection ◽  
Light Curve ◽  
Data Extraction ◽  
High Signal ◽  
Extraction Techniques ◽  
Signal To Noise ◽  
Noise Sources ◽  
New Methods ◽  
Area Of Interest

AbstractUsing CCDs to obtain time-series light curve information is an increasing area of interest in astronomy. For brighter, high signal-to-noise sources, the data collection and reduction procedures are very robust and easy to use. However, for fainter, low signal-to-noise objects we must resort to new methods. These include the use of optimum data extraction techniques, a fuller understanding of the CCD itself, and a more complete error model. This paper will provide a brief introduction to CCD time-series photometry and then explore the above new methods in relation to real observational situations.

Seabed habitat mapping employing single and multi-beam backscatter data: A case study from the western continental shelf of India

2012 ◽  
Vol 48 ◽  
pp. 40-49 ◽  
Author(s):  
K. Haris ◽  
Bishwajit Chakraborty ◽  
Baban Ingole ◽  
Andrew Menezes ◽  
Ratan Srivastava
Keyword(s):  
Continental Shelf ◽  
Habitat Mapping

scholarly journals Advances in Multi-Signal Spectrum-Image Acquisition and Data Extraction Techniques involving Spectral Cathodoluminescence

2006 ◽  
Vol 12 (S02) ◽  
pp. 1520-1521 ◽  
Author(s):  
SA Galloway ◽  
PJ Thomas
Keyword(s):  
Data Extraction ◽  
Image Acquisition ◽  
Signal Spectrum ◽  
Extraction Techniques

Extended abstract of a paper presented at Microscopy and Microanalysis 2006 in Chicago, Illinois, USA, July 30 – August 3, 2005

scholarly journals Habitat mapping and assessment in Atlantic Outer Continental Shelf wind energy areas

2017 ◽  
Author(s):  
V Kentner ◽  
V Guida ◽  
D Johnson ◽  
J Brink
Keyword(s):  
Wind Energy ◽  
Continental Shelf ◽  
Habitat Mapping ◽  
Outer Continental Shelf

scholarly journals Habitat mapping of the Maltese continental shelf using acoustic textures and bathymetric analyses

2018 ◽  
Vol 207 ◽  
pp. 483-498 ◽  
Author(s):  
Mariacristina Prampolini ◽  
Philippe Blondel ◽  
Federica Foglini ◽  
Fantina Madricardo
Keyword(s):  
Continental Shelf ◽  
Habitat Mapping

Web Harvesting

2017 ◽  
pp. 351-378 ◽  
Author(s):  
B. Umamageswari ◽  
R. Kalpana
Keyword(s):  
Web Mining ◽  
Data Extraction ◽  
Web Pages ◽  
Extraction Techniques ◽  
Web Data ◽  
Web Data Extraction ◽  
Region Extraction ◽  
Universal Applicability ◽  
Web Harvesting

Web mining is done on huge amounts of data extracted from WWW. Many researchers have developed several state-of-the-art approaches for web data extraction. So far in the literature, the focus is mainly on the techniques used for data region extraction. Applications which are fed with the extracted data, require fetching data spread across multiple web pages which should be crawled automatically. For this to happen, we need to extract not only data regions, but also the navigation links. Data extraction techniques are designed for specific HTML tags; which questions their universal applicability for carrying out information extraction from differently formatted web pages. This chapter focuses on various web data extraction techniques available for different kinds of data rich pages, classification of web data extraction techniques and comparison of those techniques across many useful dimensions.

Applications of Machine Learning in Steganography for Data Protection and Privacy

2021 ◽  
pp. 306-325
Author(s):  
Mahip M. Bartere ◽  
Sneha Bohra ◽  
Prashant Adakane ◽  
B. Santhosh Kumar
Keyword(s):  
Machine Learning ◽  
Data Hiding ◽  
Data Protection ◽  
Data Security ◽  
Data Extraction ◽  
Original Data ◽  
Extraction Techniques ◽  
Reverse Engineer ◽  
Applications Of Machine Learning

Data security is one of the most important aspects in today's scenario. Whenever we send our data from source to destination, data protection is one of the prime components. With the help of data hiding and data extraction techniques, we are able to provide the solution of different types of problems whenever we transfer our data. Steganography is a process where we can hide our data and maintain the quality of the image. At the same time, we think about data alteration. With the help of stegtanalysis method, we reverse engineer and extract the original data. In this chapter, data hiding and data extraction techniques are explained in the combination of machine learning architecture. The combination of steganography and steganalysis along with machine learning is used to identify protected data using different techniques.

Sign in / Sign up

Export Citation Format

Share Document