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Blind Procedures

Blind Procedures

Ideally, the people who observe and record the children’s behavior are unaware of who was assigned to the experimental or control group, in order to control for experimenter bias. Experimenter bias refers to the possibility that a researcher’s expectations might skew the results of the study. Remember, conducting an experiment requires a lot of planning, and the people involved in the research project have a vested interest in supporting their hypotheses. If the observers knew which...

Blinding

Blinding

Blinding is a commonly used method of not telling participants which treatment a subject is receiving. Blinding is a critical part of a randomized control trial or RCT. It reduces the bias that affects the results. In an RCT, blinding is used in the form of a placebo. A placebo effect occurs when untreated subjects falsely believe they have received the treatment and report improved symptoms. A placebo or a dummy treatment is administered to subjects to negate the bias caused by such an effect.

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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

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Big Data Blind Separation.

Mujahid N Syed¹

¹Department of Systems Engineering, King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia.

Entropy (Basel, Switzerland)

|December 3, 2020

Summary

This summary is machine-generated.

This study introduces a novel mathematical approach for non-negative data separation, enabling simultaneous validation of source assumptions and data separation. The method is efficient for big data analysis.

Keywords:

big data blind signal separation correntropy ranking locally dominant sources

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Area of Science:

Data Analysis
Signal Processing

Background:

Non-negative data separation is crucial in data analysis.
Existing methods often lack validation for sparse locally dominant source assumptions.
Current techniques typically extract mixing matrix elements sequentially.

Purpose of the Study:

To develop a mathematical modeling-based approach for non-negative data separation.
To introduce a method that simultaneously validates the locally dominant source assumption and separates data.
To propose a correntropy-based measure for reducing model size in big data separation.

Main Methods:

Mathematical modeling for simultaneous validation and separation.
Correntropy-based measure for model size reduction.
Application to non-negative matrix factorization problems.

Main Results:

A novel approach for non-negative data separation is presented.
The method effectively validates the locally dominant source assumption.
The approach is suitable for big data separation and demonstrated through numerical experiments.

Conclusions:

The proposed mathematical modeling approach offers simultaneous validation and separation for non-negative data.
Correntropy-based measure enhances efficiency for big data applications.
This work provides a validated and efficient solution for a critical data analysis problem.