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http://hdl.handle.net/10603/545081
Title: | An intelligence quotient estimator based on regression analysis |
Researcher: | Tamil Thendral M |
Guide(s): | Ganesh Babu T R |
Keywords: | Computer Science Computer Science Information Systems Engineering and Technology Image processing techniques Intelligence quotient Magnetic Resonance Imaging Regression analysis |
University: | Anna University |
Completed Date: | 2021 |
Abstract: | Intelligence is an abstract entity of the brain like the Mind. It is the most and essential entity for one to effectively operate in everyday life. It too think, learn from experience, solve problems, and adapt to new situations. It is an ability to solve difficulties. It sets one to have interpersonal skills. It is a mental quality that allows one to shape, or choose his/her environment. It is an ability to make decisions, grasp information, and reason. To measure intelligence, many types of tests do exist and intelligence quotient (IQ) is the common measure for intelligence. The most prevalent IQ tests include the Stanford-Binet Intelligence Scale, Universal Nonverbal Intelligence, Differential Ability Scales, Peabody Individual Achievement Test, Wechsler Individual Achievement Test, Wechsler Adult Intelligence Scale, Woodcock-Johnson III Tests of Cognitive Disabilities etc. For more than several decades, researchers started to explore the relationships between the concept of intelligence and brain regions. Due to this approach, image processing techniques are now become significant to identify brain regions that lead to intelligence from images. As a result, many kinds of approaches such as functional connectivity or morphometry etc. have been set so far. All these approaches engendered more computational complexity as they brought many subtle or fine brain regions into the research to measure intelligence. newline newline |
Pagination: | xviii, 143p. |
URI: | http://hdl.handle.net/10603/545081 |
Appears in Departments: | Faculty of Information and Communication Engineering |
Files in This Item:
File | Description | Size | Format | |
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01_title.pdf | Attached File | 357.25 kB | Adobe PDF | View/Open |
02_prelim pages.pdf | 2.64 MB | Adobe PDF | View/Open | |
03_content.pdf | 378.11 kB | Adobe PDF | View/Open | |
04_abstract.pdf | 221.05 kB | Adobe PDF | View/Open | |
05_chapter 1.pdf | 1.07 MB | Adobe PDF | View/Open | |
06_chapter 2.pdf | 609.23 kB | Adobe PDF | View/Open | |
07_chapter 3.pdf | 737.76 kB | Adobe PDF | View/Open | |
08_chapter 4.pdf | 1.24 MB | Adobe PDF | View/Open | |
09_chapter 5.pdf | 1.17 MB | Adobe PDF | View/Open | |
10_chapter 6.pdf | 1.71 MB | Adobe PDF | View/Open | |
11_annexures.pdf | 4.19 MB | Adobe PDF | View/Open | |
80_recommendation.pdf | 70.82 kB | Adobe PDF | View/Open |
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