Please use this identifier to cite or link to this item:
http://hdl.handle.net/10603/427556
Title: | Certain investigations on detection of autistic traits using machine learning with deep learning techniques |
Researcher: | Arunkumar, A |
Guide(s): | Surendran, D |
Keywords: | Engineering and Technology Computer Science Computer Science Information Systems Artificial Intelligence Paradigm Machine Learning |
University: | Anna University |
Completed Date: | 2022 |
Abstract: | Artificial Intelligence (AI) is used in several areas to help humans newlineand assist people in carrying day-to-day tasks and activities. Since its newlineinception from the year 1950 by Alan Turing, AI has transformed newlinetremendously with multiple facets in supporting and assisting peoples to carry newlineout tasks in several domains using intelligent approaches. AI refers to newlinebringing in intelligence into systems to observe real-world problems and newlineprovide better solutions. Recently AI made paradigm shift in the field of newlinehealthcare, transformed many aspects of activities carried in healthcare newlinedomain. Some of the key applications are diagnosis and treatment of diseases, newlineprognosis and recommendations, and supporting other administrative newlineprocesses. Machine Learning (ML) and Deep Learning (DL) are sub-fields of newlineAI. ML is where machines learn on their own without being explicitly newlineprogrammed. On other hand DL is sub-field of ML, inspired by the structure newlineof human brain involving several neurons to process any task. Both ML and newlineDL has vast applications in medical domain thereby improving the efficiency newlineand accuracy of diagnosis and treatment of several diseases. newlineAutism Spectrum Disorder (ASD) is a neurological disorder caused newlineby weak cognitive skills with other heterogeneous behavioral indications. newlineASD involves relentless challenges with social communication, lack of newlineinterests, and monotonous behavior. ASD has a trivial economic impact due newlineto the increased count in the ASD cases globally, time delay in diagnosis and newlineexpensive screening cost. Most research studies have proven that the newlinediagnosis of ASD at earlier stage can improve the physiological or social newlinecommunication of ASD patients. newline |
Pagination: | xvii,128p. |
URI: | http://hdl.handle.net/10603/427556 |
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 | 148.44 kB | Adobe PDF | View/Open |
02_prelim pages.pdf | 0 B | Adobe PDF | View/Open | |
03_content.pdf | 203.45 kB | Adobe PDF | View/Open | |
04_abstract.pdf | 184.81 kB | Adobe PDF | View/Open | |
05_chapter 1.pdf | 312.07 kB | Adobe PDF | View/Open | |
06_chapter 2.pdf | 1.74 MB | Adobe PDF | View/Open | |
07_chapter 3.pdf | 1.15 MB | Adobe PDF | View/Open | |
08_chapter 4.pdf | 1.52 MB | Adobe PDF | View/Open | |
09_chapter 5.pdf | 1.24 MB | Adobe PDF | View/Open | |
10_chapter 6.pdf | 200.3 kB | Adobe PDF | View/Open | |
11_annexures.pdf | 101.49 kB | Adobe PDF | View/Open | |
80_recommendation.pdf | 422.57 kB | Adobe PDF | View/Open |
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