| 000 | a | ||
|---|---|---|---|
| 999 |
_c10113 _d10113 |
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| 003 | OSt | ||
| 005 | 20191118095706.0 | ||
| 008 | 191118b xxu||||| |||| 00| 0 eng d | ||
| 040 |
_aAIKTC-KRRC _cAIKTC-KRRC |
||
| 100 |
_910528 _aKhullar, Vikas |
||
| 245 | _aPerformance Evaluation of Autism Diagnosis Tool at Diverse Learning Rates | ||
| 250 | _aVol.6(2), May-Aug | ||
| 260 |
_aNew Delhi _bSTM Journals _c2019 |
||
| 300 | _a92-99p. | ||
| 520 | _aArtificial Neural Networks (ANN) has been considered as a major artifact in the scenario of Machine Learning, which needs optimization for more accurate and efficient results. The main aim of this paper is to identify the optimization techniques and their learning rates for ANN with better results. In this paper, we have been utilized “Keras” library for framing ANN with different optimization techniques. The evaluation of the optimization techniques has conducted on the basis of accuracy and loss as parameters. On the basis of our comparative study, we have proposed the best ANN optimization technique out of our evaluated techniques for the diagnosis of available Autism Spectrum Disorder (ASD) dataset. | ||
| 650 | 0 |
_94622 _aComputer Engineering |
|
| 700 |
_910529 _aBala, Manju |
||
| 773 | 0 |
_dNoida STM Journals _tJournal of artificial intelligence research and advances (JoAIRA) |
|
| 856 |
_uhttp://computers.stmjournals.com/index.php?journal=JoAIRA&page=article&op=view&path%5B%5D=2091 _yClick here |
||
| 942 |
_2ddc _cAR |
||