Consecrate Recurrent Neural Network Classifier for Autism Classification
S. Padmapriya1, S. Murugan2
1S. Padmapriya*, Assistant Professor, Department of Computer Science, SRM Trichy Arts & Science College, Trichy.
2S. Murugan, Associate Professor, Department of Computer Science, Nehru Memorial College, Puthanampatti.
Manuscript received on September 22, 2019. | Revised Manuscript received on October 20, 2019. | Manuscript published on October 30, 2019. | PP: 2033-2041 | Volume-9 Issue-1, October 2019 | Retrieval Number: A9550109119/2019©BEIESP | DOI: 10.35940/ijeat.A9550.109119
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Most recent discoveries in Autism Spectrum Disorder (ASD) detection and classification studies reveal that there is a substantial relationship between Autism disorders and gene sequences. This work is indented to classify the autism spectrum disorder groups and sub-groups based on the gene sequences. The gene sequences are large data and perplexed for handling with conventional data mining or classification procedures. The Consecrate Recurrent Neural Network Classifier for Autism Classification (CRNNC-AC) work is introduced in this work to classify autism disorders using gene sequence data. A dedicated Elman  type Recurrent Neural Network (RNN) is introduced along with a legacy Long Short-Term Memory (LSTM)  in this classifier. The LSTM model is contrived to achieve memory optimization to eliminate memory overflows without affecting the classification accuracy. The classification quality metrics  such as Accuracy, Sensitivity, Specificity and F1-Score are concerned for optimization. The processing time of the proposed method is also measured to evaluate the pertinency.
Keywords: Autism Spectrum Disorder classification, Gene sequence-based autism disorder detection, Recurrent Neural Network., Elman Network, Long Short-Term Memory.