International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | Hybrid Multimodal Autism Spectrum Disorder Screening Framework |
|---|---|
| ABSTRACT | Autism Spectrum Disorder must be detected at an early stage to provide help without any delay. A multimodal ap-proach combining facial image and behavioral data is proposed in this study for improved early detection of ASD. To obtain more effective results, this approach uses a combination of methods like EfficientNetB3, MultiLayer Peceptron (MLP), Hybrid Feature Fusion and XGBoost. This framework combines behavioral data with facial image data to retrieve informative patterns related to ASD and enhance classification accuracy. This approach has achieved an accuracy of 88.31 percent after training, which is higher than the other approaches which existed previously. A clear pattern is observed that combining multiple methods improves accuracy rather than using a single approach. It also indicates that the combination of diverse models improves the performance of early-stage ASD detection. |
| TITLE | |
| AUTHOR | SWATHI IDDUM, AKI SAI SURYA KAMAL TARUN, PAIDI LAHARI, NALLANA KAVYA SRI, VEMULA SAI PAVAN Department of Computer Science & Engineering – Data Science, Anil Neerukonda Institute of Technology and Sciences, Visakhapatnam, India |
| VOLUME | 187 |
| DOI | DOI: 10.15680/IJIRCCE.2026.1408007 |
| pdf/7_Hybrid Multimodal Autism Spectrum Disorder Screening.pdf | |
| KEYWORDS | |
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