Work place: Center for Cyber Physical Systems, Vellore Institute of Technology, Chennai, 600127, India
E-mail: balasundaram.a@vit.ac.in
Website:
Research Interests: Artificial Intelligence
Biography
Balasudaram A. received the master’s degree in computer science and engineering from B. S. Abdur Rahman Crescent University, Chennai, India, and the Doctor of Philosophy (Ph.D.) degree in computer science and engineering from Anna University, India. He is currently an Associate Professor with the School of Computer Science and Engineering and is also associated with the Research Center for Cyber Physical Systems, Vellore Institute of Technology (VIT), Chennai Campus. He has an overall experience of 15 years of which he has over nine years of industrial experience working across MNCs like Cognizant Technology Solutions (CTS), Tata Consultancy Services (TCS), and iGATE Global Solutions, and five years of academic experience. His research interests include deep neural networks, computer vision, video analytics, image and video processing, artificial intelligence, data warehousing, data mining, healthcare intelligence, medical image analysis, and smart agriculture. He has received five best paper awards so far across international conferences. He has also received the Star Performer Award from Cognizant Technology Solutions and the Quality and Delivery Excellence Award from iGATE Global Solutions. He is also an active reviewer of reputed international SCIE journals of Elsevier, IEEE, and Springer. He has also served as the guest editor for special issues in a couple of SCI journals.
By Ayesha Shaik Lavish R. Jain Balasundaram A.
DOI: https://doi.org/10.5815/ijitcs.2025.05.04, Pub. Date: 8 Oct. 2025
This research work aims to utilize deep learning techniques to identify autism traits in children based on their facial features. By combining traditional convolutional approaches with attention layers, the study seeks to enhance interpretability and accuracy in identifying autism spectrum disorder (ASD) traits. The dataset includes diverse facial images of children diagnosed with ASD and neuro-typical children, ensuring comprehensive representation. Preprocessing techniques standardize and enhance image quality, mitigating biases. Integration of attention layers within the convolutional neural net-work (CNN) architecture focuses on crucial facial features, improving feature extraction and classification accuracy. This approach enhances model interpretability through eXplainable AI (XAI) techniques. Model training involves optimization and validation processes, employing hyper parameter tuning and cross-validation for robustness. The performance of this combined model yielded close to 95% accuracy outperforming existing models in terms of complexity to accuracy ratio.
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