0009-0006-4712-6622 About
Priyanga S is a graduate in Computer Science and Engineering at Amrita Vishwa Vidyapeetham, India. Her research focuses on artificial intelligence, deep learning, and computer vision, with particular emphasis on healthcare-oriented applications. Her work spans medical image analysis and intelligent predictive systems, including deep learning–based models for autism spectrum disorder detection and brain tumor segmentation using Vision Transformers and lightweight convolutional networks. She has also explored quantum-enhanced reinforcement learning frameworks for disease diagnosis, including glaucoma and sickle cell anemia. In addition, her research extends to financial and data-driven applications such as cryptocurrency price forecasting, telecom churn prediction using LSTM networks, and high-frequency trading analysis, along with NLP-based educational systems and chatbot development. Her current interests include quantum machine learning, biomedical AI, and robust real-world computer vision systems.
Employment
-
Amrita Vishwa Vidyapeetham Student2022 - 2026
Education
Education history is unavailable.
Projects & Funding
Projects & funding information is unavailable.
Publications (12)
- Quantum-Enhanced Deep Learning Framework for Predictive Ophthalmic Healthcare Save
- Quantum-Enhanced Neuromorphic Framework for Early Detection of Wilson's Disease Using SNN-QSVM Hybrid Modeling Save
- Quantum SVM-driven framework for accurate brain stroke classification Save
- A Quantum-Driven Deep Reinforcement Learning Framework for Accurate Sickle Cell Disease Diagnosis and Triage Save
- A Decision-Oriented Multi-Modal Framework for Disaster Risk Assessment and Resource Optimization Save
- Personalized Learning With AI Save
- Transforming High-Frequency Trading Predictions with CNN-LSTM Networks Save
- Crypto Prophets: Machine Learning Oracles for Price Forecasting Save
- Enhanced Brain Tumor Segmentation using UNet3D: A Deep Learning Perspective Save
- Enhanced Detection of Autism Spectrum Disorder Using ResNet: A Deep Learning Approach Save