Sachin Bhandari
Affiliation confirmed via AI analysis of OpenAlex, ORCID, and web sources.
Researcher
Faculty Researcher
Research Areas
Biomedical Subjects
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Biography and Research Information
OverviewAI-generated summary
Sachin Bhandari's research primarily focuses on the application of machine learning and artificial intelligence techniques to medical imaging, with a specific emphasis on diabetic retinopathy severity classification. He has investigated the use of advanced neural network architectures, including squeeze-and-excitation and attention U-net models, often incorporating transfer learning from Xception models to improve classification accuracy. Bhandari has also explored other AI applications, such as understanding deepfakes and classifying hateful memes using multimodal models. His work extends to predicting geometric deviations in additively manufactured parts using conditional generative adversarial networks. Bhandari has authored 18 publications, with a total of 70 citations, and holds an h-index of 4. He collaborates with researchers at Arkansas Tech University, including Tolga Ensarı, Robin Ghosh, and Sagar Dhakal.
Metrics
- h-index: 4
- Publications: 18
- Citations: 70
Selected Publications
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ATU-Tutor: A RAG-Based Socratic Tutoring System with Agentic AI Adaptive Assessment (2026)
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SmartSpend: A Unified Artificial Intelligence Powered Personal Finance Dashboard Integrating Fraud Detection, Spending Forecasting and Budget Recommendation (2026)
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Multimodal Neuromorphic Computing for Cancer Diagnosis: Architecture, Clinical Fusion and Energy Analysis (2026)
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The Advanced Health Risk Predictor: Ensemble Machine Learning System for Multi Disease Clinical Decision Support (2026)
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Spiking Neural Networks for ECG Classification and Anomaly Detection (2025)
Collaboration Network
Top Collaborators
- Spiking Neural Networks for ECG Classification and Anomaly Detection
- Spiking Neural Networks for ECG Classification and Anomaly Detection
- Spiking Neural Networks for ECG Classification and Anomaly Detection
- Spiking Neural Networks for ECG Classification and Anomaly Detection
- Spiking Neural Networks for ECG Classification and Anomaly Detection
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