Sachin Bhandari Data-verified
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Biography and Research Information
OverviewAI-generated summary
Sachin Bhandari's research focuses on the application of machine learning and artificial intelligence, particularly neural networks, for medical image analysis and prediction tasks. He has investigated the use of advanced deep learning models, such as squeeze-and-excitation and sparse light weight multi-level attention U-net with transfer learning, to improve the classification of diabetic retinopathy severity. His work also explores the potential of generative adversarial networks for predicting geometric deviations in additively manufactured parts under varying process parameters. Bhandari's scholarship metrics include an h-index of 4 and a total of 20 publications, with 73 citations. He has collaborated with researchers at Arkansas Tech University, including Tolga Ensarı and Sagar Dhakal, on shared publications.
Metrics
- h-index: 4
- Publications: 18
- Citations: 70
Selected Publications
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Spiking Neural Networks for ECG Classification and Anomaly Detection (2025)
Collaboration Network
Top Collaborators
- A Review on Swarm intelligence & Evolutionary Algorithms based Approaches for Diabetic Retinopathy Detection
- Improved diabetic retinopathy severity classification using squeeze-and-excitation and sparse light weight multi-level attention u-net with transfer learning from xception
- Improved Diabetic Retinopathy Severity Classification Using Squeeze-and-excitation and Sparse Light Weight Multi-level Attention U-net With Transfer Learning From Xception
- SACOENet: an advanced segment anything model enhanced with self-calibrated convolutions and optimized EfficientNetB7 for precise diabetic retinopathy detection
- A Review on Swarm intelligence & Evolutionary Algorithms based Approaches for Diabetic Retinopathy Detection
- Improved diabetic retinopathy severity classification using squeeze-and-excitation and sparse light weight multi-level attention u-net with transfer learning from xception
- Improved Diabetic Retinopathy Severity Classification Using Squeeze-and-excitation and Sparse Light Weight Multi-level Attention U-net With Transfer Learning From Xception
- SACOENet: an advanced segment anything model enhanced with self-calibrated convolutions and optimized EfficientNetB7 for precise diabetic retinopathy detection
- Improved diabetic retinopathy severity classification using squeeze-and-excitation and sparse light weight multi-level attention u-net with transfer learning from xception
- Improved Diabetic Retinopathy Severity Classification Using Squeeze-and-excitation and Sparse Light Weight Multi-level Attention U-net With Transfer Learning From Xception
- SACOENet: an advanced segment anything model enhanced with self-calibrated convolutions and optimized EfficientNetB7 for precise diabetic retinopathy detection
- Classification of Hateful Memes Using Multimodal Models
- Classification of Hateful Memes Using Multimodal Models
- Classification of Hateful Memes Using Multimodal Models
- A Review on Swarm intelligence & Evolutionary Algorithms based Approaches for Diabetic Retinopathy Detection
- Nail Polish Remover Induced Methemoglobinemia: An Uncommon Occurrence
- Nail Polish Remover Induced Methemoglobinemia: An Uncommon Occurrence
- Nail Polish Remover Induced Methemoglobinemia: An Uncommon Occurrence
- Nail Polish Remover Induced Methemoglobinemia: An Uncommon Occurrence
- Nail Polish Remover Induced Methemoglobinemia: An Uncommon Occurrence
- Adult T-Cell Lymphoma (ATL) With Erythroderma in Indolent Human T-Lymphotropic Virus Type I (HTLV-1) Infection
- Adult T-Cell Lymphoma (ATL) With Erythroderma in Indolent Human T-Lymphotropic Virus Type I (HTLV-1) Infection
- Adult T-Cell Lymphoma (ATL) With Erythroderma in Indolent Human T-Lymphotropic Virus Type I (HTLV-1) Infection
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