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I am a machine-learning researcher with a strong background in processing and analyzing diverse data modalities to solve real-world challenges. My expertise spans computer vision, quantum machine learning, and semi-supervised learning, where I’ve developed innovative deep learning models and solutions in healthcare diagnostics and particle physics datasets. I love working with data and specialize in uncovering insights from complex datasets using advanced tools. I aim to advance methods that maximize learning from limited data, enabling transformative applications in fields where data scarcity is a critical barrier.


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Publications

  • KACQ-DCNN: Uncertainty-Aware Interpretable Kolmogorov-Arnold Classical-Quantum Dual-Channel Neural Network for Heart Disease Detection
  • Quantum Rationale-Aware Graph Contrastive Learning for Jet Discrimination
  • Enhanced Alzheimer's Brain Scan Dataset: Normal and Synthesized
  • MosqVision-3K: A Balanced Multi-Source Dataset of 3,000 Annotated Images for Culex, Anopheles, and Aedes Mosquito Species Classification
  • Lorentz-Equivariant Quantum Graph Neural Network for High-Energy Physics
  • Multi-Layered Password-Based Steganography: A Novel Approach for Tiered Information Hiding

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Co-workers & collaborators

  • Mohammad Abu Yousuf

  • Sanjida Akter

  • Nadia Sultana

  • A K M Azad

  • Salem A Alyami

  • Mohammad Ali Moni

Md. Akmol Masud's public data