BubCNN is a bubble detection system which employs a pretrained Faster RCNN module to locate bubbles and a pretrained shape regression CNN module to approximate the bubble shape by an ellipse.

Assembled Phi-X174 genome using Overlap Graph, Kmer Composition and De-Bruijn Graph.

Full-stack pipeline to detect U.S. housing market bubbles and forecast price trends. Merges 6+ macroeconomic datasets in Snowflake to compute risk scores and price predictions using walk-forward ML models. Deployed with Streamlit for interactive insights.

A Wolfram Mathematica image processing code for bubble flow images in X-ray. Codeveloped with https://github.com/Mihails-Birjukovs

Self-calibrating Optical Mark Recognition (OMR) framework for robust bubble-sheet analysis under noisy and distorted scanning conditions.

End-to-End Python implementation of LPPLS (Log-Periodic Power Law Singularity) framework for detecting financial bubbles and critical transitions. Features Filimonov-Sornette calibration, Lagrange regularization, Lomb-Scargle spectral validation, and Monte Carlo significance testing. Complete computational replication of Hosseinzadeh (2025).

OMR Sheet Evaluation system using Python and OpenCV. Automatically detects answer bubbles, evaluates marked responses, calculates scores, and visualizes grading results. Built with Computer Vision techniques including contour detection, thresholding, morphology, and pixel-density analysis for automated exam assessment.

Offline OMR bubble marker for MCQ answer sheets (PDF/Image) using Python, OpenCV, and PyMuPDF.