Research

My ongoing research focuses on defect detection in solar panels. By utilizing multiple modalities, my research aims to address the complexities of identifying various types of defects, including cracks, hotspots, delamination, corrosion, and other structural anomalies that affect the performance and longevity of solar panels.

During my undergraduate studies, I worked on data mining and tuning conventional machine learning algorithms to predict some target class with the highest accuracy. This also led me to creating an application that could automate this process to a certain extent. Afterwards, I shifted my focus from text based datasets to image based ones, working on enabling low powered systems to make certain estimations based on color space.

Profile: Google Scholar

Publications

A Cost-Effective Framework to Predict the Ripeness of any Fruit Based on Color Space

A. Mueez
IEEE Region 10 Symposium (TENSYMP), 2020

[ResearchGate] [IEEEXplore] [Code] [Video]

This work proposes a generic model with low computational overhead, employing GrabCut, k-Means Clustering and kNN which when implemented on a Raspberry Pi 3 Model B can predict the ripeness of any fruit in well under a second with 85% accuracy.

Credit Card Fraud Detection Employing Various Sampling Techniques to Counter Class Imbalance

A. Hasan, A.Mueez
Southeast University Journal of Computer Sciences, 2021

Evaluating the Effectiveness of Variational Autoencoders with Other Sampling Techniques to Overcome Class Imbalance for Credit Card Fraud Detection

R. Islam, A. Mueez, O. Hasan, A. Khan
Southeast University Journal of Computer Sciences, 2022

Undergraduate Thesis

Exploratory Data Analysis and Success Prediction of Google Play Store Apps

[DSpace]

This work involves metadata of around 5,000 apps, comprising over 170,000 reviews scraped using a custom script to analyze patterns associated with specific ratings. Moreover, a success model was defined based on specific features and predicted with 85% accuracy.

Google Sites

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