Rate My Professor Mohammad Haque

Haque's research encompasses evolutionary computing, optimisation, ensemble methods, machine learning for imbalanced data, cloud computing service selection, network alignment, continued fractions for machine learning and mathematical modeling, medical image reconstruction, meta-analytics, and community detection in networks. His interdisciplinary work applies to biological data, business analytics, image processing, toxicology, and material science. Key publications include 'Heterogeneous Ensemble Combination Search Using Genetic Algorithm for Class Imbalanced Data Classification' (2016), 'Cloud Service Selection Using Multicriteria Decision Analysis' (2014), 'Analytic Continued Fractions for Regression: A Memetic Algorithm Approach' (2021), 'Learning to Extrapolate Using Continued Fractions: Predicting the Critical Temperature of Superconductor Materials' (2023), 'Mathematical Modelling of Peak and Residual Shear Strength of Rough Rock Discontinuities Using Continued Fractions' (2023), and 'The (α, β)-k Boolean Signatures of Molecular Toxicity: Microcystin as a Case Study' (2024). With over 30 publications and more than 500 citations, his contributions demonstrate impact across computer science and applied domains. Haque is proficient in C, C++, pattern recognition, signal and image processing, feature extraction, and classification, with experience in software design, coding, debugging, and high-pressure team projects.