CAMIL: Clustering and Assembly with Multiple Instance Learning for phenotype prediction. - Nathan LaPierre; Mohammad Arifur Rahman; Huzefa Rangwala (BIBM, 2016)

Metagenome sequence clustering with hash-based canopies. - Mohammad Arifur Rahman; Nathan Lapierre; Huzefa Rangwala; Daniel Barbará (J. Bioinform. Comput. Biol., 2017)

RegMIL: Phenotype Classification from Metagenomic Data. - Mohammad Arifur Rahman; Huzefa Rangwala (BCB, 2018)

IDMIL: an alignment-free Interpretable Deep Multiple Instance Learning (MIL) for predicting disease from whole-metagenomic data. - Mohammad Arifur Rahman; Huzefa Rangwala (Bioinform., 2020)

Phenotype Prediction from Metagenomic Data Using Clustering and Assembly with Multiple Instance Learning (CAMIL). - Mohammad Arifur Rahman; Nathan LaPierre; Huzefa Rangwala (IEEE ACM Trans. Comput. Biol. Bioinform., 2020)

Anti-aliasing deep image classifiers using novel depth adaptive blurring and activation function. - Md Tahmid Hossain; Shyh Wei Teng; Guojun Lu; Mohammad Arifur Rahman; Ferdous Sohel (Neurocomputing, 2023)

Addressing Limitations of Common Methods in Attention-Based Hyperspectral Band Selection Algorithms. - Mohammad Arifur Rahman; Shyh Wei Teng; M. Manzur Murshed; Manoranjan Paul; David Brennan (DICTA, 2024)

FSDR: A Novel Deep Learning-based Feature Selection Algorithm for Pseudo Time-Series Data using Discrete Relaxation. - Mohammad Arifur Rahman; M. Manzur Murshed; Shyh Wei Teng; Manoranjan Paul (CoRR, 2024)

Deep Learning-Based Adaptive Downsampling of Hyperspectral Bands for Soil Organic Carbon Estimation. - Mohammad Arifur Rahman; Shyh Wei Teng; M. Manzur Murshed; Manoranjan Paul; David Brennan (IEEE Access, 2025)