By Dr Md Zakir Hossain

Preprints

Hasan MR; Yao Y; Hossain MZ; Krishna A; Rudas I; Rahman S; Gedeon T, 2025, Labels Generated by Large Language Models Help Measure People's Empathy in Vitro, http://dx.doi.org/10.48550/arxiv.2501.00691

Yang H; Wang L; Hossain MZ, 2025, SRLoRA: Subspace Recomposition in Low-Rank Adaptation via Importance-Based Fusion and Reinitialization, http://dx.doi.org/10.48550/arxiv.2505.12433

Zhang Z; Ahmed KA; Hasan MR; Gedeon T; Hossain MZ, 2024, DiabetesNet: A Deep Learning Approach to Diabetes Diagnosis, http://dx.doi.org/10.48550/arxiv.2403.07483

Liu X; Hasan MR; Gedeon T; Hossain MZ, 2024, MADE-for-ASD: A Multi-Atlas Deep Ensemble Network for Diagnosing Autism Spectrum Disorder, http://dx.doi.org/10.48550/arxiv.2407.07076

Yang Y; Hossain MZ; Li X; Rahman S; Stone E, 2024, Spatial Transcriptomics Analysis of Zero-shot Gene Expression Prediction, http://dx.doi.org/10.48550/arxiv.2401.14772

Yang Y; Hossain MZ; Stone EA; Rahman S, 2022, Exemplar Guided Deep Neural Network for Spatial Transcriptomics Analysis of Gene Expression Prediction, http://dx.doi.org/10.48550/arxiv.2210.16721

Faroque MT; Yang Y; Hossain MZ; Naim SM; Mohammed N; Rahman S, 2022, Less is More: Facial Landmarks can Recognize a Spontaneous Smile, http://dx.doi.org/10.48550/arxiv.2210.04240

Yang Y; Hossain MZ; Gedeon T; Rahman S, 2021, S2FGAN: Semantically Aware Interactive Sketch-to-Face Translation, http://dx.doi.org/10.48550/arxiv.2011.14785

Chen R; Islam A; Gedeon T; Hossain MZ, 2021, Observers Pupillary Responses in Recognising Real and Posed Smiles: A Preliminary Study, http://dx.doi.org/10.48550/arxiv.2102.03994

Yang Y; Hossain MZ; Gedeon T; Rahman S, 2020, RealSmileNet: A Deep End-To-End Network for Spontaneous and Posed Smile Recognition, http://dx.doi.org/10.48550/arxiv.2010.03203

Gao R; Islam A; Gedeon T; Hossain MZ, Identifying Real and Posed Smiles from Observers’ Galvanic Skin Response and Blood Volume Pulse, http://dx.doi.org/10.36227/techrxiv.13180544

Jobayer ; Taylor A; Hasan R; Ahmed KA; Hossain Z, Machine Learning to Predict Gut Microbiomes of Agricultural Pests, http://dx.doi.org/10.1101/2024.08.12.607564

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