Siregar, S., & Azizah, Z. (2026). A reduced-order nonlinear thermo–fluid model of pressure-driven extraction in a moka pot. The European Physical Journal Plus, 141(10), Article 1134.

Mudrikah, M. A., Julia, S., Azizah, Z., & Siregar, S. (2026). Quantifying how lesion texture and shape influence deep learning segmentation in breast ultrasound. Biomedical Signal Processing and Control, 125, Article 110788.

Rahmawati, V., & Siregar, S. (2026). Enhancing breast lesion segmentation in ultrasound images using U-Net with augmentation-based preprocessing. IMAGING. https://doi.org/10.1556/1647.2026.00361

Fiarka, F., Hakim, A. R., Budiansah, I., Wicaksono, A. A., Jabar, J. M., Peters, S. M. B., Prive, B. M., Nagarajah, J., Konijnenberg, M. W., Siregar, S., Hardiansyah, D., & Glatting, G. (2026). Limitations of mono-exponential individual fitting as a reference model for single-time-point dosimetry in [177Lu]Lu-PSMA-617 therapy. Zeitschrift für Medizinische Physik.

Siregar, S. (2026). Coffee brewing as a context for teaching porous media flow and mass transfer principles. European Journal of Physics, 47(3), Article 035101.

Ceesay, M. B., Saputro, A. H., & Siregar, S. (2026). Hierarchical tissue-based MRI features with explainable machine learning for Alzheimer's disease classification. Jurnal Ilmu Fisika, 18(1), 93–104.

Siregar, S., Azizah, Z., & Julia, S. (2025). Shape feature analysis for differentiating benign and malignant breast lesions in ultrasound. In Proceedings of the 2025 International Conference on Computer Engineering, Network and Intelligent Multimedia (CENIM). IEEE.

Ceesay, M. B., Saputro, A. H., & Siregar, S. (2025). Histogram-based slice-level features for Alzheimer's disease classification using machine learning on MR images. In Proceedings of the 2025 International Conference on Computer Engineering, Network and Intelligent Multimedia (CENIM). IEEE.

Azizah, Z., & Siregar, S. (2025). An explainable deep learning pipeline for breast cancer detection using ResNet-50 and Grad-CAM. In Proceedings of the 2025 International Conference on Computer Engineering, Network and Intelligent Multimedia (CENIM). IEEE.

Siregar, S., Rafianto, A., & Lubis, L. E. (2024). Optimizing Raman spectra pre-processing and classification for prostate cancer detection in tissue specimens. AIP Conference Proceedings, 3210(1), Article 020006.

Siregar, S., Nurhikmat, A., Amdani, R. Z., Hatmi, R. U., Kobarsih, M., Kusumaningrum, A., Karim, M. A., Dameswari, A. H., Siswanto, N., Siswoprayogi, S., & Yuliyanto, P. (2024). Estimation of proximate composition in rice using ATR-FTIR spectroscopy and chemometrics. ACS Omega, 9(30), 32760–32768. https://doi.org/10.1021/acsomega.4c02816

Op De Beeck, M., Troein, C., Siregar, S., Gentile, L., Abbondanza, G., Peterson, C., Persson, P., & Tunlid, A. (2020). Regulation of fungal decomposition at the single-cell level. The ISME Journal, 14(4), 896–905.

Troein, C., Siregar, S., Op De Beeck, M., Peterson, C., Tunlid, A., & Persson, P. (2020). OCTAVVS: A graphical toolbox for high-throughput preprocessing and analysis of vibrational spectroscopy imaging data. Methods and Protocols, 3(2), Article 34. https://doi.org/10.3390/mps3020034

Siregar, S., & Saijo, Y. (2018). Photoacoustic image denoising using dictionary learning. In Proceedings of the 3rd International Conference on Biomedical Signal and Image Processing (pp. 34–37).

Siregar, S., Oktamuliani, S., & Saijo, Y. (2018). A theoretical model of laser heating carbon nanotubes. Nanomaterials, 8(8), Article 580.

Siregar, S., Nagaoka, R., Ul Haq, I., & Saijo, Y. (2018). Non-local means denoising in photoacoustic imaging. Japanese Journal of Applied Physics, 57(7S1), Article 07LB06. https://doi.org/10.7567/JJAP.57.07LB06

Siregar, S., Nagaoka, R., Ishikawa, K., & Saijo, Y. (2017). Carbon nanotubes as a potential contrast agent for photoacoustic imaging. Proceedings of Meetings on Acoustics, 32(1), Article 020018.

Ul Haq, I., Nagaoka, R., Siregar, S., & Saijo, Y. (2017). Sparse-representation-based denoising of photoacoustic images. Biomedical Physics & Engineering Express, 3(4), Article 045014.

Ul Haq, I., Siregar, S., Nagaoka, R., & Saijo, Y. (2017). Vascular bifurcation detection by symmetric analysis of eigenvalues. In Proceedings of the 8th International Conference of Pattern Recognition Systems (ICPRS 2017) (pp. 1–5).

Ul Haq, I., Nagaoka, R., Siregar, S., & Saijo, Y. (2016). Reconstruction of vasculature in optical resolution photoacoustic microscopy using wavelet and Hessian-based method. The Journal of the Acoustical Society of America, 140(4, Supplement), Article 2981.

Saito, R., Nugraha, A. R. T., Hasdeo, E. H., Siregar, S., Guo, H., & Yang, T. (2015). Ultraviolet Raman spectroscopy of graphene and transition-metal dichalcogenides. physica status solidi (b), 252(11), 2363–2374.

Liu, H.-L., Siregar, S., Hasdeo, E. H., Kumamoto, Y., Shen, C.-C., Cheng, C.-C., Li, L.-J., Saito, R., & Kawata, S. (2015). Deep-ultraviolet Raman scattering studies of monolayer graphene thin films. Carbon, 81, 807–813.

Majidi, M. A., Siregar, S., & Rusydi, A. (2014). Theoretical study of optical conductivity of graphene with magnetic and nonmagnetic adatoms. Physical Review B, 90(19), 195442. https://doi.org/10.1103/PhysRevB.90.195442