Sana Ullah

sana.ullah@uni-bielefeld.de
ORCID logo https://orcid.org/0000-0003-4112-802X

PEVZ-ID
d6c308ff-85b9-4031-8320-66589527527f

13 Publikationen

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  • [13]
    2023 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2982807 OA
    Ullah, S., Koravuna, S., Rückert, U., & Jungeblut, T. (2023). Exploring spiking neural networks: a comprehensive analysis of mathematical models and applications. Frontiers in Computational Neuroscience, 17. https://doi.org/10.3389/fncom.2023.1215824
    PUB | PDF | DOI | WoS | PubMed | Europe PMC
     
  • [12]
    2023 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2985715
    Koravuna, S., Ullah, S., Jungeblut, T., & Rückert, U. (2023). Digit Recognition Using Spiking Neural Networks on FPGA. In I. Rojas, G. Joya, & A. Catala (Eds.), Lecture Notes in Computer Science. Advances in Computational Intelligence. 17th International Work-Conference on Artificial Neural Networks, IWANN 2023, Ponta Delgada, Portugal, June 19–21, 2023, Proceedings, Part I (pp. 406-417). Cham: Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-43085-5_32
    PUB | DOI
     
  • [11]
    2023 | Kurzbeitrag Konferenz / Poster | Veröffentlicht | PUB-ID: 2985713
    Ullah, S., & Jungeblut, T. (2023). Analysis of MR Images for Early and Accurate Detection of Brain Tumor using Resource Efficient Simulator Brain Analysis. 19th International Conference on Machine Learning and Data Mining MLDM New York USA. https://doi.org/10.5281/zenodo.10457930
    PUB | DOI | Download (ext.)
     
  • [10]
    2023 | Kurzbeitrag Konferenz / Poster | Veröffentlicht | PUB-ID: 2985712
    Ullah, S., Amanullah, A., Roy, K., Lee, J. - A., Chul-Jun, S., & Jungeblut, T. (2023). A Hybrid Spiking-Convolutional Neural Network Approach for Advancing High-Quality Image Inpainting. International Conference on Computer Vision (ICCV) 2023 Paris France . https://doi.org/10.5281/zenodo.10458019
    PUB | DOI | Download (ext.)
     
  • [9]
    2023 | Konferenzbeitrag | Angenommen | PUB-ID: 2985188
    Ullah, S., Koravuna, S., Rückert, U., & Jungeblut, T. (Accepted). A Novel Spike Vision Approach for Robust Multi-Object Detection using SNNs. Presented at the Novel Trends in Data Science 2023, Congressi Stefano Franscini at Monte Verità in Ticino, Switzerland. https://doi.org/10.5281/zenodo.10262228
    PUB | DOI | Download (ext.) | Preprint
     
  • [8]
    2023 | Konferenzbeitrag | PUB-ID: 2983660
    Ullah, S., Koravuna, S., Rückert, U., & Jungeblut, T. (2023). Transforming Event-Based into Spike-Rate Datasets for Enhancing Neuronal Behavior Simulation to Bridging the Gap for SNNs. Presented at the International Conference on Computer Vision (ICCV) 2023, Paris France. https://doi.org/10.13140/RG.2.2.14469.32485
    PUB | DOI
     
  • [7]
    2023 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2982808
    Ullah, S., Koravuna, S., Rückert, U., & Jungeblut, T. (2023). Evaluation of Spiking Neural Nets-Based Image Classification Using the Runtime Simulator RAVSim. International Journal of Neural Systems, 33(09), 2350044. https://doi.org/10.1142/S0129065723500442
    PUB | DOI | WoS | PubMed | Europe PMC
     
  • [6]
    2023 | Kurzbeitrag Konferenz / Poster | PUB-ID: 2982810
    Ullah, S., Koravuna, S., Rückert, U., & Jungeblut, T. (2023). Evaluating Spiking Neural Network Models: A Comparative Performance Analysis. Presented at the . https://doi.org/10.13140/RG.2.2.21295.71847
    PUB | DOI
     
  • [5]
    2023 | Kurzbeitrag Konferenz / Poster | PUB-ID: 2982811
    Ullah, S., Koravuna, S., Rückert, U., & Jungeblut, T. (2023). Design-Space Exploration of SNN Models using Application-Specific Multi-Core Architectures. Presented at the . https://doi.org/10.13140/RG.2.2.26328.88324
    PUB | DOI
     
  • [4]
    2023 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2982809
    Ullah, S., Koravuna, S., Rückert, U., & Jungeblut, T. (2023). Streamlined Training of GCN for Node Classification with Automatic Loss Function and Optimizer Selection. In L. Iliadis, I. Maglogiannis, S. Alonso, C. Jayne, & E. Pimenidis (Eds.), Communications in Computer and Information Science. Engineering Applications of Neural Networks. 24th International Conference, EAAAI/EANN 2023, León, Spain, June 14–17, 2023, Proceedings (pp. 191-202). Cham: Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-34204-2_17
    PUB | DOI
     
  • [3]
    2022 | Kurzbeitrag Konferenz / Poster | PUB-ID: 2982814
    Ullah, S., Koravuna, S., Jungeblut, T., & Rückert, U. (2022). Real-Time Resource Efficient Simulator for SNNs-based Model Experimentation. Presented at the . https://doi.org/10.13140/RG.2.2.14584.83201/1
    PUB | DOI
     
  • [2]
    2022 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2979461
    Ullah, S., Koravuna, S., Rückert, U., & Jungeblut, T. (2022). SNNs Model Analyzing and Visualizing Experimentation Using RAVSim. In L. Iliadis, C. Jayne, A. Tefas, & E. Pimenidis (Eds.), Communications in Computer and Information Science. Engineering Applications of Neural Networks. 23rd International Conference, EAAAI/EANN 2022, Chersonissos, Crete, Greece, June 17–20, 2022, Proceedings (pp. 40-51). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-031-08223-8_4
    PUB | DOI | Download (ext.)
     
  • [1]
    2022 | Preprint | PUB-ID: 2982804
    Ullah, S., Koravuna, S., Jungeblut, T., & Rückert, U. (2022). NireHApS: Neuro-Inspired and Resource-Efficient Hardware-Architectures for Plastic SNNs. https://doi.org/10.13140/RG.2.2.16202.85444
    PUB | DOI
     
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