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. Frontiers Media SA. doi: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 (Lecture Notes in Computer Science). In I. Rojas, G. Joya & A. Catala (Hrsg.), Advances in Computational Intelligence. 17th International Work-Conference on Artificial Neural Networks, IWANN 2023, Ponta Delgada, Portugal, June 19–21, 2023, Proceedings, Part I (S. 406-417). Cham: Springer Nature Switzerland. doi: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. Gehalten auf der 19th International Conference on Machine Learning and Data Mining MLDM, New York USA. doi: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. Gehalten auf der International Conference on Computer Vision (ICCV) 2023, Paris France . doi: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. Gehalten auf der Novel Trends in Data Science 2023. doi: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. Gehalten auf der International Conference on Computer Vision (ICCV) 2023, Paris France : Published. doi: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. World Scientific Pub Co Pte Ltd. doi: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. Bielefeld : Datatninja Spring School 2023. doi: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. University of Texas at San Antonio: Neuro-Inspired Computing Elements (NICE 2023). doi: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 (Communications in Computer and Information Science). In L. Iliadis, I. Maglogiannis, S. Alonso, C. Jayne & E. Pimenidis (Hrsg.), Engineering Applications of Neural Networks. 24th International Conference, EAAAI/EANN 2023, León, Spain, June 14–17, 2023, Proceedings (S. 191-202). Cham: Springer Nature Switzerland. doi: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. Bielefeld : Datatninja Spring School 2022. doi: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 (Communications in Computer and Information Science). In L. Iliadis, C. Jayne, A. Tefas & E. Pimenidis (Hrsg.), Engineering Applications of Neural Networks. 23rd International Conference, EAAAI/EANN 2022, Chersonissos, Crete, Greece, June 17–20, 2022, Proceedings (S. 40-51). Gehalten auf der 23rd International Conference on Engineering Applications of Neural Networks (EAAAI/EANN 2022), Cham: Springer International Publishing. doi: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. Unpublished. doi:10.13140/RG.2.2.16202.85444.
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