PUBLIKATIONSSERVER

Sanaullah Sanaullah

https://orcid.org/0000-0003-4112-802X

29 Publikationen

Alle markieren

[29]
2025 | Artikel | FH-PUB-ID: 6139
H. Attaullah, S. Sanaullah, A. Peters, Q. A. Ahmed, J. Baudisch, and T. Jungeblut, “DNA: detecting early signs of neurodegenerative diseases through activity and sleep analysis,” Frontiers in Neuroscience, vol. 19, 2025.
HSBI-PUB | DOI
 
[28]
2025 | Dissertation | FH-PUB-ID: 6052
S. Sanaullah, Design-Space Exploration of Biologically-Inspired SNN Models for Application-Specific Many-Core Systems. Universität Bielefeld, 2025.
HSBI-PUB | DOI
 
[27]
2025 | Konferenzbeitrag | FH-PUB-ID: 5976
S. Sanaullah, H. Honda, K. Roy, A. Schneider, J. Waßmuth, and T. Jungeblut, “Automating Neural Model Selection in Spiking Neural Networks Using AutoML Techniques*,” in 2025 22nd International Learning and Technology Conference (L&T), Jeddah, Saudi Arabia, 2025, pp. 274–279.
HSBI-PUB | DOI
 
[26]
2025 | Artikel | FH-PUB-ID: 5366
S. Sanaullah, A. Schneider, J. Waßmuth, U. Rückert, and T. Jungeblut, “RAVSim v2.0: Enhanced visualization and comparative analysis for neural network models,” SoftwareX, vol. 29, 2025.
HSBI-PUB | DOI
 
[25]
2024 | Konferenzbeitrag | FH-PUB-ID: 4782
S. Sanaullah, S. Koravuna, U. Rückert, and T. Jungeblut, “A Spike Vision Approach for Multi-object Detection and Generating Dataset Using Multi-core Architecture on Edge Device,” in Engineering Applications of Neural Networks. 25th International Conference, EANN 2024, Corfu, Greece, June 27–30, 2024, Proceedings, Corfu, Greece, 2024, pp. 317–328.
HSBI-PUB | DOI
 
[24]
2024 | Konferenzbeitrag | FH-PUB-ID: 5367 | OA
S. Koravuna, S. Sanaullah, T. Jungeblut, and U. Rückert, “Spiking Neural Network Models Analysis on Field Programmable Gate Arrays,” in International Conference on Intelligent and Innovative Computing Applications, Pearle Beach Hotel & Spa, in the paradise island of Mauritius, 2024, vol. 2024, pp. 259–270.
HSBI-PUB | DOI | Download (ext.)
 
[23]
2024 | Konferenzbeitrag | FH-PUB-ID: 5315
H. Attaullah, S. Sanaullah, and T. Jungeblut, “FL-DL: Fuzzy Logic with Deep Learning, Hybrid Anomaly Detection and Activity Prediction in Smart Homes Data-Sets,” in 2024 IEEE 24th International Symposium on Computational Intelligence and Informatics (CINTI), Budapest, Hungary, 2024, pp. 43–48.
HSBI-PUB | DOI
 
[22]
2024 | Konferenzbeitrag | FH-PUB-ID: 5098
S. Sanaullah, H. Attaullah, and T. Jungeblut, “Encryption Techniques for Privacy-Preserving CNN Models: Performance and Practicality in Urban AI Applications,” in Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Advances in Urban-AI, Atlanta GA USA, 2024, pp. 50–53.
HSBI-PUB | DOI
 
[21]
2024 | Konferenzbeitrag | FH-PUB-ID: 5097
S. Sanaullah, S. Koravuna, U. Rückert, and T. Jungeblut, “Advancements in Neural Network Generations,” in Proceedings of the sAIOnARA Conference, Bielefeld, 2024.
HSBI-PUB | DOI
 
[20]
2024 | Konferenzbeitrag | FH-PUB-ID: 5096
S. Sanaullah, H. Attaullah, and T. Jungeblut, “Trade-offs Between Privacy and Performance in Encrypted Dataset using Machine Learning Models,” in Proceedings of the sAIOnARA Conference, Bielefeld, 2024.
HSBI-PUB | DOI
 
[19]
2024 | Artikel | FH-PUB-ID: 4997 | OA
H. Attaullah, S. Sanaullah, and T. Jungeblut, “Analyzing Machine Learning Models for Activity Recognition Using Homomorphically Encrypted Real-World Smart Home Datasets: A Case Study,” Applied Sciences, vol. 14, no. 19, 2024.
HSBI-PUB | Dateien verfügbar | DOI | Download (ext.)
 
[18]
2024 | Kurzbeitrag Konferenz | FH-PUB-ID: 4883
S. Sanaullah, K. Roy, U. Ruckert, and T. Jungeblut, “Selection of Optimal Neural Model using Spiking Neural Network for Edge Computing  ,” in 2024 IEEE 44th International Conference on Distributed Computing Systems (ICDCS), Jersey City, NJ, USA, 2024, pp. 1452–1453.
HSBI-PUB | DOI | Inspire
 
[17]
2024 | Konferenzbeitrag | FH-PUB-ID: 4811
S. Sanaullah, H. Attaullah, and T. Jungeblut, “The Next-Gen Interactive Runtime Simulator for Neural Network Programming,” in Companion Proceedings of the 8th International Conference on the Art, Science, and Engineering of Programming, Lund Sweden, 2024, pp. 8–10.
HSBI-PUB | DOI
 
[16]
2024 | Konferenzbeitrag | FH-PUB-ID: 4783 | OA
S. Sanaullah, K. Roy, U. Rückert, and T. Jungeblut, “A Hybrid Spiking-Convolutional Neural Network Approach for Advancing Machine Learning Models,” in Proceedings of the 5th Northern Lights Deep Learning Conference, UiT The Arctic University, Tromsø, Norway, 2024, vol. 233.
HSBI-PUB | Dateien verfügbar | DOI | Download (ext.) | WoS | arXiv
 
[15]
2023 | Buchbeitrag | FH-PUB-ID: 3222
S. Sanaullah, S. Koravuna, U. Rückert, and T. Jungeblut, “Streamlined Training of GCN for Node Classification with Automatic Loss Function and Optimizer Selection,” in Engineering Applications of Neural Networks. 24th International Conference, EAAAI/EANN 2023, León, Spain, June 14–17, 2023, Proceedings, L. Iliadis, I. Maglogiannis, S. Alonso, C. Jayne, and E. Pimenidis, Eds. Cham: Springer Nature Switzerland, 2023, pp. 191–202.
HSBI-PUB | DOI
 
[14]
2023 | Artikel | FH-PUB-ID: 3482 | OA
S. Sanaullah, S. Koravuna, U. Rückert, and T. Jungeblut, “Evaluation of Spiking Neural Nets-Based Image Classification Using the Runtime Simulator RAVSim,” International Journal of Neural Systems, 2023.
HSBI-PUB | DOI | Download (ext.)
 
[13]
2023 | Artikel | FH-PUB-ID: 3572 | OA
S. Sanaullah, S. Koravuna, U. Rückert, and T. Jungeblut, “Exploring spiking neural networks: a comprehensive analysis of mathematical models and applications,” Frontiers in Computational Neuroscience, vol. 17, 2023.
HSBI-PUB | DOI | Download (ext.)
 
[12]
2023 | Artikel | FH-PUB-ID: 3665 | OA
S. Sanaullah, S. Koravuna, U. Ruckert, and T. Jungeblut, “Design-Space Exploration of SNN Models using Application-Specific Multi-Core Architectures,” Preprint, 2023.
HSBI-PUB | DOI | Download (ext.)
 
[11]
2023 | Konferenzbeitrag | FH-PUB-ID: 3644 | OA
S. Sanaullah, S. Koravuna, U. Rückert, and T. Jungeblut, “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, 2023.
HSBI-PUB | DOI | Download (ext.)
 
[10]
2023 | Konferenzbeitrag | FH-PUB-ID: 4207 | OA
S. Sanaullah and T. Jungeblut, “Analysis of MR Images for Early and Accurate Detection of Brain Tumor using Resource Efficient Simulator Brain Analysis,” presented at the 19th International Conference on Machine Learning and Data Mining MLDM, New York USA, 2023.
HSBI-PUB | DOI | Download (ext.)
 
[9]
2023 | Kurzbeitrag Konferenz | FH-PUB-ID: 4206 | OA
S. Sanaullah, A. Amanullah, K. Roy, J.-A. Lee, S. Chul-Jun, and T. Jungeblut, “A Hybrid Spiking-Convolutional Neural Network Approach for Advancing High-Quality Image Inpainting,” presented at the International Conference on Computer Vision (ICCV) 2023, Paris France, 2023.
HSBI-PUB | DOI | Download (ext.)
 
[8]
2023 | Konferenzbeitrag | FH-PUB-ID: 4205 | OA
S. Sanaullah, S. Koravuna, U. Rückert, and T. Jungeblut, “A Novel Spike Vision Approach for Robust Multi-Object Detection using SNNs,” presented at the Conference: Novel Trends in Data Science 2023, Congressi Stefano Franscini at Monte Verità in Ticino, Switzerland, 2023.
HSBI-PUB | DOI | Download (ext.)
 
[7]
2023 | Konferenzbeitrag | FH-PUB-ID: 4208
S. Koravuna, S. Sanaullah, T. Jungeblut, and U. Rückert, “Digit Recognition Using Spiking Neural Networks on FPGA,” in Advances in Computational Intelligence. 17th International Work-Conference on Artificial Neural Networks, IWANN 2023, Ponta Delgada, Portugal, June 19–21, 2023, Proceedings, Part I, 2023, pp. 406–417.
HSBI-PUB | DOI
 
[6]
2023 | Kurzbeitrag Konferenz | FH-PUB-ID: 2919 | OA
S. Sanaullah, S. Koravuna, U. Rückert, and T. Jungeblut, “Evaluating Spiking Neural Network Models: A Comparative Performance Analysis,” presented at the Datatninja Spring School 2023.
HSBI-PUB | DOI | Download (ext.)
 
[5]
2022 | Konferenzbeitrag | FH-PUB-ID: 2757
S. Sanaullah, S. Koravuna, T. Jungeblut, and U. Rückert, “NireHApS: Neuro-Inspired and Resource-Efficient Hardware-Architectures for Plastic SNNs,” presented at the DataNinja – Networking Event, Bielefeld, 2022.
HSBI-PUB | DOI
 
[4]
2022 | Kurzbeitrag Konferenz | FH-PUB-ID: 2753
S. Sanaullah, S. Koravuna, U. Rückert, and T. Jungeblut, “Real-Time Resource Efficient Simulator for SNNs-based Model Experimentation.” Bielefeld University, Germany , 2022.
HSBI-PUB | DOI
 
[3]
2022 | Konferenzbeitrag | FH-PUB-ID: 3480 | OA
S. Sanaullah, S. Koravuna, U. Ruckert, and T. Jungeblut, “Real-Time SNNs Model Analyzing and Visualizing Experimentation,” 2022.
HSBI-PUB | DOI | Download (ext.)
 
[2]
2022 | Konferenzbeitrag | FH-PUB-ID: 2276
S. Sanaullah, S. Koravuna, U. Rückert, and T. Jungeblut, “SNNs Model Analyzing and Visualizing Experimentation Using RAVSim,” in Engineering Applications of Neural Networks. 23rd International Conference, EAAAI/EANN 2022, Chersonissos, Crete, Greece, June 17–20, 2022, Proceedings, Greta, 2022, pp. 40–51.
HSBI-PUB | DOI
 
[1]
2019 | Konferenzbeitrag | FH-PUB-ID: 2975 | OA
S. Sanaullah, H. Baig, and J. A. Lee, “Accelerating the Threshold and Timing Analysis of Genetic Logic Circuit Models,” presented at the 11th International Workshop on Bio-Design Automation, Cambridge England.
HSBI-PUB | DOI | Download (ext.)
 
Markierte Publikation(en)

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Publikationen filtern

Darstellung / Sortierung

Zitationsstil: IEEE

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29 Publikationen

Alle markieren

[29]
2025 | Artikel | FH-PUB-ID: 6139
H. Attaullah, S. Sanaullah, A. Peters, Q. A. Ahmed, J. Baudisch, and T. Jungeblut, “DNA: detecting early signs of neurodegenerative diseases through activity and sleep analysis,” Frontiers in Neuroscience, vol. 19, 2025.
HSBI-PUB | DOI
 
[28]
2025 | Dissertation | FH-PUB-ID: 6052
S. Sanaullah, Design-Space Exploration of Biologically-Inspired SNN Models for Application-Specific Many-Core Systems. Universität Bielefeld, 2025.
HSBI-PUB | DOI
 
[27]
2025 | Konferenzbeitrag | FH-PUB-ID: 5976
S. Sanaullah, H. Honda, K. Roy, A. Schneider, J. Waßmuth, and T. Jungeblut, “Automating Neural Model Selection in Spiking Neural Networks Using AutoML Techniques*,” in 2025 22nd International Learning and Technology Conference (L&T), Jeddah, Saudi Arabia, 2025, pp. 274–279.
HSBI-PUB | DOI
 
[26]
2025 | Artikel | FH-PUB-ID: 5366
S. Sanaullah, A. Schneider, J. Waßmuth, U. Rückert, and T. Jungeblut, “RAVSim v2.0: Enhanced visualization and comparative analysis for neural network models,” SoftwareX, vol. 29, 2025.
HSBI-PUB | DOI
 
[25]
2024 | Konferenzbeitrag | FH-PUB-ID: 4782
S. Sanaullah, S. Koravuna, U. Rückert, and T. Jungeblut, “A Spike Vision Approach for Multi-object Detection and Generating Dataset Using Multi-core Architecture on Edge Device,” in Engineering Applications of Neural Networks. 25th International Conference, EANN 2024, Corfu, Greece, June 27–30, 2024, Proceedings, Corfu, Greece, 2024, pp. 317–328.
HSBI-PUB | DOI
 
[24]
2024 | Konferenzbeitrag | FH-PUB-ID: 5367 | OA
S. Koravuna, S. Sanaullah, T. Jungeblut, and U. Rückert, “Spiking Neural Network Models Analysis on Field Programmable Gate Arrays,” in International Conference on Intelligent and Innovative Computing Applications, Pearle Beach Hotel & Spa, in the paradise island of Mauritius, 2024, vol. 2024, pp. 259–270.
HSBI-PUB | DOI | Download (ext.)
 
[23]
2024 | Konferenzbeitrag | FH-PUB-ID: 5315
H. Attaullah, S. Sanaullah, and T. Jungeblut, “FL-DL: Fuzzy Logic with Deep Learning, Hybrid Anomaly Detection and Activity Prediction in Smart Homes Data-Sets,” in 2024 IEEE 24th International Symposium on Computational Intelligence and Informatics (CINTI), Budapest, Hungary, 2024, pp. 43–48.
HSBI-PUB | DOI
 
[22]
2024 | Konferenzbeitrag | FH-PUB-ID: 5098
S. Sanaullah, H. Attaullah, and T. Jungeblut, “Encryption Techniques for Privacy-Preserving CNN Models: Performance and Practicality in Urban AI Applications,” in Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Advances in Urban-AI, Atlanta GA USA, 2024, pp. 50–53.
HSBI-PUB | DOI
 
[21]
2024 | Konferenzbeitrag | FH-PUB-ID: 5097
S. Sanaullah, S. Koravuna, U. Rückert, and T. Jungeblut, “Advancements in Neural Network Generations,” in Proceedings of the sAIOnARA Conference, Bielefeld, 2024.
HSBI-PUB | DOI
 
[20]
2024 | Konferenzbeitrag | FH-PUB-ID: 5096
S. Sanaullah, H. Attaullah, and T. Jungeblut, “Trade-offs Between Privacy and Performance in Encrypted Dataset using Machine Learning Models,” in Proceedings of the sAIOnARA Conference, Bielefeld, 2024.
HSBI-PUB | DOI
 
[19]
2024 | Artikel | FH-PUB-ID: 4997 | OA
H. Attaullah, S. Sanaullah, and T. Jungeblut, “Analyzing Machine Learning Models for Activity Recognition Using Homomorphically Encrypted Real-World Smart Home Datasets: A Case Study,” Applied Sciences, vol. 14, no. 19, 2024.
HSBI-PUB | Dateien verfügbar | DOI | Download (ext.)
 
[18]
2024 | Kurzbeitrag Konferenz | FH-PUB-ID: 4883
S. Sanaullah, K. Roy, U. Ruckert, and T. Jungeblut, “Selection of Optimal Neural Model using Spiking Neural Network for Edge Computing  ,” in 2024 IEEE 44th International Conference on Distributed Computing Systems (ICDCS), Jersey City, NJ, USA, 2024, pp. 1452–1453.
HSBI-PUB | DOI | Inspire
 
[17]
2024 | Konferenzbeitrag | FH-PUB-ID: 4811
S. Sanaullah, H. Attaullah, and T. Jungeblut, “The Next-Gen Interactive Runtime Simulator for Neural Network Programming,” in Companion Proceedings of the 8th International Conference on the Art, Science, and Engineering of Programming, Lund Sweden, 2024, pp. 8–10.
HSBI-PUB | DOI
 
[16]
2024 | Konferenzbeitrag | FH-PUB-ID: 4783 | OA
S. Sanaullah, K. Roy, U. Rückert, and T. Jungeblut, “A Hybrid Spiking-Convolutional Neural Network Approach for Advancing Machine Learning Models,” in Proceedings of the 5th Northern Lights Deep Learning Conference, UiT The Arctic University, Tromsø, Norway, 2024, vol. 233.
HSBI-PUB | Dateien verfügbar | DOI | Download (ext.) | WoS | arXiv
 
[15]
2023 | Buchbeitrag | FH-PUB-ID: 3222
S. Sanaullah, S. Koravuna, U. Rückert, and T. Jungeblut, “Streamlined Training of GCN for Node Classification with Automatic Loss Function and Optimizer Selection,” in Engineering Applications of Neural Networks. 24th International Conference, EAAAI/EANN 2023, León, Spain, June 14–17, 2023, Proceedings, L. Iliadis, I. Maglogiannis, S. Alonso, C. Jayne, and E. Pimenidis, Eds. Cham: Springer Nature Switzerland, 2023, pp. 191–202.
HSBI-PUB | DOI
 
[14]
2023 | Artikel | FH-PUB-ID: 3482 | OA
S. Sanaullah, S. Koravuna, U. Rückert, and T. Jungeblut, “Evaluation of Spiking Neural Nets-Based Image Classification Using the Runtime Simulator RAVSim,” International Journal of Neural Systems, 2023.
HSBI-PUB | DOI | Download (ext.)
 
[13]
2023 | Artikel | FH-PUB-ID: 3572 | OA
S. Sanaullah, S. Koravuna, U. Rückert, and T. Jungeblut, “Exploring spiking neural networks: a comprehensive analysis of mathematical models and applications,” Frontiers in Computational Neuroscience, vol. 17, 2023.
HSBI-PUB | DOI | Download (ext.)
 
[12]
2023 | Artikel | FH-PUB-ID: 3665 | OA
S. Sanaullah, S. Koravuna, U. Ruckert, and T. Jungeblut, “Design-Space Exploration of SNN Models using Application-Specific Multi-Core Architectures,” Preprint, 2023.
HSBI-PUB | DOI | Download (ext.)
 
[11]
2023 | Konferenzbeitrag | FH-PUB-ID: 3644 | OA
S. Sanaullah, S. Koravuna, U. Rückert, and T. Jungeblut, “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, 2023.
HSBI-PUB | DOI | Download (ext.)
 
[10]
2023 | Konferenzbeitrag | FH-PUB-ID: 4207 | OA
S. Sanaullah and T. Jungeblut, “Analysis of MR Images for Early and Accurate Detection of Brain Tumor using Resource Efficient Simulator Brain Analysis,” presented at the 19th International Conference on Machine Learning and Data Mining MLDM, New York USA, 2023.
HSBI-PUB | DOI | Download (ext.)
 
[9]
2023 | Kurzbeitrag Konferenz | FH-PUB-ID: 4206 | OA
S. Sanaullah, A. Amanullah, K. Roy, J.-A. Lee, S. Chul-Jun, and T. Jungeblut, “A Hybrid Spiking-Convolutional Neural Network Approach for Advancing High-Quality Image Inpainting,” presented at the International Conference on Computer Vision (ICCV) 2023, Paris France, 2023.
HSBI-PUB | DOI | Download (ext.)
 
[8]
2023 | Konferenzbeitrag | FH-PUB-ID: 4205 | OA
S. Sanaullah, S. Koravuna, U. Rückert, and T. Jungeblut, “A Novel Spike Vision Approach for Robust Multi-Object Detection using SNNs,” presented at the Conference: Novel Trends in Data Science 2023, Congressi Stefano Franscini at Monte Verità in Ticino, Switzerland, 2023.
HSBI-PUB | DOI | Download (ext.)
 
[7]
2023 | Konferenzbeitrag | FH-PUB-ID: 4208
S. Koravuna, S. Sanaullah, T. Jungeblut, and U. Rückert, “Digit Recognition Using Spiking Neural Networks on FPGA,” in Advances in Computational Intelligence. 17th International Work-Conference on Artificial Neural Networks, IWANN 2023, Ponta Delgada, Portugal, June 19–21, 2023, Proceedings, Part I, 2023, pp. 406–417.
HSBI-PUB | DOI
 
[6]
2023 | Kurzbeitrag Konferenz | FH-PUB-ID: 2919 | OA
S. Sanaullah, S. Koravuna, U. Rückert, and T. Jungeblut, “Evaluating Spiking Neural Network Models: A Comparative Performance Analysis,” presented at the Datatninja Spring School 2023.
HSBI-PUB | DOI | Download (ext.)
 
[5]
2022 | Konferenzbeitrag | FH-PUB-ID: 2757
S. Sanaullah, S. Koravuna, T. Jungeblut, and U. Rückert, “NireHApS: Neuro-Inspired and Resource-Efficient Hardware-Architectures for Plastic SNNs,” presented at the DataNinja – Networking Event, Bielefeld, 2022.
HSBI-PUB | DOI
 
[4]
2022 | Kurzbeitrag Konferenz | FH-PUB-ID: 2753
S. Sanaullah, S. Koravuna, U. Rückert, and T. Jungeblut, “Real-Time Resource Efficient Simulator for SNNs-based Model Experimentation.” Bielefeld University, Germany , 2022.
HSBI-PUB | DOI
 
[3]
2022 | Konferenzbeitrag | FH-PUB-ID: 3480 | OA
S. Sanaullah, S. Koravuna, U. Ruckert, and T. Jungeblut, “Real-Time SNNs Model Analyzing and Visualizing Experimentation,” 2022.
HSBI-PUB | DOI | Download (ext.)
 
[2]
2022 | Konferenzbeitrag | FH-PUB-ID: 2276
S. Sanaullah, S. Koravuna, U. Rückert, and T. Jungeblut, “SNNs Model Analyzing and Visualizing Experimentation Using RAVSim,” in Engineering Applications of Neural Networks. 23rd International Conference, EAAAI/EANN 2022, Chersonissos, Crete, Greece, June 17–20, 2022, Proceedings, Greta, 2022, pp. 40–51.
HSBI-PUB | DOI
 
[1]
2019 | Konferenzbeitrag | FH-PUB-ID: 2975 | OA
S. Sanaullah, H. Baig, and J. A. Lee, “Accelerating the Threshold and Timing Analysis of Genetic Logic Circuit Models,” presented at the 11th International Workshop on Bio-Design Automation, Cambridge England.
HSBI-PUB | DOI | Download (ext.)
 
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Darstellung / Sortierung

Zitationsstil: IEEE

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