الذكاء الاصطناعي المعتمد على الشبكات العصبية للتنبؤ بالحساسية وزمن الاستجابة في الحساسات الغازية

Narkhede, Parag., Walambe, Rahee., Mandaokar, Shruti., Chandel, Pulkit., Kotecha, Ketan., & Ghinea, George. (2021). Gas Detection and Identification Using Multimodal Artificial Intelligence Based Sensor Fusion. Applied System Innovation, 4(1), 3. https://doi.org/10.3390/asi4010003

Walambe, Rahee., Narkhede, Parag., Mandaokar, Shruti., et al. (2023). Gas Detection and Identification Using Multimodal AI-Based Sensor Fusion. Data Science Week Poster Presentation.

Zhang, Yue., Li, Hong., Chen, Xiaohui., & Wang, Jian. (2024). Gas Detection and Classification Using Multimodal Data Based on Deep Learning. Sensors, 24(18), 5904. https://doi.org/10.3390/s24185904

Chakraborty, Sourav., Mittermaier, Stefan., & Carbonelli, Claudio. (2022). Understanding the Behavior of Gas Sensors Using Explainable AI. Engineering Proceedings, 27(1), 61. https://doi.org/10.3390/ecsa-9-13350

Zhuang, Yuxuan., Yin, Dong., Wu, Lei., Niu, Guanghui., & Wang, Feng. (2024). A deep learning approach for gas sensor data regression: Incorporating surface state model and GRU-based model. APL Machine Learning, 2(1), 016104. https://doi.org/10.1063/5.0160983

Attallah, Omar., & Morsi, Islam. (2024). Multitask Deep Learning-Based Pipeline for Gas Leakage Detection via E-Nose and Thermal Imaging Multimodal Fusion. Chemosensors.

Liu, Ming., & Zhao, Qiang. (2024). 2024 Breakthroughs in Smart Gas Sensor Technology: A Review. Gas Detection Journal. Retrieved from https://gasdetection.com/articles/2024-breakthroughs-in-smart-gas-sensor-technology-a-review/.

Ahmed, Shahbaz., Kumar, Rajesh., & Lee, Donghyun. (2024). Assessing Gas Leakage Detection Performance Using Machine Learning with Different Modalities. Journal of Intelligent & Fuzzy Systems, 45(2), 123–135. https://link.springer.com/article/10.1007/s42341-024-00545-0

Angstenberger, Stefan., Sterl, Florian., Theelke, Kilian., Giessen, Harald., & Schwarz, Ulrich. (2025). Real-time detection of low gas concentrations using coherently controlled quartz-enhanced photoacoustic spectroscopy. Optica, 12(1), 45–52. https://doi.org/10.1364/OPTICA.544448.

Al-Hadeethi, Yousef., Alghamdi, Abdullah., Alshahrani, Hassan., Alqahtani, Mohammed. and Alshahrani, Fahad., 2023. Fabrication of ZnO/WO₃ nanocomposite thin films using sacrificial template method for enhanced VOC gas sensing. Nanomaterials, 13(4), p.733. Available at: https://doi.org/10.3390/nano13040733

Korotcenkov, Ghenadii., Brinzari, Valeriu., Cho, Byung-Keun., Gulina, Larisa. and Han, Seung-Hwan., 2014. The influence of heterojunctions on the gas sensing properties of SnO₂–WO₃ composite thin films. Sensors, 14(11), pp.20480–20512. Available at: https://doi.org/10.3390/s141120480

Zhang, Jian., Liu, Xiaobo., Neri, Giovanni. and Pinna, Nicola., 2015. Nanostructured materials for room-temperature gas sensors. Nanoscale, 7(6), pp.2028–2043. Available at: https://doi.org/10.1039/C5NR02571K.

Al-Hadeethi, Yousef., Alshahrani, Hassan. and Alqahtani, Mohammed., 2024. Enhanced NO₂ gas sensing performance of SnO₂-WO₃ nanocomposite thin films prepared by chemical bath deposition. Journal of Electronic Materials. Available at: https://doi.org/10.1007/s11664-024-11381-6

Hosseini-Golgoo, Seyed Mohammad., Yousefi, Mohammad Hossein., & Ghasemi, Saeid., 2013. Application of neural networks for gas identification using temperature-modulated sensors. Sensor Letters, 11(3), pp.556–561.

Zhou, Yue. and Liu, Xiaobo., 2021. Gas recognition based on time-series neural networks and sensor arrays. Sensors, 21(14), p.4826. https://doi.org/10.3390/s21144826

Dehnaw, Ali. et al., 2024. Deep Neural Network Optimization for Efficient Gas Detection Systems in Edge Intelligence Environments. Processes, 12(12), p.2638. https://doi.org/10.3390/pr12122638

Zong, Boyang. et al., 2025. Smart Gas Sensors: Recent Developments and Future Prospective. Nano-Micro Letters, 17(54). https://doi.org/10.1007/s40820-024-01543-w