Vision-language models (VLMs) offer a promising alternative to conventional fire detection systems by reasoning about the semantic context of a scene and thus reducing false alarms, yet their large model size makes deployment on embedded fire sensors impractical. We develop a teacher-student knowledge distillation framework in which large VLMs fine-tuned for fire understanding are distilled into lightweight students, then deploy the distilled models on a commercial Detectium fire detection sensor and evaluate accuracy, latency, and memory. Compact students preserve most of their teachers’ fire-understanding capability, and Qwen2.5-0.5B provides the strongest overall deployment trade-off.
@article{kazzazi2026distill,title={Distilling Vision-Language Models for On-Device Fire Understanding},author={Kazzazi, Mohammad and Liu, Zixuan and Khajavi, Siavash},journal={arXiv preprint arXiv:2609.05782},year={2026},month=sep,archiveprefix={arXiv},primaryclass={cs.AI},doi={10.48550/arXiv.2609.05782},url={https://arxiv.org/abs/2609.05782},}
We improve skin cancer diagnosis by combining late discrete wavelet transform features with new swarm-based optimizers for medical image analysis.
@article{kazzazi2026skin,title={Enhancing skin cancer diagnosis using late discrete wavelet transform and new swarm-based optimizers},author={Mousa, Ramin and Chamani, Saeed and Morsali, Mohammad and Kazzazi, Mohammad and Hatami, Parsa and Sarabi, Soroush},journal={Machine Learning with Applications},volume={23},pages={100811},year={2026},month=mar,publisher={Elsevier},doi={10.1016/j.mlwa.2025.100811},url={https://doi.org/10.1016/j.mlwa.2025.100811},}
CLEAR is a closed-form localization estimator with a reduced sensor network. The proposed method is a computationally efficient, two-stage estimator that fuses time-difference-of-arrival (TDOA) and frequency-difference-of-arrival (FDOA) measurements with a minimal number of sensors to localize a moving source in IoT settings.
@article{kazzazi2025clear,title={{CLEAR}: A Closed-Form Minimal-Sensor {TDOA}/{FDOA} Estimator for Moving-Source {IoT} Localization},author={Kazzazi, Mohammad and Morsali, Mohammad and Amiri, Rouhollah},journal={arXiv preprint arXiv:2510.04160},year={2025},month=oct,archiveprefix={arXiv},primaryclass={eess.SP},doi={10.48550/arXiv.2510.04160},url={https://arxiv.org/abs/2510.04160},}
