A hybrid deep learning framework for SEM-based air pollutant analysis: Mamba integration and GAN-augmented training.

Area of Science:

• Environmental Science
• Computer Science
• Artificial Intelligence

Background:

• Air pollution analysis is critical for public health and ecological balance.
• Accurate identification of airborne pollutants requires advanced analytical methods.
• Current methods face challenges with data limitations and computational efficiency.

Purpose of the Study:

• To develop a novel deep learning framework for efficient air pollutant classification using image data.
• To improve detection accuracy and inference speed compared to existing models.
• To address the issue of limited labeled data through data augmentation.

Main Methods:

• Integration of the Mamba mechanism (a state space model) with convolutional layers for image classification.
• Utilizing Mamba blocks for global semantic representation and convolutional layers for local feature extraction.
• Employing a Conditional Generative Adversarial Network (CGAN) for data augmentation to synthesize realistic particulate images.

Main Results:

• The proposed model demonstrates superior long-range dependency modeling and linear computational complexity.
• Significant improvements in detection accuracy and inference speed over traditional CNN and Transformer baselines.
• Effective mitigation of overfitting and enhanced generalization capabilities due to GAN-based data augmentation.

Conclusions:

• The novel deep learning framework offers an efficient and accurate solution for classifying airborne pollutant components from images.
• The integration of Mamba and CGAN effectively addresses challenges of limited data and computational demands.
• The approach shows strong potential for real-world applications in environmental monitoring and air quality assessment.