Harnessing Deep Convolutional Neural Networks Detecting Synthetic Cannabinoids: A Hybrid Learning Strategy for Handling Class Imbalances in Limited Datasets

Abstract

The aim of this research was to develop and deploy efficient deep convolutional neural network (DCNN) frameworks for detecting and discriminating between various categories of designer drugs. These are of particular relevance in forensic contexts, aiding efforts to prevent and counter drug use and trafficking and supporting associated legal investigations. Our multinomial classification architectures, based on Attenuated Total Reflectance Fourier-Transform Infrared (ATR-FTIR) spectra, are primarily tailored to accurately identify synthetic cannabinoids. Within the scope of our dataset, they also adeptly detect other forensically significant drugs and misused prescription medications. The artificial intelligence (AI) models we developed use two platforms: our custom-designed, pre-trained Convolutional Autoencoder (CAE) and a structure derived from the Vision Transformer Trained on ImageNet Competition Data (ViT-B/32) model. In order to compare and refine our models, various loss functions (cross-entropy and focal loss) and optimization algorithms (Adaptive Moment Estimation, Stochastic Gradient Descent, Sign Stochastic Gradient Descent, and Root Mean Square Propagation) were tested and evaluated at differing learning rates. This study shows that innovative transfer learning methods, which integrate both unsupervised and supervised techniques with spectroscopic data pre-processing (ATR correction, normalization, smoothing) and present significant benefits. Their effectiveness in training AI systems on limited, imbalanced datasets is particularly notable. The strategic deployment of CAEs, complemented by data augmentation and synthetic sample generation using the Synthetic Minority Oversampling Technique (SMOTE) and class weights, effectively address the challenges posed by such datasets. The robustness and adaptability of our DCNN models are discussed, emphasizing their reliability and portability for real-world applications. Beyond their primary forensic utility, these systems demonstrate versatility, making them suitable for broader computer vision tasks, notably image classification and object detection.

Overview of Data Augmentation Techniques in Time Series Analysis

“…Data augmentation transcends various learning paradigms, playing a significant role in both supervised and unsupervised learning contexts. In supervised learning, it addresses challenges like class imbalance and enriches small datasets, enhancing model accuracy and reliability [4]. In unsupervised learning, augmentation techniques help in extracting more robust features and patterns from unlabeled data, a vital aspect in domains such as natural language processing and computer vision [5].…”