Convolutional Neural Network Detecting Synthetic Cannabinoids

Praisler

2022

Inventions

This paper presents the alternative training strategies we tested for an Artificial Neural Network (ANN) designed to detect JWH synthetic cannabinoids. In order to increase the model performance in terms of output sensitivity, we used the Neural Designer data science and machine learning platform combined with the programming language Python. We performed a comparative analysis of several optimization algorithms, error parameters and regularization methods. Finally, we performed a new goodness-of-fit analysis between the testing samples in the data set and the corresponding ANN outputs in order to investigate their sensitivity. The effectiveness of the new methods combined with the optimization algorithms is discussed.

Section: Methodsmentioning

confidence: 99%

Sensitivity Analysis of Artificial Neural Networks Identifying JWH Synthetic Cannabinoids Built with Alternative Training Strategies and Methods

Praisler

2022

Inventions

“…The group of positives contains 50 JWH synthetic cannabinoids, while the group of negatives includes 100 compounds (50 non-JWH cannabinoids and 50 others drugs). The list of these compounds was presented in a previous paper [2].…”

Section: Amentioning

confidence: 99%

Computerized Detection of JWH Synthetic Cannabinoids Class Membership Based on Machine Learning Algorithms and Molecular Descriptors

Praisler

2022 IEEE International Conference on Automation, Quality and Testing, Robotics (AQTR)

2022

Self Cite

An Artificial Neural Networks (ANN) model identifying JWH Synthetic Cannabinoids, that we have developed based on a combination of topological, 3D-MoRSE (Molecule Representation of Structure based on Electron diffraction) and ADMET (Absorption, Distribution, Metabolism, Excretion and Toxicity) molecular descriptors, is described and analyzed. The validation results indicate that this computerized system has a very high potential for efficiently predicting the class membership of JWH and discriminating them from a large variety of (non-JWH) substances of forensic interest.

“…Our research is part of ongoing efforts addressing the growing challenges of synthetic drug proliferation. In a prior study, we obtained promising results with a DCNN model based on the adapted Inception-V3 architecture [14]. In this work, we have presented the performances of a custom-designed DCNN, which was developed from scratch and combined with a specially designed pre-trained Convolutional Autoencoder.…”

Section: Introductionmentioning

confidence: 99%

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

Burlacu,

Praisler

et al. 2023

Inventions

Self Cite

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.