The presence of aflatoxin M1 in breast milk poses a serious risk to the health of infants because of its potential to cause cancer and have negative effects on development. Detecting AFM1 in milk samples using conventional methods is often time-consuming and may not provide real-time monitoring capabilities. The use of machine learning techniques to forecast aflatoxin M1 levels in breast milk samples is examined in this study. To develop predictive models of aflatoxin M1 in breast milk, we employed well-known supervised machine learning algorithms such as Random Forest and Gradient Boosting. The findings show that machine learning can be used for the identification of aflatoxin M1 in breast milk. By actively monitoring breast quality, this research highlights the significance of machine learning in protecting babies’ health and advances the prediction skills in food safety.

Mycotoxins are toxic compounds produced by certain fungi, whose health effects may be significant when they contaminate fresh eggs. Conventional methods of mycotoxin analysis, while accurate, are labor-intensive, time-consuming, and impractical for large-scale screening applications. This study attempts to use using machine learning techniques to predict the concentration and presence of deoxynivalenol (DON), aflatoxin B1 (AFB1), and ochratoxin A (OTA) in fresh eggs from Jordan. Rather than replacing analytical detection methods, the proposed approach can enable a risk-based prioritization of samples for laboratory testing by identifying high-risk samples based on environmental and production factors. A dataset consisting of 1250 poultry egg samples collected between January and July 2024 under several factors involving environmental conditions and chemical assay results regarding mycotoxin content in eggs was used. Several machine learning algorithms were used in this study to build predictive models, including decision trees, support vector machines, and neural networks. The results indicate that machine learning can accurately and reliably predict mycotoxin contamination, which demonstrates the potential for integrating machine learning into food safety protocols. This study contributes toward developing predictive analytics for food safety and lays the groundwork for future research aimed at improving contamination monitoring systems.

In this research, we propose a graph-based approach to classify ECG signals. We model the ECG signal as a time-varying graph and examine its dynamics at two levels: intra-beat and inter-beat. The proposed framework employs a two-level graph representation of the ECG. At the intra-beat level, each beat is modeled as a graph in which the P, Q, R, S, and T waves serve as nodes, and temporal distance information is incorporated into the node feature vector. At the inter-beat level, the ECG signal is represented as a graph of beats. We investigated two node representations: learnable MLP embeddings derived from handcrafted features and reduced features obtained via PCA. Unlike conventional temporal GCNs, graph attention networks (GATs), and transformers, which predominantly rely on single-level representations of ECG signals, the proposed approach builds hierarchical graph structures directly from ECG signals. The dual model was trained on 1024 ECG segments from the PTB Diagnostic ECG Database (PTBDB) and tested on 398 ECG segments from the MIT-BIH Arrhythmia Database to evaluate its performance. The results indicate that the best-performing configuration, which employs PCA-reduced node features, outperforms the other configurations tested. The model achieves a mean accuracy of $97.67\%$ and a mean F1-score of $97.18\%$ over five runs.