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.

Connected and Autonomous Vehicles are promising for advancing traffic safety and efficiency. However, the increased connectivity makes these vehicles vulnerable to a broad array of cyber threats. This paper presents a novel hybrid approach for intrusion detection in in-vehicle networks, specifically focusing on the Controller Area Network bus. Ensemble learning techniques are combined with sophisticated optimization techniques and dynamic adaptation mechanisms to develop a robust, accurate, and computationally efficient intrusion detection system. The proposed system is evaluated on real-world automotive network datasets that include various attack types (e.g., Denial of Service, fuzzy, and spoofing attacks). With these results, the proposed hybrid adaptive system achieves an unprecedented accuracy of 99.995% with a 0.00001% false positive rate, which is significantly more accurate than traditional methods. In addition, the system is very robust to novel attack patterns and is tolerant to varying computational constraints and is suitable for deployment on a real-time basis in various automotive platforms. As this research represents a significant advancement in automotive cybersecurity, a scalable and proactive defense mechanism is necessary to safely operate next-generation vehicles.

This work investigates the potential of ChatGPT, a cutting-edge large language model (LLM), for software design analysis specifically in detecting architectural patterns and tactics. The evaluation involves comparing ChatGPT’s performance with that of Archie, a traditional Eclipse plugin designed for architectural analysis. The study uses the source code of five open-source software systems as the testing ground. Results reveal that ChatGPT achieves noteworthy performance in both pattern and tactic detection tasks. Specifically, for pattern detection, ChatGPT demonstrates an accuracy of up to 47.06%, while for tactic detection, it achieves a precision of 28.25%. While ChatGPT’s current capabilities are not yet a replacement for specialized tools like Archie, it offers significant potential as a complementary tool in architectural analysis workflows. By bridging the gap between natural language understanding and software engineering, ChatGPT could pave the way for more intelligent and automated solutions in the field. However, a key limitation is its difficulties in handling foundational or traditional tactics, resulting in a lower detection rate in certain areas. This research contributes valuable insights into the application of LLMs in software engineering, highlighting both the strengths and the limitations of ChatGPT in addressing complex architectural tasks.