Often in open multiagent systems, agents interact with other agents to meet their own goals. Trust is, therefore, considered essential to make such interactions effective. However, trust is a complex, multifaceted concept and includes more than just evaluating others’ honesty. Many trust evaluation models have been proposed and implemented in different areas; most of them focused on algorithms for trusters to model the trustworthiness of trustees in order to make effective decisions about which trustees to select. For this purpose, many trust evaluation models use third party information sources such as witnesses, but slight consideration is paid for locating such third party information sources. Unlike most trust models, the proposed model defines a scalable way to locate a set of witnesses, and combines a suspension technique with reinforcement learning to improve the model responses to dynamic changes in the system. Simulation results indicate that the proposed model benefits trusters while demanding less message overhead.

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.

The Internet of Agents (IoA) is an emerging field of research that aims to combine the advantages of multi-agent systems and Internet of Things (IoT), by adding autonomy and smartness to, traditionally, dummy things used in IoT. Multi-agent systems can be used to model distributed systems of smart grids, such as smart grid operations, power system control, electricity market, and monitoring and diagnostic. Trust management can be considered a key component for successful interactions between autonomous agents in IoA, especially when agents cannot assure that potential interactions’ partners share the same core beliefs, or make accurate statements regarding their competencies and abilities. When interactions are based on trust, trust establishment mechanisms can be used to direct trustees, instead of trustors, to build a higher level of trust and have a greater impact on the results of interactions. This paper presents a trust establishment model that uses a multi-criteria (multidimensional) approach to help trustees in IoA environment to adjust their behaviors to improve their perceived trustworthiness, to attract more interactions with trustors. It calculates the necessary improvement per criterion when only a single aggregated satisfaction value is provided per interaction, where the model attempts to predicted both the appropriate value per criteria and its importance. The proposed model is evaluated through simulation, and results indicate that trustees empowered with the proposed model have higher levels of trust and better chances to be selected as interaction partners when such selection is based on trust.

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.