With the increase in globalization, the degree of electronic communication increases. Such an increase is also experienced by many sectors including the medical sector, which communicates and generates large amounts of data related to the spread of diseases as observed in the case of the COVID-19 pandemic. This has led to the deployment of Internet of Things (IoT) networks in many medical centers. However, one main challenge is how to maintain the security of the data and devices in the network. In this study, we discuss the cybersecurity risk associated with IoT networks used for medical services and provide a solution for protecting medical data and devices using an agent-based approach. Unlike most conventional cybersecurity models that use agents based on deterministic logic or independent learning agents to detect and prevent cybersecurity attacks, we propose a cybersecurity model using a collaborative network of learning agents, called Collabo, that share both mutual and causal values regarding their actions on a common security target. Our experimental results demonstrate the significance of our model over conventional models.

The use of generative learning models in natural language processing (NLP) has significantly contributed to the advancement of natural language applications, such as sentimental analysis, topic modeling, text classification, chatbots, and spam filtering. With a large amount of text generated each day from different sources, such as web-pages, blogs, emails, social media, and articles, one of the most common tasks in NLP is the classification of a text corpus. This is important in many institutions for planning, decision-making, and creating archives of their projects. Many algorithms exist to automate text classification tasks but the most intriguing of them is that which also learns these tasks automatically. In this study, we present a new model to infer and learn from data using probabilistic logic and apply it to text classification. This model, called GenCo, is a multi-input single-output (MISO) learning model that uses a collaboration of partial classifications to generate the desired output. It provides a heterogeneity measure to explain its classification results and enables a reduction in the curse of dimensionality in text classification. Experiments with the model were carried out on the Twitter US Airline dataset, the Conference Paper dataset, and the SMS Spam dataset, outperforming baseline models with 98.40%, 89.90%, and 99.26% accuracy, respectively.

Automotive driving assistant systems (ADAS) and Automated driving (AD) technologies are commonly employed in the control of unmanned vehicles along a path. However, like many other technologies, they come with risks, including potential misdirection and collisions with obstacles along the vehicle's route. To mitigate these risks, various tracking systems, including radar tracking systems, are employed to detect and monitor targets along the trajectories of ADAS/AD vehicles. Nevertheless, the effectiveness of these tracking operations is crucial in assessing the reliability of both tracking systems and ADAS/AD technologies, especially at the edge-computing level. In this study, we introduce a tracking technique using a collaborative predictive model in a time series, named CoPreMo, aimed at enhancing the reliability of radar system tracking operations. We conducted three experiments with this model on a simulated radar system to track a target's range at varying speeds across three ADAS/AD scenarios. The experiments yielded range tracking errors of 0.21m, 0.26m, and 0.32m, outperforming the baseline models.

The use of artificial intelligence in natural language processing (NLP) has significantly contributed to the advancement of natural language applications such as sentimental analysis, topic modeling, text classification, chatbots, and spam filtering. With a large amount of text generated each day from different sources such as webpages, blogs, emails, social media, and articles, one of the most common tasks in natural language processing is the classification of a text corpus. This is important in many institutions for planning, decision-making, and archives of their projects. Many algorithms exist to automate text classification operations but the most intriguing of them is that which also learns these operations automatically. In this study, we present a new model to infer and learn from data using probabilistic logic and apply it to text classification. This model, called GenCo, is a multi-input single-output (MISO) learning model that uses a collaboration of partial classifications to generate the desired output. It provides a heterogeneity measure to explain its classification results and enables the reduction of the curse of dimensionality in text classification. The classification results are compared with those of conventional text classification models, and it shows that our proposed model has a higher classification performance than conventional models.

Knowledge is a property that measures the degree of awareness of an agent about a target in an environment. The goal in conventional intelligent and cognitive agent development is to build agents that can be trained to gain knowledge about a target. The definition and operations of this knowledge associated to the agent is not clear, whereas these are required for developing a reliable, scalable and flexible agent. In this paper, we provide a concise theoretical framework for the description and quantification of the knowledge property needed for an efficient design of cognitive and rational intelligent agents. We relate the quantification scheme to the epistemological description of knowledge and present many illustrative examples on the usefulness of the quantification scheme.

Automotive driving assistant systems (ADAS) and Automated driving (AD) technologies are widely used in unmanned vehicle control along a trajectory, and just like many other technologies, they present some risks such as the risk of misdirection and collision of the vehicle with an obstacle along its trajectory. To avoid these, many tracking systems such as the radar tracking system are used to detect and track targets and trajectories of ADAS/AD vehicles. However, the performance of the tracking operation is an important factor in determining the reliability of the tracking systems and ADAS/AD technologies, especially at the edge computing level. In this study, we proposed a tracking technique using a collaborative predictive model in time series, called CoPreMo, to improve the reliability of the tracking operation of a radar system. We carried out three experiments with the model on a simulated radar system to track the range of a target with different speeds in three ADAS/AD scenarios and achieve a range tracking error of 0.21m, 0.26m, and 0.32m, better than those of the baseline models.