Building norms-adaptable agents from Potential Norms Detection Technique (PNDT)

This paper presents a contribution to research on norms detection by proposing a technique, which is called the Potential Norms Detection Technique (PNDT). The literature proposes that an agent changes or updates its norms based on the variables of the local environment and the amount of thinking about its behaviour. Consequently, any changes on these two variables cause the agent to use the PNDT to update the norms in complying with the domain’s normative protocol. This technique enables an agent to update its norms even in the absence of sanctions from a third-party enforcement authority as found in some work, which entail sanctions by a third-party to detect and identify the norms. The PNDT consists of five components: agent’s belief base; observation process; Potential Norms Mining Algorithm (PNMA) to detect the potential norms and identify the normative protocol; verification process, which verifies the detected potential norms; and updating process, which updates the agent’s belief base with new normative protocol. The authors then demonstrate the operation of the algorithm by testing it on a typical scenario and analyse the results on several issues

Review of iris segmentation and recognition using deep learning to improve biometric application

Biometric recognition is essential for identifying people in security, surveillance, and mobile device authentication. Iris recognition (IR) biometrics is exact because it uses unique iris patterns to identify individuals. Iris segmentation, which isolates the iris from the rest of the ocular image, determines iris identification accuracy. The main problem is concerned with selecting the best deep learning (DL) algorithm to classify and estimate biometric iris biometric iris. This study proposed a comprehensive review of DL-based methods to improve biometric iris segmentation and recognition. It also evaluates reliability, specificity, memory, and F-score. It was reviewed with iris image analysis, edge detection, and classification literature. DL improves iris segmentation and identification in biometric authentication, especially when combined with additional biometric modalities like fingerprint fusion. Besides, that DL in iris detection requires large training datasets and is challenging to use with noisy or low-quality photos. In addition, it examines DL for iris segmentation and identification efforts to improve biometric application understanding. It also suggests ways to improve precision and reliability. DL may be used in biometric identification; however, further study is needed to overcome current limits and improve IR processes

Nature-Inspired Drone Swarming for Wildfires Suppression Considering Distributed Fire Spots and Energy Consumption

Wildfires are among the biggest problems faced worldwide. They are increasing in severity and frequency, causing economic losses, human death, and significant environmental damage. Environmental factors, such as wind and large forest areas, contribute to the fire spreading over multiple fire spots, all of which grow continuously, making fire suppression extremely difficult. Therefore, fire spots should be coverage simultaneously to contain the spread and prevent coalescence. Therefore, this study presents a new model based on the principles of nature-inspired metaheuristics that uses Swarm Intelligence (SI) to test the effectiveness of using an autonomous and decentralized behaviour for a swarm of Unmanned Aerial Vehicles (UAVs) or drones to detect all distributed fire spots and extinguishing them cooperatively. To achieve this goal, we used the improved random walk algorithm to explore the distributed fire spots and a self-coordination mechanism based on the stigmergy as an indirect communication between the swarm drones, taking into account the collision avoidance factor, the amount of extinguishing fluid, and the flight range of the drones. Numerical analysis and extensive simulations were performed to investigate the behaviour of the proposed methods and analyze their performance in terms of the area-coverage rate and total energy required by the drone swarm to complete the task. Our quantitative tests show that the improved model has the best coverage (95.3%, 84.3% and 65.8%, respectively) compared to two other methods Levy Flight (LF) algorithm and Particle Swarm Optimization (PSO), which use the same initial parameter values. The simulation results show that the proposed model performs better than its competitors and saves energy, especially in more complicated situations

The semantics of norms mining in multi-agent systems

In this paper, we present the semantics of our proposed norms mining technique for an agent to detect the norms of a group of agents in order to comply with the group’s normative protocol. We define the semantics of entities and processes that are involved in the norms mining technique. The technique entails an agent exploiting the resources of the host system,implementing data formatting and filtering, and identifying the norms’ components that contribute to the strength of the norms to identify the group’s potential norms. We then present an algorithm of the norms mining operation and demonstrate the success of the technique in detecting the potential norms

Norms detection and assimilation in multi-agent systems: a conceptual approach

In this paper, we propose a technique for a software agent to detect the norms of a community of agents and assimilate its behaviour to comply with the local normative protocol, failing which, the agent is refused services and resources. In this technique, the software agent is equipped with an algorithm, which detects and analyzes the normative interactions between local agents. When the detection is successful, it launches another algorithm to request for its assimilation to the local normative protocol, indicating its acceptance by the group of local agents

From 5G to 6G Technology: Meets Energy, Internet-of-Things and Machine Learning: A Survey

Due to the rapid development of the fifth-generation (5G) applications, and increased demand for even faster communication networks, we expected to witness the birth of a new 6G technology within the next ten years. Many references suggested that the 6G wireless network standard may arrive around 2030. Therefore, this paper presents a critical analysis of 5G wireless networks’, significant technological limitations and reviews the anticipated challenges of the 6G communication networks. In this work, we have considered the applications of three of the highly demanding domains, namely: energy, Internet-of-Things (IoT) and machine learning. To this end, we present our vision on how the 6G communication networks should look like to support the applications of these domains. This work presents a thorough review of 370 papers on the application of energy, IoT and machine learning in 5G and 6G from three major libraries: Web of Science, ACM Digital Library, and IEEE Explore. The main contribution of this work is to provide a more comprehensive perspective, challenges, requirements, and context for potential work in the 6G communication standard