بحوث سكوبس — م.د. نور عبد الكريم محمد علي

The rapid evolution of 6G networks necessitates intelligent and secure spectrum allocation to meet the demands of ultra-dense connectivity, low latency, and high data throughput. Multi-Agent Reinforcement Learning (MARL) offers a promising direction for enabling adaptive decision-making across distributed network agents. However, existing spectrum allocation methods often suffer from limited adaptability to dynamic environments and are vulnerable to security threats such as jamming and unauthorized access. To address these limitations, this study proposes a novel framework based on Multi-Agent Deep Q-Network (MADQN), where multiple agents learn collaborative and secure spectrum allocation strategies through deep reinforcement learning. The framework integrates adversarial training to detect and mitigate malicious behaviors while ensuring efficient and conflict-free spectrum sharing. The proposed method is particularly suitable for dynamic spectrum sharing in dense urban 6G environments, enabling real-time adaptation to traffic load, user mobility, and interference patterns. Experimental results demonstrate that MADQN significantly improves spectrum utilization efficiency by 97.6%, minimizes interference by 35%, and enhances system security by 98.4%, as well as scalability by 96.5%, compared to traditional centralized and rule-based approaches. This framework represents a scalable and robust solution for future 6G networks requiring autonomous and secure spectrum management. © 2025 IEEE.

The usage of blockchain technology contributes to ensuring transparency, security, and confidence in addition to transparency. The purpose of this study is to compare a standard supply chain to a blockchain-enhanced model in terms of transparency. This research underlines that implementing distributed ledger technology can reduce fraud and delays and improve traceability through a performance evaluation based on cost-effectiveness, trust enhancement, and operational streamlining. To evaluate the practical use of blockchain in real-world applications, this study incorporates expert interviews and case studies from diverse commercial sectors. The studies revealed that blockchain boosts supply chain visibility to a major degree, automates contract execution through smart contracts, and fosters trust in stakeholders. Nonetheless, concerns like scaling issues, regulatory uncertainties, and integration hurdles still exist. A comparison of supply chains based on traditional indicates a distinct efficiency in data integrity and real-time monitoring. Moreover, opinions of manufacturers, logistics service providers, regulators, and consumers underline the necessity for unified policy and industry-wide cooperation. Strategies and laws must be established to solve the economic and technological constraints. This report contributes to the knowledge on blockchain-assisted supply chains and gives realworld recommendations for boosting adoption for governments and industry players. Future studies should look into a mixture of blockchain models and AI for improvement. © 2025 IEEE.

This study investigates the significant effects of combining robotics, Artificial Intelligence (AI), neural network technology, and 6G networks in the surgical area. The combination of these technologies is improving decision-making, precision, and connectivity while completely changing surgical procedures. These technologies will provide insights into the revolutionary potential and potential implications of current achievements in the medical profession through an extensive overview. Beyond historical bounds and changing the landscape of surgical operations, the combination of AI, robots, neural networks, and the impending advent of 6 G networks has ushered in a new age for remote surgery. An overview of the revolutionary synergy between these state-of-the-art technologies and their combined effects on accuracy, usability, and patient-centred care in the context of remote surgery is given in this research. Robotics integration has given remote surgery previously unheard-of accuracy and dexterity. Currently, surgeons can operate robotic arms from a distance, surpassing the restrictions of traditional teleoperation. The surgical capabilities are enhanced by this robotic synergy, enabling more complex manoeuvres and delicate treatments. © 2024 University of Split, FESB.

Neural networks are effectively used in a variety of applications including data mining. The neural network can realize different complex nonlinear functions by making them attractive to identify a system. One of the most important issues of classifying datasets through neural networks is the formation of an ideal network, that consists of many successive steps like set parameters. Perhaps the most prominent parameter is the learning rate. Indeed, choosing an appropriate learning rate value is one of the things that greatly helps to control the overall network performance. In contrast, any inappropriate value for the learning rate negatively affects the classification model and can therefore destabilize the model’s performance and thus seriously deteriorate its quality. This paper presents a new model by adopting a cyclical learning rate instead of using a constant value for training deep neural networks by Multi-Layer Perceptron (MLP) architecture. This model is tested on various real-world datasets; Electricity, NSL- KDD, and four sub-datasets of HuGaDB (HuGaDB-01-01, HuGaDB-05- 12, HuGaDB-13-11, and HuGaDB-14-05). The proposed model achieves an accuracy of, 89.57%, 99.12%, 99.2%, 97.83%, 96.19%, and 99.85% for these datasets respectively. Accordingly, the proposed model outperforms many previous models. As a result, the deep neural network models can be more effective when they adopt an appropriate value for the learning rate. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.