The rapid growth of crypto asset markets has outpaced the capacity of regulatory frameworks to respond with agility and coherence. In Canada, the challenge is particularly complex due to overlapping provincial and federal jurisdictions, and the absence of a unified digital asset policy. This paper proposes a novel approach to crypto asset governance through a game-theoretic simulation framework that models regulator–firm interactions within Canada’s decentralized system. By combining classical game theory with agent-based modeling, the framework allows regulators to simulate and evaluate the outcomes of different policy strategies under varying levels of enforcement, coordination, and compliance. Using Canada as a case study, the simulation reveals how misaligned incentives between federal and provincial regulators can weaken enforcement, while coordinated signaling and adaptive regulation can improve outcomes. The paper contributes both theoretically and practically by offering a tool that can help regulators test the impact of proposed rules before implementation. The framework is scalable and adaptable to other jurisdictions facing similar decentralization challenges. The study’s primary objectives are to (1) develop an integrated game-theoretic and agent-based simulation framework for multi-level crypto asset regulation, (2) model Canada’s unique federal-provincial regulatory dynamics, and (3) identify optimal regulatory calibration points that balance compliance, innovation, and consumer protection. Simulation results demonstrate that a coordinated regulatory approach improves compliance rates by 13 percentage points and reduces consumer harm by 22%, while a purely punitive approach reduces the innovation score by 35%. These findings provide evidence-based guidance for policymakers navigating the complex trade-offs inherent in governing emerging digital asset markets.

Deep learning has significantly improved image quality in enhancement and retouching tasks. However, current methods, such as HDRNet, CSRNet, and 3D LUT, primarily rely on low-level visual features and lack in-depth utilization of image semantic information, resulting in global average enhancement, color inconsistencies, and loss of brightness details. Noise and blur in images intensify the blending of distinct features and contribute to information degradation, making it more challenging to extract semantic details and thereby limiting the recovery performance. In order to solve the above problem, this paper proposes SUNet, an image restoration model enhanced with semantic information. By incorporating fine-grained semantic information into UNet and employing orthogonal and decoupled feature representations, SUNet significantly improves restoration performance without relying on specific segmentation annotations. The introduction of semantic information enables the model to differentiate between different regions in the image distinctly, making feature extraction more targeted and progressively reducing feature coupling. This enhances the model’s ability to represent semantically relevant features while avoiding interference from blurry or noisy features. Our contribution effectively bridges the gap between global enhancement techniques and the need for local semantic accuracy, laying the foundation for more sophisticated image enhancement methods. Experimental evaluations on public datasets demonstrate that our approach outperforms state-of-the-art methods.

The exponential growth in data communication and increase in network size have led to various intrusions and attacks. An Intrusion Detection System (IDS) can be provided as a crucial component of a network or database to ensure the security of data communication over a network. The network size is large, a large dataset may comprise more irrelevant, redundant, and high-dimensional features that impact feature classification, thus affecting the intrusion detection rate. This study presents a new hybrid enhanced normalised Crow Search Algorithm (CSA) and Particle Swarm Optimisation (PSO) technique to address feature selection issues and to classify global best features using a random-forest classifier. In the proposed algorithm, the benefits of the CSA between the search strategy and rapid convergence phenomenon of the PSO algorithm are utilised to select the global best solution in a large search space. A random-forest classifier is used to classify the features after they are updated with weight values for significant features, assessing the asymptotic variance of features and points that are closest to the optimal solution. The asymptotic features are subjected to the weighted least mean square (WLS) method to eliminate large deviations among the features. The random-forest classifier distinguishes between normal records and abnormal intrusion records. The performance assessment of the proposed hybrid IDS model is performed by utilising two datasets, which reveals that the proposed model outperforms other existing models. The simulation outcomes show higher accuracy rate, precision value, recall factor, and F1-Score, revealing the efficacy of the IDS model.