Credit Card Fraud (CCF) detection is a major challenge in financial security, especially in detecting unauthorized transactions. As fraudsters' patterns increase, traditional methods face challenges in detecting them. Hence, this paper proposes a Sea Lion - Self-Supervised Network (SL-SSNet) to improve detection accuracy. This research study aims to enhance performance by optimizing data quality feature extraction and achieving better results in fraud detection. The innovative approach for CCF detection using a hybrid optimization model combines the strengths of Sea Lion optimization and Self-Supervised Networks to improve both model accuracy and performance. Initially, the CCF dataset is collected from Kaggle. Then the data goes through a pre-processing phase where irrelevant data points, noise, and low-quality data are removed. The relevant data is selected in the next phase. Feature extraction is performed to select the most important and influential features related to fraud detection. The final phase is prediction and performance evaluation. The results show that the SL-SSNet model performs better than other methods in fraud detection. Specifically, the model achieved 99.98% accuracy, 82.46% precision, 97.23% recall, and 89.97% F1-score. These results prove the effectiveness and robustness of SL-SSNet in detecting fraudulent transactions.

Healthcare is on the verge of revolutionary change through the combination of urban computing solutions with edge computing in the period of urban expansion and technological progress. The key objectives of the chapter will focus on understanding how edge computing can optimize data processing at the source, minimizing latency and enabling immediate decision-making in urgent healthcare scenarios, and identifying specific challenges faced by urban healthcare systems, including data overload, resource allocation inefficiencies, and accessibility issues in densely populated areas. The chapter uses a combination of qualitative and quantitative research methods as its methodology framework. A study of urban healthcare facilities through different case examples evaluates positive results as well as best practices in implementation. The book chapter investigates the fundamental framework of edge computing within healthcare systems which specifically targets main goals to improve patient outcomes together with operational refinement and immediate data evaluation capabilities.

In the rapidly evolving landscape of blockchain technology, accurately classifying blockchain addresses is crucial for enhancing security, tracking illicit activities, and understanding transaction behaviors. This paper presents a novel classification method leveraging a Graph Neural Network (GNN) framework, which utilizes an account balance model to analyze relationships and transactions between blockchain addresses. By constructing a graph representation of the blockchain network, we capture both the structural and functional attributes of addresses. Our approach demonstrates significant improvements in classification accuracy compared to traditional methods, offering a robust solution for identifying various address types, including exchanges, personal wallets, and service providers. Experimental results show that our model effectively distinguishes between different classes of addresses, thereby contributing to the broader field of blockchain analytics and security.

Blockchain technology, originally associated with digital currencies, possesses key features such as decentralization, anonymity, robustness, and resistance to tampering, making it an ideal platform for building covert communication channels. This chapter begins by introducing the concept of network covert channels, discussing their definition, historical development, and the architecture of blockchain technology with an exploration of traditional network covert channels. It then highlights the advantages of blockchain-based covert communication channels over conventional methods. The chapter classifies blockchain network covert channels based on several key components. It provides a detailed analysis of the advantages and disadvantages of these channels in terms of covert nature, transmission efficiency, and communication costs. Finally, the chapter addresses existing challenges and limitations within blockchain network covert channels and offers insights into future research directions to improve their efficiency, security, and scalability in the context of secure communication.

Credit risk poses a substantial problem to the banking and financial industries, especially when borrowers fail to satisfy their repayment commitments. Conventional approaches have various challenges in effectively anticipating credit risk evaluations, including the incidence of fraudulent activity. Therefore, to avoid these problems, a new approach called the Pigeon U Net Prediction System (PUNPS) has been developed for credit risk prediction and classification. The credit card transaction dataset was collected using the Kaggle platform. The dataset was then preprocessed to remove duplicate items. The feature selection approach was used to keep only relevant variables. Credit risk prediction was successfully carried out using the fitness function of pigeon optimization. Furthermore, the classified credit risk forecasts were processed with the U-Net framework. Finally, the performance of the model was evaluated, and the findings were compared with standard approaches. This method offers significant advantages compared to conventional models, demonstrating improved performance in predicting credit risk through improved accuracy. The performance of this model is evaluated through various risk assessment metrics such as F1 score, Precision, recall, Accuracy, and error rate. It shows an impressive accuracy of 99.8%, accompanied by a precision score of 99.9% and a recall score of 99.7%. An F1 score of 99.6% confirms its effective balance between Precision and recall, establishing it as a reliable and accurate tool for credit risk assessment. In addition, it maintains a minimal error rate of only 0.2%.