The rapid increase of fraud attacks on banking systems, financial institutions, and even credit card holders demonstrate the high demand for enhanced fraud detection (FD) systems for these attacks. This paper provides a systematic review of enhanced techniques using Artificial Intelligence (AI), machine learning (ML), deep learning (DL), and meta-heuristic optimization (MHO) algorithms for credit card fraud detection (CCFD). Carefully selected recent research papers have been investigated to examine the effectiveness of these AI-integrated approaches in recognizing a wide range of fraud attacks. These AI techniques were evaluated and compared to discover the advantages and disadvantages of each one, leading to the exploration of existing limitations of ML or DL-enhanced models. Discovering the limitation is crucial for future work and research to increase the effectiveness and robustness of various AI models. The key finding from this study demonstrates the need for continuous development of AI models that could be alert to the latest fraudulent activities.

This paper explores the transformative impact of the Internet of Medical Things (IoMT) on healthcare. By integrating medical equipment and sensors with the internet, IoMT enables real-time monitoring of patient health, remote patient care, and individualized treatment plans. IoMT significantly improves several healthcare domains, including managing chronic diseases, patient safety, and drug adherence, resulting in better patient outcomes and reduced expenses. Technologies like blockchain, Artificial Intelligence (AI), and cloud computing further boost IoMT’s capabilities in healthcare. Blockchain enhances data security and interoperability, AI analyzes massive volumes of health data to find patterns and make predictions, and cloud computing offers scalable and cost-effective data processing and storage. Therefore, this paper provides a comprehensive review of the Internet of Things (IoT) and IoMT-based edge-intelligent smart healthcare, focusing on publications published between 2018 and 2024. The review addresses numerous studies on IoT, IoMT, AI, edge and cloud computing, security, Deep Learning, and blockchain. The obstacles facing IoMT are also covered in this paper, including interoperability issues, regulatory compliance, and privacy and data security concerns. Finally, recommendations for further studies are provided.

The rise of digital finance has led to a surge in fraudulent activities, particularly in credit card transactions and cryptocurrency ecosystems. With financial crimes becoming more sophisticated, traditional fraud detection methods often fail to identify complex fraudulent patterns. This research explores the application of machine learning (ML) and artificial intelligence (AI) techniques to enhance the security of digital finance by detecting fraudulent activities in credit card transactions and cryptocurrency wallets within the USA. The study utilizes large-scale transaction datasets containing key financial indicators such as transaction frequency, spending patterns, anomaly scores, and network behaviors. To develop an AI-driven fraud detection framework, we implement and compare six machine learning models: XGBoost, RLightGBM, Decision Trees, K-Nearest Neighbors (KNN), Convolutional Neural Networks (CNNs), and Autoencoders. The models are trained on both structured financial data (e.g., credit card transaction logs) and unstructured blockchain transaction records (e.g., Bitcoin wallet addresses and transaction flows). To address data imbalance, the study applies the Synthetic Minority Over-sampling Technique (SMOTE), ensuring fair representation of fraudulent transactions. Model performance is evaluated using Precision, Recall, F1-score, and ROC-AUC metrics to determine the most effective fraud detection approach. Additionally, the research emphasizes data privacy and security, incorporating anonymization techniques and regulatory compliance measures to safeguard sensitive financial information. This study contributes to the ongoing fight against financial fraud by demonstrating how AI-based solutions can enhance the security and resilience of digital finance systems in the USA.