Financial fraud, considered as deceptive tactics for gaining financial benefits, has recently become a widespread menace in companies and organizations. Conventional techniques such as manual verifications and inspections are imprecise, costly, and time consuming for identifying such fraudulent activities. With the advent of artificial intelligence, machine-learning-based approaches can be used intelligently to detect fraudulent transactions by analyzing a large number of financial data. Therefore, this paper attempts to present a systematic literature review (SLR) that systematically reviews and synthesizes the existing literature on machine learning (ML)-based fraud detection. Particularly, the review employed the Kitchenham approach, which uses well-defined protocols to extract and synthesize the relevant articles; it then report the obtained results. Based on the specified search strategies from popular electronic database libraries, several studies have been gathered. After inclusion/exclusion criteria, 93 articles were chosen, synthesized, and analyzed. The review summarizes popular ML techniques used for fraud detection, the most popular fraud type, and evaluation metrics. The reviewed articles showed that support vector machine (SVM) and artificial neural network (ANN) are popular ML algorithms used for fraud detection, and credit card fraud is the most popular fraud type addressed using ML techniques. The paper finally presents main issues, gaps, and limitations in financial fraud detection areas and suggests possible areas for future research.

With the integration of Internet of Things (IoT) technology, smart cities possess the capability to advance their public transportation modalities, address prevalent traffic congestion challenges, refine infrastructure, and optimize communication frameworks, thereby augmenting their progression towards heightened urbanization. Through the integration of sensors, cell phones, artificial intelligence (AI), data analytics, and cloud computing, smart cities worldwide are evolving to be more efficient, productive, and responsive to their residents’ needs. While the promise of smart cities has been marked over the past decade, notable challenges, especially in the realm of security, threaten their optimal realization. This research provides a comprehensive survey on IoT in smart cities. It focuses on the IoT-based smart city components. Moreover, it provides explanation for integrating different technologies with IoT for smart cities such as AI, sensing technologies, and networking technologies. Additionally, this study provides several case studies for smart cities. In addition, this study investigates the challenges of adopting IoT in smart cities and provides prevention methods for each challenge. Moreover, this study provides future directions for the upcoming researchers. It serves as a foundational guide for stakeholders and emphasizes the pressing need for a balanced integration of innovation and safety in the smart city landscape.

Flood mapping using remote sensing data is critical to disaster response, especially in real-time monitoring and edge deployment. However, existing deep learning models often face challenges related to computational complexity, latency, and limited scalability in dynamic and resource-constrained environments. This paper proposes FloodNet-Lite, a lightweight and scalable deep learning framework for real-time flood mapping in 6G-enabled environments. The system integrates an optimized UNet architecture with MobileNetV3 as a backbone, enhanced by depthwise separable convolutions, attention mechanisms, and advanced model compression techniques such as quantization-aware training and structured pruning. Knowledge distillation transfers learning from a high-capacity teacher model to a compact student model, while self-supervised learning further reduces dependency on labeled data to boost efficiency and robustness. Crucially, this framework is designed for deployment in 6G edge computing infrastructures, leveraging 6G capabilities such as ultra-reliable low-latency communication (URLLC), AI-native edge nodes, and space-air-ground integrated networks (SAGIN). These features enable real-time flood detection via UAVs, ground sensors, and satellite imagery with minimal latency and maximal interoperability. The model achieves a mean IoU of 0.85 while maintaining an ultra-low model size of 18 MB and an inference speed of 14.3 FPS on Jetson Nano. This work provides a robust, scalable, and edge-ready flood mapping solution aligned with the vision and capabilities of future 6G-enabled intelligent disaster management systems.

An innovative approach is proposed to improve capacity performance by including dynamic reconfigurable intelligent surfaces (DRIS) in the downlink (DL) non-orthogonal multiple access (NOMA) power domain (PD) systems with massive multiple-input, multiple-output) (mMIMO) in the setting of 6G wireless networks. To guarantee the system’s best performance in different scenarios, we used a unique optimization approach to distribute power among users efficiently using the water-filling algorithm. Analysis of the influence of different deployment densities of static, and DRIS on the performance of the system is presented in this paper. The effect on the effective area spectral efficiency (EASE), and the ability of RIS to reduce latency and handle higher user loads are also discussed. The paper also demonstrates practical 6G configurations, including 256 quadrature amplitude modulation (256-QAM), channel state information (CSI), and successive interference cancellation (SIC). The results indicate that including DRIS in the mMIMO DL NOMA PD system significantly boosts capacity, and EASE while decreasing latency. Implementing logarithmic water filling has proven to be a highly effective method for distributing power location to maximize the capabilities of suggested systems. These results establish crucial information for enhancing future wireless communication systems, and in agreement with the estimated equation, the Monte Carlo results show that our work is accurate and reliable. Integrating the DRIS with four distinct user groups (4, 8, 16, and 32) improves the system’s capacity performance by 25%, 25.01%, 25.02%, and 25.03% respectively, compared to the performance conventional static RIS that applied in other related works.

This article examines the performance of cooperative NOMA systems in a massive MIMO configuration inside the sixth-generation (6G) network, including scenarios with and without reconfigurable intelligent surfaces (RIS). The emphasis is on comprehending the effects of implementing static, dynamic, and intelligent RIS on the capacity of the cooperative NOMA system with differing user quantities. The study examines how different user loads and RIS densities affect average latency and effective area spectral efficiency (EASE). Integration of a proposal system in a novel way with water-filling in the multi-user channel logarithm to enhance capacity, average latency, and EASE for diverse user densities. The results show that RIS significantly improves capacity, average latency, and EASE, especially with intelligent RIS. All scenarios, in conjunction with the suggested algorithm, markedly improve network performance, particularly under conditions of increased user demand. Additionally, it enhances the system's capability and spectrum efficiency, especially when deployment numbers rise.