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The implementation of smart cities is necessary in order to enhance the management of urban resources through the use of cloud and fog computing, owing to a multitude of pressing concerns and evolving urban dynamics. In this paper, the incorporation of cloud and fog computing with a Cyber-Physical System (CPS) is presented to enhance the efficiency of urban resource management in the context of smart cities. As the process of urbanization intensifies, cities are confronted with unparalleled obstacles in their pursuit of sustainability, optimal resource allocation, and enhanced well-being for their inhabitants. Smart cities, which are constructed based on the concepts of technology-driven efficiency and sustainability, provide novel resolutions to these intricate urban challenges. The integration of cloud and fog computing technologies is the fundamental basis for the development of CPS. A CPS-based framework is presented that serves as a bridge between the physical and digital realms. An algorithm for task placement for urban resource management is proposed that reduces the latency and network consumption to facilitate the seamless integration of real-time data monitoring, analysis, and control mechanisms for essential resources. In the simulation, the iFogSim toolkit is used for performing the experiments. The results show the effectiveness of the proposed framework. Through the utilization of these technologies, smart cities have the potential to establish a foundation for urban environments that are characterized by enhanced sustainability, efficiency, and livability. [ABSTRACT FROM AUTHOR]

The healthcare sector faces many challenges, such as rising expenses, rising patient demands, and a lack of qualified healthcare workers. To tackle these difficulties and enhance patient outcomes, healthcare systems are progressively using advanced technologies such as Artificial Intelligence (AI), the Internet of Things (IoT), Fog computing, and Cloud computing. This study investigates these technologies' potential and integration to significantly transform the healthcare system, enhance patient care, and optimize operational efficiency. Furthermore, this paper examines the possible synergies that can be achieved via the harmonious integration of different technologies, leading to a more efficient and effective healthcare system. Moreover, this study delves into sustainable healthcare, emphasizing the imperative of striking a harmonious equilibrium between providing superior care, upholding environmental stewardship, and enduring sustainability. Implementing sustainable healthcare strategies, in conjunction with incorporating artificial intelligence (AI), Internet of Things (IoT), Fog computing, and Cloud computing, has the potential to revolutionize the healthcare sector. This transformation is expected to result in improved healthcare quality, reduced costs, and innovative approaches to promoting health and well-being. [ABSTRACT FROM AUTHOR]

In the healthcare industry, telemedicine has emerged as a game-changing technology that makes medical treatments accessible from a distance. However, the critical factors for delivering high-quality healthcare are the assurance of low latency and efficient resource utilization. This research study presents a new methodology to tackle these obstacles by incorporating Genetic Algorithms (GAs) into a Fog Computing framework for dynamic load balancing in telemedicine systems. By conducting a series of experiments, we conducted a comparative analysis between this approach and conventional cloud and fog computing configurations. The findings illustrate that our proposed strategy exhibits persistent superiority over alternative methods, resulting in a notable latency reduction and improved resource use optimization. Our load balancing technique, based on genetic algorithms (GA), enhances the quality of telemedicine services by effectively adjusting to varying workloads and network circumstances. This adaptation capability can increase patient care and ensure scalability for upcoming healthcare systems. [ABSTRACT FROM AUTHOR]

Fraud detection systems (FDS) are an important factor in e-commerce trading as online fraud grows and spreads quickly. The incidence of fraud involving online payments by credit cards has recently increased considerably, forcing all banks and e-commerce companies to carry out large-scale operational logs to create automatic methods to detect fraud. The controlled binary classification systems that are appropriately trained from pre-determined sampled data sets appear to be one of the most promising techniques for the detection of unlawful transactions, separating fraudulent and non-fraudulent cases. So the problems related to fraud detection are also classified as a novel challenge that is an assessment based on several advanced algorithms, such as random woodlands, Naive Bays (NB), and generative opponent networks. The problem is a classification problem. In this article, random forest and generative adversary networks are developed to further increase the accuracy of the detection rate of fraud via credit cards. Extensive experiments have shown that the technique offered exceeds current techniques. [ABSTRACT FROM AUTHOR]