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With the promotion and development of oversized data technology, many data analysis platforms based on super large data storage and computing frameworks have emerged in the industry. While the platforms with oversized economic data analysis combined with machine learning models are still relatively lacking. And oversized data also brings a new problem, that is the security of economic development. It is an important and difficult task to analyse and detect risks from oversized economic data. Based on machine learning, data analysis, economic market and other multidisciplinary fields, this paper proposes a machine learning method, which is a genetic algorithm (GA) based feature selection method: FSGA. This method abstracts every possible feature selection result into an individual in GA, generates a population through genetic operation, and measures the merits of the individual through fitness. In addition, this paper has conducted multitudinous simulation experiments on the GA‐based FSGA method and the traditional LSTM data analysis method respectively. The accuracy rate and other indicators are obtained by comparing the training. The experimental results show that the GA‐based FSGA machine learning method has higher prediction accuracy when analysing oversized economic data. And it is practical to accelerate the development of digital economy. [ABSTRACT FROM AUTHOR]

As the deployment of Internet of Things (IoT) devices becomes more widespread, the demand for data analysis services is also increasing in both range and volume. Despite the increasing demand for services due to the rise of IoT devices, efficiently processing latency-sensitive data analysis tasks (DATs) remains a significant challenge, especially in areas where communication infrastructure is limited or unavailable. Unmanned aerial vehicles (UAVs) can enhance communication coverage and quality by leveraging their flexible deployment characteristics, particularly in areas where network coverage is limited for IoT devices. UAVs can provide edge computing services while simultaneously offering network coverage, making them a valuable asset for improving communication in such areas. In this paper, we propose an Mobile Edge Computing (MEC) system that utilizes multiple UAVs equipped with edge servers to offer latency-sensitive analytical services to IoT devices on the ground. However, traditional task scheduling algorithms are challenging to adapt to dynamic and complex edge network environments, particularly for scheduling tasks with dependencies. To address this challenge, we employ deep reinforcement learning (DRL) to develop Directed Acyclic Graph (DAG) task scheduling algorithms and UAV deployment optimization algorithms. Our algorithm enables us to obtain optimal scheduling and deployment adjustment strategies in dynamic and changing environments. Simulation experiments demonstrate that in a MEC system comprising of multiple UAVs, our algorithm can swiftly converge to the optimal value, resulting in a significant reduction in DAT response time and cluster energy consumption compared to baseline algorithms. [ABSTRACT FROM AUTHOR]

The construction of Smart Cities is inseparable from the healthy operation of markets. Reasonable data analysis can provide a crucial foundation for the development of market behavior by considering the enormous amount of data generated by a market economy. To this end, we propose enhanced cluster generative adversarial networks (eClusterGAN) to achieve latent space clustering. However, data storage security is crucial. Moreover, we suggest a GAN-based network intrusion detection system (GAN NIDS) that uses adversarial learning to assist the generator in learning the spatial distribution of normal network flows. The simulation results showed that the proposed eClusterGAN and GAN NIDS outperformed the benchmarks in terms of clustering accuracy, running time, precision, recall, and F1, which can support researchers in studying economic data trends. The construction of Smart Cities can effectively ensure healthy market development by discovering and disseminating the potential value of market economic data. [ABSTRACT FROM AUTHOR]