Journal of Computing and Information Technology最新文献

Optimization and Benefit Assessment of Production Supply Chain Networks Using Graph Neural Network Models 利用图神经网络模型对生产供应链网络进行优化和效益评估

Journal of Computing and Information Technology Pub Date : 2024-07-15 DOI: 10.20532/cit.2024.1005804

Ting Dong, M. Samonte

{"title":"Optimization and Benefit Assessment of Production Supply Chain Networks Using Graph Neural Network Models","authors":"Ting Dong, M. Samonte","doi":"10.20532/cit.2024.1005804","DOIUrl":"https://doi.org/10.20532/cit.2024.1005804","url":null,"abstract":"With the flourishing development of global economy, effective management of production supply chains is crucial for the competitiveness of enterprises. Optimizing supply chain networks can not only improve the efficiency of resource allocation but also enhance market responsiveness and systemic risk resistance. Traditional supply chain network optimization methods, focusing mostly on linear models and static analysis, fall short in addressing the growing complexity and dynamism. The emergence of Graph Neural Network (GNN) models in recent years has offered new opportunities to tackle non-linearity and structural dynamism in supply chain networks. However, existing research still faces methodological limitations in supply chain node relationship mining and benefit assessment. This study introduces an optimization and benefit assessment method for production supply chain networks based on GNNs. Firstly, by developing a node role type-aware graph neural network model, this paper achieves in-depth mining and optimization of node relationships within production supply chain networks. Secondly, a hierarchical factor analysis method is used to comprehensively assess the benefits of the production supply chain. This method can dynamically capture changes in node roles and relationships within the supply chain network, optimize the network structure, and provides a multidimensional, multilevel framework for benefit assessment. This study not only expands the application of GNN in the field of supply chain management but also provides a new analytical tool for the comprehensive assessment of supply chain benefits.","PeriodicalId":38688,"journal":{"name":"Journal of Computing and Information Technology","volume":"14 23","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141645778","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Short-Term Power Load Forecasting Method Based on GRU-Transformer Combined Neural Network Model 基于 GRU 变压器组合神经网络模型的短期电力负荷预测方法

Journal of Computing and Information Technology Pub Date : 2024-07-15 DOI: 10.20532/cit.2024.1005783

Weiwei Mao, S. Yu, Wenqing Chen

{"title":"Short-Term Power Load Forecasting Method Based on GRU-Transformer Combined Neural Network Model","authors":"Weiwei Mao, S. Yu, Wenqing Chen","doi":"10.20532/cit.2024.1005783","DOIUrl":"https://doi.org/10.20532/cit.2024.1005783","url":null,"abstract":"Load Forecast (LF) is an important task in the planning, control and application of public power systems. Accurate Short Term Load Forecast (STLF) is the premise of safe and economical operation of a power system. In the research of short-term power load forecasting, machine learning and deep learning are the most popular methods at present, but there still exists a problem that the single and simple structure of power load forecasting model leads to low accuracy of load forecasting. In order to improve the accuracy of STLF, a Gated Cycle Unit (GRU)-Transformer combined neural network model is proposed. Transformer encoder structure is used as feature extractor to mine the complex mapping relationships between the input features and load. The advantage of self-attention mechanism is used to solve the problem of information loss of long sequences in short-term power load forecasting. At the same time, the multivariate time series model of GRU is used for model training. The experimental results on the power load data set of a certain region in southwest China and Panama City show that the proposed combined model prediction method has higher accuracy than those proposed in other literatures, which further proves its feasibility and superiority.","PeriodicalId":38688,"journal":{"name":"Journal of Computing and Information Technology","volume":"30 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141649336","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

High-Frequency Quantitative Trading of Digital Currencies Based on Fusion of Deep Reinforcement Learning Models with Evolutionary Strategies 基于深度强化学习模型与进化策略融合的数字货币高频量化交易

Journal of Computing and Information Technology Pub Date : 2024-07-15 DOI: 10.20532/cit.2024.1005825

Yijun He, Bo Xu, Xinpu Su

{"title":"High-Frequency Quantitative Trading of Digital Currencies Based on Fusion of Deep Reinforcement Learning Models with Evolutionary Strategies","authors":"Yijun He, Bo Xu, Xinpu Su","doi":"10.20532/cit.2024.1005825","DOIUrl":"https://doi.org/10.20532/cit.2024.1005825","url":null,"abstract":"High-frequency quantitative trading in the emerging digital currency market poses unique challenges due to the lack of established methods for extracting trading information. This paper proposes a deep evolutionary reinforcement learning (DERL) model that combines deep reinforcement learning with evolutionary strategies to address these challenges. Reinforcement learning is applied to data cleaning and factor extraction from a high-frequency, microscopic viewpoint to quantitatively explain the supply and demand imbalance and to create trading strategies. In order to determine whether the algorithm can successfully extract the significant hidden features in the factors when faced with large and complex high-frequency factors, this paper trains the agent in reinforcement learning using three different learning algorithms, including Q-learning, evolutionary strategies, and policy gradient. The experimental dataset, which contains data on sharp up, sharp down, and continuous oscillation situations, was chosen to test Bitcoin in January-February, September, and November of 2022. According to the experimental results, the evolutionary strategies algorithm achieved returns of 59.18%, 25.14%, and 22.72%, respectively. The results demonstrate that deep reinforcement learning based on the evolutionary strategies outperforms Q-learning and policy gradient concerning risk resistance and return capability. The proposed approach offers a robust and adaptive solution for high-frequency trading in the digital currency market, contributing to the development of effective quantitative trading strategies.","PeriodicalId":38688,"journal":{"name":"Journal of Computing and Information Technology","volume":"51 41","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141644548","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Editorial for Vol.32, No.1 第 32 卷第 1 期社论

Journal of Computing and Information Technology Pub Date : 2024-07-15 DOI: 10.20532/cit.2024.1005852

引用次数: 0

A Brief Survey on Safety of Large Language Models 大型语言模型安全性简述

Journal of Computing and Information Technology Pub Date : 2024-07-15 DOI: 10.20532/cit.2024.1005778

Zhengjie Gao, Xuanzi Liu, Yuanshuai Lan, Zheng Yang

引用次数: 0

Editorial for Vol.31, No.2 第 31 卷第 2 期社论

Journal of Computing and Information Technology Pub Date : 2024-02-27 DOI: 10.20532/cit.2023.100579

引用次数: 0

Research on Keywords Variations in Linguistics Based on TF-IDF and N-gram 基于TF-IDF和N-gram的语言学关键词变异研究

Journal of Computing and Information Technology Pub Date : 2023-09-28 DOI: 10.20532/cit.2022.1005566

Yuyao Li, Xueyi Wen, Xingyu Liu

引用次数: 0

A Case Study of Edge Computing Implementations: Multi-access Edge Computing, Fog Computing and Cloudlet 边缘计算实现的案例研究:多访问边缘计算、雾计算和Cloudlet

Journal of Computing and Information Technology Pub Date : 2023-09-28 DOI: 10.20532/cit.2022.1005646

Liang Tian, Xiaorou Zhong

{"title":"A Case Study of Edge Computing Implementations: Multi-access Edge Computing, Fog Computing and Cloudlet","authors":"Liang Tian, Xiaorou Zhong","doi":"10.20532/cit.2022.1005646","DOIUrl":"https://doi.org/10.20532/cit.2022.1005646","url":null,"abstract":"With the explosive growth of intelligent and mobile devices, the current centralized cloud computing paradigm is encountering difficult challenges. Since the primary requirements have shifted towards implementing real-time response and supporting context awareness and mobility, there is an urgent need to bring resources and functions of centralized clouds to the edge of networks, which has led to the emergence of the edge computing paradigm. Edge computing increases the responsibilities of network edges by hosting computation and services, therefore enhancing performances and improving quality of experience (QoE). Fog computing, multi-access edge computing (MEC), and cloudlet are three typical and promising implementations of edge computing. Fog computing aims to build a system that enables cloud-to-thing service connectivity and works in concert with clouds, MEC is seen as a key technology of the fifth generation (5G) system, and Cloudlet is a micro-data center deployed in close proximity. In terms of deployment scenarios, Fog computing focuses on the Internet of Things (IoT), MEC mainly provides mobile RAN application solutions for 5G systems, and cloudlet offloads computing power at the network edge. In this paper, we present a comprehensive case study on these three edge computing implementations, including their architectures, differences, and their respective application scenario in IoT, 5G wireless systems, and smart edge. We discuss the requirements, benefits, and mechanisms of typical co-deployment cases for each paradigm and identify challenges and future directions in edge computing.","PeriodicalId":38688,"journal":{"name":"Journal of Computing and Information Technology","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135386294","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Short-Term Power Demand Forecasting Using Blockchain-Based Neural Networks Models 基于区块链的神经网络模型的短期电力需求预测

Journal of Computing and Information Technology Pub Date : 2023-09-28 DOI: 10.20532/cit.2022.1005614

Ruohan Wang, Yunlong Chen, Entang Li, Hongwei Xing, Jianhui Zhang, Jing Li

{"title":"Short-Term Power Demand Forecasting Using Blockchain-Based Neural Networks Models","authors":"Ruohan Wang, Yunlong Chen, Entang Li, Hongwei Xing, Jianhui Zhang, Jing Li","doi":"10.20532/cit.2022.1005614","DOIUrl":"https://doi.org/10.20532/cit.2022.1005614","url":null,"abstract":"With the rapid development of blockchain technology, blockchain-based neural network short-term power demand forecasting has become a research hot spot in the power industry. This paper aims to combine neural network algorithms with blockchain technology to establish a trustworthy and efficient short-term demand forecasting model. By leveraging the distributed ledger and immutability features of blockchain, we ensure the security and reliability of power demand data. Meanwhile, short-term power demand forecasting research using neural networks has the potential to increase the stability of the power system and offer opportunities for improved operations. In this paper, the root mean-square-error model evaluation indicator was used to compare the back propagation (BP) neural network algorithm and the traditional forecasting algorithm. The evaluation was performed on the randomly selected five household power datasets. The results show that, by comparing the long short-term memory network (LSTM) model with the BP neural network model, it was determined that the average prediction impact increases by about 25.7% under stable power demand. The short-term power prediction model of the BP neural network has the average error values more than two times lower than the traditional prediction model. It was shown that the use of the BP neural network algorithm and blockchain could increase the accuracy of short-term power demand forecasting, allowing the neural network-based algorithm to be implemented and taken into account in the research on short-term power demand forecasting.","PeriodicalId":38688,"journal":{"name":"Journal of Computing and Information Technology","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135387270","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Research on the Design of Financial Management Model Based on SOM-PNN Driven by Digital Economy 数字经济驱动下基于SOM-PNN的财务管理模型设计研究

Journal of Computing and Information Technology Pub Date : 2023-09-28 DOI: 10.20532/cit.2022.1005615

Di Fan

{"title":"Research on the Design of Financial Management Model Based on SOM-PNN Driven by Digital Economy","authors":"Di Fan","doi":"10.20532/cit.2022.1005615","DOIUrl":"https://doi.org/10.20532/cit.2022.1005615","url":null,"abstract":"This study proposes a novel financial risk prediction methodology by harnessing the power of self-organizing mapping (SOM) neural network and probabilistic neural network (PNN). The amalgamation of SOM and PNN's advantageous characteristics is seamlessly integrated into the algorithm posited within this paper. In order to collate and prognosticate data, the SOM network employs a two-dimensional topological framework comprising of two layers of neurons. Subsequently, the PNN model expeditiously furnishes the final classification outcomes by processing the output results obtained from the SOM model. The technique developed atop this composite model offers accelerated computation, effectively mitigates the impact of noisy samples, and significantly augments model accuracy. Finally, the effectiveness of the proposed method was demonstrated through a comprehensive financial risk analysis of listed companies from 2016 to 2020. The experimental results show that the SOM-PNN method has achieved high accuracy in predicting the financial difficulties experienced by traditional companies in the selected company samples, exceeding 85%. Especially when the sample data is insufficient, its accuracy reaches 80%, surpassing other algorithms. Statement: In the modern era, financial institutions use big data to perform background analysis and review, continuously optimize, and adjust, in order to introduce quantitative analysis methods into every link of risk management as far as possible. This allows financial institutions to quickly achieve balance in the game process of risk and income, and achieve Profit maximization in local or even more space.","PeriodicalId":38688,"journal":{"name":"Journal of Computing and Information Technology","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135387257","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0