Journal of Computational Methods in Sciences and Engineering - Volume 24, issue 2

Authors: Yang, Hanqi | Wang, Xiaoyu

Article Type: Research Article

Abstract: The global market competition is becoming increasingly fierce, and manufacturing enterprises need to invest and expand. However, the traditional financial optimization of manufacturing enterprises has faced problems such as low efficiency and inaccurate search for the optimal solution, which has made manufacturing enterprises likely to face financial risks. The investment portfolio can enable enterprises to obtain the maximum profit on a certain risk level, or reduce their investment risk on a certain return level as far as possible. If the combinatorial optimization is realized, it can be applied to the optimal selection of manufacturing enterprises’ financialization. This article analyzed the …respective characteristics of Genetic Algorithm (GA) and Simulated Annealing (SA) algorithms, and analyzed the combination of GA and SA algorithms to solve the optimal investment portfolio through GA-SA algorithm, thereby helping manufacturing enterprises to make the optimal choice for financialization. The experimental results of this article indicated that the GA-SA algorithm solved the problem of GA algorithm easily falling into local optima, SA algorithm’s initial temperature and generation mechanism, and improved the efficiency of finding the optimal solution. Meanwhile, the experimental results showed that the average optimal solutions of Genetic Algorithm, Simulated Annealing algorithm, and GA-SA algorithms for 36 stock portfolios in Enterprise 1 were 69, 69, and 107, respectively. The average optimal solutions of the three algorithms for 36 stock portfolios in Enterprise 2 were 73, 90, and 112, respectively. This proves that the number of optimal solutions searched by GA-SA algorithm is higher than that of GA and SA algorithm, and also proves that it is effective to use GA-SA algorithm to optimize investment portfolio and help manufacturing enterprises to make optimal financial choices. Show more

Keywords: Simulated annealing, genetic algorithm, portfolio theory, manufacturing enterprise

DOI: 10.3233/JCM-247179

Citation: Journal of Computational Methods in Sciences and Engineering, vol. 24, no. 2, pp. 623-638, 2024

Authors: Ma, Xiaowen

Article Type: Research Article

Abstract: Aiming to address the timely dissemination of news information, this work explores the clever utilization of data mining (DM) technology and deep learning (DL) algorithms to construct an intelligent real-time news image acquisition system to meet the urgency of news dissemination needs. First, this work introduces an intelligent real-time news image acquisition system and provides a detailed analysis of its principles and advantages. Throughout this process, the crucial role of DM technology in news image classification and automation is emphasized, especially in dealing with rapidly evolving news events. Next, the work establishes an intelligent real-time news image acquisition model based …on DL algorithms, which integrates the essence of DM technology. Through this fusion, the research objective is to enhance the performance of the news image acquisition system to achieve higher real-time and accuracy, which is vital for the swift delivery of news information. Finally, this work investigates the application of the intelligent news image acquisition system in network communication to ensure its adaptability to various network communication scenarios while maintaining accuracy and real-time capabilities. The research results demonstrate that the application of DM technology in combination with DL algorithms can effectively meet the practical needs of the news industry, enhancing the automation of news image processing and enabling faster information delivery to the audience. Notably, the AlexNet model employed performs exceptionally well, achieving recognition rates of up to 99.6% after data augmentation or equalization processing, with an accuracy of 90.9% and a high specificity of 93.38%. This indicates outstanding overall classification accuracy and negative class accuracy, even when distinguishing between news and non-news scenarios. These results clearly underscore the connection between DM technology and news acquisition and editing practices, and emphasize its potential to improve the efficiency and accuracy of real-time information dissemination. The research’s contribution and innovation lie in the fusion of DM technology with DL algorithms to build an intelligent real-time news image acquisition system. This fusion enhances the automation and classification performance of news images, thereby improving the real-time and accuracy of news information. Furthermore, the work strongly emphasizes improving the real-time and accuracy of the news image acquisition system to ensure the swift delivery of information, which is of utmost importance in rapidly changing news events. Show more

Keywords: Deep learning, data mining, real-time image acquisition, network security, AlexNet model

DOI: 10.3233/JCM-237131

Citation: Journal of Computational Methods in Sciences and Engineering, vol. 24, no. 2, pp. 639-656, 2024

Authors: Huang, Furong

Article Type: Research Article

Abstract: As global economic integration continues to advance, international trade has become increasingly vital for the economic development and growth of nations. This research aims to assess the trends in industrial technology security within China’s international trade and provide practical guidance for policy-making, corporate strategies, and international cooperation. The significance of the rising trend in security within China’s international trade industry lies in its establishment of a robust foundation for the long-term development of China’s international trade, contributing to its cooperation and competitiveness with other countries. In addressing the limitations of traditional measurement methods and providing a more comprehensive and accurate …assessment of industrial technology security, this research presents an approach based on a discrete Hopfield Neural Network (HNN) for evaluating industrial technology security in international trade. This method integrates multiple indicators, including technology gap rates, to construct the Superior Quality Engineering (SQE) comprehensive evaluation model. The research employs a combination model of “entropy-grey relational-Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS)-discrete HNN” to assess industrial technology security. This research evaluates international trade industry technology security using patent data from 2015 to 2022 as samples. The results indicate an overall upward trend in security in China’s international trade industry. Within this trend, the research observes a stepwise increase in scale components, leading to continuous improvement in security. In terms of quality components, although security develops relatively slowly overall, it exhibits a trend of initial gradual decline followed by rapid growth. Regarding efficiency components, there is overall slow growth with periodic fluctuations. This research outcome provides substantial support for the research of industrial technology in international trade. The proposed method can assist businesses in evaluating their technological security in international trade and offer robust support for international trade decision-making. Show more

Keywords: Measurement, international trade, technological innovation, high-tech industries, discrete hopfield neural network, gray relation

DOI: 10.3233/JCM-237128

Citation: Journal of Computational Methods in Sciences and Engineering, vol. 24, no. 2, pp. 657-674, 2024

Authors: Meng, Yan | Jungjin, Kim | Hak-Chun, Lee | Je, Cho Dong | Cheng, Peiyun

Article Type: Research Article

Abstract: With the rapid development of Artificial Intelligence (AI) and Big Data (BD), they provide people with new ways and tools in information acquisition, processing and dissemination. The purpose of this study is to deeply discuss the influence of AI and BD technologies on the development of Information Security (InfoSec) in media economy, and analyze the opportunities and challenges it brings. Firstly, the evaluation system of the influence of BD technology on the development of InfoSec, a media enterprise, is constructed. Then, this study discusses the development mode of the multilateral platform of media economy supported by BD technology. Finally, taking …S company and X platform as examples, this study analyses the influence of BD technology on the development of InfoSec and the development mode of media economy supported by BD technology. The research results show that with the support of BD technology, the proportion of InfoSec products in enterprises has reached 0.2426, and the InfoSec score of S company has reached 77.74, reaching a high level. The profit margin of digital network media is three times that of traditional media. The number of users of multilateral digital media economic platform X has increased by 21%, and its turnover has increased by 54%. AI and BD technologies have brought opportunities and challenges to the InfoSec development of media economy. While applying these technologies, it is necessary to take effective measures to protect users’ privacy and strengthen the information review and verification mechanism to ensure the sustainable development of media economy. The uniqueness of this study lies in the construction of a comprehensive BD technology impact assessment system and the discussion of the development model of multilateral media economic platform, which provides a powerful reference and guidance for media enterprises. In addition, this study also reveals the positive influence of BD technology on InfoSec and the development of media economy, which provides beneficial enlightenment for the future development of media industry. In response to the challenges of privacy and InfoSec, this study puts forward important policy and practical suggestions to ensure the sustainable growth of media economy and the protection of users’ rights and interests. Therefore, this study has important contribution and innovative value in both theory and practice. Show more

Keywords: Artificial intelligence, big data technology, media economy, information security, multilateral platforms

DOI: 10.3233/JCM-237126

Citation: Journal of Computational Methods in Sciences and Engineering, vol. 24, no. 2, pp. 675-695, 2024

Authors: Zhang, Zhongpeng | Wang, Guibao

Article Type: Research Article

Abstract: This work aims to advance the security management of complex networks to better align with evolving societal needs. The work employs the Ant Colony Optimization algorithm in conjunction with Long Short-Term Memory neural networks to reconstruct and optimize task networks derived from time series data. Additionally, a trend-based noise smoothing scheme is introduced to mitigate data noise effectively. The approach entails a thorough analysis of historical data, followed by applying trend-based noise smoothing, rendering the processed data more scientifically robust. Subsequently, the network reconstruction problem for time series data originating from one-dimensional dynamic equations is addressed using an algorithm based …on the principles of Stochastic Gradient Descent (SGD). This algorithm decomposes time series data into smaller samples and yields optimal learning outcomes in conjunction with an adaptive learning rate SGD approach. Experimental results corroborate the remarkable fidelity of the weight matrix reconstructed by this algorithm to the true weight matrix. Moreover, the algorithm exhibits efficient convergence with increasing data volume, manifesting shorter time requirements per iteration while ensuring the attainment of optimal solutions. When the sample size remains constant, the algorithm’s execution time is directly proportional to the square of the number of nodes. Conversely, as the sample size scales, the SGD algorithm capitalizes on the availability of more information, resulting in improved learning outcomes. Notably, when the noise standard deviation is 0.01, models predicated on SGD and the Least-Squares Method (LSM) demonstrate reduced errors compared to instances with a noise standard deviation of 0.1, highlighting the sensitivity of LSM to noise. The proposed methodology offers valuable insights for advancing research in complex network studies. Show more

Keywords: Heuristic algorithm, long short-term memory neural network, optimal task network security, topology time series, stochastic gradient descent

DOI: 10.3233/JCM-237124

Citation: Journal of Computational Methods in Sciences and Engineering, vol. 24, no. 2, pp. 697-714, 2024

Authors: Liu, Peijun

Article Type: Research Article

Abstract: English reading and writing are important parts of language teaching. In order to improve the English reading and writing ability of college students, the TLBO (teaching learning-based optimization) algorithm is used in this research to improve the way that English reading and writing are taught in colleges and universities. It is chosen as the primary model for this study. The TLBO algorithm is further optimized in this paper, and a convergence analysis is performed between the optimized model M-TLBO (multi-learning teaching learning-based optimization) algorithm and other TLBO algorithms in order to address the issues that the TLBO algorithm has an …excessively single teaching ability and readily settles into local optimal solutions for some large-scale complex problems. In terms of stability and convergence accuracy, M-TLBO outperforms other algorithms. In order to investigate the impact of the M-TLBO algorithm on students’ writing performance, this paper uses the teaching-learning optimization algorithm to conduct a pre-and post-test on students’ English reading and writing performance in five dimensions. The study’s findings revealed that students’ pre-test writing scores had a mean value of 8.4770 and a standard deviation of 1.72449, and that their post-study writing scores had increased by 5.05 points. The English reading and writing information-based teaching model can improve students’ English writing performance. It is hoped to promote the development of English teaching and improve the efficiency of students’ English learning. Show more

Keywords: Intelligent optimization algorithm, TLBO algorithm, M-TLBO model, information-based teaching, English reading and writing, teaching model

DOI: 10.3233/JCM-237101

Citation: Journal of Computational Methods in Sciences and Engineering, vol. 24, no. 2, pp. 715-730, 2024

Authors: Xia, Yunqing

Article Type: Research Article

Abstract: With the development of technology and the widespread collection of data, high-dimensional data analysis has become a research hotspot in many fields. Traditional parameter methods often face problems such as dimensional disasters in high-dimensional data analysis. Non parametric methods have broad application prospects in high-dimensional data because they do not rely on specific parameter distribution assumptions. The Bayesian rule is more suitable for dealing with noise and outliers in high-dimensional data because it takes uncertainty into account. Therefore, it is of great significance to combine non parametric methods with Bayesian methods for application research in high-dimensional data analysis. In this …paper, the nonparametric Bayesian method was applied to the analysis of high-dimensional data, and the Dirichlet process Mixture model was used to cluster high-dimensional data. The regression analysis of high-dimensional data was carried out through the prediction model of nonparametric Bayesian regression. In this paper, the nonparametric Bayesian method based on Bayesian sparse linear model was used for feature selection of high-dimensional data. In order to determine the superiority of nonparametric Bayesian methods in high-dimensional data analysis, this paper conducted experiments on nonparametric Bayesian methods and traditional parametric methods in high-dimensional data analysis from five aspects of cluster analysis, classification analysis, regression analysis, feature selection and anomaly detection, and evaluated them through multiple indicators. This article explored the application of non parametric Bayesian methods in high-dimensional data analysis from these aspects through simulation experiments. The experimental results show that the clustering accuracy of the non parametric Bayesian clustering algorithm was 0.93, and the accuracy of the non parametric Bayesian classification algorithm was between 0.93 and 0.99; the coefficient of determination of nonparametric Bayesian regression algorithm was 0.98; the F1 values of non parametric Bayesian methods in anomaly detection ranged from 0.86 to 0.91, which was superior to traditional methods. Non parametric Bayesian methods have broad application prospects in high-dimensional data analysis, and can be applied in multiple fields such as clustering, classification, regression, etc. Show more

Keywords: High dimensional data, nonparametric bayesian method, cluster analysis, classification analysis, regression analysis, feature selection

DOI: 10.3233/JCM-237104

Citation: Journal of Computational Methods in Sciences and Engineering, vol. 24, no. 2, pp. 731-743, 2024

Authors: Dong, Peilin | Wang, Xiaoyu | Shi, Zhouhao

Article Type: Research Article

Abstract: The financial market has randomness, and the prediction of the financial market is an important task in the financial market. In traditional financial market prediction models, the prediction results are often unsatisfactory. So it needs to introduce new models for financial analysis. To solve this problem, this paper analyzed a financial market trend prediction model based on LSTM (Long Short-Term Memory) NN (Neural Network) algorithm, and conducted an empirical analysis on the Shanghai stock index dataset. This paper first introduced the LSTM NN algorithm, and then divided it into training set, test set and comparison set according to the data …characteristics. At last, this paper used the data preprocessing method to verify the LSTM NN algorithm. The experimental results showed that the LSTM NN algorithm analyzed in this paper can effectively improve the generalization ability of financial market trend prediction models while ensuring the prediction accuracy. Through experimental analysis, this paper found that the average accuracy rate of using LSTM NN algorithm was 2.25% higher than that of using traditional NN algorithm. This research is primarily aimed at developing effective methods for predicting stock market trends in the continuously evolving Chinese securities market. The core objective is to empower investors with precise guidance by enabling them to make well-informed investment decisions. Achieving accurate predictions holds the potential to significantly impact economic operations in a positive way. Therefore, this research direction is of paramount importance, offering substantial value both in academic exploration and practical application. Show more

Keywords: Long short-term memory, neural network algorithm, financial market, prediction model

DOI: 10.3233/JCM-237097

Citation: Journal of Computational Methods in Sciences and Engineering, vol. 24, no. 2, pp. 745-755, 2024

Authors: Weng, Minming

Article Type: Research Article

Abstract: In the background of the Internet, traditional media companies are facing very serious challenges. This paper firstly gave a brief introduction to the development of traditional media, pointed out the necessity of digital transformation of traditional media companies, and analyzed Phoenix Media using political, economic, social and technological (PEST) analysis and strength, weaknesses, opportunity, and threat (SWOT) analysis. Moreover, the current development of the company was analyzed, and its digital transformation path was elaborated. Finally, the existing problems of the company were pointed out, and some suggestions were given. This study provides a reference for traditional media companies to carry …out digital transformation. Show more

Keywords: Internet, traditional media, digitalization, transformation path

DOI: 10.3233/JCM-237092

Citation: Journal of Computational Methods in Sciences and Engineering, vol. 24, no. 2, pp. 757-768, 2024

Authors: Sun, Ji

Article Type: Research Article

Abstract: OBJECTIVES: This paper aims to analyze factors affecting the employment quality of contemporary college students to improve it. METHODS: Data related to the influencing factors and employment quality were collected by means of a questionnaire. After verifying the effectiveness of the data, a structural equation model of employment quality was constructed. FINDINGS: The data collected by the questionnaire were effective. The structural equation model analysis results showed that individual factors, educational factors, and environmental factors were all significantly related to employment quality. NOVELTY: The novelty of this article lies in …the use of a structural equation model to quantitatively analyze the relationship between influencing factors and employment quality, providing an effective reference for improving the employment quality of college students. Show more

Keywords: Employment quality, college students, structural equation model, influencing factors

DOI: 10.3233/JCM-237094

Citation: Journal of Computational Methods in Sciences and Engineering, vol. 24, no. 2, pp. 769-777, 2024