Most recent issue published online in the International Journal of Computational Systems Engineering.
International Journal of Computational Systems Engineering
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International Journal of Computational Systems Engineering
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International Journal of Computational Systems Engineering
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http://www.inderscience.com/browse/index.php?journalID=378&year=2023&vol=7&issue=2/3/4
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A method of forecasting cross-border e-commerce stocking for SMEs based on demand characteristics and sequence trends under sustainable development strategy
http://www.inderscience.com/link.php?id=132908
With the continuous acceleration of economic globalisation, cross-border e-commerce enterprises have started to apply big data technology to find business information, among which the accurate forecast of stock availability has become an important influencing factor on consumers' online shopping experience. In order to improve the accuracy of cross-border e-commerce stocking prediction, this study first analyses the demand feature-based selection and prediction method, followed by the analysis of the serial trend-based stocking prediction method, and then proposes a stocking prediction method based on demand features fused with serial trend, and finally analyses the results of cross-border e-commerce stocking prediction for SMEs by the proposed method. The results show that the contribution rate of class A goods is the highest, which can be considered to build a stocking warehouse overseas for stocking, while stocking at the origin, and using multiple batches of small lot stocking to reduce inventory costs ensuring capital security.
A method of forecasting cross-border e-commerce stocking for SMEs based on demand characteristics and sequence trends under sustainable development strategy
Hua Yang; Lihui Yu
International Journal of Computational Systems Engineering, Vol. 7, No. 2/3/4 (2023) pp. 57 - 66
With the continuous acceleration of economic globalisation, cross-border e-commerce enterprises have started to apply big data technology to find business information, among which the accurate forecast of stock availability has become an important influencing factor on consumers' online shopping experience. In order to improve the accuracy of cross-border e-commerce stocking prediction, this study first analyses the demand feature-based selection and prediction method, followed by the analysis of the serial trend-based stocking prediction method, and then proposes a stocking prediction method based on demand features fused with serial trend, and finally analyses the results of cross-border e-commerce stocking prediction for SMEs by the proposed method. The results show that the contribution rate of class A goods is the highest, which can be considered to build a stocking warehouse overseas for stocking, while stocking at the origin, and using multiple batches of small lot stocking to reduce inventory costs ensuring capital security.]]>
10.1504/IJCSYSE.2023.132908
International Journal of Computational Systems Engineering, Vol. 7, No. 2/3/4 (2023) pp. 57 - 66
Hua Yang
Lihui Yu
School of Finance and Business, Zhongshan Torch Polytechnic, Zhongshan, 528436, China ' School of Finance and Business, Zhongshan Torch Polytechnic, Zhongshan, 528436, China
sustainable development
big data
demand characteristics
sequence trends
cross-border e-commerce
stocking forecast
2023-08-16T23:20:50-05:00
Copyright © 2023 Inderscience Enterprises Ltd.
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66
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Business model innovation and development path selection of international cultural trade under circular economy
http://www.inderscience.com/link.php?id=132906
With the economy's rapid development, people have higher requirements for quality of cultural products and services, which requires model innovation and path selection. To study the innovation of international cultural trade business model and the choice of development path, this study first analyses the integration of cultural tourism industry in province A by using the fusion measurement method, then studies the influencing factors by using the grey correlation and regression analysis. From 2012 to 2022, the highest value of cultural industry to tourism industry is 0.052, 0.039, 0.035 and 0.028, respectively. The highest values of tourism industry to cultural industry are 0.043, 0.031, 0.054 and 0.071, respectively, which do not reach 0.1. Integration degree between industries increased from 0.0035 in 2012 to 0.0186 in 2017, but decreased to 0.0130 in 2022, indicating that integration degree between the two industries is low, and the integration development is not stable.
Business model innovation and development path selection of international cultural trade under circular economy
Yuefeng Han; Guan Wang
International Journal of Computational Systems Engineering, Vol. 7, No. 2/3/4 (2023) pp. 67 - 76
With the economy's rapid development, people have higher requirements for quality of cultural products and services, which requires model innovation and path selection. To study the innovation of international cultural trade business model and the choice of development path, this study first analyses the integration of cultural tourism industry in province A by using the fusion measurement method, then studies the influencing factors by using the grey correlation and regression analysis. From 2012 to 2022, the highest value of cultural industry to tourism industry is 0.052, 0.039, 0.035 and 0.028, respectively. The highest values of tourism industry to cultural industry are 0.043, 0.031, 0.054 and 0.071, respectively, which do not reach 0.1. Integration degree between industries increased from 0.0035 in 2012 to 0.0186 in 2017, but decreased to 0.0130 in 2022, indicating that integration degree between the two industries is low, and the integration development is not stable.]]>
10.1504/IJCSYSE.2023.132906
International Journal of Computational Systems Engineering, Vol. 7, No. 2/3/4 (2023) pp. 67 - 76
Yuefeng Han
Guan Wang
School of Digital Commerce, Zhejiang Yuexiu University, Shaoxing, 312000, China ' School of International Trade and Economics, Jilin University of Finance and Economics, Changchun, 130017, China
business model
circular economy
cultural tourism industry
development path
international cultural trade
2023-08-16T23:20:50-05:00
Copyright © 2023 Inderscience Enterprises Ltd.
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76
2023-08-16T23:20:50-05:00
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Research on e-commerce personalised transaction processing model based on reinforcement learning
http://www.inderscience.com/link.php?id=132922
Aiming at increasing the amount of transaction processing issues with the rapid development of the e-commerce industry, this study proposes the personalised transaction processing model of e-commerce based on reinforcement learning. The DBSCAN method is used to pre-process the high-dimensional and low-density data in e-commerce, and the improved DR-PSO method is used to reduce the dimension of the data so as to obtain the optimal data set. Then, an e-commerce transaction processing model is constructed based on learning algorithm and the distributed multi-objective synthetic TOPE algorithm. The research results show that TOPE algorithm is the most economical, which is conducive to the long-term development of e-commerce. The results show that the e-commerce transaction processing system model proposed in this study has high adaptability and effectiveness. This study provides a reference for the progress of e-commerce in the era of artificial intelligence.
Research on e-commerce personalised transaction processing model based on reinforcement learning
Jinling Chi
International Journal of Computational Systems Engineering, Vol. 7, No. 2/3/4 (2023) pp. 77 - 85
Aiming at increasing the amount of transaction processing issues with the rapid development of the e-commerce industry, this study proposes the personalised transaction processing model of e-commerce based on reinforcement learning. The DBSCAN method is used to pre-process the high-dimensional and low-density data in e-commerce, and the improved DR-PSO method is used to reduce the dimension of the data so as to obtain the optimal data set. Then, an e-commerce transaction processing model is constructed based on learning algorithm and the distributed multi-objective synthetic TOPE algorithm. The research results show that TOPE algorithm is the most economical, which is conducive to the long-term development of e-commerce. The results show that the e-commerce transaction processing system model proposed in this study has high adaptability and effectiveness. This study provides a reference for the progress of e-commerce in the era of artificial intelligence.]]>
10.1504/IJCSYSE.2023.132922
International Journal of Computational Systems Engineering, Vol. 7, No. 2/3/4 (2023) pp. 77 - 85
Yuefeng Han
Guan Wang
Department of Finance and Economics, Huaibei Vocational and Technical College, Huaibei, 235000, China
reinforcement learning
artificial intelligence
electronic commerce
e-commerce
transaction processing model
2023-08-16T23:20:50-05:00
Copyright © 2023 Inderscience Enterprises Ltd.
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85
2023-08-16T23:20:50-05:00
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Exploring the costing method of steel enterprises based on PSO algorithm under the concept of sustainable development
http://www.inderscience.com/link.php?id=132909
According to the established raw material cost difference accounting model of iron and steel enterprises, AAD-MOPSO algorithm is used to solve the model. The model, a very accurate cost accounting technique, balances the quantity and price variations of steel goods. The experiments are compared with particle swarm optimisation (PSO) and multi-objective particle swarm optimisation (MOPSO) to verify the excellent performance of the proposed model. The results of AAD-MOPSO algorithm in the SP test function show that its SP, IGD and <i>I<SUB align="right">H</i> indicators are lower than the comparison algorithm. The best performances of the three indicators are 3.419E-4, 2.154E-4 and 1.017E-3. As a result, the AAD-MOPSO algorithm enhances the iron and steel industry's cost accounting accuracy and promotes the long-term growth of businesses.
Exploring the costing method of steel enterprises based on PSO algorithm under the concept of sustainable development
Jinping Qiu
International Journal of Computational Systems Engineering, Vol. 7, No. 2/3/4 (2023) pp. 86 - 95
According to the established raw material cost difference accounting model of iron and steel enterprises, AAD-MOPSO algorithm is used to solve the model. The model, a very accurate cost accounting technique, balances the quantity and price variations of steel goods. The experiments are compared with particle swarm optimisation (PSO) and multi-objective particle swarm optimisation (MOPSO) to verify the excellent performance of the proposed model. The results of AAD-MOPSO algorithm in the SP test function show that its SP, IGD and <i>I<SUB align="right">H</i> indicators are lower than the comparison algorithm. The best performances of the three indicators are 3.419E-4, 2.154E-4 and 1.017E-3. As a result, the AAD-MOPSO algorithm enhances the iron and steel industry's cost accounting accuracy and promotes the long-term growth of businesses.]]>
10.1504/IJCSYSE.2023.132909
International Journal of Computational Systems Engineering, Vol. 7, No. 2/3/4 (2023) pp. 86 - 95
Yuefeng Han
Guan Wang
School of Accounting, Chongqing College of Finance and Economics, Chongqing 402160, China
particle swarm optimisation
PSO
costing
sustainability
least squares
multi-objective particle swarm optimisation
MOPSO
2023-08-16T23:20:50-05:00
Copyright © 2023 Inderscience Enterprises Ltd.
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95
2023-08-16T23:20:50-05:00
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Research on e-commerce neural network financial accounting crisis early warning model combined with partial least squares
http://www.inderscience.com/link.php?id=132913
The study establishes a variable system based on the financial accounting crisis early warning theory, and uses partial least squares method to screen variables in order to accurately predict various incentives for the financial crisis in the actual operation of enterprises in the e-commerce industry. According to the findings of the experiment, when the quantity of hidden layer nodes in L-1~3 years is 9, 10 and 11 respectively, the convergence rate of the model can reach the best state. In the prediction of 2020 and 2021, the accuracy rate of L-2 and L-3 is less than 90%, and L-1 has an accuracy rate of more than 90%. In conclusion, the PLS-BP financial crisis early warning model developed and studied can be highly accurate and useful, and it can quickly identify financial crisis signals for businesses in the e-commerce industry and develop efficient measures.
Research on e-commerce neural network financial accounting crisis early warning model combined with partial least squares
Xiaoyang Meng
International Journal of Computational Systems Engineering, Vol. 7, No. 2/3/4 (2023) pp. 96 - 105
The study establishes a variable system based on the financial accounting crisis early warning theory, and uses partial least squares method to screen variables in order to accurately predict various incentives for the financial crisis in the actual operation of enterprises in the e-commerce industry. According to the findings of the experiment, when the quantity of hidden layer nodes in L-1~3 years is 9, 10 and 11 respectively, the convergence rate of the model can reach the best state. In the prediction of 2020 and 2021, the accuracy rate of L-2 and L-3 is less than 90%, and L-1 has an accuracy rate of more than 90%. In conclusion, the PLS-BP financial crisis early warning model developed and studied can be highly accurate and useful, and it can quickly identify financial crisis signals for businesses in the e-commerce industry and develop efficient measures.]]>
10.1504/IJCSYSE.2023.132913
International Journal of Computational Systems Engineering, Vol. 7, No. 2/3/4 (2023) pp. 96 - 105
Yuefeng Han
Guan Wang
Accounting Institute, Jiaozuo University, Jiaozuo, 454000, China
partial least squares
PLS
BP neural network
financial crisis
logistic regression
online retailers
variable indicators
2023-08-16T23:20:50-05:00
Copyright © 2023 Inderscience Enterprises Ltd.
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2/3/4
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105
2023-08-16T23:20:50-05:00
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Data mining research on sustainable business model innovation of enterprises based on particle swarm algorithm
http://www.inderscience.com/link.php?id=132912
To achieve fast and accurate data mining, this research proposes a data mining method based on particle swarm optimisation algorithm, which first introduces a time factor to optimise the fractional-order particle swarm algorithm (TFFV-PSO), and then implements automatic clustering on the basis of improved fractional-order PSO. In the result part, when searching in the early stage, the TFFV-PSO proposed in this paper can avoid forming local optimisation in multimodal function, and has better robustness. The convergence speed of the TFFV-PSO algorithm is faster and more accurate in the two-dimensional case than in the ten-dimensional case. The number of clusters of the new algorithm in different datasets is consistent with the actual, and the correct rates in dataset 1 and dataset 2 can reach the algorithm's average DBI values are lower. The average DBI values of the algorithm are lower than those of other algorithms.
Data mining research on sustainable business model innovation of enterprises based on particle swarm algorithm
Jianxiong Hu
International Journal of Computational Systems Engineering, Vol. 7, No. 2/3/4 (2023) pp. 106 - 114
To achieve fast and accurate data mining, this research proposes a data mining method based on particle swarm optimisation algorithm, which first introduces a time factor to optimise the fractional-order particle swarm algorithm (TFFV-PSO), and then implements automatic clustering on the basis of improved fractional-order PSO. In the result part, when searching in the early stage, the TFFV-PSO proposed in this paper can avoid forming local optimisation in multimodal function, and has better robustness. The convergence speed of the TFFV-PSO algorithm is faster and more accurate in the two-dimensional case than in the ten-dimensional case. The number of clusters of the new algorithm in different datasets is consistent with the actual, and the correct rates in dataset 1 and dataset 2 can reach the algorithm's average DBI values are lower. The average DBI values of the algorithm are lower than those of other algorithms.]]>
10.1504/IJCSYSE.2023.132912
International Journal of Computational Systems Engineering, Vol. 7, No. 2/3/4 (2023) pp. 106 - 114
Yuefeng Han
Guan Wang
School of Economics, Tongling University, Tongling, 244061, China
particle swarm algorithm
enterprise
sustainability
business model creation
data mining
2023-08-16T23:20:50-05:00
Copyright © 2023 Inderscience Enterprises Ltd.
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114
2023-08-16T23:20:50-05:00
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Visualisation and analysis method of enterprise financial expenditure data based on historical database
http://www.inderscience.com/link.php?id=132910
Under the influence of the trend of data informatisation, the storage and utilisation of enterprise financial expenditure information is more dependent on technologies such as databases. How to use enterprise information databases more effectively and make the storage and utilisation of enterprise financial expenditure data more efficient and easier is a concern of users. The study proposes a visual analysis model of enterprise financial expenditure data based on real-time historical database, which is constructed based on auto-encoder and K-mean clustering algorithm. It improves both algorithms in the design process to reduce the negative impact of their defects on the visual analysis model. The performance test of the visual analysis model of financial expenditure data shows that the loss value is as low as 0.02 and the error sum of squares is as low as 0.18. This indicates the value of the model for visual analysis of financial expenditure data of large enterprises.
Visualisation and analysis method of enterprise financial expenditure data based on historical database
Ping Chen
International Journal of Computational Systems Engineering, Vol. 7, No. 2/3/4 (2023) pp. 115 - 123
Under the influence of the trend of data informatisation, the storage and utilisation of enterprise financial expenditure information is more dependent on technologies such as databases. How to use enterprise information databases more effectively and make the storage and utilisation of enterprise financial expenditure data more efficient and easier is a concern of users. The study proposes a visual analysis model of enterprise financial expenditure data based on real-time historical database, which is constructed based on auto-encoder and K-mean clustering algorithm. It improves both algorithms in the design process to reduce the negative impact of their defects on the visual analysis model. The performance test of the visual analysis model of financial expenditure data shows that the loss value is as low as 0.02 and the error sum of squares is as low as 0.18. This indicates the value of the model for visual analysis of financial expenditure data of large enterprises.]]>
10.1504/IJCSYSE.2023.132910
International Journal of Computational Systems Engineering, Vol. 7, No. 2/3/4 (2023) pp. 115 - 123
Yuefeng Han
Guan Wang
School of Exhibition and Economic Management, Shanghai Institute of Tourism, Shanghai, 201418, China
historical database
financial data
visual analysis
neural network
K-means clustering
2023-08-16T23:20:50-05:00
Copyright © 2023 Inderscience Enterprises Ltd.
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123
2023-08-16T23:20:50-05:00
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Influence of technology optimisation based on machine learning algorithm on financial management innovation of e-commerce enterprises
http://www.inderscience.com/link.php?id=132911
In order to improve the financial ability of e-commerce enterprises to deal with risks and optimise their financial early warning effect, a complete random forest-based financial early warning method for e-commerce enterprises based on k-nearest neighbours is proposed. Firstly, in order to improve the classification effect of complete random forest algorithm on dynamic data, a complete random forest algorithm based on k-nearest neighbour is proposed; then, on this basis, the financial risk evaluation system of e-commerce enterprises is established by using the analytic hierarchy process, so as to complete the construction of the financial early warning model of e-commerce enterprises; and finally its application effect is tested and analysed. The results show that the minimum prediction accuracy and F1 value of the model remain at 0.7, which are 0.58 and 0.3 higher than the NB model, respectively.
Influence of technology optimisation based on machine learning algorithm on financial management innovation of e-commerce enterprises
Rui Min
International Journal of Computational Systems Engineering, Vol. 7, No. 2/3/4 (2023) pp. 124 - 134
In order to improve the financial ability of e-commerce enterprises to deal with risks and optimise their financial early warning effect, a complete random forest-based financial early warning method for e-commerce enterprises based on k-nearest neighbours is proposed. Firstly, in order to improve the classification effect of complete random forest algorithm on dynamic data, a complete random forest algorithm based on k-nearest neighbour is proposed; then, on this basis, the financial risk evaluation system of e-commerce enterprises is established by using the analytic hierarchy process, so as to complete the construction of the financial early warning model of e-commerce enterprises; and finally its application effect is tested and analysed. The results show that the minimum prediction accuracy and F1 value of the model remain at 0.7, which are 0.58 and 0.3 higher than the NB model, respectively.]]>
10.1504/IJCSYSE.2023.132911
International Journal of Computational Systems Engineering, Vol. 7, No. 2/3/4 (2023) pp. 124 - 134
Yuefeng Han
Guan Wang
School of Economics and Management, Jiangsu Maritime Institute, Nanjing, 210000, China
k-nearest neighbour
KNN
random forest
machine learning
e-commerce
corporate finance
2023-08-16T23:20:50-05:00
Copyright © 2023 Inderscience Enterprises Ltd.
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134
2023-08-16T23:20:50-05:00
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A study on the impact of personalised recommendation algorithms in webcasting on the development of rural e-commerce entrepreneurship
http://www.inderscience.com/link.php?id=132914
The vigorous development of rural e-commerce has brought a great positive effect on the development of rural economy and the improvement of local people's living material conditions. Among them, online live broadcasting provides a new perspective for the personalised development of e-commerce entrepreneurship. At the same time, based on the advantages of collaborative filtering (CF) algorithm in formulating user scoring criteria, the random forest (RF) algorithm is introduced to realise the research on the correlation of some features, so as to improve the information anti-noise ability and optimise the algorithm performance. And the intelligent recommendation algorithm that combines RF algorithm and improved CF algorithm is applied to rural e-commerce entrepreneurship recommendation. The results show that the fusion algorithm combines the advantages of the RF algorithm and the improved CF algorithm, which makes it have better performance in content recommendation.
A study on the impact of personalised recommendation algorithms in webcasting on the development of rural e-commerce entrepreneurship
Jie Li
International Journal of Computational Systems Engineering, Vol. 7, No. 2/3/4 (2023) pp. 135 - 145
The vigorous development of rural e-commerce has brought a great positive effect on the development of rural economy and the improvement of local people's living material conditions. Among them, online live broadcasting provides a new perspective for the personalised development of e-commerce entrepreneurship. At the same time, based on the advantages of collaborative filtering (CF) algorithm in formulating user scoring criteria, the random forest (RF) algorithm is introduced to realise the research on the correlation of some features, so as to improve the information anti-noise ability and optimise the algorithm performance. And the intelligent recommendation algorithm that combines RF algorithm and improved CF algorithm is applied to rural e-commerce entrepreneurship recommendation. The results show that the fusion algorithm combines the advantages of the RF algorithm and the improved CF algorithm, which makes it have better performance in content recommendation.]]>
10.1504/IJCSYSE.2023.132914
International Journal of Computational Systems Engineering, Vol. 7, No. 2/3/4 (2023) pp. 135 - 145
Yuefeng Han
Guan Wang
School of Economics and Management, Fujian Vocational and Technical College of Water Conservancy and Electric Power, Yongan, 366000, China
collaborative filtering
rural e-commerce
random forest
webcasting
2023-08-16T23:20:50-05:00
Copyright © 2023 Inderscience Enterprises Ltd.
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145
2023-08-16T23:20:50-05:00
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A comprehensive survey on recommender system techniques
http://www.inderscience.com/link.php?id=132915
The recommender system (RecSys) is a relatively emergent research area in machine learning that helps users to get personalised products, friends, documents, places and other online services with minimal time. RecSys has been proved as an important solution for information overload problems, by providing more proactive and personalised information services. It performs like a gateway for users to be recommended as to what decision would be right and predicts future post decision. RecSys utilised to support the venture to implement one-to-one marketing strategies in e-commerce. These strategies present enormous advantages namely satisfying the customer's interest increase the possibility of cross-selling and demonstrating the customer loyalty. This paper presents the overview of recommender system approaches, applications and challenges and directly supports the researchers in their understanding of this field. Further, we surveyed collaborative filtering-based RecSys in detailed manner and scrutinised the strengths and limitations to assist the future researchers.
A comprehensive survey on recommender system techniques
Thenmozhi Ganesan; R. Anandha Jothi; Palanisamy Vellaiyan
International Journal of Computational Systems Engineering, Vol. 7, No. 2/3/4 (2023) pp. 146 - 158
The recommender system (RecSys) is a relatively emergent research area in machine learning that helps users to get personalised products, friends, documents, places and other online services with minimal time. RecSys has been proved as an important solution for information overload problems, by providing more proactive and personalised information services. It performs like a gateway for users to be recommended as to what decision would be right and predicts future post decision. RecSys utilised to support the venture to implement one-to-one marketing strategies in e-commerce. These strategies present enormous advantages namely satisfying the customer's interest increase the possibility of cross-selling and demonstrating the customer loyalty. This paper presents the overview of recommender system approaches, applications and challenges and directly supports the researchers in their understanding of this field. Further, we surveyed collaborative filtering-based RecSys in detailed manner and scrutinised the strengths and limitations to assist the future researchers.]]>
10.1504/IJCSYSE.2023.132915
International Journal of Computational Systems Engineering, Vol. 7, No. 2/3/4 (2023) pp. 146 - 158
Thenmozhi Ganesan
R. Anandha Jothi
Palanisamy Vellaiyan
Department of Computer Applications, Alagappa University, Karaikudi, 630003, India ' Department of Computer Applications, Alagappa University, Karaikudi, 630003, India ' Department of Computer Applications, Alagappa University, Karaikudi, 630003, India
recommender system
RecSys
machine learning
information overload
personalised recommendation
classification and prediction
2023-08-16T23:20:50-05:00
Copyright © 2023 Inderscience Enterprises Ltd.
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2023-08-16T23:20:50-05:00
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Machine learning in financial risk forecasting and management for trading firms
http://www.inderscience.com/link.php?id=132917
To improve the financial risk prediction ability of e-commerce enterprises, this study combines BP neural network and PLS method to construct a financial crisis warning model for e-commerce enterprises, namely the BP-PLS model. The experiment first analyses and selects financial crisis warning indicators for e-commerce enterprises, and then extracts components using PLS method. Then, the results of component extraction are used as input vectors for the BP neural network. Finally, the experiment used the BP-PLS model to construct financial crisis warning models for e-commerce enterprises in T-1, T-2, and T-3 years, respectively. The experimental results show that the accuracy of both T-1 and T-2 models is above 90%. The accuracy of the T-3 model exceeds 85%. Therefore, the established model can meet the needs of financial crisis warning. In addition, the model has excellent performance and its training error convergence effect is superior to other models.
Machine learning in financial risk forecasting and management for trading firms
Zhiqing Zhou
International Journal of Computational Systems Engineering, Vol. 7, No. 2/3/4 (2023) pp. 159 - 167
To improve the financial risk prediction ability of e-commerce enterprises, this study combines BP neural network and PLS method to construct a financial crisis warning model for e-commerce enterprises, namely the BP-PLS model. The experiment first analyses and selects financial crisis warning indicators for e-commerce enterprises, and then extracts components using PLS method. Then, the results of component extraction are used as input vectors for the BP neural network. Finally, the experiment used the BP-PLS model to construct financial crisis warning models for e-commerce enterprises in T-1, T-2, and T-3 years, respectively. The experimental results show that the accuracy of both T-1 and T-2 models is above 90%. The accuracy of the T-3 model exceeds 85%. Therefore, the established model can meet the needs of financial crisis warning. In addition, the model has excellent performance and its training error convergence effect is superior to other models.]]>
10.1504/IJCSYSE.2023.132917
International Journal of Computational Systems Engineering, Vol. 7, No. 2/3/4 (2023) pp. 159 - 167
Thenmozhi Ganesan
R. Anandha Jothi
Palanisamy Vellaiyan
School of Humanities and Management, Xi'an Traffic Engineering Institute, Xi'an, 710300, China
machine learning
e-commerce enterprise
financial risk
forecast
BPNN
2023-08-16T23:20:50-05:00
Copyright © 2023 Inderscience Enterprises Ltd.
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167
2023-08-16T23:20:50-05:00
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Research on credit risk assessment of e-commerce enterprises based on improved multi-objective clustering algorithm
http://www.inderscience.com/link.php?id=132916
With the increasing share of e-commerce business, many companies are also facing credit risk issues of varying degrees. Aiming at the problem of credit risk assessment, this study proposes an improved multi-objective clustering algorithm to assess corporate credit risk. By comparing the conventional FMC algorithm and K-means algorithm, the performance of the proposed improved MOEC algorithm is analysed. It can be seen from the PR curves of the three algorithms that the AP values of the three algorithms are 0.9324, 0.9455, and 0.9972, respectively. In contrast, the improved MOEC algorithm has higher accuracy and stability. Tested on the UCI dataset, it was found that in dataset 3, the RI value of the improved MOEC algorithm was 0.97; in dataset 5, the NMI value of the improved MOEC algorithm was 0.96.
Research on credit risk assessment of e-commerce enterprises based on improved multi-objective clustering algorithm
Danyan Zhong
International Journal of Computational Systems Engineering, Vol. 7, No. 2/3/4 (2023) pp. 168 - 176
With the increasing share of e-commerce business, many companies are also facing credit risk issues of varying degrees. Aiming at the problem of credit risk assessment, this study proposes an improved multi-objective clustering algorithm to assess corporate credit risk. By comparing the conventional FMC algorithm and K-means algorithm, the performance of the proposed improved MOEC algorithm is analysed. It can be seen from the PR curves of the three algorithms that the AP values of the three algorithms are 0.9324, 0.9455, and 0.9972, respectively. In contrast, the improved MOEC algorithm has higher accuracy and stability. Tested on the UCI dataset, it was found that in dataset 3, the RI value of the improved MOEC algorithm was 0.97; in dataset 5, the NMI value of the improved MOEC algorithm was 0.96.]]>
10.1504/IJCSYSE.2023.132916
International Journal of Computational Systems Engineering, Vol. 7, No. 2/3/4 (2023) pp. 168 - 176
Thenmozhi Ganesan
R. Anandha Jothi
Palanisamy Vellaiyan
Accounting Department, Changzhou University Huaide College, Changzhou, 214500, China
e-commerce
corporate credit
clustering algorithm
multi-objective
risk assessment
finance
2023-08-16T23:20:50-05:00
Copyright © 2023 Inderscience Enterprises Ltd.
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176
2023-08-16T23:20:50-05:00
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A study on corporate financial crisis prediction strategy based on particle swarm improved fuzzy clustering method from accounting perspective
http://www.inderscience.com/link.php?id=132923
The research focuses on improving the particle swarm algorithm and uses the improved particle swarm algorithm as a tool to optimise the probabilistic neural network and fuzzy clustering algorithm respectively. The results show that, in the comparison of homogeneous classifiers, the accuracy of the IFP model studied and designed has the highest broken line position, with the highest point reaching 88.76%, and the error rates in the first and second types of errors are 8.57% and 8.62% respectively, which are the lowest among similar models; It can be seen that the enterprise financial crisis prediction model designed in this study can guarantee higher prediction accuracy in practical application, help enterprises dynamically monitor their financial operation status in the operation process, real-time alert the coming financial crisis, and lay a theoretical foundation for a new enterprise financial monitoring system.
A study on corporate financial crisis prediction strategy based on particle swarm improved fuzzy clustering method from accounting perspective
Juan Ye
International Journal of Computational Systems Engineering, Vol. 7, No. 2/3/4 (2023) pp. 177 - 189
The research focuses on improving the particle swarm algorithm and uses the improved particle swarm algorithm as a tool to optimise the probabilistic neural network and fuzzy clustering algorithm respectively. The results show that, in the comparison of homogeneous classifiers, the accuracy of the IFP model studied and designed has the highest broken line position, with the highest point reaching 88.76%, and the error rates in the first and second types of errors are 8.57% and 8.62% respectively, which are the lowest among similar models; It can be seen that the enterprise financial crisis prediction model designed in this study can guarantee higher prediction accuracy in practical application, help enterprises dynamically monitor their financial operation status in the operation process, real-time alert the coming financial crisis, and lay a theoretical foundation for a new enterprise financial monitoring system.]]>
10.1504/IJCSYSE.2023.132923
International Journal of Computational Systems Engineering, Vol. 7, No. 2/3/4 (2023) pp. 177 - 189
Thenmozhi Ganesan
R. Anandha Jothi
Palanisamy Vellaiyan
Accounting Institute, Chongqing College of Finance and Economics, Chongqing, 402160, China
particle swarm
fuzzy clustering
financial crisis
financial forecasting
2023-08-16T23:20:50-05:00
Copyright © 2023 Inderscience Enterprises Ltd.
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189
2023-08-16T23:20:50-05:00
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The construction of college students' job recommendation model based on improved k-means-CF
http://www.inderscience.com/link.php?id=132918
Based on the internet of things (IoT) technology, building a digital management platform for employment and entrepreneurship service system, recommending suitable corporate positions for students and promoting students' employment and entrepreneurship have become an important issue for each university. At present, the recommendation accuracy and recommendation efficiency of most digital management platforms of employment and entrepreneurship service system are not ideal and not very practical. To this end, the research is based on the idea of data mining, combining collaborative filtering (CF) algorithm, k-means algorithm and dichotomous k-means algorithm to build a personalised recommendation model for graduate jobs, and improve and optimise the career recommendation of the digital management platform of employment and entrepreneurship service system based on this model. The experimental results show that the accuracy rate of model 4 reaches 99.78%, which is significantly higher than the other three models. Therefore, the personalised recommendation model constructed by the study can efficiently and accurately provide students with employment and entrepreneurship information, thus promoting students' employment and entrepreneurship and providing some relief to the huge employment pressure in the current society.
The construction of college students' job recommendation model based on improved k-means-CF
Ping Ouyang
International Journal of Computational Systems Engineering, Vol. 7, No. 2/3/4 (2023) pp. 190 - 198
Based on the internet of things (IoT) technology, building a digital management platform for employment and entrepreneurship service system, recommending suitable corporate positions for students and promoting students' employment and entrepreneurship have become an important issue for each university. At present, the recommendation accuracy and recommendation efficiency of most digital management platforms of employment and entrepreneurship service system are not ideal and not very practical. To this end, the research is based on the idea of data mining, combining collaborative filtering (CF) algorithm, k-means algorithm and dichotomous k-means algorithm to build a personalised recommendation model for graduate jobs, and improve and optimise the career recommendation of the digital management platform of employment and entrepreneurship service system based on this model. The experimental results show that the accuracy rate of model 4 reaches 99.78%, which is significantly higher than the other three models. Therefore, the personalised recommendation model constructed by the study can efficiently and accurately provide students with employment and entrepreneurship information, thus promoting students' employment and entrepreneurship and providing some relief to the huge employment pressure in the current society.]]>
10.1504/IJCSYSE.2023.132918
International Journal of Computational Systems Engineering, Vol. 7, No. 2/3/4 (2023) pp. 190 - 198
Thenmozhi Ganesan
R. Anandha Jothi
Palanisamy Vellaiyan
School of Economics and Management, Foshan Polytechnic, Foshan, 528137, China
internet of things
IoT
employment entrepreneurship
k-means algorithm
collaborative filtering algorithm
dichotomous k-means algorithm
data mining
2023-08-16T23:20:50-05:00
Copyright © 2023 Inderscience Enterprises Ltd.
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198
2023-08-16T23:20:50-05:00
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The impact of green supply chain management on sustainability performance in Chinese manufacturing companies
http://www.inderscience.com/link.php?id=132920
This paper utilises technological innovation to investigate and examine the impact of GSCM on the sustainable development of companies. To achieve this objective, we conducted a survey among manufacturing companies in China and analysed data collected from 451 firms. We employed statistical software such as SPSS 23.0 and AMOS 23.0. The Chinese manufacturing industry served as our research focus, and we conducted an empirical study on the effects of GSCM and sustainability performance, considering the mediating variable of technological innovation. Our findings revealed that technological innovation, acting as a mediator, has a partial mediating effect on the relationship between GSCM and sustainability performance. To continually enhance sustainable development, companies must persist in investing in scientific research, embracing cutting-edge technologies and talent, and reinforcing their competitiveness. The contribution of our research lies in expanding the GSCM framework by incorporating technological innovation into the theory, while offering valuable guidance through empirical evidence.
The impact of green supply chain management on sustainability performance in Chinese manufacturing companies
Tielin Gao; Xiaohan Dang; Donghai Xu; Zhaoxia Zhao; Tianxiang Liu; Yaning Zhang
International Journal of Computational Systems Engineering, Vol. 7, No. 2/3/4 (2023) pp. 199 - 210
This paper utilises technological innovation to investigate and examine the impact of GSCM on the sustainable development of companies. To achieve this objective, we conducted a survey among manufacturing companies in China and analysed data collected from 451 firms. We employed statistical software such as SPSS 23.0 and AMOS 23.0. The Chinese manufacturing industry served as our research focus, and we conducted an empirical study on the effects of GSCM and sustainability performance, considering the mediating variable of technological innovation. Our findings revealed that technological innovation, acting as a mediator, has a partial mediating effect on the relationship between GSCM and sustainability performance. To continually enhance sustainable development, companies must persist in investing in scientific research, embracing cutting-edge technologies and talent, and reinforcing their competitiveness. The contribution of our research lies in expanding the GSCM framework by incorporating technological innovation into the theory, while offering valuable guidance through empirical evidence.]]>
10.1504/IJCSYSE.2023.132920
International Journal of Computational Systems Engineering, Vol. 7, No. 2/3/4 (2023) pp. 199 - 210
Tielin Gao
Xiaohan Dang
Donghai Xu
Zhaoxia Zhao
Tianxiang Liu
Yaning Zhang
College of Business, Hebei Normal University, Shijiazhuang 050024, China ' College of Management, Hebei GEO University, Shijiazhuang, 050031, China ' College of Business, Hebei Normal University, Shijiazhuang 050024, China ' College of Business, Hebei Normal University, Shijiazhuang 050024, China ' Department of Public Administration of Law and Politics, Hebei GEO University, Shijiazhuang, 050031, China ' College of Public Administration, Hebei University of Economics and Business, Shijiazhuang, 050061, China
green supply chain management
GSCM
technological innovation
sustainability performance
manufacturing companies
2023-08-16T23:20:50-05:00
Copyright © 2023 Inderscience Enterprises Ltd.
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199
210
2023-08-16T23:20:50-05:00
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Text document categorisation using random forest and C4.5 decision tree classifier
http://www.inderscience.com/link.php?id=132924
In reality, documentation is the most significant and rapidly developing field due to the restricted amount of time in the preparation of the documentation. Applications for text classification include language and item identification, document indexing, populating hierarchical catalogues of web resources, and word sense disambiguation. There are numerous texts that serve as documentation and strategies for categorisation have been created to improve efficiency. The proposed system focused on categorising and documenting text using the ensemble learning technique of random forest method and the C4.5 decision tree classifier. This system's processes include construction of decision tree text classifiers, training the constructed models as a part of implementation, dimension reduction, tf/idf indexing of the documents, clustering the terms using brown clustering and running the testing dataset through the classifiers as a part of document categorisation. Orange tool and Python libraries are used to implement the system. It is found that in random forest approach efficiency is increased due to proper construction of text classifiers.
Text document categorisation using random forest and C4.5 decision tree classifier
Sumathi Pawar; Manjula Gururaj Rao; Karuna Pandith
International Journal of Computational Systems Engineering, Vol. 7, No. 2/3/4 (2023) pp. 211 - 220
In reality, documentation is the most significant and rapidly developing field due to the restricted amount of time in the preparation of the documentation. Applications for text classification include language and item identification, document indexing, populating hierarchical catalogues of web resources, and word sense disambiguation. There are numerous texts that serve as documentation and strategies for categorisation have been created to improve efficiency. The proposed system focused on categorising and documenting text using the ensemble learning technique of random forest method and the C4.5 decision tree classifier. This system's processes include construction of decision tree text classifiers, training the constructed models as a part of implementation, dimension reduction, tf/idf indexing of the documents, clustering the terms using brown clustering and running the testing dataset through the classifiers as a part of document categorisation. Orange tool and Python libraries are used to implement the system. It is found that in random forest approach efficiency is increased due to proper construction of text classifiers.]]>
10.1504/IJCSYSE.2023.132924
International Journal of Computational Systems Engineering, Vol. 7, No. 2/3/4 (2023) pp. 211 - 220
Sumathi Pawar
Manjula Gururaj Rao
Karuna Pandith
Information Science and Engineering, NMAMIT, Nitte University, Karkala, Karnataka, 574110, India ' Information Science and Engineering, NMAMIT, Nitte University, Karkala, Karnataka, 574110, India ' Information Science and Engineering, NMAMIT, Nitte University, Karkala, Karnataka, 574110, India
dimensionality reduction
KE approach
indexing
machine learning
tf/idf
ensemble learning
2023-08-16T23:20:50-05:00
Copyright © 2023 Inderscience Enterprises Ltd.
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211
220
2023-08-16T23:20:50-05:00