Construction and optimization of vending machine decision support system based on improved C4.5 decision tree
The intensification of market competition makes refined operation management become the focus of attention of major manufacturers. As an important branch of artificial intelligence (AI), machine learning (ML) plays a key role in it, and has its application prospect in various systems. Based on this...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-02-01
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| Series: | Heliyon |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844024010557 |
| _version_ | 1797304591667167232 |
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| author | Ping Li Fang Xiong Xibei Huang Xiaojun Wen |
| author_facet | Ping Li Fang Xiong Xibei Huang Xiaojun Wen |
| author_sort | Ping Li |
| collection | DOAJ |
| description | The intensification of market competition makes refined operation management become the focus of attention of major manufacturers. As an important branch of artificial intelligence (AI), machine learning (ML) plays a key role in it, and has its application prospect in various systems. Based on this situation, this paper takes vending machines as the research object. On the one hand, the product classification model of vending machine is constructed based on decision tree algorithm. On the other hand, based on neural network (NN), the sales forecast model of vending machines is built. Finally, based on the above research, the theoretical framework of decision support system (DSS) for vending machines is constructed. The research shows that: (1) The accuracy of C4.5 algorithm can reach 87 % at the highest and 68 % at the lowest. The accuracy of the improved C4.5 algorithm can reach 87 % at the highest and 67 % at the lowest, with little difference between them. (2) The maximum running time of the improved C4.5 algorithm is about 5500 ms, and the minimum is close to 1 ms. In addition, the running time of all seven datasets is better than that of the unmodified algorithm. (3) When the back propagation neural network (BPNN) is used to forecast the sales of vending machines, the curve of the predicted data basically coincides with the curve of the actual data, which shows that its accuracy is high. This paper aims to build a convenient and secure DSS by taking vending machines as an example. In addition, this paper also uses reinforcement learning to optimize the research methods of this paper. It can further optimize the performance and efficiency of vending machines and provide better service experience for customers. Meanwhile, the use of reinforcement learning can make the whole system more intelligent and adaptive to better cope with the changing market environment. |
| first_indexed | 2024-03-08T00:11:38Z |
| format | Article |
| id | doaj.art-ec80efd5a1e042a7b05edb98a67e83f0 |
| institution | Directory Open Access Journal |
| issn | 2405-8440 |
| language | English |
| last_indexed | 2024-03-08T00:11:38Z |
| publishDate | 2024-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj.art-ec80efd5a1e042a7b05edb98a67e83f02024-02-17T06:39:20ZengElsevierHeliyon2405-84402024-02-01103e25024Construction and optimization of vending machine decision support system based on improved C4.5 decision treePing Li0Fang Xiong1Xibei Huang2Xiaojun Wen3Corresponding author.; School of Information and Mechatronic Engineering, Hunan International Economics University, Changsha, 410205, ChinaSchool of Information and Mechatronic Engineering, Hunan International Economics University, Changsha, 410205, ChinaSchool of Information and Mechatronic Engineering, Hunan International Economics University, Changsha, 410205, ChinaSchool of Information and Mechatronic Engineering, Hunan International Economics University, Changsha, 410205, ChinaThe intensification of market competition makes refined operation management become the focus of attention of major manufacturers. As an important branch of artificial intelligence (AI), machine learning (ML) plays a key role in it, and has its application prospect in various systems. Based on this situation, this paper takes vending machines as the research object. On the one hand, the product classification model of vending machine is constructed based on decision tree algorithm. On the other hand, based on neural network (NN), the sales forecast model of vending machines is built. Finally, based on the above research, the theoretical framework of decision support system (DSS) for vending machines is constructed. The research shows that: (1) The accuracy of C4.5 algorithm can reach 87 % at the highest and 68 % at the lowest. The accuracy of the improved C4.5 algorithm can reach 87 % at the highest and 67 % at the lowest, with little difference between them. (2) The maximum running time of the improved C4.5 algorithm is about 5500 ms, and the minimum is close to 1 ms. In addition, the running time of all seven datasets is better than that of the unmodified algorithm. (3) When the back propagation neural network (BPNN) is used to forecast the sales of vending machines, the curve of the predicted data basically coincides with the curve of the actual data, which shows that its accuracy is high. This paper aims to build a convenient and secure DSS by taking vending machines as an example. In addition, this paper also uses reinforcement learning to optimize the research methods of this paper. It can further optimize the performance and efficiency of vending machines and provide better service experience for customers. Meanwhile, the use of reinforcement learning can make the whole system more intelligent and adaptive to better cope with the changing market environment.http://www.sciencedirect.com/science/article/pii/S2405844024010557Machine learningDecision support systemDecision treeNeural networkVending system |
| spellingShingle | Ping Li Fang Xiong Xibei Huang Xiaojun Wen Construction and optimization of vending machine decision support system based on improved C4.5 decision tree Heliyon Machine learning Decision support system Decision tree Neural network Vending system |
| title | Construction and optimization of vending machine decision support system based on improved C4.5 decision tree |
| title_full | Construction and optimization of vending machine decision support system based on improved C4.5 decision tree |
| title_fullStr | Construction and optimization of vending machine decision support system based on improved C4.5 decision tree |
| title_full_unstemmed | Construction and optimization of vending machine decision support system based on improved C4.5 decision tree |
| title_short | Construction and optimization of vending machine decision support system based on improved C4.5 decision tree |
| title_sort | construction and optimization of vending machine decision support system based on improved c4 5 decision tree |
| topic | Machine learning Decision support system Decision tree Neural network Vending system |
| url | http://www.sciencedirect.com/science/article/pii/S2405844024010557 |
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