Pub Date : 2023-09-20DOI: 10.1007/s44196-023-00332-4
Qijun Zou
Abstract With the continuous development of economic globalisation, China has established free trade zones (FTA). To promote the diversification of cross-border e-commerce in FTA and increase industry competitiveness, the Porter's Five Forces model (PFFM) was used to analyse the profit model of e-commerce enterprises. Based on fuzzy logic, an evaluation model for the profit model of cross-border e-commerce was constructed, and this evaluation model was used to evaluate the profitability of cross-border e-commerce. The results show that the evaluation model constructed based on fuzzy logic can better reflect the profitability of enterprises. The accuracy of multilevel fuzzy evaluation is above 80% every year, with the highest accuracy being in 2017, and the evaluation accuracy for that year is 98.5%. The study of a cross-border e-commerce profit evaluation model based on multilevel fuzzy evaluation method can better reflect the profitability of enterprises and help them clarify their future development direction.
随着经济全球化的不断发展，中国建立了自由贸易区。为了促进自贸区内跨境电子商务的多元化发展，提高行业竞争力，本文运用波特五力模型(Porter’s Five Forces model, PFFM)对电子商务企业的盈利模式进行了分析。基于模糊逻辑，构建了跨境电子商务盈利模式的评价模型，并利用该评价模型对跨境电子商务的盈利能力进行了评价。结果表明，基于模糊逻辑构建的评价模型能较好地反映企业的盈利能力。多级模糊评价的准确率每年都在80%以上，2017年准确率最高，当年的评价准确率为98.5%。研究基于多级模糊评价法的跨境电子商务利润评价模型，可以更好地反映企业的盈利能力，帮助企业明确未来的发展方向。
{"title":"A Multilevel Fuzzy Evaluation of Cross-Border E-Commerce Profitability Model","authors":"Qijun Zou","doi":"10.1007/s44196-023-00332-4","DOIUrl":"https://doi.org/10.1007/s44196-023-00332-4","url":null,"abstract":"Abstract With the continuous development of economic globalisation, China has established free trade zones (FTA). To promote the diversification of cross-border e-commerce in FTA and increase industry competitiveness, the Porter's Five Forces model (PFFM) was used to analyse the profit model of e-commerce enterprises. Based on fuzzy logic, an evaluation model for the profit model of cross-border e-commerce was constructed, and this evaluation model was used to evaluate the profitability of cross-border e-commerce. The results show that the evaluation model constructed based on fuzzy logic can better reflect the profitability of enterprises. The accuracy of multilevel fuzzy evaluation is above 80% every year, with the highest accuracy being in 2017, and the evaluation accuracy for that year is 98.5%. The study of a cross-border e-commerce profit evaluation model based on multilevel fuzzy evaluation method can better reflect the profitability of enterprises and help them clarify their future development direction.","PeriodicalId":54967,"journal":{"name":"International Journal of Computational Intelligence Systems","volume":"85 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136313535","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-20DOI: 10.1007/s44196-023-00328-0
Mingjing Pei, Ningzhong Liu, Bing Zhao, Han Sun
Abstract Industrial image anomaly detection (AD) is a critical issue that has been investigated in different research areas. Many works have attempted to detect anomalies by simulating anomalous samples. However, how to simulate abnormal samples remains a significant challenge. In this study, a method for simulating anomalous samples is designed. First, for the object category, patch extraction and patch paste are designed to ensure that the extracted image patches come from the objects and are pasted to the objects in the image. Second, based on the statistical analysis of various anomalies’ presence, a combination of data augmentation is proposed to cover various anomalies as much as possible. The method is evaluated on MVTec AD and BTAD datasets; the experimental results demonstrate that our method achieves an overall detection AUC of 97.6% in MVTec AD datasets, outperforming the baseline by 1.5%, and the improvement over VT-ADL method is 4.3% on the BTAD datasets, demonstrating our method’s effectiveness and generalization.
{"title":"Self-Supervised Learning for Industrial Image Anomaly Detection by Simulating Anomalous Samples","authors":"Mingjing Pei, Ningzhong Liu, Bing Zhao, Han Sun","doi":"10.1007/s44196-023-00328-0","DOIUrl":"https://doi.org/10.1007/s44196-023-00328-0","url":null,"abstract":"Abstract Industrial image anomaly detection (AD) is a critical issue that has been investigated in different research areas. Many works have attempted to detect anomalies by simulating anomalous samples. However, how to simulate abnormal samples remains a significant challenge. In this study, a method for simulating anomalous samples is designed. First, for the object category, patch extraction and patch paste are designed to ensure that the extracted image patches come from the objects and are pasted to the objects in the image. Second, based on the statistical analysis of various anomalies’ presence, a combination of data augmentation is proposed to cover various anomalies as much as possible. The method is evaluated on MVTec AD and BTAD datasets; the experimental results demonstrate that our method achieves an overall detection AUC of 97.6% in MVTec AD datasets, outperforming the baseline by 1.5%, and the improvement over VT-ADL method is 4.3% on the BTAD datasets, demonstrating our method’s effectiveness and generalization.","PeriodicalId":54967,"journal":{"name":"International Journal of Computational Intelligence Systems","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136308089","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract Wake re-categorization (RECAT) has been implemented to improve runway capacity, and consequently, aircraft arrival runway occupancy time has become a crucial factor influencing runway capacity. Accurate prediction of the runway occupancy time can assist controllers in determining aircraft separation, thereby enhancing the operational efficiency of the runway. In this study, the GA–PSO algorithm is utilized to optimize the Back Propagation neural network prediction model using Quick access recorder data from various domestic airports, achieving high-precision prediction. Additionally, the SHapley Additive explanation model is applied to quantify the effect of each characteristic parameter on the arrival runway occupancy time, resulting in the prediction of aircraft arrival runway occupancy time. This model can provide a foundation for improving runway operation efficiency and technical support for the design of airport runway and taxiway structure.
{"title":"Prediction of Aircraft Arrival Runway Occupancy Time Based on Machine Learning","authors":"Haoran Gao, Yubing Xie, Changjiang Yuan, Xin He, Tiantian Niu","doi":"10.1007/s44196-023-00333-3","DOIUrl":"https://doi.org/10.1007/s44196-023-00333-3","url":null,"abstract":"Abstract Wake re-categorization (RECAT) has been implemented to improve runway capacity, and consequently, aircraft arrival runway occupancy time has become a crucial factor influencing runway capacity. Accurate prediction of the runway occupancy time can assist controllers in determining aircraft separation, thereby enhancing the operational efficiency of the runway. In this study, the GA–PSO algorithm is utilized to optimize the Back Propagation neural network prediction model using Quick access recorder data from various domestic airports, achieving high-precision prediction. Additionally, the SHapley Additive explanation model is applied to quantify the effect of each characteristic parameter on the arrival runway occupancy time, resulting in the prediction of aircraft arrival runway occupancy time. This model can provide a foundation for improving runway operation efficiency and technical support for the design of airport runway and taxiway structure.","PeriodicalId":54967,"journal":{"name":"International Journal of Computational Intelligence Systems","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135155319","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-14DOI: 10.1007/s44196-023-00327-1
Rizk M. Rizk-Allah, Islam M. Eldesoky, Ekram A. Aboali, Sarah M. Nasr
Abstract In this paper, a heap-based optimizer algorithm with chaotic search has been presented for the global solution of nonlinear programming problems. Heap-based optimizer (HBO) is a modern human social behavior-influenced algorithm that has been presented as an effective method to solve nonlinear programming problems. One of the difficulties that faces HBO is that it falls into locally optimal solutions and does not reach the global solution. To recompense the disadvantages of such modern algorithm, we integrate a heap-based optimizer with a chaotic search to reach the global optimization for nonlinear programming problems. The proposed algorithm displays the advantages of both modern techniques. The robustness of the proposed algorithm is inspected on a wide scale of different 42 problems including unimodal, multi-modal test problems, and CEC-C06 2019 benchmark problems. The comprehensive results have shown that the proposed algorithm effectively deals with nonlinear programming problems compared with 11 highly cited algorithms in addressing the tasks of optimization. As well as the rapid performance of the proposed algorithm in treating nonlinear programming problems has been proved as the proposed algorithm has taken less time to find the global solution.
{"title":"Heap-Based Optimizer Algorithm with Chaotic Search for Nonlinear Programming Problem Global Solution","authors":"Rizk M. Rizk-Allah, Islam M. Eldesoky, Ekram A. Aboali, Sarah M. Nasr","doi":"10.1007/s44196-023-00327-1","DOIUrl":"https://doi.org/10.1007/s44196-023-00327-1","url":null,"abstract":"Abstract In this paper, a heap-based optimizer algorithm with chaotic search has been presented for the global solution of nonlinear programming problems. Heap-based optimizer (HBO) is a modern human social behavior-influenced algorithm that has been presented as an effective method to solve nonlinear programming problems. One of the difficulties that faces HBO is that it falls into locally optimal solutions and does not reach the global solution. To recompense the disadvantages of such modern algorithm, we integrate a heap-based optimizer with a chaotic search to reach the global optimization for nonlinear programming problems. The proposed algorithm displays the advantages of both modern techniques. The robustness of the proposed algorithm is inspected on a wide scale of different 42 problems including unimodal, multi-modal test problems, and CEC-C06 2019 benchmark problems. The comprehensive results have shown that the proposed algorithm effectively deals with nonlinear programming problems compared with 11 highly cited algorithms in addressing the tasks of optimization. As well as the rapid performance of the proposed algorithm in treating nonlinear programming problems has been proved as the proposed algorithm has taken less time to find the global solution.","PeriodicalId":54967,"journal":{"name":"International Journal of Computational Intelligence Systems","volume":"111 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134911740","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-14DOI: 10.1007/s44196-023-00330-6
Abdelrahman I. Saad, Fahima A. Maghraby, Osama Badawy
Abstract A deep convolution neural network image segmentation model based on a cost-effective active learning mechanism is proposed and named PolySeg Plus. It is intended to address polyp segmentation with a lack of labeled data and a high false-positive rate of polyp discovery. In addition to applying active learning, which assisted in labeling more image samples, a comprehensive polyp dataset formed of five benchmark datasets was generated to increase the number of images. To enhance the captured image features, the locally shared feature method is used, which utilizes the power of employing neighboring features together with one another to improve the quality of image features and overcome the drawbacks of the Conditional Random Features method. Medical image segmentation was performed using ResUNet++, ResUNet, UNet++, and UNet models. Gaussian noise was removed from the images using a gaussian filter, and the images were then augmented before being fed into the models. In addition to optimizing model performance through hyperparameter tuning, grid search is used to select the optimum parameters to maximize model performance. The results demonstrated a significant improvement and applicability of the proposed method in polyp segmentation when compared to state-of-the-art methods on the datasets CVC-ClinicDB, CVC-ColonDB, ETIS Larib Polyp DB, KVASIR-SEG, and Kvasir-Sessile, with Dice coefficients of 0.9558, 0.8947, 0.7547, 0.9476, and 0.6023, respectively. Not only did the suggested method improve the dice coefficients on the individual datasets, but it also produced better results on the comprehensive dataset, which will contribute to the development of computer-aided diagnosis systems.
{"title":"PolySeg Plus: Polyp Segmentation Using Deep Learning with Cost Effective Active Learning","authors":"Abdelrahman I. Saad, Fahima A. Maghraby, Osama Badawy","doi":"10.1007/s44196-023-00330-6","DOIUrl":"https://doi.org/10.1007/s44196-023-00330-6","url":null,"abstract":"Abstract A deep convolution neural network image segmentation model based on a cost-effective active learning mechanism is proposed and named PolySeg Plus. It is intended to address polyp segmentation with a lack of labeled data and a high false-positive rate of polyp discovery. In addition to applying active learning, which assisted in labeling more image samples, a comprehensive polyp dataset formed of five benchmark datasets was generated to increase the number of images. To enhance the captured image features, the locally shared feature method is used, which utilizes the power of employing neighboring features together with one another to improve the quality of image features and overcome the drawbacks of the Conditional Random Features method. Medical image segmentation was performed using ResUNet++, ResUNet, UNet++, and UNet models. Gaussian noise was removed from the images using a gaussian filter, and the images were then augmented before being fed into the models. In addition to optimizing model performance through hyperparameter tuning, grid search is used to select the optimum parameters to maximize model performance. The results demonstrated a significant improvement and applicability of the proposed method in polyp segmentation when compared to state-of-the-art methods on the datasets CVC-ClinicDB, CVC-ColonDB, ETIS Larib Polyp DB, KVASIR-SEG, and Kvasir-Sessile, with Dice coefficients of 0.9558, 0.8947, 0.7547, 0.9476, and 0.6023, respectively. Not only did the suggested method improve the dice coefficients on the individual datasets, but it also produced better results on the comprehensive dataset, which will contribute to the development of computer-aided diagnosis systems.","PeriodicalId":54967,"journal":{"name":"International Journal of Computational Intelligence Systems","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134912354","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-12DOI: 10.1007/s44196-023-00320-8
Sarada Mohapatra, Prabhujit Mohapatra
Abstract Golden Jackal Optimization (GJO) is a recently developed nature-inspired algorithm that is motivated by the collaborative hunting behaviours of the golden jackals in nature. However, the GJO has the disadvantage of poor exploitation ability and is easy to get stuck in an optimal local region. To overcome these disadvantages, in this paper, an enhanced variant of the golden jackal optimization algorithm that incorporates the opposition-based learning (OBL) technique (OGJO) is proposed. The OBL technique is implemented into GJO with a probability rate, which can assist the algorithm in escaping from the local optima. To validate the efficiency of OGJO, several experiments have been performed. The experimental outcomes revealed that the proposed OGJO has more efficiency than GJO and other compared algorithms.
{"title":"An Improved Golden Jackal Optimization Algorithm Using Opposition-Based Learning for Global Optimization and Engineering Problems","authors":"Sarada Mohapatra, Prabhujit Mohapatra","doi":"10.1007/s44196-023-00320-8","DOIUrl":"https://doi.org/10.1007/s44196-023-00320-8","url":null,"abstract":"Abstract Golden Jackal Optimization (GJO) is a recently developed nature-inspired algorithm that is motivated by the collaborative hunting behaviours of the golden jackals in nature. However, the GJO has the disadvantage of poor exploitation ability and is easy to get stuck in an optimal local region. To overcome these disadvantages, in this paper, an enhanced variant of the golden jackal optimization algorithm that incorporates the opposition-based learning (OBL) technique (OGJO) is proposed. The OBL technique is implemented into GJO with a probability rate, which can assist the algorithm in escaping from the local optima. To validate the efficiency of OGJO, several experiments have been performed. The experimental outcomes revealed that the proposed OGJO has more efficiency than GJO and other compared algorithms.","PeriodicalId":54967,"journal":{"name":"International Journal of Computational Intelligence Systems","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135824690","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-08DOI: 10.1007/s44196-023-00317-3
Ping Chen
{"title":"Research on Financial Risk Evaluation and Control of Tourism Enterprises Based on Improved GA Algorithm","authors":"Ping Chen","doi":"10.1007/s44196-023-00317-3","DOIUrl":"https://doi.org/10.1007/s44196-023-00317-3","url":null,"abstract":"","PeriodicalId":54967,"journal":{"name":"International Journal of Computational Intelligence Systems","volume":" ","pages":""},"PeriodicalIF":2.9,"publicationDate":"2023-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41990054","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-07DOI: 10.1007/s44196-023-00324-4
Binbin Huang, Ciyu Wang
{"title":"Research on Data Analysis of Efficient Innovation and Entrepreneurship Practice Teaching Based on LightGBM Classification Algorithm","authors":"Binbin Huang, Ciyu Wang","doi":"10.1007/s44196-023-00324-4","DOIUrl":"https://doi.org/10.1007/s44196-023-00324-4","url":null,"abstract":"","PeriodicalId":54967,"journal":{"name":"International Journal of Computational Intelligence Systems","volume":" ","pages":""},"PeriodicalIF":2.9,"publicationDate":"2023-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45903865","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-01DOI: 10.1007/s44196-023-00310-w
Jianxia Chen, Liwei Pan, Shi Dong, Tianci Yu, Liang Xiao, Meihan Yao, Shijie Luo
{"title":"Multi-temporal Sequential Recommendation Model Based on the Fused Learning Preferences","authors":"Jianxia Chen, Liwei Pan, Shi Dong, Tianci Yu, Liang Xiao, Meihan Yao, Shijie Luo","doi":"10.1007/s44196-023-00310-w","DOIUrl":"https://doi.org/10.1007/s44196-023-00310-w","url":null,"abstract":"","PeriodicalId":54967,"journal":{"name":"International Journal of Computational Intelligence Systems","volume":" ","pages":""},"PeriodicalIF":2.9,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46699767","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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