Pub Date : 2023-10-04DOI: 10.1007/s44196-023-00337-z
Boting Liu, Weili Guan, Changjin Yang, Zhijie Fang, Zhiheng Lu
Abstract Graph convolutional network (GCN) is an effective tool for feature clustering. However, in the text classification task, the traditional TextGCN (GCN for Text Classification) ignores the context word order of the text. In addition, TextGCN constructs the text graph only according to the context relationship, so it is difficult for the word nodes to learn an effective semantic representation. Based on this, this paper proposes a text classification method that combines Transformer and GCN. To improve the semantic accuracy of word node features, we add a part of speech (POS) to the word-document graph and build edges between words based on POS. In the layer-to-layer of GCN, the Transformer is used to extract the contextual and sequential information of the text. We conducted the experiment on five representative datasets. The results show that our method can effectively improve the accuracy of text classification and is better than the comparison method.
图卷积网络(GCN)是一种有效的特征聚类工具。然而，在文本分类任务中，传统的textcn (GCN for text classification)忽略了文本的上下文词序。此外，TextGCN仅根据上下文关系构建文本图，因此单词节点很难学习到有效的语义表示。在此基础上，本文提出了一种结合Transformer和GCN的文本分类方法。为了提高词节点特征的语义准确性，我们在词-文档图中加入词性(POS)，并基于词性(POS)在词与词之间建立边缘。在分层GCN中，使用Transformer提取文本的上下文信息和顺序信息。我们在五个有代表性的数据集上进行了实验。结果表明，该方法能有效提高文本分类的准确率，优于比较法。
{"title":"Transformer and Graph Convolutional Network for Text Classification","authors":"Boting Liu, Weili Guan, Changjin Yang, Zhijie Fang, Zhiheng Lu","doi":"10.1007/s44196-023-00337-z","DOIUrl":"https://doi.org/10.1007/s44196-023-00337-z","url":null,"abstract":"Abstract Graph convolutional network (GCN) is an effective tool for feature clustering. However, in the text classification task, the traditional TextGCN (GCN for Text Classification) ignores the context word order of the text. In addition, TextGCN constructs the text graph only according to the context relationship, so it is difficult for the word nodes to learn an effective semantic representation. Based on this, this paper proposes a text classification method that combines Transformer and GCN. To improve the semantic accuracy of word node features, we add a part of speech (POS) to the word-document graph and build edges between words based on POS. In the layer-to-layer of GCN, the Transformer is used to extract the contextual and sequential information of the text. We conducted the experiment on five representative datasets. The results show that our method can effectively improve the accuracy of text classification and is better than the comparison method.","PeriodicalId":54967,"journal":{"name":"International Journal of Computational Intelligence Systems","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135591657","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 : 2023-10-04DOI: 10.1007/s44196-023-00342-2
Shixuan Li, Wenxuan Shi
Abstract Textual-based factors have been widely regarded as a promising feature that can be applied to financial issues. This study focuses on extracting both basic and semantic textual features to supplement the traditionally used financial indicators. The main is to improve Chinese listed companies’ financial distress prediction (FDP). A unique paradigm is proposed in this study that combines financial and multi-type textual predictive factors, feature selection methods, classifiers, and time spans to achieve the optimal FDP. The frequency counts, TF-IDF, TextRank, and word embedding approaches are employed to extract frequency count-based, keyword-based, sentiment, and readability indicators. The experimental results prove that financial domain sentiment lexicons, word embedding-based readability analysis approaches, and the basic textual features of Management Discussion and Analysis can be important elements of FDP. Moreover, the finding highlights the fact that incorporating financial and textual features can achieve optimal performance 4 or 5 years before the expected baseline year; applying the RF-GBDT combined model can also outperform other classifiers. This study makes an innovative contribution, since it expands the multiple text analysis method in the financial text mining field and provides new findings on how to provide early warning signs related to financial risk. The approaches developed in this research can serve as a template that can be used to resolve other financial issues.
{"title":"Incorporating Multiple Textual Factors into Unbalanced Financial Distress Prediction: A Feature Selection Methods and Ensemble Classifiers Combined Approach","authors":"Shixuan Li, Wenxuan Shi","doi":"10.1007/s44196-023-00342-2","DOIUrl":"https://doi.org/10.1007/s44196-023-00342-2","url":null,"abstract":"Abstract Textual-based factors have been widely regarded as a promising feature that can be applied to financial issues. This study focuses on extracting both basic and semantic textual features to supplement the traditionally used financial indicators. The main is to improve Chinese listed companies’ financial distress prediction (FDP). A unique paradigm is proposed in this study that combines financial and multi-type textual predictive factors, feature selection methods, classifiers, and time spans to achieve the optimal FDP. The frequency counts, TF-IDF, TextRank, and word embedding approaches are employed to extract frequency count-based, keyword-based, sentiment, and readability indicators. The experimental results prove that financial domain sentiment lexicons, word embedding-based readability analysis approaches, and the basic textual features of Management Discussion and Analysis can be important elements of FDP. Moreover, the finding highlights the fact that incorporating financial and textual features can achieve optimal performance 4 or 5 years before the expected baseline year; applying the RF-GBDT combined model can also outperform other classifiers. This study makes an innovative contribution, since it expands the multiple text analysis method in the financial text mining field and provides new findings on how to provide early warning signs related to financial risk. The approaches developed in this research can serve as a template that can be used to resolve other financial issues.","PeriodicalId":54967,"journal":{"name":"International Journal of Computational Intelligence Systems","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135644083","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 : 2023-09-26DOI: 10.1007/s44196-023-00326-2
Yuhui Zhang, Wenhong Wei, Shaohao Xie, Zijia Wang
Abstract Real-world optimization problems often have multiple optimal solutions and simultaneously finding these optimal solutions is beneficial yet challenging. Brain storm optimization (BSO) is a relatively new paradigm of swarm intelligence algorithm that has been shown to be effective in solving global optimization problems, but it has not been fully exploited for multimodal optimization problems. A simple control strategy for the step size parameter in BSO cannot meet the need of optima finding task in multimodal landscapes and can possibly be refined and optimized. In this paper, we propose an adaptive BSO (ABSO) algorithm that adaptively adjusts the step size parameter according to the quality of newly created solutions. Extensive experiments are conducted on a set of multimodal optimization problems to evaluate the performance of ABSO and the experimental results show that ABSO outperforms existing BSO algorithms and some recently developed algorithms. BSO has great potential in multimodal optimization and is expected to be useful for solving real-world optimization problems that have multiple optimal solutions.
{"title":"Brain Storm Optimization Algorithm with an Adaptive Parameter Control Strategy for Finding Multiple Optimal Solutions","authors":"Yuhui Zhang, Wenhong Wei, Shaohao Xie, Zijia Wang","doi":"10.1007/s44196-023-00326-2","DOIUrl":"https://doi.org/10.1007/s44196-023-00326-2","url":null,"abstract":"Abstract Real-world optimization problems often have multiple optimal solutions and simultaneously finding these optimal solutions is beneficial yet challenging. Brain storm optimization (BSO) is a relatively new paradigm of swarm intelligence algorithm that has been shown to be effective in solving global optimization problems, but it has not been fully exploited for multimodal optimization problems. A simple control strategy for the step size parameter in BSO cannot meet the need of optima finding task in multimodal landscapes and can possibly be refined and optimized. In this paper, we propose an adaptive BSO (ABSO) algorithm that adaptively adjusts the step size parameter according to the quality of newly created solutions. Extensive experiments are conducted on a set of multimodal optimization problems to evaluate the performance of ABSO and the experimental results show that ABSO outperforms existing BSO algorithms and some recently developed algorithms. BSO has great potential in multimodal optimization and is expected to be useful for solving real-world optimization problems that have multiple optimal solutions.","PeriodicalId":54967,"journal":{"name":"International Journal of Computational Intelligence Systems","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134958736","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 : 2023-09-26DOI: 10.1007/s44196-023-00339-x
Annisa Darmawahyuni, Bambang Tutuko, Siti Nurmaini, Muhammad Naufal Rachmatullah, Muhammad Ardiansyah, Firdaus Firdaus, Ade Iriani Sapitri, Anggun Islami
Abstract Fetal heart monitoring during pregnancy plays a critical role in diagnosing congenital heart disease (CHD). A noninvasive fetal electrocardiogram (fECG) provides additional clinical information for fetal heart monitoring. To date, the analysis of noninvasive fECG is challenging due to the cancellation of maternal QRS-complexes, despite significant advances in electrocardiography. Fetal QRS-complex is highly considered to measure fetal heart rate to detect some fetal abnormalities such as arrhythmia. In this study, we proposed a deep learning (DL) framework that stacked a convolutional layer and bidirectional long short-term memory for fetal QRS-complexes classification. The fECG signals are first preprocessed using discrete wavelet transform (DWT) to remove the noise or inferences. The following step beats and QRS-complex segmentation. The last step is fetal QRS-complex classification based on DL. In the experiment of Physionet/Computing in Cardiology Challenge 2013, this study achieved 100% accuracy, sensitivity, specificity, precision, and F1-score. A stacked DL model demonstrates an effective tool for fetal QRS-complex classification and contributes to clinical applications for long-term maternal and fetal monitoring.
妊娠期胎儿心脏监测对先天性心脏病(CHD)的诊断具有重要意义。无创胎儿心电图(fECG)为胎儿心脏监测提供了额外的临床信息。迄今为止，尽管心电图技术取得了重大进展，但由于母体qrs复合物的取消，无创性fECG分析仍具有挑战性。胎儿qrs复合体在检测胎儿心律失常等胎儿异常时被高度重视。在这项研究中，我们提出了一个深度学习(DL)框架，该框架堆叠了卷积层和双向长短期记忆，用于胎儿qrs复合物的分类。首先使用离散小波变换(DWT)对fECG信号进行预处理以去除噪声或推断。接下来的步骤是节拍和qrs复合分割。最后一步是基于DL的胎儿qrs复合体分类。在Physionet/Computing In Cardiology Challenge 2013的实验中，本研究达到100%的准确度、灵敏度、特异性、精密度和f1评分。堆叠DL模型是一种有效的胎儿qrs复杂分类工具，有助于临床应用于母体和胎儿的长期监测。
{"title":"Accurate Fetal QRS-Complex Classification from Abdominal Electrocardiogram Using Deep Learning","authors":"Annisa Darmawahyuni, Bambang Tutuko, Siti Nurmaini, Muhammad Naufal Rachmatullah, Muhammad Ardiansyah, Firdaus Firdaus, Ade Iriani Sapitri, Anggun Islami","doi":"10.1007/s44196-023-00339-x","DOIUrl":"https://doi.org/10.1007/s44196-023-00339-x","url":null,"abstract":"Abstract Fetal heart monitoring during pregnancy plays a critical role in diagnosing congenital heart disease (CHD). A noninvasive fetal electrocardiogram (fECG) provides additional clinical information for fetal heart monitoring. To date, the analysis of noninvasive fECG is challenging due to the cancellation of maternal QRS-complexes, despite significant advances in electrocardiography. Fetal QRS-complex is highly considered to measure fetal heart rate to detect some fetal abnormalities such as arrhythmia. In this study, we proposed a deep learning (DL) framework that stacked a convolutional layer and bidirectional long short-term memory for fetal QRS-complexes classification. The fECG signals are first preprocessed using discrete wavelet transform (DWT) to remove the noise or inferences. The following step beats and QRS-complex segmentation. The last step is fetal QRS-complex classification based on DL. In the experiment of Physionet/Computing in Cardiology Challenge 2013, this study achieved 100% accuracy, sensitivity, specificity, precision, and F1-score. A stacked DL model demonstrates an effective tool for fetal QRS-complex classification and contributes to clinical applications for long-term maternal and fetal monitoring.","PeriodicalId":54967,"journal":{"name":"International Journal of Computational Intelligence Systems","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134958600","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 : 2023-09-26DOI: 10.1007/s44196-023-00331-5
Beiqin Zhang
Abstract The COVID-19 pandemic has caused drastic fluctuations in the economies of various countries. Meanwhile, the governments’ ability to save the economy depends on how banks provide credit to troubled companies. Therefore, the impact of the epidemic on bank credit and inclusive finance are worth exploring. However, most of the existing studies focus on the reform of the financial and economic system, only paying attention to the theoretical mechanism analysis and effect adjustment, scant data support, and insufficient scheme landing. At the same time, with the rise and rapid development of artificial intelligence technology in recent years, all walks of life have introduced it into real scenes for multi-source heterogeneous big data analysis and decision-making assistance. Therefore, we first take the Chinese mainland as an example in this paper. By studying the impact of the epidemic on bank credit preference and the mechanism of inclusive finance, we can provide objective decision-making basis for the financial system in the post-epidemic era to better flow credit funds into various entities and form a new perspective for related research. Then, we put forward a model based on Bi-directional Long Short-term Memory Network (BiLSTM) and Attention Mechanism to predict the number of newly diagnosed cases during the COVID-19 pandemic every day. It is not only suitable for COVID-19 pandemic data characterized by time series and nonlinearity, but also can adaptively select the most relevant input data by introducing an Attention Mechanism, which can solve the problems of huge calculation and inaccurate prediction results. Finally, through experiments and empirical research, we draw the following conclusions: (1) The impact of the COVID-19 pandemic will promote enterprises to increase credit. (2) Banks provide more credit to large enterprises. (3) The epidemic has different impacts on credit in different regions, with the most significant one on central China. (4) Banks tend to provide more credit to manufacturing industries under the epidemic. (5) Digital inclusive finance plays a (positive) regulating effect on bank credit in COVID-19 pandemic. Inspired by the research results, policymakers can consider further solving the information asymmetry and strengthening the construction of a credit system, and more direct financial support policies for enterprises should be adopted. (6) By adopting the COVID-19 prediction model based on the BiLSTM-Attention network to accurately predict the epidemic situation in the COVID-19 pandemic, it can provide an important basis for the formulation of epidemic prevention and control policies.
{"title":"COVID-19 Forecast and Bank Credit Decision Model Based on BiLSTM-Attention Network","authors":"Beiqin Zhang","doi":"10.1007/s44196-023-00331-5","DOIUrl":"https://doi.org/10.1007/s44196-023-00331-5","url":null,"abstract":"Abstract The COVID-19 pandemic has caused drastic fluctuations in the economies of various countries. Meanwhile, the governments’ ability to save the economy depends on how banks provide credit to troubled companies. Therefore, the impact of the epidemic on bank credit and inclusive finance are worth exploring. However, most of the existing studies focus on the reform of the financial and economic system, only paying attention to the theoretical mechanism analysis and effect adjustment, scant data support, and insufficient scheme landing. At the same time, with the rise and rapid development of artificial intelligence technology in recent years, all walks of life have introduced it into real scenes for multi-source heterogeneous big data analysis and decision-making assistance. Therefore, we first take the Chinese mainland as an example in this paper. By studying the impact of the epidemic on bank credit preference and the mechanism of inclusive finance, we can provide objective decision-making basis for the financial system in the post-epidemic era to better flow credit funds into various entities and form a new perspective for related research. Then, we put forward a model based on Bi-directional Long Short-term Memory Network (BiLSTM) and Attention Mechanism to predict the number of newly diagnosed cases during the COVID-19 pandemic every day. It is not only suitable for COVID-19 pandemic data characterized by time series and nonlinearity, but also can adaptively select the most relevant input data by introducing an Attention Mechanism, which can solve the problems of huge calculation and inaccurate prediction results. Finally, through experiments and empirical research, we draw the following conclusions: (1) The impact of the COVID-19 pandemic will promote enterprises to increase credit. (2) Banks provide more credit to large enterprises. (3) The epidemic has different impacts on credit in different regions, with the most significant one on central China. (4) Banks tend to provide more credit to manufacturing industries under the epidemic. (5) Digital inclusive finance plays a (positive) regulating effect on bank credit in COVID-19 pandemic. Inspired by the research results, policymakers can consider further solving the information asymmetry and strengthening the construction of a credit system, and more direct financial support policies for enterprises should be adopted. (6) By adopting the COVID-19 prediction model based on the BiLSTM-Attention network to accurately predict the epidemic situation in the COVID-19 pandemic, it can provide an important basis for the formulation of epidemic prevention and control policies.","PeriodicalId":54967,"journal":{"name":"International Journal of Computational Intelligence Systems","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134958603","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 : 2023-09-26DOI: 10.1007/s44196-023-00334-2
Zujingyang Wang
Abstract The trend of Chinese enterprises has defied the hard-hit international investment and trade activities of recent years. The scale and globalization of local enterprises continue to develop, and the enterprise management efficiency has become increasingly prominent. This paper delves into the strategic importance of FSSC in augmenting corporate value, and examines the link between digital transformation and the formation of FSSC. The introduction of FSSC and its accompanying digital technology marks a new juncture for corporate reform. Based on RBV and DCV, this paper studies the strategic significance of FSSC to enhance corporate value, and deconstructs the relationship between digital transformation and the development process of financial shared service centers. Based on the time-varying DID, 335 listed companies were selected for a quasi-natural experiment. The results showed that: (1) financial shared service centers can significantly promote the improvement of corporate value. (2) Digital transformation can promote the establishment and development of financial sharing service centers, thus promoting the improvement of corporate value. (3) The effects of FSSC and digital transformation are characterized by heterogeneity. The worth of financial shared service centers for non-state-owned and manufacturing companies is substantial. Digital transformation has a noteworthy positive moderating effect on them but has no considerable moderating effect on state-owned and non-manufacturing companies. The government ought to bolster policy direction, keep up the digital transformation of businesses, and aid them in achieving rapid financial transformation; senior executives should remain cognizant of their strategic position and strive to execute a satisfactory job of both internal and external coordination, as the research findings suggest.
{"title":"Financial Shared Service, Digital Transformation and Corporate Value Creation","authors":"Zujingyang Wang","doi":"10.1007/s44196-023-00334-2","DOIUrl":"https://doi.org/10.1007/s44196-023-00334-2","url":null,"abstract":"Abstract The trend of Chinese enterprises has defied the hard-hit international investment and trade activities of recent years. The scale and globalization of local enterprises continue to develop, and the enterprise management efficiency has become increasingly prominent. This paper delves into the strategic importance of FSSC in augmenting corporate value, and examines the link between digital transformation and the formation of FSSC. The introduction of FSSC and its accompanying digital technology marks a new juncture for corporate reform. Based on RBV and DCV, this paper studies the strategic significance of FSSC to enhance corporate value, and deconstructs the relationship between digital transformation and the development process of financial shared service centers. Based on the time-varying DID, 335 listed companies were selected for a quasi-natural experiment. The results showed that: (1) financial shared service centers can significantly promote the improvement of corporate value. (2) Digital transformation can promote the establishment and development of financial sharing service centers, thus promoting the improvement of corporate value. (3) The effects of FSSC and digital transformation are characterized by heterogeneity. The worth of financial shared service centers for non-state-owned and manufacturing companies is substantial. Digital transformation has a noteworthy positive moderating effect on them but has no considerable moderating effect on state-owned and non-manufacturing companies. The government ought to bolster policy direction, keep up the digital transformation of businesses, and aid them in achieving rapid financial transformation; senior executives should remain cognizant of their strategic position and strive to execute a satisfactory job of both internal and external coordination, as the research findings suggest.","PeriodicalId":54967,"journal":{"name":"International Journal of Computational Intelligence Systems","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134958738","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 : 2023-09-26DOI: 10.1007/s44196-023-00335-1
Ruijuan Zhang
Abstract In order to improve the accuracy of e-commerce credit risk assessment, this paper suggests utilizing an artificial immune network to upgrade the text mining algorithm. Through this process, a new e-commerce risk assessment model reliant on the improved algorithm can be constructed with the intention of decreasing the likelihood of risk in digital transactions. The results show that the accuracy and loss rate of the improved clustering algorithm are 97.3% and 4.3%, respectively, both of which are better than the comparison algorithm. Then, the empirical analysis of the e-commerce credit risk assessment model proposed in the study shows that the average fitness and accuracy of the model after stability are 0.0022 and 95.63%, respectively, demonstrating superior performance compared to the comparison model. The above results show that the improved algorithm and the risk assessment model have good performance. Therefore, using this model to evaluate the credit risk of e-commerce can not only improve the accuracy of credit evaluation and promote the sustainable development of e-commerce. Furthermore, it can catalyze the adoption of innovative credit evaluation methods and promote the application of artificial intelligence technology in e-commerce.
{"title":"The Application of Artificial Immune Network in E-Commerce Credit Risk Assessment","authors":"Ruijuan Zhang","doi":"10.1007/s44196-023-00335-1","DOIUrl":"https://doi.org/10.1007/s44196-023-00335-1","url":null,"abstract":"Abstract In order to improve the accuracy of e-commerce credit risk assessment, this paper suggests utilizing an artificial immune network to upgrade the text mining algorithm. Through this process, a new e-commerce risk assessment model reliant on the improved algorithm can be constructed with the intention of decreasing the likelihood of risk in digital transactions. The results show that the accuracy and loss rate of the improved clustering algorithm are 97.3% and 4.3%, respectively, both of which are better than the comparison algorithm. Then, the empirical analysis of the e-commerce credit risk assessment model proposed in the study shows that the average fitness and accuracy of the model after stability are 0.0022 and 95.63%, respectively, demonstrating superior performance compared to the comparison model. The above results show that the improved algorithm and the risk assessment model have good performance. Therefore, using this model to evaluate the credit risk of e-commerce can not only improve the accuracy of credit evaluation and promote the sustainable development of e-commerce. Furthermore, it can catalyze the adoption of innovative credit evaluation methods and promote the application of artificial intelligence technology in e-commerce.","PeriodicalId":54967,"journal":{"name":"International Journal of Computational Intelligence Systems","volume":"88 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134958598","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 : 2023-09-26DOI: 10.1007/s44196-023-00338-y
Juanjuan Peng
Abstract The study of logistics distribution network under e-commerce environment is conducive to the establishment of efficient logistics distribution system, but also to promote the further development of e-commerce and improve social benefits of great significance. This study considers multiple fuzzy factors and introduces a customer fuzzy time window with variable coefficients, establishes a multi-objective set allocation integrated multi-level location path planning model, and proposes an archive type multi-objective simulated annealing improvement algorithm based on master–slave parallel framework embedded taboo search to solve the model. Tabu search and large-scale neighborhood algorithm are used to solve the initial solutions of the first level network and the second level network respectively, and archival reception criterion is introduced to deal with the multi-objective problem. The results of the proposed algorithm for the two-level site-routing problem are less than 6% different from the internationally known optimal solution. The master–slave parallel computing framework improves the efficiency of the algorithm by about 6.38%. The experimental results prove the effectiveness and necessity of the improved optimization. In addition, this study simulates the site-routing problem model constructed by the study by extending the data of standard examples. The experimental results prove the correctness and reference significance of the multilevel site-routing problem model with multiple fuzzy factors.
{"title":"Effectiveness of Mixed Fuzzy Time Window Multi-objective Allocation in E-Commerce Logistics Distribution Path","authors":"Juanjuan Peng","doi":"10.1007/s44196-023-00338-y","DOIUrl":"https://doi.org/10.1007/s44196-023-00338-y","url":null,"abstract":"Abstract The study of logistics distribution network under e-commerce environment is conducive to the establishment of efficient logistics distribution system, but also to promote the further development of e-commerce and improve social benefits of great significance. This study considers multiple fuzzy factors and introduces a customer fuzzy time window with variable coefficients, establishes a multi-objective set allocation integrated multi-level location path planning model, and proposes an archive type multi-objective simulated annealing improvement algorithm based on master–slave parallel framework embedded taboo search to solve the model. Tabu search and large-scale neighborhood algorithm are used to solve the initial solutions of the first level network and the second level network respectively, and archival reception criterion is introduced to deal with the multi-objective problem. The results of the proposed algorithm for the two-level site-routing problem are less than 6% different from the internationally known optimal solution. The master–slave parallel computing framework improves the efficiency of the algorithm by about 6.38%. The experimental results prove the effectiveness and necessity of the improved optimization. In addition, this study simulates the site-routing problem model constructed by the study by extending the data of standard examples. The experimental results prove the correctness and reference significance of the multilevel site-routing problem model with multiple fuzzy factors.","PeriodicalId":54967,"journal":{"name":"International Journal of Computational Intelligence Systems","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134958595","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 : 2023-09-21DOI: 10.1007/s44196-023-00336-0
Yancang Li, Weizhi Li, Qiuyu Yuan, Huawang Shi, Muxuan Han
Abstract Aiming at the shortcomings of seagull optimization algorithm in the process of searching for optimization, such as slow convergence speed, low precision, easy falling into local optimal, and performance dependent on the selection of parameters, this paper proposes an improved gull optimization algorithm based on multi-strategy fusion based on the analysis of gull population characteristics. Firstly, L–C cascade chaotic mapping is used to initialize the population so that seagulls are more evenly distributed in the initial solution space. Secondly, to improve the algorithm’s global exploration ability in the early stage, the nonlinear convergence factor is incorporated to adjust the position of seagulls in the migration stage. At the same time, the group learning strategy was introduced after the population position update to improve the population quality and optimization accuracy further. Finally, in the late stage of the algorithm, the golden sine strategy of the Levy flight guidance mechanism is used to update the population position to improve the population’s diversity and enhance the local development ability of the algorithm in the late stage. To verify the optimization performance of the improved algorithm, CEC2017 and CEC2022 test suites are selected for simulation experiments, and box graphs are drawn. The test results show that the proposed algorithm has apparent convergence speed, accuracy, and stability advantages. The engineering case results demonstrate the proposed algorithm’s advantages in solving complex problems with unknown search spaces.
{"title":"Multi-strategy Improved Seagull Optimization Algorithm","authors":"Yancang Li, Weizhi Li, Qiuyu Yuan, Huawang Shi, Muxuan Han","doi":"10.1007/s44196-023-00336-0","DOIUrl":"https://doi.org/10.1007/s44196-023-00336-0","url":null,"abstract":"Abstract Aiming at the shortcomings of seagull optimization algorithm in the process of searching for optimization, such as slow convergence speed, low precision, easy falling into local optimal, and performance dependent on the selection of parameters, this paper proposes an improved gull optimization algorithm based on multi-strategy fusion based on the analysis of gull population characteristics. Firstly, L–C cascade chaotic mapping is used to initialize the population so that seagulls are more evenly distributed in the initial solution space. Secondly, to improve the algorithm’s global exploration ability in the early stage, the nonlinear convergence factor is incorporated to adjust the position of seagulls in the migration stage. At the same time, the group learning strategy was introduced after the population position update to improve the population quality and optimization accuracy further. Finally, in the late stage of the algorithm, the golden sine strategy of the Levy flight guidance mechanism is used to update the population position to improve the population’s diversity and enhance the local development ability of the algorithm in the late stage. To verify the optimization performance of the improved algorithm, CEC2017 and CEC2022 test suites are selected for simulation experiments, and box graphs are drawn. The test results show that the proposed algorithm has apparent convergence speed, accuracy, and stability advantages. The engineering case results demonstrate the proposed algorithm’s advantages in solving complex problems with unknown search spaces.","PeriodicalId":54967,"journal":{"name":"International Journal of Computational Intelligence Systems","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136235308","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/s44196-023-00319-1
Rubén E. Nogales, Marco E. Benalcázar
Abstract Hand gestures are widely used in human-to-human and human-to-machine communication. Therefore, hand gesture recognition is a topic of great interest. Hand gesture recognition is closely related to pattern recognition, where overfitting can occur when there are many predictors relative to the size of the training set. Therefore, it is necessary to reduce the dimensionality of the feature vectors through feature selection techniques. In addition, the need for portability in hand gesture recognition systems limits the use of deep learning algorithms. In this sense, a study of feature selection and extraction methods is proposed for the use of traditional machine learning algorithms. The feature selection methods analyzed are: maximum relevance and minimum redundancy (MRMR), Sequential, neighbor component analysis without parameters (NCAsp), neighbor component analysis with parameters (NCAp), Relief-F, and decision tree (DT). We also analyze the behavior of feature selection methods using classification and recognition accuracy and processing time. Feature selection methods were fed through seventeen feature extraction functions, which return a score proportional to its importance. The functions are then ranked according to their scores and fed to machine learning algorithms such as Artificial Neural Networks (ANN), Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Decision Tree (DT). This work demonstrates that all feature selection methods evaluated on ANN provide better accuracy. In addition, the combination and number of feature extraction functions influence the accuracy and processing time.
{"title":"Analysis and Evaluation of Feature Selection and Feature Extraction Methods","authors":"Rubén E. Nogales, Marco E. Benalcázar","doi":"10.1007/s44196-023-00319-1","DOIUrl":"https://doi.org/10.1007/s44196-023-00319-1","url":null,"abstract":"Abstract Hand gestures are widely used in human-to-human and human-to-machine communication. Therefore, hand gesture recognition is a topic of great interest. Hand gesture recognition is closely related to pattern recognition, where overfitting can occur when there are many predictors relative to the size of the training set. Therefore, it is necessary to reduce the dimensionality of the feature vectors through feature selection techniques. In addition, the need for portability in hand gesture recognition systems limits the use of deep learning algorithms. In this sense, a study of feature selection and extraction methods is proposed for the use of traditional machine learning algorithms. The feature selection methods analyzed are: maximum relevance and minimum redundancy (MRMR), Sequential, neighbor component analysis without parameters (NCAsp), neighbor component analysis with parameters (NCAp), Relief-F, and decision tree (DT). We also analyze the behavior of feature selection methods using classification and recognition accuracy and processing time. Feature selection methods were fed through seventeen feature extraction functions, which return a score proportional to its importance. The functions are then ranked according to their scores and fed to machine learning algorithms such as Artificial Neural Networks (ANN), Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Decision Tree (DT). This work demonstrates that all feature selection methods evaluated on ANN provide better accuracy. In addition, the combination and number of feature extraction functions influence the accuracy and processing time.","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":"136308352","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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