Machine learning with applications最新文献_第4页

Machine learning techniques and multi-objective programming to select the best suppliers and determine the orders

Machine learning with applications Pub Date : 2025-01-17 DOI: 10.1016/j.mlwa.2025.100623

Asma ul Husna, Saman Hassanzadeh Amin, Ahmad Ghasempoor

{"title":"Machine learning techniques and multi-objective programming to select the best suppliers and determine the orders","authors":"Asma ul Husna, Saman Hassanzadeh Amin, Ahmad Ghasempoor","doi":"10.1016/j.mlwa.2025.100623","DOIUrl":"10.1016/j.mlwa.2025.100623","url":null,"abstract":"<div><div>Selection of appropriate suppliers and allocation the orders among them have become the two key strategic decisions regarding purchasing. In this study, a two-phase integrated approach is proposed for solving supplier selection and order allocation problems. Phase 1 contains four techniques from statistics and Machine Learning (ML), including Auto-Regressive Integrated Moving Average, Random Forest, Gradient Boosting Regression, and Long Short-term Memory for forecasting the demands, using large amounts of real historical data. In Phase 2, suppliers’ qualitative weights are determined by a fuzzy logic model. Then, a new multi-objective programming model is designed, considering multiple periods and products. In this phase, the results of Phase 1 and the results of the fuzzy model are utilized as inputs for the multi-objective model. The weighted-sum method is applied for solving the multi-objective model. The results show Random Forest model leads to more accurate predictions than the other examined models in this study. In addition, based on the results, the selection of the forecasting techniques and different weights of suppliers affect both supplier selection and the related orders.</div></div>","PeriodicalId":74093,"journal":{"name":"Machine learning with applications","volume":"19 ","pages":"Article 100623"},"PeriodicalIF":0.0,"publicationDate":"2025-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143168591","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

S&P-500 vs. Nasdaq-100 price movement prediction with LSTM for different daily periods

Machine learning with applications Pub Date : 2025-01-13 DOI: 10.1016/j.mlwa.2024.100617

Xiang Zhang, Eugene Pinsky

{"title":"S&P-500 vs. Nasdaq-100 price movement prediction with LSTM for different daily periods","authors":"Xiang Zhang, Eugene Pinsky","doi":"10.1016/j.mlwa.2024.100617","DOIUrl":"10.1016/j.mlwa.2024.100617","url":null,"abstract":"<div><div>This paper explores the efficiency of LSTM neural networks in predicting price movements for the two major U.S. stock indices: the S&P-500 and the Nasdaq-100 index. We consider three distinct daily periods: “overnight” (Close-to-Open), “daytime” (Open-to-Close) and “24-hour” (Close-to-Close) trading sessions. Using historical pricing data for these indices since 2000, this study shows how well the standard LSTM model captures price movement patterns to improve short-term trading strategies. The findings reveal that, for the S&P-500, a one-year training with 24-hour periods delivers a 14.5% more return over the Buy-and-Hold strategy. Moreover, combining “overnight” and “daytime” strategies delivers more than 40% return compared to passive index investing. By contrast, for the Nasdaq-100, a shorter training period of three months for “24-hour” periods delivers 90% more return than passive index investing. These results suggest that LSTM effectively learns the unique market dynamics associated with each index and different time periods, offering further insights into how deep learning can enhance financial forecasting and trading opportunities.</div></div>","PeriodicalId":74093,"journal":{"name":"Machine learning with applications","volume":"19 ","pages":"Article 100617"},"PeriodicalIF":0.0,"publicationDate":"2025-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143170230","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multi-agent deep reinforcement learning with online and fair optimal dispatch of EV aggregators

Machine learning with applications Pub Date : 2025-01-09 DOI: 10.1016/j.mlwa.2025.100620

Arian Shah Kamrani , Anoosh Dini , Hanane Dagdougui , Keyhan Sheshyekani

{"title":"Multi-agent deep reinforcement learning with online and fair optimal dispatch of EV aggregators","authors":"Arian Shah Kamrani , Anoosh Dini , Hanane Dagdougui , Keyhan Sheshyekani","doi":"10.1016/j.mlwa.2025.100620","DOIUrl":"10.1016/j.mlwa.2025.100620","url":null,"abstract":"<div><div>The growing popularity of electric vehicles (EVs) and the unpredictable behavior of EV owners have attracted attention to real-time coordination of EVs charging management. This paper presents a hierarchical structure for charging management of EVs by integrating fairness and efficiency concepts within the operations of the distribution system operator (DSO) while utilizing a multi-agent deep reinforcement learning (MADRL) framework to tackle the complexities of energy purchasing and distribution among EV aggregators (EVAs). At the upper level, DSO calculates the maximum allowable power for each EVA based on power flow constraints to ensure grid safety. Then, it finds the optimal efficiency-Jain tradeoff (EJT) point, where it sells the highest energy amount while ensuring equitable energy distribution. At the lower level, initially, each EVA acts as an agent employing a double deep Q-network (DDQN) with adaptive learning rates and prioritized experience replay to determine optimal energy purchases from the DSO. Then, the real-time smart dispatch (RSD) controller prioritizes EVs for energy dispatch based on relevant EVs information. Findings indicate the proposed enhanced DDQN outperforms deep deterministic policy gradient (DDPG) and proximal policy optimization (PPO) in cumulative rewards and convergence speed. Finally, the framework’s performance is evaluated against uncontrolled charging and the first come first serve (FCFS) scenario using the 118-bus distribution system, demonstrating superior performance in maintaining safe operation of the grid while reducing charging costs for EVAs. Additionally, the framework’s integration with renewable energy sources (RESs), such as photovoltaic (PV), demonstrates its potential to enhance grid reliability.</div></div>","PeriodicalId":74093,"journal":{"name":"Machine learning with applications","volume":"19 ","pages":"Article 100620"},"PeriodicalIF":0.0,"publicationDate":"2025-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143170229","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Predicting customer subscription in bank telemarketing campaigns using ensemble learning models

Machine learning with applications Pub Date : 2025-01-07 DOI: 10.1016/j.mlwa.2025.100618

Michael Peter , Hawa Mofi , Said Likoko , Julius Sabas , Ramadhani Mbura , Neema Mduma

{"title":"Predicting customer subscription in bank telemarketing campaigns using ensemble learning models","authors":"Michael Peter , Hawa Mofi , Said Likoko , Julius Sabas , Ramadhani Mbura , Neema Mduma","doi":"10.1016/j.mlwa.2025.100618","DOIUrl":"10.1016/j.mlwa.2025.100618","url":null,"abstract":"<div><div>This study investigates the use of ensemble learning models bagging, boosting, and stacking to enhance the accuracy and reliability of predicting customer subscriptions in bank telemarketing campaigns. Recognizing the challenges posed by class imbalance and complex customer behaviors, we employ multiple ensemble techniques to build a robust predictive framework. Our analysis demonstrates that stacking models achieve the best overall performance, with an accuracy of 91.88% and an Receiver Operating Characteristic Area Under the Curve (ROC-AUC) score of 0.9491, indicating a strong capability to differentiate between subscribers and non-subscribers. Additionally, feature importance analysis reveals that contact duration, economic indicators like the Euro interbank offered (Euribor) rate, and customer age are the most influential factors in predicting subscription likelihood. These findings suggest that by focusing on customer engagement and economic trends, banks can improve telemarketing campaign effectiveness. We recommend the integration of advanced balancing techniques and real-time prediction systems to further enhance model performance and adaptability. Future work could explore deep learning models and interpretability techniques to gain deeper insights into customer behavior patterns. Overall, this study highlights the potential of ensemble models in predictive modeling for telemarketing, providing a data-driven foundation for more targeted and efficient customer acquisition strategies.</div></div>","PeriodicalId":74093,"journal":{"name":"Machine learning with applications","volume":"19 ","pages":"Article 100618"},"PeriodicalIF":0.0,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143170241","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Improving mango ripeness grading accuracy: A comprehensive analysis of deep learning, traditional machine learning, and transfer learning techniques

Machine learning with applications Pub Date : 2025-01-06 DOI: 10.1016/j.mlwa.2025.100619

Md․ Saon Sikder, Mohammad Shamsul Islam, Momenatul Islam, Md․ Suman Reza

{"title":"Improving mango ripeness grading accuracy: A comprehensive analysis of deep learning, traditional machine learning, and transfer learning techniques","authors":"Md․ Saon Sikder, Mohammad Shamsul Islam, Momenatul Islam, Md․ Suman Reza","doi":"10.1016/j.mlwa.2025.100619","DOIUrl":"10.1016/j.mlwa.2025.100619","url":null,"abstract":"<div><div>Bangladesh ranks among the top 10 countries globally in mango output. Mangoes can be classified based on their ripeness, with skin color being the most significant aspect. The current classification procedure is done manually, leading to mistakes and vulnerability to human error. Most research often focuses on using a single method to assess the ripeness of fruits. The study comprises a set of comprehensive tests showcasing different tactics for determining the most efficient methods through various models. One unique dataset was used for all five models: Gaussian Naive Bayes (GNB), Support Vector Machine (SVM), Gradient Boosting (GB), Random Forest (RF), and K-Nearest Neighbors (KNN). Utilizing convolutional neural networks (CNNs) and VGG16, a pre-trained CNN model, to extract features and train the dataset. Used these training datasets as input to calculate the average accuracy of the five models during testing. In addition to these experiments, these five models using standard techniques also evaluated. The study also included a comparative analysis that emphasized the best performance of each model in various scenarios. This analysis shows that the CNN model consistently performs better than the transfer learning model (VGG16) and classical machine learning methods. Except for the KNN and Naive Bayes scenarios, the VGG16 model achieved much higher accuracy compared to typical machine learning methods. In three other models, classical machine learning outperforms the VGG16 model. The Gradient Boosting model in deep learning (CNN) demonstrated the highest accuracy of 96.28 % compared to other models and techniques.</div></div>","PeriodicalId":74093,"journal":{"name":"Machine learning with applications","volume":"19 ","pages":"Article 100619"},"PeriodicalIF":0.0,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143170240","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Stairway to heaven: An emotional journey in Divina Commedia with threshold-based Naïve Bayes classifier

Machine learning with applications Pub Date : 2024-12-24 DOI: 10.1016/j.mlwa.2024.100613

Maurizio Romano, Claudio Conversano

{"title":"Stairway to heaven: An emotional journey in Divina Commedia with threshold-based Naïve Bayes classifier","authors":"Maurizio Romano, Claudio Conversano","doi":"10.1016/j.mlwa.2024.100613","DOIUrl":"10.1016/j.mlwa.2024.100613","url":null,"abstract":"<div><div>Computational literary uses data science and computer science techniques to study literature. In this framework, we investigate how an expert system can acquire knowledge from the specific content of a narrative text without any pre-existing information about it. We utilize the Threshold-based Naïve Bayes (Tb-NB) classifier to analyze the content of Dante Alighieri’s Divina Commedia poem. Tb-NB is a probabilistic data-driven model that predicts the polarity of a binary response based on the probability of an event occurring given certain features, and assigns a log-likelihood score to each word in a text. Our first task is understanding if and how the links between lexical forms and meanings characterize the three parts of the poem (Inferno, Purgatorio and Paradiso) in order to predict if a Canto belongs to Inferno or Paradiso based on its specific content, and to determine if a Canto of Purgatorio is more similar to those of Inferno or to those of Paradiso. We show Tb-NB outperform other similar approaches and achieves the same performance of Random Forest (F1-score <span><math><mo>=</mo></math></span> 0.985) but providing much more information to interpret the specific content and the lexical forms used by Dante Alighieri in its poem. The Tb-NB’s scores are the base of knowledge for the implementation of an expert system, like a search engine, that can help users to identify the most informative verses of a Canto or by better comprehend or discover the content of the poem from a word related to a particular feeling or emotion.</div></div>","PeriodicalId":74093,"journal":{"name":"Machine learning with applications","volume":"19 ","pages":"Article 100613"},"PeriodicalIF":0.0,"publicationDate":"2024-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143170231","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Unified wound diagnostic framework for wound segmentation and classification

Machine learning with applications Pub Date : 2024-12-20 DOI: 10.1016/j.mlwa.2024.100616

Mustafa Alhababi , Gregory Auner , Hafiz Malik , Muteb Aljasem , Zaid Aldoulah

{"title":"Unified wound diagnostic framework for wound segmentation and classification","authors":"Mustafa Alhababi , Gregory Auner , Hafiz Malik , Muteb Aljasem , Zaid Aldoulah","doi":"10.1016/j.mlwa.2024.100616","DOIUrl":"10.1016/j.mlwa.2024.100616","url":null,"abstract":"<div><div>Chronic wounds affect millions worldwide, posing significant challenges for healthcare systems and a heavy economic burden globally. The segmentation and classification (S&C) of chronic wounds are critical for wound care management and diagnosis, aiding clinicians in selecting appropriate treatments. Existing approaches have utilized either traditional machine learning or deep learning methods for S&C. However, most focus on binary classification, with few addressing multi-class classification, often showing degraded performance for pressure and diabetic wounds. Wound segmentation has been largely limited to foot ulcer images, and there is no unified diagnostic tool for both S&C tasks. To address these gaps, we developed a unified approach that performs S&C simultaneously. For segmentation, we proposed Attention-Dense-UNet (Att-<span>d</span>-UNet), and for classification, we introduced a feature concatenation-based method. Our framework segments wound images using Att-<span>d</span>-UNet, followed by classification into one of the wound types using our proposed method. We evaluated our models on publicly available wound classification datasets (AZH and Medetec) and segmentation datasets (FUSeg and AZH). To test our unified approach, we extended wound classification datasets by generating segmentation masks for Medetec and AZH images. The proposed unified approach achieved 90% accuracy and an 86.55% dice score on the Medetec dataset and 81% accuracy and an 86.53% dice score on the AZH dataset These results demonstrate the effectiveness of our separate models and unified approach for wound S&C.</div></div>","PeriodicalId":74093,"journal":{"name":"Machine learning with applications","volume":"19 ","pages":"Article 100616"},"PeriodicalIF":0.0,"publicationDate":"2024-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143170239","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Practical classification accuracy of sequential data using neural networks

Machine learning with applications Pub Date : 2024-12-18 DOI: 10.1016/j.mlwa.2024.100611

Mamoru Mimura

{"title":"Practical classification accuracy of sequential data using neural networks","authors":"Mamoru Mimura","doi":"10.1016/j.mlwa.2024.100611","DOIUrl":"10.1016/j.mlwa.2024.100611","url":null,"abstract":"<div><div>Many existing studies on neural network accuracy utilize datasets that may not always reflect real-world conditions. While it has been demonstrated that accuracy tends to decrease as the number of benign samples increases, this effect has not been quantitatively assessed within neural networks. Moreover, its relevance to security tasks beyond malware classification remains unexplored. In this research, we refined the metric to evaluate the degradation of accuracy with an increased number of benign samples in test data. Utilizing both standard and specific neural network models, we conducted experiments to adapt this metric to neural networks and various feature extraction techniques. Using the FFRI dataset, comprising 150,000 malware and 400,000 benign samples, along with the URL dataset, containing 3143 malicious and 106,545,781 benign samples, we increased benign samples in the test set while keeping the training set’s malicious and benign samples constant. Our findings indicate that neural networks can indeed overestimate their accuracy with a smaller count of benign samples. Importantly, our refined metric is not only applicable to neural networks but is also effective for other feature extraction methods and security tasks beyond malware detection.</div></div>","PeriodicalId":74093,"journal":{"name":"Machine learning with applications","volume":"19 ","pages":"Article 100611"},"PeriodicalIF":0.0,"publicationDate":"2024-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143170232","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Combinations of distributional regression algorithms with application in uncertainty estimation of corrected satellite precipitation products

Machine learning with applications Pub Date : 2024-12-16 DOI: 10.1016/j.mlwa.2024.100615

Georgia Papacharalampous, Hristos Tyralis, Nikolaos Doulamis, Anastasios Doulamis

{"title":"Combinations of distributional regression algorithms with application in uncertainty estimation of corrected satellite precipitation products","authors":"Georgia Papacharalampous, Hristos Tyralis, Nikolaos Doulamis, Anastasios Doulamis","doi":"10.1016/j.mlwa.2024.100615","DOIUrl":"10.1016/j.mlwa.2024.100615","url":null,"abstract":"<div><div>To facilitate effective decision-making, precipitation datasets should include uncertainty estimates. Quantile regression with machine learning has been proposed for issuing such estimates. Distributional regression offers distinct advantages over quantile regression, including the ability to model intermittency as well as a stronger ability to extrapolate beyond the training data, which is critical for predicting extreme precipitation. Therefore, here, we introduce the concept of distributional regression in precipitation dataset creation, specifically for the spatial prediction task of correcting satellite precipitation products. Building upon this concept, we formulated new ensemble learning methods that can be valuable not only for spatial prediction but also for other prediction problems. These methods exploit conditional zero-adjusted probability distributions estimated with generalized additive models for location, scale and shape (GAMLSS), spline-based GAMLSS and distributional regression forests as well as their ensembles (stacking based on quantile regression and equal-weight averaging). To identify the most effective methods for our specific problem, we compared them to benchmarks using a large, multi-source precipitation dataset. Stacking was shown to be superior to individual methods at most quantile levels when evaluated with the quantile loss function. Moreover, while the relative ranking of the methods varied across different quantile levels, stacking methods, and to a lesser extent mean combiners, exhibited lower variance in their performance across different quantiles compared to individual methods that occasionally ranked extremely low. Overall, a task-specific combination of multiple distributional regression algorithms could yield significant benefits in terms of stability.</div></div>","PeriodicalId":74093,"journal":{"name":"Machine learning with applications","volume":"19 ","pages":"Article 100615"},"PeriodicalIF":0.0,"publicationDate":"2024-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143168592","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Adaptive gate residual connection and multi-scale RCNN for fake news detection

Machine learning with applications Pub Date : 2024-12-15 DOI: 10.1016/j.mlwa.2024.100612

QunHui Zhou, Tijian Cai

{"title":"Adaptive gate residual connection and multi-scale RCNN for fake news detection","authors":"QunHui Zhou, Tijian Cai","doi":"10.1016/j.mlwa.2024.100612","DOIUrl":"10.1016/j.mlwa.2024.100612","url":null,"abstract":"<div><div>Detection of false news based on text classification technology has significant research significance and practical value in the current information age. However, existing methods overlook the problem of uneven sample distribution in the false news dataset and fail to consider the mutual influence between news articles. In light of this, this paper proposes a new method for false news detection. Firstly, news texts are embedded using Electra (Efficiently Learning an Encoder that Classifies Token Replacements Accurately) to obtain word embedding representations. Secondly, Multi-Scale Recurrent Convolutional Neural Network (RCNN) is employed to further extract contextual information from news texts. Self-attention is introduced to calculate attention scores between news articles, allowing for mutual influence between news features. The establishment of connections between modules is achieved through adaptive gated residual connections. Finally, the focal loss function is used to balance the relationship between few-sample and multi-sample data in the dataset. Experimental results on publicly available false news detection datasets demonstrate that the proposed method achieves higher prediction accuracy than the comparative methods. This method provides a new perspective for the field of false news detection, playing a positive role in promoting information authenticity and protecting public interests.</div></div>","PeriodicalId":74093,"journal":{"name":"Machine learning with applications","volume":"19 ","pages":"Article 100612"},"PeriodicalIF":0.0,"publicationDate":"2024-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143169032","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0