Annals of Data Science最新文献_第9页

Spatial Functional Data and Classification Techniques to Control a Hand Prosthesis Using Silent Speech 空间功能数据与分类技术在无声语音控制手部假肢中的应用

Annals of Data Science Pub Date : 2025-04-24 DOI: 10.1007/s40745-025-00596-x

Valeria Bejarano, Martha Patricia Bohorquez Castañeda, Juan Carlos Riaño, Luis Carlos Sarmiento Vela, Rafael Rentería

{"title":"Spatial Functional Data and Classification Techniques to Control a Hand Prosthesis Using Silent Speech","authors":"Valeria Bejarano, Martha Patricia Bohorquez Castañeda, Juan Carlos Riaño, Luis Carlos Sarmiento Vela, Rafael Rentería","doi":"10.1007/s40745-025-00596-x","DOIUrl":"10.1007/s40745-025-00596-x","url":null,"abstract":"<div><p>We propose a novel alternative to generate and classify electroencephalography images, involving the spatial correlation structure among the functional brain-signals. We combine spatial functional methods with image classification techniques to carry out supervised classification of a new silent speech thought, in one specific option that determines an action of a hand prosthesis. Using functional kriging, we find the optimized prediction of the brain signals on unsampled brain locations. Based on these spatial functional predictions, we build a sequence of images in the frequency domain, and then we apply machine learning and deep learning algorithms to this sequence of images. The proposal is applied to a dataset of electroencephalography signals collected on the language area of the brain while people are thinking in silence on each of the five vowels in the Spanish language. Our methodology is implemented in real-life computer interface machines using Google Compute Engine, testing the period for the machine calibration and training the classification algorithm.</p></div>","PeriodicalId":36280,"journal":{"name":"Annals of Data Science","volume":"13 4","pages":"805 - 828"},"PeriodicalIF":0.0,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s40745-025-00596-x.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148613934","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

Sustainable Development of Green Tourism Supply Chain Considering Blockchain Traceability and Government Subsidies 考虑可追溯性和政府补贴的绿色旅游供应链可持续发展

Annals of Data Science Pub Date : 2025-04-21 DOI: 10.1007/s40745-025-00588-x

Jixia Zheng, Rui Chen, Qinggen Zeng, Yanan Chen, Qianlin Ye

{"title":"Sustainable Development of Green Tourism Supply Chain Considering Blockchain Traceability and Government Subsidies","authors":"Jixia Zheng, Rui Chen, Qinggen Zeng, Yanan Chen, Qianlin Ye","doi":"10.1007/s40745-025-00588-x","DOIUrl":"10.1007/s40745-025-00588-x","url":null,"abstract":"<div><p>This study examines the issue of green information distortion and its impact on tourists’ purchasing decisions, as well as the associated high transaction costs within the green tourism supply chain. By selecting a green tourism supply chain with varying government subsidy schemes as the focus of the research, the objective is to explore optimal subsidy strategies and assess the implications of blockchain integration. A three-level Stackelberg game model is established, featuring the government as the leader and a scenic spot (SS) and travel agency (TA) as participants. Key findings include: (1) Production subsidies are more effective in boosting market demand than environmental investment subsidies, particularly when tourist green trust and preferences are high. (2) Blockchain enhances greenness, market demand, and social welfare, positively influencing the green tourism supply chain (GTSC). (3) Tourist green preference and trust significantly affect GTSC optimization, especially as preferences increase. Additionally, a cost-sharing smart contract mechanism is designed to mitigate environmental investment's negative impact and optimize social welfare and product greenness.</p></div>","PeriodicalId":36280,"journal":{"name":"Annals of Data Science","volume":"12 4","pages":"1315 - 1342"},"PeriodicalIF":0.0,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145167864","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Deep CP-CXR: A Deep Learning Model for Classification of Covid-19 and Pneumonia Disease Using Chest X-Ray Images 深度CP-CXR：基于胸部x射线图像的Covid-19和肺炎疾病分类的深度学习模型

Annals of Data Science Pub Date : 2025-04-16 DOI: 10.1007/s40745-025-00601-3

Kuljeet Singh, Anubha Gaur, Sachin Kumar, Sourabh Shastri, Vibhakar Mansotra

{"title":"Deep CP-CXR: A Deep Learning Model for Classification of Covid-19 and Pneumonia Disease Using Chest X-Ray Images","authors":"Kuljeet Singh, Anubha Gaur, Sachin Kumar, Sourabh Shastri, Vibhakar Mansotra","doi":"10.1007/s40745-025-00601-3","DOIUrl":"10.1007/s40745-025-00601-3","url":null,"abstract":"<div><p>The global spread of the Coronavirus has caused a disastrous effect, affecting millions of people and making it crucial to take action. Numerous experts have worked extensively to create viable vaccines in the fight against this infectious disease. The current study offers hope by suggesting a deep learning model, Deep CP-CXR, for determining patients with Covid-19 and pneumonia. Our study encompasses two significant investigations. First, we used images from chest X-rays for binary classification to distinguish Covid-19-diagnosed patients from normal patients. Second, using chest X-ray images, we expanded the study to include several groups, such as pneumonia, Covid-19, and normal instances. The results of our studies were extremely promising. The binary classification achieved a remarkable average accuracy of 100%, allowing for accurate classification between Covid-19 patients and normal cases. In addition, the multiple-category classification was able to distinguish between Covid-19, pneumonia, and normal individuals with a remarkable average accuracy of 98.57%. These astounding findings lead us to conclude that the Deep CP-CXR method we’ve suggested for classifying Covid-19 and pneumonia patients enables medical professionals to perform it accurately. Healthcare providers worldwide will benefit significantly from this development since it has the potential to enhance both the detection and treatment of these ailments. The proposed deep learning approach improves the speed and precision in classifying the disease with which doctors can diagnose and treat their patients effectively.</p></div>","PeriodicalId":36280,"journal":{"name":"Annals of Data Science","volume":"13 4","pages":"849 - 872"},"PeriodicalIF":0.0,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148613154","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Symmetric Purity Measure for Clustering Comparison 聚类比较的对称纯度度量

Annals of Data Science Pub Date : 2025-04-01 DOI: 10.1007/s40745-025-00594-z

José Luis Vázquez Noguera, Edgar López Pezoa, Gabriel Espínola, Sebastián Alberto Grillo

引用次数: 0

Within Importance Score Aggregation for Wrapper-Based Feature Selection and Its Stability 基于包装器特征选择的重要度积分聚合及其稳定性

Annals of Data Science Pub Date : 2025-03-23 DOI: 10.1007/s40745-025-00595-y

Reem Salman, Ayman Alzaatreh, Hana Sulieman

{"title":"Within Importance Score Aggregation for Wrapper-Based Feature Selection and Its Stability","authors":"Reem Salman, Ayman Alzaatreh, Hana Sulieman","doi":"10.1007/s40745-025-00595-y","DOIUrl":"10.1007/s40745-025-00595-y","url":null,"abstract":"<div><p>Feature selection remains a central topic of research across various application domains. It is widely employed to reduce dimensionality, eliminate irrelevant data, enhance learning accuracy, and improve the interpretability of results. Among the various feature selection techniques, wrapper methods are widely recognized for their strong performance, albeit at the cost of high computational expense. In this paper, we implement a general framework for the ensemble of multiple feature selection methods based on bootstrap-induced diversity. This framework offers a streamlined methodology for enhancing both the predictive accuracy and stability of wrapper-based feature selection methods. Experimental results, derived from five simulated datasets and ten real-world datasets, underscore the effectiveness of regression-based wrappers in supervised learning problems. Notably, among the real datasets, partial least squares and logworth scoring methods demonstrate superior accuracy and stability overall. Furthermore, the degree of collinearity in the input data emerges as a decisive factor in determining the optimal approach between these two methods.</p></div>","PeriodicalId":36280,"journal":{"name":"Annals of Data Science","volume":"13 2","pages":"329 - 353"},"PeriodicalIF":0.0,"publicationDate":"2025-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147607016","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

STL-LSTM Hybrid Model for Forecasting Seasonal Agricultural Price Series 季节性农产品价格序列预测的STL-LSTM混合模型

Annals of Data Science Pub Date : 2025-03-20 DOI: 10.1007/s40745-025-00590-3

Ronit Jaiswal, Girish K. Jha, Rajeev Ranjan Kumar, Kapil Choudhary

{"title":"STL-LSTM Hybrid Model for Forecasting Seasonal Agricultural Price Series","authors":"Ronit Jaiswal, Girish K. Jha, Rajeev Ranjan Kumar, Kapil Choudhary","doi":"10.1007/s40745-025-00590-3","DOIUrl":"10.1007/s40745-025-00590-3","url":null,"abstract":"<div><p>In this study, we propose an ensemble hybrid model called seasonal trend decomposition based on Loess and long short-term memory (STL-LSTM) for forecasting non-stationary, nonlinear, and seasonal agricultural price series. The model integrates STL, a decomposition technique, with a recurrent neural network-based forecasting method, long short-term memory (LSTM). The STL technique decomposes the original price series into its seasonal, trend, and remainder components, while the LSTM network forecasts each component individually. The predictions from all components are then aggregated to produce the final ensemble forecast. The developed model effectively captures the temporal patterns of complex time series through the analysis of the simplified decomposed components. Empirical results from monthly potato price data of two major markets in India reveal the superior forecasting ability of the proposed model. The STL-LSTM achieves up to 12% lower root mean square error (RMSE) and 9% lower mean absolute percentage error (MAPE) compared to benchmark models which includes both individual and hybrid ones. Additionally, the Diebold-Mariano (DM) test confirms the statistical significance of the model's improved performance. The results highlight the STL-LSTM hybrid model as a promising tool for agricultural price forecasting and provide a foundation for further advancements in this domain.</p></div>","PeriodicalId":36280,"journal":{"name":"Annals of Data Science","volume":"13 2","pages":"279 - 302"},"PeriodicalIF":0.0,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147607133","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Gaussian Copula Joint Models for Mixed Longitudinal (k, l)-Inflated Conway–Maxwell Poisson and Continuous Responses 混合纵向(k, l)-膨胀康威-麦克斯韦泊松和连续响应的高斯Copula联合模型

Annals of Data Science Pub Date : 2025-03-15 DOI: 10.1007/s40745-025-00593-0

Fateme Rezaee, Ehsan Bahrami Samani

{"title":"Gaussian Copula Joint Models for Mixed Longitudinal (k, l)-Inflated Conway–Maxwell Poisson and Continuous Responses","authors":"Fateme Rezaee, Ehsan Bahrami Samani","doi":"10.1007/s40745-025-00593-0","DOIUrl":"10.1007/s40745-025-00593-0","url":null,"abstract":"<div><p>In this paper, we propose joint model for claim counts and amounts in non-life insurance using a Gaussian Copula joint model for analyzing longitudinal (<i>k</i>, <i>l</i>)-inflated Conway–Maxwell Poisson and Normal responses with random effects. We develop a Gaussian Copula-based regression model that accounts for associations between count responses which are inflated in two points. Our approach utilizes underlying latent variables to reveal the hidden mechanisms that produce count responses, particularly in cases where some of these responses exhibit inflation at two specific points. The Gaussian Copula approach are used to investigate both of the correlation between mixed responses and the correlation of longitudinal nature. The full likelihood-based inference method is applied for the estimation of parameters to obtain maximum likelihood estimates of the parameters. To illustrate the utility of the models, some simulations are illustrated. A simulation study is performed in which for count response (<i>k</i>, <i>l</i>)-Inflated Conway–Maxwell Poisson and distributions are considered. Finally, the proposed models are applied to real-life insurance data, specifically an Iranian automobile insurance dataset, which exhibits overdispersion and is derived from an observational study.\u0000</p></div>","PeriodicalId":36280,"journal":{"name":"Annals of Data Science","volume":"13 1","pages":"41 - 62"},"PeriodicalIF":0.0,"publicationDate":"2025-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147339306","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

On Bivariate Dynamic Past Inaccuracy Measure 二元动态过去误差测量

Annals of Data Science Pub Date : 2025-02-26 DOI: 10.1007/s40745-025-00592-1

K. V. Viswakala, E. V. Gijo, E. I. Abdul Sathar

引用次数: 0

A Decision-Theoretic Approach to Model Choice 模型选择的决策理论方法

Annals of Data Science Pub Date : 2025-02-12 DOI: 10.1007/s40745-025-00589-w

Markku Karhunen

{"title":"A Decision-Theoretic Approach to Model Choice","authors":"Markku Karhunen","doi":"10.1007/s40745-025-00589-w","DOIUrl":"10.1007/s40745-025-00589-w","url":null,"abstract":"<div><p>Model choice algorithms are usually compared based on their accuracy, i.e. ability to find true models. However, conservative algorithms (such as BIC minimisation) are accurate when no true effects exist, while more liberal algorithms (such as Lasso) are accurate when there are plenty of true effects. There is ambiguity, then, regarding the correct algorithm. The purpose of this paper is to show how expected utility maximisation and Monte Carlo simulations can be used to compare model choice algorithms. Two loss functions are derived from the expected utility function of the researcher. Both loss functions turn out to be linear combinations of specificity and one or two kinds of sensitivity which are discussed in this paper. Subsequently, this paper experiments with four parametrisations of these loss functions, and then uses these parametrised versions to compare nine algorithms within the contexts of both logistic and Gaussian regression. The results demonstrate that researchers who avoid false positives should either use BIC or BICc for model choice or report nothing at all. AIC does not seem to be the optimal method for the range of parameters covered in this study.</p></div>","PeriodicalId":36280,"journal":{"name":"Annals of Data Science","volume":"13 1","pages":"29 - 39"},"PeriodicalIF":0.0,"publicationDate":"2025-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s40745-025-00589-w.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147338433","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

Two-Sample Test for Testing Equality of Location Vectors Based on Outlying Function 基于离群函数的位置向量等价性检验的双样本检验

Annals of Data Science Pub Date : 2025-02-11 DOI: 10.1007/s40745-025-00591-2

M. S. Barale, D. T. Shirke

引用次数: 0