Annals of Data Science最新文献

{"title":"EdgeSculpt-HO: A hybrid optimization AI model for Real-Time 3D sculpture and pottery design","authors":"Lu kun Huang","doi":"10.1007/s40745-025-00669-x","DOIUrl":"10.1007/s40745-025-00669-x","url":null,"abstract":"<div><p>Digital sculpting is becoming increasingly important in creative design, education, and cultural heritage preservation. Yet, existing techniques for 3D sculpture and pottery modeling often face significant limitations, including poor adaptability to user input, computational inefficiency, and inadequate responsiveness in real-time or edge-computing environments. These challenges hinder intuitive interaction and dynamic design exploration. To overcome these barriers, this work introduces EdgeSculpt-HO, a Hybrid Optimization AI model for real-time, gesture-based 3D sculpture and pottery creation. The framework integrates four key modules: SPFeat-FuseNet for extracting rich multimodal features from spatial, stylistic, and temporal data; EdgeSculptNet, a NAS-enhanced 3D generator optimized for edge deployment; C-GreyGenSelect, a chaotic Grey Wolf and Genetic Algorithm-based selector for robust feature reduction; and the Touch2Form Interaction System, which enables real-time sculptural manipulation using gestures, supported by reinforcement learning, Vision Transformers, and RNN-based haptic feedback. Notably, the system collects Internet of Things-based tactile and environmental input, and utilizes software-defined networking to dynamically manage low-latency data routing between modules across edge devices. Tested on three datasets—6K Sculptures, Art Images, and 3D Model Samples—EdgeSculpt-HO outperformed MeshGAN, AtlasNet, and 3D-StyleGAN, achieving a Dice Similarity of 0.97, Chamfer Distance of 0.0023, 94.6% optimization accuracy, System Usability Scale of 97, Mean Opinion Score of 4.6, and Net Promoter Score of + 72, validating its artistic quality, responsiveness, and deployment readiness.</p></div>","PeriodicalId":36280,"journal":{"name":"Annals of Data Science","volume":"13 2","pages":"455 - 487"},"PeriodicalIF":0.0,"publicationDate":"2026-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147606930","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}

{"title":"Understanding Patient Satisfaction in Online Healthcare: A Study from Doctor-Patient Interaction Perspective","authors":"Yalin Su,&nbsp;Junli Zhang,&nbsp;Xiaodan Yu","doi":"10.1007/s40745-026-00688-2","DOIUrl":"10.1007/s40745-026-00688-2","url":null,"abstract":"<div><p>The digital transformation of healthcare has introduced online healthcare services that offer unprecedented accessibility and convenience. However, the shift to virtual doctor-patient interactions has unveiled new challenges in ensuring patient satisfaction. Prior research has primarily focused on the doctor's perspective, with scant attention given to the patient's perception of informational and emotional support within these digital interactions. Drawing the perspective from Media Synchronicity Theory, this study investigates how online healthcare interaction patterns influence patient perceived satisfaction of informational support and emotional support, and how different interaction patterns across varied media types impact these satisfaction facets. Through an empirical analysis of 7374 patient consultation records from \"www.haodf.com\", this study analyzes the influence of interaction pattern such as doctors' response time, interaction rounds, frequency and media synchronicity in patients' satisfaction. Research results indicate that while doctor's response time might not be the most decisive factor, other interactive factors— interaction rounds and consultation frequency positively influence patient satisfaction with both perceived informational support and emotional support, and media synchronicity positively moderates the impact of the number of interaction rounds on patient satisfaction with perceived informational support and emotional support. By introducing a patient-centric approach to understanding satisfaction in online healthcare interactions, this study highlights the importance of media synchronicity in optimizing communication and provides empirical evidence for the role of interaction patterns in shaping patient satisfaction. The findings offer insights for healthcare providers and platform designers to enhance online service delivery and patient engagement.</p></div>","PeriodicalId":36280,"journal":{"name":"Annals of Data Science","volume":"13 2","pages":"501 - 521"},"PeriodicalIF":0.0,"publicationDate":"2026-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147606497","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}

{"title":"Research on Data Monitoring of Power Grid Operation Status Based on Internet of Things Technology","authors":"Qiang Li,&nbsp;Weijian Zhang,&nbsp;Weizhi Lu,&nbsp;Yuan Liu,&nbsp;Di Cai","doi":"10.1007/s40745-026-00684-6","DOIUrl":"10.1007/s40745-026-00684-6","url":null,"abstract":"<div><p>With the advancement of power grid informatization, the utilization of Internet of Things (IoT) technology has become increasingly widespread. This study primarily focuses on monitoring the operational status of the power grid, specifically targeting load status. Environmental and load data were collected using IoT technology. Then, the least squares support vector machine (LSSVM) was selected as the predictive model. An improved beluga whale optimization (IBWO) algorithm was developed to optimize the parameters of the LSSVM model, resulting in an IBWO-LSSVM model for load status prediction. An experiment was conducted using the collected data. It was found that the load state predictions generated by the IBWO-LSSVM model closely matched the actual values. The mean absolute error achieved was 30.56 MW, the root mean square error was 38.45 MW, and the mean absolute percentage error was 2.12%. These results also surpassed those of several other prediction methods, demonstrating the effectiveness of this model in load state prediction and its capability to enhance load state data monitoring. The findings validate the efficacy of the IBWO-LSSVM model and highlight its potential application in actual data monitoring of grid operational status.</p></div>","PeriodicalId":36280,"journal":{"name":"Annals of Data Science","volume":"13 2","pages":"489 - 500"},"PeriodicalIF":0.0,"publicationDate":"2026-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147606496","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}

{"title":"Cancelable Speaker Identification Based on Speech Deconvolution Methods","authors":"Marwa A. Elsayed,&nbsp;Walid El-Shafai,&nbsp;Mohsen A. Rashwan,&nbsp;Moawad I. Dessouky,&nbsp;Adel S. El-Fishawy,&nbsp;Fathi E. Abd El-Samie","doi":"10.1007/s40745-024-00579-4","DOIUrl":"10.1007/s40745-024-00579-4","url":null,"abstract":"<div><p>Biometric authentication systems, which use unique biological traits for identification, have gained popularity in various fields as a replacement for traditional password- or token-based systems. While offering enhanced security, these systems remain vulnerable to hacking attempts. To address this concern, this paper presents a novel masking technique for speaker identification, enhancing security and privacy. The core concept involves generating an alternate version of the original speaker template using a one-way transformation. This ensures that the original template remains protected and unused within the system. The transformation draws inspiration from deconvolution methods, commonly used for signal processing. These methods are applied in the presence of noise to magnify the noise in order to mask the original templates. We explore three specific deconvolution methods for creating cancelable biometric templates, namely Linear Minimum Mean Square Error (LMMSE), inverse filter and regularized deconvolution. The LMMSE method depends on statistical techniques to minimize the error between the original signal and the deconvolution output. The paper discusses some assumptions that help reduce the computational complexity of the LMMSE solution. Inverse filter deconvolution directly reverses the convolution process through multiplication with the inverse filter transfer function in frequency domain. Finally, regularized deconvolution incorporates additional constraints during the deconvolution process to improve the robustness of the transformed templates. Additionally, we investigate two further methods for comparison: Discrete Wavelet Transform (DWT) and wavelet thresholding. These methods offer alternative approaches to signal processing and feature extraction. Simulation results demonstrate the effectiveness of the deconvolution-based cancelable biometric schemes, displaying their potential for enhancing the security and privacy of speaker identification systems. A novel finding from this research is that the inverse filter deconvolution method, despite its known limitations, offers superior performance in comparison to other methods when applied in cancelable speaker identification. This unexpected outcomes change the conventional understanding of the method applicability and opens new directions for research in the field.</p></div>","PeriodicalId":36280,"journal":{"name":"Annals of Data Science","volume":"13 1","pages":"1 - 27"},"PeriodicalIF":0.0,"publicationDate":"2025-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147342382","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}

{"title":"Revisiting Computing Paradigm for Partitional Clustering Analysis and Recommendations","authors":"Hakam Singh,&nbsp;Yugal Kumar,&nbsp;Nagesh Kumar","doi":"10.1007/s40745-025-00663-3","DOIUrl":"10.1007/s40745-025-00663-3","url":null,"abstract":"<div><p>The business world is concentric around the imperative of information extraction and analysis. The information extraction processes are blended with different data mining techniques like clustering, classification, etc. Clustering is an explorative technique that extracts imperative information from large databases. The adaptability of clustering methods in various disciplines leads to momentous research; immersive work is in this field. Several algorithms are reported for clustering to handle dissimilar clustering problems like initialization, local optima, diversity, and convergence rate. In addition to this, clustering methods are automated, improved/hybridized to obtain new/robust clustering algorithms. An extensive literature survey was carried out to provide in-depth knowledge of partitional clustering algorithms, datasets, and performance measures. This study considers the last twelve years substantial work on partitional clustering and well summarized to promote profound understanding and in-depth learning. The outcome of this research would highlight the key aspects determining the most suitable partitioning and clustering approaches, in turn influencing future work. Furthermore, it offers valuable insights for researchers working in the domain of clustering and data analytics.</p></div>","PeriodicalId":36280,"journal":{"name":"Annals of Data Science","volume":"13 2","pages":"423 - 453"},"PeriodicalIF":0.0,"publicationDate":"2025-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147607328","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}

{"title":"Adaptive Hierarchical Attention for Multivariate Time Series Anomaly Detection","authors":"Xiaohan You,&nbsp;Xiaobo Guo,&nbsp;Binfeng Wang,&nbsp;Changbin Wang","doi":"10.1007/s40745-025-00662-4","DOIUrl":"10.1007/s40745-025-00662-4","url":null,"abstract":"<div><p>In multivariate time series (MTS) anomaly detection, existing graph neural network (GNN) methods often neglect multi-level feature representations, relying solely on the final layer output and fixed thresholds, which leads to information loss and high false-positive rates. To address this issue, we propose a MTS adaptive hierarchical attention (MTS-AHA) model that integrates cross-layer features from local to global topological structures across GNN layers. Additionally, we introduce a dynamic threshold module that adaptively adjusts the threshold by fitting the tail features of the distribution. Extensive experiments on three datasets validate the superiority of our method and its components.</p></div>","PeriodicalId":36280,"journal":{"name":"Annals of Data Science","volume":"13 2","pages":"401 - 421"},"PeriodicalIF":0.0,"publicationDate":"2025-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147606498","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}

{"title":"System of Systems Engineering Foundation","authors":"Mo Jamshidi","doi":"10.1007/s40745-025-00652-6","DOIUrl":"10.1007/s40745-025-00652-6","url":null,"abstract":"<div><p>This paper introduces the concept of system of systems (SoS) and the challenges ahead to extend systems engineering (SE) to system of systems engineering. The birth of a new engineering field may be on the horizon - System of Systems Engineering (SoSE). A SoS is a collection of individual, possibly heterogeneous, but functional systems integrated together to enhance the overall robustness, lower the cost of operation, and increase reliability of the overall complex (SoS) system. Having said that the field has large vacuum from basic definition, to theory, to management and implementation. Many key issues like architecture, modeling, simulation, identification, emergence, standards, net-centricity, control, etc. are all begging for attention. In this review paper, we will be going through all these issues briefly and bring out the challenges to the attention of interested readers. This paper is based on a keynote presented at the ITQM International Conference, Bucharest, Romania, August 23-24, 2024</p></div>","PeriodicalId":36280,"journal":{"name":"Annals of Data Science","volume":"12 6","pages":"1857 - 1881"},"PeriodicalIF":0.0,"publicationDate":"2025-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145537717","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}

{"title":"Will Market Participants Affect the Volume-Price Relationship in the Chinese Stock Market? An Empirical Analysis Based on the DCC-MIDAS Model","authors":"Zhanpeng Cai,&nbsp;Yong Tang,&nbsp;Jingjing Fang,&nbsp;Shunfeng Wan","doi":"10.1007/s40745-025-00655-3","DOIUrl":"10.1007/s40745-025-00655-3","url":null,"abstract":"<div><p>The connection between stock market prices and trading volume has long been a subject of extensive research interest among scholars. This study examines whether the effective utilization of exogenous information from market participants contributes to a better understanding of the volume-price relationship. Using a DCC-MIDAS model that incorporates multiple mixed-frequency exogenous variables, this study empirically analyzes the influence of various market participants on the volume-price relationship in the Chinese stock market by incorporating a DCC-MIDAS model with multiple mixed-frequency exogenous variables. The results indicate that a dynamic correlation between stock market returns and trading volume in China. The increase in volume-price correlation can be attributed to improvements in the performance and governance of listed companies, accurate information dissemination by intermediary organs, and economic policy uncertainty. Among these factors, the influence of intermediary institutions’ information dissemination is the most persistent. However, the impact of investor sentiment on the volume-price relationship is not significant, as short-term shocks caused by emotional information quickly decay and dissipate. Additionally, the heterogeneity analysis reveals that different market participants exhibit distinct bull and bear market periodic features and industry characteristics in their influence on the volume-price relationship. This study is significant for understanding how exogenous information influences the volume-price relationship in the stock market. Regulatory authorities can formulate appropriate measures to stabilize the volume-price correlation based on both the influence path and the declining speed.</p></div>","PeriodicalId":36280,"journal":{"name":"Annals of Data Science","volume":"13 2","pages":"379 - 400"},"PeriodicalIF":0.0,"publicationDate":"2025-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147607014","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}

{"title":"Special Issue of the Eleventh International Conference on Information Technology and Quantitative Management (ITQM 2024)","authors":"Yong Shi,&nbsp;Florin Gheorghe Filip","doi":"10.1007/s40745-025-00661-5","DOIUrl":"10.1007/s40745-025-00661-5","url":null,"abstract":"","PeriodicalId":36280,"journal":{"name":"Annals of Data Science","volume":"12 6","pages":"1771 - 1774"},"PeriodicalIF":0.0,"publicationDate":"2025-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145537716","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}

{"title":"Correction: Exponentiated Odd Lindley-X Power Series Class of Distributions: Properties and Applications","authors":"Fastel Chipepa,&nbsp;Nonhle Mdziniso,&nbsp;Shahid Mohammad,&nbsp;Sher Chhetri","doi":"10.1007/s40745-025-00653-5","DOIUrl":"10.1007/s40745-025-00653-5","url":null,"abstract":"","PeriodicalId":36280,"journal":{"name":"Annals of Data Science","volume":"13 2","pages":"375 - 376"},"PeriodicalIF":0.0,"publicationDate":"2025-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s40745-025-00653-5.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147607270","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}