Frontiers in Big Data最新文献_第9页

Spatiotemporal deep learning framework for predictive behavioral threat detection in surveillance footage. 用于监控录像中预测行为威胁检测的时空深度学习框架。

IF 3.8

Frontiers in Big Data Pub Date : 2026-02-27 eCollection Date: 2026-01-01 DOI: 10.3389/fdata.2026.1770989

Asha Aruna Sheela Matta, Venkata Purna Chandra Sekhara Rao Manukonda

{"title":"Spatiotemporal deep learning framework for predictive behavioral threat detection in surveillance footage.","authors":"Asha Aruna Sheela Matta, Venkata Purna Chandra Sekhara Rao Manukonda","doi":"10.3389/fdata.2026.1770989","DOIUrl":"10.3389/fdata.2026.1770989","url":null,"abstract":"Anomaly detection in video surveillance remains a challenging problem due to complex human behaviors, temporal variability, and limited annotated data. This study proposes an optimized spatiotemporal deep learning (DL) framework that integrates a Convolutional Neural Network (CNN) for spatial feature extraction with a Long Short-Term Memory (LSTM) network for temporal dependency modeling. The CNN processes frame-level appearance information, while the LSTM captures sequential motion patterns across video frames, enabling effective representation of anomalous activities. Hyperparameter optimization and regularization strategies are employed to improve convergence stability and generalization performance. The proposed model is evaluated on the DCSASS surveillance dataset and the experimental results demonstrate that the optimized CNN-LSTM framework achieves an accuracy of 98.1%, with consistently high precision, recall, and F1-score across 3-fold, 5-fold, and 10-fold cross-validation settings. Comparative analysis shows that the proposed method outperforms conventional machine learning models and recent deep learning baselines, highlighting its effectiveness and robustness for practical video-based anomaly detection in surveillance environments.","PeriodicalId":52859,"journal":{"name":"Frontiers in Big Data","volume":"9 ","pages":"1770989"},"PeriodicalIF":3.8,"publicationDate":"2026-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12982034/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147470340","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

Federated learning for teacher data privacy protection: a study in the context of the PIPL. 面向教师数据隐私保护的联邦学习：PIPL背景下的研究。

IF 2.4

Frontiers in Big Data Pub Date : 2026-02-09 eCollection Date: 2026-01-01 DOI: 10.3389/fdata.2026.1681382

Shanwei Chen, Xiu Zhi Qi, Xue Hui Han, Zhao Chen Fan, Le Le Wang

{"title":"Federated learning for teacher data privacy protection: a study in the context of the PIPL.","authors":"Shanwei Chen, Xiu Zhi Qi, Xue Hui Han, Zhao Chen Fan, Le Le Wang","doi":"10.3389/fdata.2026.1681382","DOIUrl":"https://doi.org/10.3389/fdata.2026.1681382","url":null,"abstract":"Background: The Personal Information Protection Law (PIPL) in China imposes strict requirements on personal data handling, particularly in educational contexts where teacher data privacy is critical. Traditional centralized machine learning approaches pose significant risks of data breaches and non-compliance. Federated Learning (FL) offers a promising decentralized alternative by enabling collaborative model training without sharing raw data.Methods: This study combines quantitative simulations and qualitative compliance analysis to evaluate FL frameworks under PIPL principles, with a focus on Differential Privacy as the primary empirically validated mechanism for noise addition and privacy guarantee. Other techniques, such as Secure Multi-Party Computation (SMC), are analyzed theoretically for their alignment with PIPL requirements like data minimization, anonymization, and encrypted transmission.Results: Experimental simulations demonstrate that FL effectively reduces data breach risks compared to centralized methods. It achieves principle-level compliance with PIPL through local data processing, differential privacy mechanisms, and secure aggregation, leading to improved privacy preservation while maintaining model performance.Conclusion: FL conceptually supports teacher data privacy protection under the PIPL framework. This study proposes a tailored compliance framework that integrates FL with privacy-enhancing technologies, offering theoretical foundations and practical recommendations for educational institutions and technology implementers to deploy privacy-preserving machine learning solutions.","PeriodicalId":52859,"journal":{"name":"Frontiers in Big Data","volume":"9 ","pages":"1681382"},"PeriodicalIF":2.4,"publicationDate":"2026-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12926099/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147286276","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

A genetic algorithm-based framework for online sparse feature selection in data streams. 基于遗传算法的数据流在线稀疏特征选择框架。

IF 3.8

Frontiers in Big Data Pub Date : 2026-02-09 eCollection Date: 2026-01-01 DOI: 10.3389/fdata.2026.1782461

Guanyu Liu, Jinhang Liu, Guifan He, Yifan Liu, Huabo Bai, Min Zhou

{"title":"A genetic algorithm-based framework for online sparse feature selection in data streams.","authors":"Guanyu Liu, Jinhang Liu, Guifan He, Yifan Liu, Huabo Bai, Min Zhou","doi":"10.3389/fdata.2026.1782461","DOIUrl":"10.3389/fdata.2026.1782461","url":null,"abstract":"High-dimensional streaming data implementations commonly utilize online streaming feature selection (OSFS) techniques. In practice, however, incomplete data due to equipment failures and technical constraints often poses a significant challenge. Online Sparse Streaming Feature Selection (OS2FS) tackles this issue by performing missing data imputation via latent factor analysis. Nevertheless, existing OS2FS approaches exhibit considerable limitations in feature evaluation, resulting in degraded performance. To address these shortcomings, this paper introduces a novel genetic algorithm-based online sparse streaming feature selection (GA-OS2FS) in data streams, which integrates two key innovations: (1) imputation of missing values using a latent factor analysis model, and (2) application of genetic algorithm to assess feature importance. Comprehensive experiments conducted on six real-world datasets show that GA-OS2FS surpasses state-of-the-art OSFS and OS2FS methods, consistently attaining higher accuracy through the selection of optimal feature subsets.","PeriodicalId":52859,"journal":{"name":"Frontiers in Big Data","volume":"9 ","pages":"1782461"},"PeriodicalIF":3.8,"publicationDate":"2026-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12926108/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147286229","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

Dynamic transfer learning with co-occurrence-guided multi-source fusion for urban spatio-temporal crime prediction. 基于共现引导的多源融合动态迁移学习的城市犯罪时空预测。

IF 3.8

Frontiers in Big Data Pub Date : 2026-02-05 eCollection Date: 2026-01-01 DOI: 10.3389/fdata.2026.1697392

Chen Cui, Ziwan Zheng, Hao Du, Wen Wang

引用次数: 0

Algorithmic recourse in sequential decision-making for long-term fairness. 长期公平顺序决策中的算法追索权。

IF 2.4

Frontiers in Big Data Pub Date : 2026-02-04 eCollection Date: 2026-01-01 DOI: 10.3389/fdata.2026.1750906

Francisco Gumucio, Lu Zhang

{"title":"Algorithmic recourse in sequential decision-making for long-term fairness.","authors":"Francisco Gumucio, Lu Zhang","doi":"10.3389/fdata.2026.1750906","DOIUrl":"https://doi.org/10.3389/fdata.2026.1750906","url":null,"abstract":"Long-term fairness in sequential decision-making is critical yet challenging, as decisions at each time step influence future opportunities and outcomes, potentially exacerbating existing disparities over time. While existing methods primarily achieve fairness by directly adjusting decision models, in this work, we study a complementary perspective based on sequential algorithmic recourse, in which fairness is pursued through actionable interventions for individuals. We introduce Sequential Causal Algorithmic Recourse for Fairness (SCARF), a causally grounded framework that generates temporally coherent recourse trajectories by integrating structural causal modeling with sequential generative modeling. By explicitly incorporating both short-term and long-term fairness constraints, as well as practical budget limitations, SCARF generates personalized recourse plans that effectively mitigate disparities over multiple decision cycles. Through experiments on synthetic and semi-synthetic datasets, we empirically examine how different recourse strategies influence fairness dynamics over time, illustrating the trade-offs between short-term and long-term fairness under sequential interventions. The results demonstrate that SCARF provides a practical and informative framework for analyzing long-term fairness in dynamic decision-making settings.","PeriodicalId":52859,"journal":{"name":"Frontiers in Big Data","volume":"9 ","pages":"1750906"},"PeriodicalIF":2.4,"publicationDate":"2026-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12913111/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146229780","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

Depression detection through dual-stream modeling with large language models: a fusion-based transfer learning framework integrating BERT and T5 representations. 通过大型语言模型的双流建模进行抑郁检测：集成BERT和T5表示的基于融合的迁移学习框架。

IF 3.8

Frontiers in Big Data Pub Date : 2026-02-04 eCollection Date: 2025-01-01 DOI: 10.3389/fdata.2025.1651290

Na Wang, Weijia Zhang, Raja Kamil, Ian Renner, Syed Abdul Rahman Al-Haddad, Normala Ibrahim, Zhen Zhao

{"title":"Depression detection through dual-stream modeling with large language models: a fusion-based transfer learning framework integrating BERT and T5 representations.","authors":"Na Wang, Weijia Zhang, Raja Kamil, Ian Renner, Syed Abdul Rahman Al-Haddad, Normala Ibrahim, Zhen Zhao","doi":"10.3389/fdata.2025.1651290","DOIUrl":"10.3389/fdata.2025.1651290","url":null,"abstract":"Millions of people around the world suffer from depression. While early diagnosis is essential for timely intervention, it remains a significant challenge due to limited access to clinically diagnosed data and privacy restrictions on mental health records. These limitations hinder the training of robust AI models for depression detection. To tackle this, this article proposes a parallel transfer learning framework for depression detection that integrates BERT and T5 through a fusion mechanism, combining the complementary advantages of these two large language models (LLMs). By integrating their semantic embeddings, the method captures a broader range of linguistic cues from transcribed speech. These embeddings are processed through a model with two parallel branches: a one-dimensional convolutional neural network and a dense neural network are used to construct each branch for preliminary prediction, which are then fused for final prediction. Evaluations on the E-DAIC dataset demonstrate that the proposed method outperforms baseline models, achieving a 3.0% increase in accuracy (91.3%), a 6.9% increase in precision (95.2%), and a 1.7% improvement in F1-score (90.0%). The experimental results verify the effectiveness of BERT and T5 fusion in enhancing depression detection performance and highlight the potential of transfer learning for scalable and privacy-conscious mental health applications.","PeriodicalId":52859,"journal":{"name":"Frontiers in Big Data","volume":"8 ","pages":"1651290"},"PeriodicalIF":3.8,"publicationDate":"2026-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12913122/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146229722","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

Modeling household adoption of IoT-based home security in Dhaka: a PLS-machine learning framework. 达卡基于物联网的家庭安全建模：pls -机器学习框架。

IF 3.8

Frontiers in Big Data Pub Date : 2026-02-04 eCollection Date: 2026-01-01 DOI: 10.3389/fdata.2026.1718710

Arif Mahmud, Ashikur Rahman, Fahmid Al Farid, Jia Uddin, Hezerul Bin Abdul Karim

{"title":"Modeling household adoption of IoT-based home security in Dhaka: a PLS-machine learning framework.","authors":"Arif Mahmud, Ashikur Rahman, Fahmid Al Farid, Jia Uddin, Hezerul Bin Abdul Karim","doi":"10.3389/fdata.2026.1718710","DOIUrl":"10.3389/fdata.2026.1718710","url":null,"abstract":"Introduction: Despite several strategies, Bangladesh has a poor rate of internet of things (IoT) deployment. This study therefore seeks to investigate the factors shaping IoT adoption for residential security in Dhaka and to analyze their respective contributions.Method: Hence, this study combined two important theories, namely protection motivation theory (PMT) along with attitude-social influence-self-efficacy (ASE) in which a hybrid PLS-Machine learning approach has been used to identify both linear and nonlinear correlations with high predictive accuracy. Snowball sampling method was utilized to choose 348 valid replies from a survey of household heads. Afterward, partial least squares (PLS) followed by artificial neural networks (ANN) and machine learning (ML) classifiers were the procedures that made up the complete assessment method.Results: The variables that affected intention with a variance of 34.9% and accuracy of 74.28% were severity, vulnerability, response efficacy, response cost, and attitude. On the other hand, vulnerability was the most significant predictor, followed by response cost, attitude, response efficacy, self-efficacy, social influence, and severity.Discussion: The theoretical contribution of this study lies in its novel integration of PMT and ASE models, offering new insights into their combined effect on technology adoption in emerging markets. Besides, the findings contribute to the literature by increasing the public awareness of home security that can enhance Dhaka's overall state of public order and safety. Moreover, the findings may offer valuable insights for companies and entrepreneurs, as incorporating these factors into marketing strategies and investment initiatives is likely to foster greater consumer adoption.","PeriodicalId":52859,"journal":{"name":"Frontiers in Big Data","volume":"9 ","pages":"1718710"},"PeriodicalIF":3.8,"publicationDate":"2026-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12913148/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146229812","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 core-enhanced latent factor model for highly accurate QoS prediction. 高精度QoS预测的自适应核心增强潜在因子模型。

IF 3.8

Frontiers in Big Data Pub Date : 2026-02-02 eCollection Date: 2026-01-01 DOI: 10.3389/fdata.2026.1775728

Siqi Ai, Peixin Li, Hao Fang, Yonghui Xia

{"title":"Adaptive core-enhanced latent factor model for highly accurate QoS prediction.","authors":"Siqi Ai, Peixin Li, Hao Fang, Yonghui Xia","doi":"10.3389/fdata.2026.1775728","DOIUrl":"10.3389/fdata.2026.1775728","url":null,"abstract":"Accurate prediction of Quality of Service (QoS) plays a crucial role in service recommendation and selection across large-scale distributed environments. Latent factor (LF) models have become a mainstream solution for QoS prediction owing to their simplicity and scalability, yet typical formulations struggle to capture complex latent interactions and usually rely on manually tuned regularization, which often limits prediction accuracy. To address these challenges, we propose an Adaptive Core-Enhanced Latent Factor (ACELF) model that integrates a learnable core interaction mechanism with an incremental Proportional-Integral-Derivative (PID)-driven adaptive regularization strategy. Specifically, a learnable core interaction matrix is introduced to model interactions between latent user and service factors, enabling richer representation learning beyond standard bilinear assumptions. To further enhance robustness, we design an incremental PID controller that dynamically adjusts the regularization coefficient of the core interaction matrix according to the training dynamics, allowing the optimization process to automatically balance model expressiveness and overfitting. Extensive experiments on real-world QoS datasets demonstrate that ACELF consistently outperforms several state-of-the-art methods in terms of prediction accuracy.","PeriodicalId":52859,"journal":{"name":"Frontiers in Big Data","volume":"9 ","pages":"1775728"},"PeriodicalIF":3.8,"publicationDate":"2026-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12907136/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146214876","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

EnDuSecFed: an ensemble approach for privacy preserving Federated Learning with dual-security framework for sustainable healthcare. EnDuSecFed：一种用于保护隐私的集成方法，具有用于可持续医疗保健的双安全框架的联邦学习。

IF 2.4

Frontiers in Big Data Pub Date : 2026-01-22 eCollection Date: 2025-01-01 DOI: 10.3389/fdata.2025.1659026

Bela Shrimali, Jenil Gajjar, Swapnoneel Roy, Sanjay Patel, Kanu Patel, Ramesh Ram Naik

引用次数: 0

Examining the influence of deterrent and enhancement factors on QR-code mobile payment continuance intention: insights from PLS-SEM and IPMA analysis. 研究威慑和增强因素对二维码移动支付持续意愿的影响：来自PLS-SEM和IPMA分析的见解。

IF 2.4

Frontiers in Big Data Pub Date : 2026-01-22 eCollection Date: 2025-01-01 DOI: 10.3389/fdata.2025.1679897

Ashikur Rahman, Fahmid Al Farid, Mohammad Abul Bashar, Jia Uddin, Arif Mahmud, Hezerul Abdul Karim

{"title":"Examining the influence of deterrent and enhancement factors on QR-code mobile payment continuance intention: insights from PLS-SEM and IPMA analysis.","authors":"Ashikur Rahman, Fahmid Al Farid, Mohammad Abul Bashar, Jia Uddin, Arif Mahmud, Hezerul Abdul Karim","doi":"10.3389/fdata.2025.1679897","DOIUrl":"10.3389/fdata.2025.1679897","url":null,"abstract":"Introduction: The rise of contactless payment has made quick response (QR) code-mobile payment (QR-MP) platform increasingly popular among mobile financial service (MFS) users, especially in emerging economies. It has been demonstrated that the ongoing use of QR payments can significantly drive the growth of emerging economies. However, despite its importance, the continued use of this technology has not been satisfactory. Thus, this study seeks to explore the modified Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) model, including four additional constructs: amotivation (AM), alternative attractiveness (AA), QR transaction anxiety (QTA), and transaction convenience (TC) to examine the MFS users' sustained usage of QR payment.Methods: Data were collected from 247 MFS users in Bangladesh using an online survey and analyzed through SEM-PLS and non-linear analysis of IPMA.Results: The research findings reveal that effort expectancy is the most influential factor, and that both moderator factors, QTA and TC, are significant. However, social influence and hedonic motivation were found to be insignificant. Furthermore, our extended research model explains 76.5% of the variance in CINT without the moderation effect.Discussion: The IPMA findings help to find the best-performing variables and provide practical insights for this study. Theoretical and managerial implications are provided to enrich the existing literature on the study of information technology, indicating how MFS providers in developing countries can retain their existing users.","PeriodicalId":52859,"journal":{"name":"Frontiers in Big Data","volume":"8 ","pages":"1679897"},"PeriodicalIF":2.4,"publicationDate":"2026-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12873545/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146144564","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