Big Data最新文献

{"title":"An Extensive Review of Security Challenges in Wireless Body Area Networks.","authors":"Arnab Ghosh, Azees Maria, Arun Sekar Rajasekaran, Vijayakumar Pandi, Hsin-Hung Cho, Shih-Yun Huang","doi":"10.1177/2167647X261475615","DOIUrl":"https://doi.org/10.1177/2167647X261475615","url":null,"abstract":"<p><p>Wireless body area networks (WBANs) became one of the most pioneering technologies in the medical sector and are known for continuous health monitoring and real-time medical data transmission. However, holding a secure WBAN environment is crucial to protect health-sensitive data and patient privacy from various cyber threats. This article serves as a comprehensive survey of potential security risks, focusing on security in WBAN. A detailed taxonomy of possible attacks is introduced, categorizing threats based on fundamental security services: authenticity, integrity, confidentiality, availability, and nonrepudiation. To alleviate these challenges, an in-depth classification of countermeasures has been provided, portraying detection mechanisms and defense strategies. This article emphasizes the need to implement multilayered security frameworks, integrating strong encryption, authentication mechanisms, and advanced anomaly detection mechanisms to safeguard WBAN systems. Moreover, this study serves as a foundation for future research on enhancing resilience in WBAN technologies.</p>","PeriodicalId":51314,"journal":{"name":"Big Data","volume":" ","pages":"2167647X261475615"},"PeriodicalIF":2.0,"publicationDate":"2026-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148764189","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}

{"title":"Big Data-Driven Explainable Agentic AI Decision Frameworks for Enterprise Innovation in FinTech Ecosystems.","authors":"Hangbo Liu, Jia Li, Yujia Nie, Yulin Wang","doi":"10.1177/2167647X261432592","DOIUrl":"10.1177/2167647X261432592","url":null,"abstract":"<p><p>FinTech ecosystems are growing at a rapid pace, creating large-scale, heterogeneous, and highly interconnected data environments that pose challenges to traditional frameworks for innovation management and decision support. Even while artificial intelligence (AI) is being used more and more to make use of this data, the majority of current methods are still opaque, reactive, and not well-suited to the needs of human-centered decision-making. In order to facilitate enterprise innovation in intricate FinTech ecosystems, this study suggests an explainable agentic AI-driven big data decision framework. The platform combines explainable big data analytics and visual analytics pipelines with autonomous AI agents that are capable of goal-directed reasoning, adaptive collaboration, and continuous learning. The suggested method permits transparent investigation of extensive financial, transactional, and behavioral data by fusing network-aware data modeling, agent-based decision orchestration, and interpretable machine learning processes. By converting agent recommendations into clear, traceable insights for strategic innovation planning, visual analytics interfaces further support human-AI co-decision-making. When compared with black-box AI models, the framework's capacity to improve decision accuracy, adaptability, and trust is demonstrated through a case-driven evaluation inside real FinTech scenarios. The findings show that by coordinating AI with organizational, ethical, and legal restrictions, explainable agentic AI can greatly enhance company innovation outcomes. By providing a scalable and comprehensible decision framework for next-generation FinTech innovation ecosystems, this work advances the developing field of agentic AI for explainable large data exploration and visual analytics.</p>","PeriodicalId":51314,"journal":{"name":"Big Data","volume":" ","pages":"259-267"},"PeriodicalIF":2.0,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148206858","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}

{"title":"Explainable Machine Learning-Based Prediction of Postoperative Hypoxemia in Elderly Patients Undergoing General Anesthesia.","authors":"Qin Sha, Long Zhang, Lijun Song, Yunjin Ji","doi":"10.1177/2167647X261466190","DOIUrl":"10.1177/2167647X261466190","url":null,"abstract":"<p><p>In order to predict the risk of postoperative hypoxemia in elderly patients undergoing general anesthesia, this study developed, validated, and interpreted explainable machine learning-based models. One thousand six hundred senior individuals (≥60 years) participated in a retrospective, single-center cohort research. Comprehensive perioperative data were gathered, including laboratory indices, intraoperative physiological parameters, comorbidities, and demographics. To improve the interpretability of the model, feature selection was carried out using least absolute shrinkage and selection operator (LASSO) regression after thorough data preparation. A held-out test dataset was used to design and internally validate four machine learning algorithms: logistic regression, support vector machine, random forest, and extreme gradient boosting (XGBoost). The area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, calibration curves, and decision curve analysis were used to evaluate the model's performance. To give clear justifications for model predictions, feature importance evaluations were carried out. About 15.0% (240/1,600) of patients experienced postoperative hypoxemia. Preoperative serum albumin level, lowest intraoperative mean arterial pressure, age, total crystalloid infusion volume, and a history of chronic obstructive pulmonary disease were important prognostic factors that were consistently found across models. The XGBoost-based explainable AI model outperformed the other models in terms of predictive performance, with the highest AUC of 0.994 (95% CI: 0.990-0.998), good calibration, and a notable net therapeutic benefit on decision curve analysis. The suggested explainable machine learning XGBoost model predicts postoperative hypoxemia in elderly patients receiving general anesthesia with accuracy and interpretability. This method facilitates focused interventions to improve surgical respiratory outcomes by supporting early risk stratification and informed perioperative decision-making by clarifying important risk factors influencing individual predictions.</p>","PeriodicalId":51314,"journal":{"name":"Big Data","volume":" ","pages":"304-315"},"PeriodicalIF":2.0,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148377937","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}

{"title":"Big Data-Driven Video Anomaly Detection Using VideoMAE for Visual Analytics in CCTV Surveillance.","authors":"Pinnapureddy Manasa, Maragoni Mahendar, Purude Vaishali Narayanrao, Satwik Komuravelli, Sreena Lakhani, Shakila Basheer, Mohammad Tabrez Quasim","doi":"10.1177/2167647X261463938","DOIUrl":"10.1177/2167647X261463938","url":null,"abstract":"<p><p>In the rapidly evolving industry, the need for surveillance is highly needed for the safety of confidential and highly sensitive organizations. With this rise, the demand for efficient security systems has become critical. So, the question is: How to achieve the highest form of security? The obvious answer, which already exists, is a manual monitoring of video footage to check for any suspicious activity. Traditional systems require a lot of manpower as video footage is monitored manually. Imagine a surveillance room full of 50 monitors that display the security footage, and there are just two men observing the footage. How chaotic it is for just two men to manage the whole room and also be vigilant always, to ensure no wrong activity happens. But this isn't very convenient for today's time, it not only overwhelms the observers but also risks critical events being missed due to human limitations. To resolve the issue, we have come up with an intelligent artificial intelligence (AI)-powered system that is capable of spotting anomalous activities in the surveillance footage without any manual effort or stress. An AI-driven system offers seamless, accurate, and proactive monitoring, paving the way for enhanced security in high-stakes environments. We have made use of a deep learning framework for video understanding tasks, which is the Video Masked Autoencoder model, and also a transformer model-Video Vision transformers-for effortless detection of suspicious activity. We have also designed a very simple and attractive web interface where the user can upload the security footage and wait for the model to do its magic. The model detects the anomaly and displays the type of anomaly with the time frame in which the anomaly is occurring. This technique is very effective to find suspicious activities more quickly and efficiently.</p>","PeriodicalId":51314,"journal":{"name":"Big Data","volume":" ","pages":"291-303"},"PeriodicalIF":2.0,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148354733","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}

{"title":"Special Issue on \"Agentic AI for Explainable Big Data Exploration and Visual Analytics\".","authors":"Sunil Prajapat, Mohammad Shabaz, Muhammad Asim Saleem, Shams Al Ajrawi","doi":"10.1177/2167647X261472285","DOIUrl":"10.1177/2167647X261472285","url":null,"abstract":"","PeriodicalId":51314,"journal":{"name":"Big Data","volume":" ","pages":"257-258"},"PeriodicalIF":2.0,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148551325","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}

{"title":"Personalized Driven Instruction Through Explainable Agentic AI in Multicultural Higher Education Environments.","authors":"Conglin Qiu, Kunkun Cui, Zilong Wang, Lifeng Hou","doi":"10.1177/2167647X261463927","DOIUrl":"10.1177/2167647X261463927","url":null,"abstract":"<p><p>The need for intelligent, transparent, and adaptive personalized instruction systems has increased due to the quick diversification of higher education settings and the exponential expansion of educational big data. In order to address the pedagogical, cultural, and cognitive variability present in multicultural higher education settings, this study suggests a Big Data-Driven Personalized Instruction framework powered by Explainable Agentic Artificial Intelligence (X-AI). The suggested approach uses autonomous agentic AI architectures that can orchestrate goal-directed learning, dynamic learner profiling, and real-time instructional adaption by utilizing large-scale, multimodal educational data, namely the HarvardX-MITx Person-Course Dataset. Explainability mechanisms are incorporated at both the model and decision levels to guarantee pedagogical trustworthiness and ethical deployment. This allows for interpretable insights into learner performance projections, instructional recommendations, and adaptive intervention tactics. To assist teachers in comprehending cross-cultural learning patterns and reducing algorithmic bias, the system incorporates feature attribution, causal inference, and visual analytics. When compared to traditional data-driven personalization techniques, the suggested methodology dramatically increases learning outcome prediction accuracy <math><mi>F</mi><mn>1</mn><mi> </mi><mo>=</mo><mi> </mi><mn>0.88</mn></math>, instructional relevance, and student engagement, according to experimental evaluations carried out on diverse higher education datasets. This work advances a scalable and culturally responsive paradigm for personalized education by combining big data analytics, agentic autonomy, and explainable AI. It provides educators, institutions, and policymakers looking to transform higher education systems in an equitable, transparent, and data-driven manner with practical insights.</p>","PeriodicalId":51314,"journal":{"name":"Big Data","volume":" ","pages":"316-322"},"PeriodicalIF":2.0,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148347361","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}

{"title":"Explainable Agentic AI for Big Data-Driven Evaluation and Visual Analytics of Digital Literacy in Higher Vocational Teacher Education.","authors":"Wen Shao","doi":"10.1177/2167647X261432584","DOIUrl":"10.1177/2167647X261432584","url":null,"abstract":"<p><p>Large-scale, diverse data produced by higher vocational teacher colleges' digital transformation challenges traditional methods for evaluating digital literacy. The reliability of current analytics and black-box artificial intelligence (AI) models for educational decision-making is limited by their frequent lack of autonomy and transparency. In order to assess digital literacy at higher vocational teacher colleges using big data and visual analytics, this study suggests an Explainable Agentic AI framework. In order to facilitate adaptive data exploration, competency evaluation, and insight generation across multimodal educational data, such as learning behavior logs, assessment records, and digital engagement indicators, the framework combines autonomous agentic intelligence with explainable AI (XAI). While XAI methods offer clear explanations of literacy aspects, decision rationale, and uncertainty, agentic components dynamically handle data processing, feature reasoning, and model selection. Effective human-AI collaboration is made possible by an interactive visual analytics layer that allows for layered investigation of learner patterns, temporal dynamics, and cohort heterogeneity. When compared with traditional machine learning techniques, experimental results on large-scale datasets from higher vocational teacher colleges show better assessment accuracy, robustness, and interpretability. This work demonstrates the promise of agentic AI for explainable big data exploration and promotes reliable instructional intelligence by combining agentic autonomy, explainability, and visual analytics within a scalable big data paradigm.</p>","PeriodicalId":51314,"journal":{"name":"Big Data","volume":" ","pages":"268-279"},"PeriodicalIF":2.0,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147576227","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}

{"title":"Agentic Artificial Intelligence-Driven Explainable Deep Learning for Deciphering Noncoding Pathogenic Mechanisms of Delirium Through Genomic Big Data Integration.","authors":"Jidong Yang, Xiong Wang, Lishuang Peng, Jianfu Tang, Shanshan Cai, Yuting Xue","doi":"10.1177/2167647X261463929","DOIUrl":"10.1177/2167647X261463929","url":null,"abstract":"<p><p>Delirium is a prevalent neuropsychiatric syndrome affecting up to 50% of hospitalized elderly patients, associated with increased mortality, cognitive decline, and health care costs exceeding $164 billion annually. Despite its clinical burden, the genetic architecture underlying susceptibility to delirium remains poorly characterized. We developed an agentic artificial intelligence pipeline integrating genome-wide association study (GWAS) data from FinnGen Release 12 (6854 cases; 384,461 controls) with causal transcriptome-wide association study (cTWAS) using brain-specific eQTL data from Genotype-Tissue Expression. Spatial transcriptomics analysis (gsMap) was performed on the human dorsolateral prefrontal cortex to characterize layer-specific expression patterns. Deep learning models (Enformer, SpliceTransformer) were applied to predict functional consequences of identified variants. GWAS identified a strongly associated locus on chromosome 19q13.32, with lead variant rs429358 (<i>P</i> = 2.79 × 10^-70) defining the apolipoprotein E (<i>APOE</i>) ε4 allele. cTWAS analysis identified two causal genes: <i>APOE</i> (posterior inclusion probabilities [PIP] = 0.871, <i>Z</i> = -8.44) and <i>ZNF226</i> (PIP = 0.604, <i>Z</i> = 5.63), exhibiting opposite directional effects. <i>APOE</i> downregulation increased susceptibility to delirium, while <i>ZNF226</i> upregulation conferred an elevated risk. Spatial transcriptomics revealed significant GWAS enrichment in Layer 5 (<i>P</i> < 0.001), with distinct layer-specific expression patterns for both genes. Enformer predicted that rs429358 substantially altered chromatin accessibility at promoter regions (H3K4me3 SNP Activity Difference = 0.30). SpliceTransformer identified significant splice site perturbations (maximum Δ = 0.45). This study provides a comprehensive genetic dissection of delirium through agentic artificial intelligence-driven multiomics integration. We identify <i>APOE</i> and <i>ZNF226</i> as causal genes with distinct mechanisms and spatial expression patterns. These findings establish a molecular framework for understanding delirium pathophysiology and identify potential therapeutic targets.</p>","PeriodicalId":51314,"journal":{"name":"Big Data","volume":" ","pages":"280-290"},"PeriodicalIF":2.0,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148347356","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}

{"title":"Constructing an Artificial Intelligence-Driven Multilingual Medical Health Education Chatbot with Domain-Specific Medical Knowledge.","authors":"Tai-Liang Chen, Yi-Hui Liu","doi":"10.1177/2167647X261463925","DOIUrl":"10.1177/2167647X261463925","url":null,"abstract":"<p><p>Language barriers remain a critical challenge in health care delivery, particularly for migrant and multicultural populations. This study presents the design, implementation, and evaluation of an artificial intelligence (AI)-driven multilingual medical health education chatbot to enhance health care communication and accessibility. The proposed system introduces a user-state-driven multi-layer logical architecture, which enables efficient multilingual interaction by dynamically routing user queries to language-specific processing modules. In addition, a hybrid retrieval-generative framework is developed to combine expert-curated health education content with conversational AI (ChatGPT), ensuring both information reliability and interaction flexibility. The system is deployed on the LINE platform and evaluated using the System Usability Scale, Net Promoter Score, and Customer Satisfaction Score with 85 participants from diverse linguistic and cultural backgrounds. The results demonstrate satisfactory usability and user acceptance, with consistent performance observed across nationality groups, supporting the system's cross-cultural applicability. Overall, the findings indicate that the proposed architecture provides a scalable, efficient, and practical solution for multilingual health care communication, reducing language barriers and improving patient understanding in real-world clinical environments.</p>","PeriodicalId":51314,"journal":{"name":"Big Data","volume":" ","pages":"2167647X261463925"},"PeriodicalIF":2.0,"publicationDate":"2026-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148383085","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}

{"title":"AI-Based Forecasting of National Tourism Revenues: Integrating Economic, Fiscal, Political, and Environmental Determinants Through Regression-Oriented Hybrid Models.","authors":"Yueyan Liu, Hyung Jong Na, Jiang Xue","doi":"10.1177/2167647X261431683","DOIUrl":"10.1177/2167647X261431683","url":null,"abstract":"<p><p>This study proposes an advanced framework for forecasting national tourism revenues by systematically comparing machine learning (ML), deep learning (DL), and hybrid architectures on a country-year panel. Baseline models using only trade and economic indicators have limited explanatory power, whereas adding fiscal, political, and environmental variables substantially improves accuracy. Among ML methods, LightGBM performs best; among DL models, the Transformer excels by capturing nonlinear interactions and temporal dependencies. Building on these results, we introduce a hybrid residual boosting model that integrates the Transformer's predictive strength with LightGBM's structural interpretability. The hybrid model outperforms single models across mean absolute error, root mean square error, mean absolute percentage error, and <i>R</i>², simultaneously minimizing errors and maximizing explanatory power. Methodologically and theoretically, the framework advances tourism economics while offering policymakers actionable guidance on fiscal planning, political stability, and environmental sustainability. Importantly, the empirical results are correlational and reflect predictive associations; they should not be interpreted as causal effects of policy interventions. Methodological novelty lies in a regression-oriented, two-stage residual-boosting design that (1) learns a Transformer as the primary forecaster on the country-year panel, (2) fits LightGBM to the Transformer residuals to correct systematic errors under distributional heterogeneity, and (3) yields a decomposed forecast (base + residual correction) that facilitates transparent error attribution beyond prior DL-DL stacking hybrids. Importantly, the reported relationships are associational and derived from predictive modeling; they should not be interpreted as causal effects of policy levers.</p>","PeriodicalId":51314,"journal":{"name":"Big Data","volume":" ","pages":"219-243"},"PeriodicalIF":2.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147576253","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}