Big Data大数据
Big Data(英文缩写 BIG DATA-US),ISSN 2167-6461,eISSN 2167-647X,中文译名:大数据 是一本学术期刊。本页汇总该期刊的最新影响因子、分区信息以及最新收录于 PubMed 的文献,帮助您快速了解期刊全貌。
发文量统计区间：2025-10-01 至 2026-09-30，按本站收录文献的发表日期统计。
期刊介绍
历年影响因子趋势
| JCR 数据年份 | 影响因子 | JCR 分区 |
|---|---|---|
| 2021 | 4.426 | Q1 |
| 2022 | 4.600 | Q1 |
| 2023 | 2.600 | Q2 |
| 2024 | 2.000 | Q2 |
| 2025 | 2.000 | Q2 |
Big Data 最新收录文献
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1. Neural Gas Network-Based Distinction Between Brain Cysts and Tumors from Magnetic Resonance Images Using a Hybrid Deep Learning Algorithm.
PMID:日期:2026-09-26The early and accurate diagnosis of brain tumors is critically important, as timely intervention significantly reduces mortality and improves patient outcomes. While magnetic resonance imaging (MRI) is the preferred diagnostic tool, differentiating malignant brain tumors from benign cysts remains a clinical challenge due to overlapping visual features. This study introduces a technically novel hybrid deep learning framework that integrates denoising, segmentation, and classification stages in a unified pipeline. First, a neural gas network is employed for precise segmentation of MRI images, enhancing boundary delineation between cystic and tumorous regions. To mitigate class imbalance and improve generalization, the Synthetic Minority Oversampling Technique is applied. A unique architectural contribution of this study lies in the integration of three pretrained convolutional neural networks (CNNs)-EfficientNetB0, MobileNetV2, and ResNet50-into a fused hybrid model that combines their complementary feature extraction capabilities. This model is fine-tuned using a grid search-based hyperparameter optimization strategy, early stopping, and cross-validation. Comparative experiments on four distinct datasets (including balanced and segmented versions) demonstrate that the proposed hybrid model consistently outperforms conventional models such as VGG16, InceptionV3, and individual CNN baselines. Achieving a peak accuracy of 97% on the balanced, augmented, and segmented dataset, this work highlights a novel and robust Artificial Intelligence (AI)-based strategy for distinguishing brain cysts from tumors in MRI, with superior performance in complex diagnostic scenarios.
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2. Big Data-Driven Production Scheduling Risk Identification Using a Lightweight Adaptive Graph Attention Network.
PMID:日期:2026-09-18The advancement of Industry 4.0 and smart manufacturing has led to the generation of high-volume, multisource data that evolve in real-time within production scheduling systems. This poses significant challenges to traditional risk identification methods. Models that ignore topological relationships lack robustness when handling complex dependencies. Black-box deep learning models offer insufficient interpretability to support decision-making. Furthermore, complex graph neural networks entail substantial computational costs and cannot meet real-time requirements. To address these issues, this article proposes an attention-based lightweight graph neural network (AGNN) for big data analytics. The model incorporates a context-aware dynamic attention mechanism that adaptively adjusts node weights based on the global state, thereby enhancing robustness against uncertainties in the scheduling environment. The visualiation of attention weights, combined with a business key performance indicator-aligned risk labeling strategy, enhances interpretability and provides a transparent basis for decisions. A lightweight two-layer, single-head architecture maintains high accuracy while compressing the parameter count to 9.8K and achieving an inference speed of 0.163 ms per sample, enabling real-time edge deployment. Importantly, AGNN establishes a data-to-decision link for big data-driven production decision-support systems. It not only identifies risks but also interprets their business impact and propagation pathways, enabling schedulers to prioritize interventions and optimie resource allocation in a timely manner. This enhances the adaptability and intelligence of manufacturing systems in dynamic environments. Comparative experiments on a real-world marine shafting dataset demonstrate that AGNN significantly outperforms other baseline models. This study offers a robust, interpretable, and efficient risk identification solution for big data-driven smart manufacturing systems and promotes the application of computational intelligence in complex industrial scenarios within a big data context.
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3. Multimodal Graph Neural Networks and Evolutionary Knowledge Fusion for Secure and Explainable Governance in Intelligent IoT Interactive Media Systems.
PMID:日期:2026-09-02This research proposes a secure, explainable, and context-aware governance framework for blockchain-based digital media contracts in multimodal artificial intelligence-enabled AIoT interactive systems. As digital licensing, NFT copyright management, royalty distribution, and cross-chain content circulation become increasingly embedded in smart media ecosystems, existing contract auditing approaches remain limited by unimodal analysis, weak explainability, black-box decision processes, and insufficient cross-platform generalization. To address these challenges, the proposed framework integrates cross-modal data fusion, graph neural networks, and evolutionary expert knowledge fusion to model smart contract code, abstract syntax trees, control-flow graphs, intercontract transactions, cross-chain records, licensing metadata, NFT copyright attributes, and multimodal IoT interaction logs as unified graph representations. By incorporating causal priors and differentiable rules, the framework supports transparent risk reasoning, verifiable explanations, and trustworthy decision support. The study contributes to explainable blockchain security, multimodal AI governance, and intelligent media systems by enabling more robust detection of copyright misuse, unauthorized licensing, abnormal content distribution, and cross-chain transaction risks.
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4. Implicit Bias of Gradient-Based Learning Under Non-Convex Constraints.
PMID:日期:2026-08-28Gradient-based learning under non-convex constraints exhibits a notable phenomenon: Despite the existence of many equivalent global minimizers, optimization algorithms consistently converge to a small subset of structured solutions. This behavior, known as implicit bias, remains insufficiently understood in constrained and non-convex settings. In this article, we investigate the mechanism of implicit bias induced by projected gradient-based optimization over general non-convex feasible sets. By modeling projected gradient descent as a continuous-time dynamical system, we derive a projected gradient flow characterized by tangent and normal cone decompositions, which capture the local geometry of the constraint set. Based on this formulation, we show that constraint geometry continuously filters gradient directions along the optimization trajectory, leading to a trajectory-dependent implicit regularization effect without modifying the objective function. We further formalize this effect through a cumulative normal projection energy functional and prove that the optimization dynamics converge to solutions minimizing both empirical risk and geometric incompatibility with the constraint set. Extensive experiments on synthetic and real-world datasets validate the theoretical predictions, demonstrating consistent alignment between solution geometry, optimization trajectories, and generalization performance. These results provide a unified geometric and dynamical explanation of implicit bias in constrained learning systems.
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5. An Extensive Review of Security Challenges in Wireless Body Area Networks.
PMID:日期:2026-08-17Wireless 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.
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6. Special Issue on "Agentic AI for Explainable Big Data Exploration and Visual Analytics".
PMID:日期:2026-07-22该文献暂无摘要。
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7. Constructing an Artificial Intelligence-Driven Multilingual Medical Health Education Chatbot with Domain-Specific Medical Knowledge.
PMID:日期:2026-07-03Language 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.
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8. Explainable Machine Learning-Based Prediction of Postoperative Hypoxemia in Elderly Patients Undergoing General Anesthesia.
8. 基于机器学习的老年全身麻醉患者术后低氧血症的可解释预测PMID:日期:2026-07-03In 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.
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9. Big Data-Driven Video Anomaly Detection Using VideoMAE for Visual Analytics in CCTV Surveillance.
PMID:日期:2026-06-30In 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.
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10. Agentic Artificial Intelligence-Driven Explainable Deep Learning for Deciphering Noncoding Pathogenic Mechanisms of Delirium Through Genomic Big Data Integration.
PMID:日期:2026-06-29Delirium 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 ( = 2.79 × 10) defining the apolipoprotein E () ε4 allele. cTWAS analysis identified two causal genes: (posterior inclusion probabilities [PIP] = 0.871, = -8.44) and (PIP = 0.604, = 5.63), exhibiting opposite directional effects. downregulation increased susceptibility to delirium, while upregulation conferred an elevated risk. Spatial transcriptomics revealed significant GWAS enrichment in Layer 5 ( < 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 and 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.