Frontiers in Big Data | New and Recent Articles https://www.frontiersin.org/journals/big-data RSS Feed for Frontiers in Big Data | New and Recent Articles en-us Frontiers Feed Generator,version:1 2026-09-15T23:09:42.985+00:00 60 https://www.frontiersin.org/articles/10.3389/fdata.2026.1776172 https://www.frontiersin.org/articles/10.3389/fdata.2026.1776172 <![CDATA[From crisis to new routine: shifts in urban shopping mobility and socioeconomic inequality during and after the COVID-19 shock]]> 2026-09-15T00:00:00Z Original Research Yilun XuMohsen BahramiAlex Pentland https://www.frontiersin.org/articles/10.3389/fdata.2026.1927033 https://www.frontiersin.org/articles/10.3389/fdata.2026.1927033 <![CDATA[Algorithmic advances in smart TV content recommendation: a structured evidence-mapping review]]> 2026-09-14T00:00:00Z Review Zhe ChenJing HeYuanjia GongJunge LiangJilong Li https://www.frontiersin.org/articles/10.3389/fdata.2026.1763520 https://www.frontiersin.org/articles/10.3389/fdata.2026.1763520 <![CDATA[An empirical test of antecedents and perceived performance of big data analytics in telecommunication companies]]> 2026-09-14T00:00:00Z Original Research Ibrahim MagboulDiaeldin OsmanFadi HerzallahAlnour Nadir OsmanDexter Gittens https://www.frontiersin.org/articles/10.3389/fdata.2026.1914133 https://www.frontiersin.org/articles/10.3389/fdata.2026.1914133 <![CDATA[Voluntary disclosure, banking stability, and AI-augmented forensic accounting: an exploratory econometric and machine-learning study of Palestinian banks]]> 2026-09-14T00:00:00Z Original Research Bahaa Subhi RaziaNajwan Ibrahim JadallahQasim ZureigatReem KhamisBahaa Subhi Awwad https://www.frontiersin.org/articles/10.3389/fdata.2026.1917723 https://www.frontiersin.org/articles/10.3389/fdata.2026.1917723 <![CDATA[Data-driven adaptive hybrid models for exchange rate return forecasting]]> 2026-09-11T00:00:00Z Original Research Olumide Sunday AdesinaLawrence Ogechukwu Obokoh https://www.frontiersin.org/articles/10.3389/fdata.2026.1900836 https://www.frontiersin.org/articles/10.3389/fdata.2026.1900836 <![CDATA[Fine-tuning multilingual sentence transformers for low-resource skill-concept matching across Kazakh, Russian, and English]]> 2026-09-10T00:00:00Z Original Research Sandugash SerikbayevaMadina SambetbayevaValiya RamazanovaAigerim YerimbetovaZhanar LamashevaZhanna SadirmekovaArdak BatyrkhanovYersaiyn Mailybayev https://www.frontiersin.org/articles/10.3389/fdata.2026.1954626 https://www.frontiersin.org/articles/10.3389/fdata.2026.1954626 <![CDATA[Editorial: Smart forecasting: deep learning and explainable AI for real-world time series prediction]]> 2026-09-10T00:00:00Z Editorial Leonard BarolliAntonino FerraroAntonio GalliFrancesco Moscato https://www.frontiersin.org/articles/10.3389/fdata.2026.1885965 https://www.frontiersin.org/articles/10.3389/fdata.2026.1885965 <![CDATA[IOTTRUST: graph-based anomaly detection for IoT intrusion using network flow topology and community structure analysis on UNSW-NB15]]> 2026-09-10T00:00:00Z Original Research Nachaat MohamedHamed Taherdoost https://www.frontiersin.org/articles/10.3389/fdata.2026.1918383 https://www.frontiersin.org/articles/10.3389/fdata.2026.1918383 <![CDATA[A comparative study of DistilBERT and Perceiver for deceptive review detection in hospitality review data]]> 2026-09-09T00:00:00Z Original Research Ioana Ximena RemeşFelicia Mirabela CosteaCornelia Aurora Gyorödi https://www.frontiersin.org/articles/10.3389/fdata.2026.1938279 https://www.frontiersin.org/articles/10.3389/fdata.2026.1938279 <![CDATA[AI-driven cybersecurity for industrial internet of things: architectures, challenges, datasets, and future research directions]]> 2026-09-07T00:00:00Z Review Siddhartha SinghalKakelli Anil Kumar https://www.frontiersin.org/articles/10.3389/fdata.2026.1876811 https://www.frontiersin.org/articles/10.3389/fdata.2026.1876811 <![CDATA[Reconstruction-aware urban crime analytics from incomplete judicial records using heterogeneous graph temporal imputation]]> 2026-09-07T00:00:00Z Original Research Wen XuYong DaiQi ZhangKeyu ChenSanxia Zeng https://www.frontiersin.org/articles/10.3389/fdata.2026.1925931 https://www.frontiersin.org/articles/10.3389/fdata.2026.1925931 <![CDATA[HK-DeepIV: heat-kernel geometric diagnostics and early-warning signals for curvature-induced interference in financial correlation networks]]> 2026-09-03T00:00:00Z Original Research Ntebogang Dinah Moroke https://www.frontiersin.org/articles/10.3389/fdata.2026.1901121 https://www.frontiersin.org/articles/10.3389/fdata.2026.1901121 <![CDATA[HyperIDS: a hypergraph learning and quantum-inspired transformer ensemble framework for IoT intrusion detection]]> 2026-09-01T00:00:00Z Original Research Samayank GoelLogeswari GovindarajTamilarasi Kathirvel Murugan https://www.frontiersin.org/articles/10.3389/fdata.2026.1924721 https://www.frontiersin.org/articles/10.3389/fdata.2026.1924721 <![CDATA[GICPIdb: an archival repository of multimodal data focusing on pathological images for gastrointestinal cancers]]> 2026-08-31T00:00:00Z Original Research Huang ChenLing TongJinyang LiuKai LiuXintao LiShufang ShiShuxue XiGeng TianMeijun ZhangDingrong ZhongShijun LiJialiang Yang https://www.frontiersin.org/articles/10.3389/fdata.2026.1888885 https://www.frontiersin.org/articles/10.3389/fdata.2026.1888885 <![CDATA[Study of deep learning cues for cross linguistic part of speech tagging in English– Malayalam code-mixed data]]> 2026-08-28T00:00:00Z Original Research Parvathy PadmakumarShreya S. NairA. K. PrajishaS. Thara https://www.frontiersin.org/articles/10.3389/fdata.2026.1863692 https://www.frontiersin.org/articles/10.3389/fdata.2026.1863692 <![CDATA[BRI DataLab: an AI-assisted research platform for Chinese infrastructure finance]]> 2026-08-27T00:00:00Z Technology and Code Sanoop Sajan Koshy https://www.frontiersin.org/articles/10.3389/fdata.2026.1897881 https://www.frontiersin.org/articles/10.3389/fdata.2026.1897881 <![CDATA[A novel three-parameter Gompertz-Lomax Distribution with a physics-informed survival network: statistical theory, hazard shape characterization, and real-world applications]]> 2026-08-27T00:00:00Z Original Research Furqan Ahmed P.Sujatha V. https://www.frontiersin.org/articles/10.3389/fdata.2026.1889044 https://www.frontiersin.org/articles/10.3389/fdata.2026.1889044 <![CDATA[A cognitively inspired feature-level fusion framework for interpretable retail sales forecasting using integration of extreme gradient boost machine, artificial neural network and attention mechanism model]]> 2026-08-20T00:00:00Z Original Research Munienge MbodilaOmobayo Ayokunle Esan https://www.frontiersin.org/articles/10.3389/fdata.2026.1812391 https://www.frontiersin.org/articles/10.3389/fdata.2026.1812391 <![CDATA[Interviewer effects on underreporting of traditional contraceptive use: evidence from National Family Health Survey-5]]> 2026-08-20T00:00:00Z Original Research Rupalee Singh ChauhanLaxmi Kant DwivediPriyanka DixitShiva S. Halli https://www.frontiersin.org/articles/10.3389/fdata.2026.1927115 https://www.frontiersin.org/articles/10.3389/fdata.2026.1927115 <![CDATA[A novel two-parameter discrete exponentiated exponential distribution with its neutrosophic extension: mathematical characterization and real-world count data modeling]]> 2026-08-18T00:00:00Z Original Research Furqan Ahmed P.Sujatha V. 1) and the dispersion regime (over-dispersion for small α, under-dispersion for large α), a dual flexibility that most competing discrete models cannot replicate without structural modification. These properties are rigorously established in closed form via the Glaser technique and numerical investigation of the dispersion index. Closed-form expressions are derived for the probability mass function, cumulative distribution function, survival and hazard rate functions, moment generating and characteristic functions, raw and central moments up to the fourth order, and the mean residual life function. Parameter estimation is carried out via maximum likelihood, and a comprehensive Monte Carlo simulation study assesses the finite-sample performance of the estimators and benchmarks it against four competing discrete models under identical settings. The practical advantage of the DEE distribution is demonstrated through three real-world count datasets from oncology, education, and nephrology; the DEE model exhibits competitive and frequently superior performance relative to seven benchmark discrete distributions. Finally, a neutrosophic generalization (DNEE) extends the framework to count data characterized by vagueness, incompleteness, or indeterminacy, with accompanying estimation and illustrative numerical analysis.]]>