Results 131 to 140 of about 9,045 (254)

Consumer Behavior Analysis in Digital Marketing Using AI and Big Data Analytics: A Narrative Review and Methodological Taxonomy

open access: yesWIREs Data Mining and Knowledge Discovery, Volume 16, Issue 3, September 2026.
AI and Big Data in Consumer Behavior Analysis. ABSTRACT The rapid expansion of digital consumer data has challenged traditional approaches to understanding behavior in digital marketing. Existing reviews often focus on individual methods and give limited guidance on how analytical techniques compare or how they should be selected for specific marketing
Leonidas Theodorakopoulos   +1 more
wiley   +1 more source

Recent Advances in Text Anonymization: A Systematic Review

open access: yesWIREs Data Mining and Knowledge Discovery, Volume 16, Issue 3, September 2026.
This survey presents a unified, cross‐domain overview of text anonymization from 2021 to 2025, covering methods from rule‐based to LLM‐based approaches, relevant datasets, benchmarks, and evaluation strategies. It highlights the critical trade‐off between privacy and utility, underscores the importance of standardizing evaluation protocols and metrics,
Marina Litvak, Alípio Jorge
wiley   +1 more source

After the Hype: Resilience Seeking in Emerging Technology Ecosystems

open access: yesJournal of Product Innovation Management, Volume 43, Issue 5, Page 793-826, September 2026.
ABSTRACT Academic Summary Hype often helps emerging technology ecosystems gain early support for their innovative value propositions, but the initial excitement around the technology typically vanishes at some point. This decrease in excitement and support may lead some ecosystems to fail while others are resilient and recover.
Fiona Schweitzer   +2 more
wiley   +1 more source

Hackathons in Statistics and Data Science Education and Experiences from ASA DataFest

open access: yesTeaching Statistics, Volume 48, Issue S1, Page S13-S21, Summer 2026.
Abstract Data hackathons provide a platform for students to work with real and challenging data, allowing them to practice both technical and transferable skills, such as data wrangling, visualization, modeling, effective communication, and teamwork. This level of active learning is difficult to achieve in a typical classroom setting.
Serveh Sharifi Far   +3 more
wiley   +1 more source

STAID: A Self‐Refining Deep Learning Framework for Spatial Cell‐Type Deconvolution with Biologically Informed Modeling

open access: yesAdvanced Science, Volume 13, Issue 43, 3 August 2026.
STAID is a unified deep learning framework that couples iterative pseudo‐spot refinement with neural network training through a feedback loop and exploits gene co‐expression information to model higher‐order interactions, achieving accurate and robust cell‐type deconvolution in spatial transcriptomics.
Jixin Liu   +5 more
wiley   +1 more source

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