Results 141 to 150 of about 119,587 (309)
Data Mining : Opportunities and Challenges chapter 1: A Survey of Bayesian Data Mining [PDF]
, 2003 Data Mining: Opportunities and Challenges presents an overview of the state of the art approaches in this new and multidisciplinary field of data mining.
Arnborg, Stefan,
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Large Language Model in Materials Science: Roles, Challenges, and Strategic Outlook
Advanced Intelligent Discovery, EarlyView.Large language models (LLMs) are reshaping materials science. Acting as Oracle, Surrogate, Quant, and Arbiter, they now extract knowledge, predict properties, gauge risk, and steer decisions within a traceable loop. Overcoming data heterogeneity, hallucinations, and poor interpretability demands domain‐adapted models, cross‐modal data standards, and ...
Jinglan Zhang +4 more
wiley +1 more source
Differentiating between data-mining and text-mining terminology
South African Journal of Information Management, 2004 J.H. Kroeze, M.C. Matthee, T.J.D. Bothma
doaj +1 more source
Advanced Experiment Design Strategies for Drug Development
Advanced Intelligent Discovery, EarlyView.Wang et al. analyze 592 drug development studies published between 2020 and 2024 that applied design of experiments methodologies. The review surveys both classical and emerging approaches—including Bayesian optimization and active learning—and identifies a critical gap between advanced experimental strategies and their practical adoption in ...
Fanjin Wang +3 more
wiley +1 more source
Text Mining in Python through the HTRC Feature Reader
The Programming Historian, 2016 We introduce a toolkit for working with the 13.6 million volume Extracted Features Dataset from the HathiTrust Research Center. You will learn how to peer at the words and trends of any book in the collection, while developing broadly useful Python data ...
Peter Organisciak, Boris Capitanu
doaj
The Challenge of Handling Structured Missingness in Integrated Data Sources
Advanced Intelligent Discovery, EarlyView.As data integration becomes ever more prevalent, a new research question that emerges is how to handle missing values that will inevitably arise in these large‐scale integrated databases? This missingness can be described as structured missingness, encompassing scenarios involving multivariate missingness mechanisms and deterministic, nonrandom ...
James Jackson +6 more
wiley +1 more source
Advanced Intelligent Discovery, EarlyView.A physics‐guided machine learning framework estimates Young's modulus in multilayered multimaterial hyperelastic cylinders using contact mechanics. A semiempirical stiffness law is embedded into a custom neural network, ensuring physically consistent predictions. Validation against experimental and numerical data on C.
Christoforos Rekatsinas +4 more
wiley +1 more source
Advanced Intelligent Discovery, EarlyView.This perspective highlights how knowledge‐guided artificial intelligence can address key challenges in manufacturing inverse design, including high‐dimensional search spaces, limited data, and process constraints. It focused on three complementary pillars—expert‐guided problem definition, physics‐informed machine learning, and large language model ...
Hugon Lee +3 more
wiley +1 more source
Text Mining Technique For Data Mining Application
, 2007 {"references": ["Themis P.Exarchos, Markos G. Tsipouras, Costas P. Exarchos, Costas\nPapaloukas, Dimitrios I. Fotiadis, Lampros K. Michalis, \"A\nmethodology for the automated creation of fuzzy expert systems for\nischaemic and arrhymic beat classification based on a set of rules\nobtained by a decision tree\" Artificial Intelligence in medicine (2007)\
openaire +2 more sources
Resource-aware very fast K-Means for ubiquitous data stream mining [PDF]
, 2005 Developments in data streams, coupled with the growth in mobile and pervasive devices, have led to the emergence of Ubiquitous Data Mining (UDM). UDM aims to perform data stream mining in a ubiquitous environment with resource-constrained and/or mobile ...
Rahul Shah +5 more
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