Results 181 to 190 of about 15,452 (256)
Abstract This study develops an explainable machine learning model to predict cryptocurrency delistings using Binance data. It combines quantitative indicators (price, volume) with qualitative data from real‐time news and Reddit. Latent Dirichlet Allocation (LDA) is used to extract topic trends and community reactions, which are transformed into time ...
Sungju Yang, Hunyeong Kwon
wiley +1 more source
This study aimed to compare both the gut and salivary microbiota over time between allergic and tolerant patients and matched controls. Allergic patients with food protein‐induced enterocolitis syndrome exhibited lower gut and salivary alpha‐diversities, several genus‐level differences, and higher acetate levels than controls.
Anais Lemoine +8 more
wiley +1 more source
GPU-powered Shotgun Stochastic Search for Dirichlet process mixtures of Gaussian Graphical Models. [PDF]
Mukherjee C, Rodriguez A.
europepmc +1 more source
ABSTRACT This paper examines the current state of the art regarding the contribution of Higher Education Institutions (HEIs) to the achievement of the Sustainable Development Goals (SDGs). We conducted a Systematic Literature Review Analysis (SLRA), which integrates a traditional Systematic Literature Review (SLR) with Bibliographic Analysis (BA), on a
Pasquale Latella, Stefania Veltri
wiley +1 more source
An extension of the basic local independence model to multiple observed classifications
Abstract The basic local independence model (BLIM) is appropriate in situations where populations do not differ in the probabilities of the knowledge states and the probabilities of careless errors and lucky guesses of the items. In some situations, this is not the case. This work introduces the multiple observed classification local independence model
Pasquale Anselmi +8 more
wiley +1 more source
Reliability measures in knowledge structure theory
Abstract In knowledge structure theory (KST) framework, this study evaluates the reliability of knowledge state estimation by introducing two key measures: the expected accuracy rate and the expected discrepancy. The accuracy rate quantifies the likelihood that the estimated knowledge state aligns with the true state, while the expected discrepancy ...
Debora de Chiusole +3 more
wiley +1 more source
From tetrachoric to kappa: How to assess reliability on binary scales
Abstract Reliability is crucial in psychometrics, reflecting the extent to which a measurement instrument can discriminate between individuals or items. While classical test theory and intraclass correlation coefficients are well‐established for quantitative scales, estimating reliability for binary outcomes presents unique challenges due to their ...
Sophie Vanbelle
wiley +1 more source
Abstract Hidden Markov diagnostic classification models capture how students' cognitive attributes evolve over time. This paper introduces a Bayesian Markov chain Monte Carlo algorithm for diagnostic classification models that jointly estimates time‐varying Q matrices, latent attributes, item parameters, attribute class proportions and transition ...
Chen‐Wei Liu
wiley +1 more source
LLM‐based prior elicitation for Bayesian graphical modeling
ABSTRACT In the Bayesian graphical modeling framework, priors on network structure encode theoretical assumptions and uncertainty about the topology of psychological constructs under study. For instance, the Bernoulli prior specifies the probability of each pairwise interaction, the Beta–Bernoulli prior governs expected network density, and the ...
Nikola Sekulovski +2 more
wiley +1 more source

