Results 61 to 70 of about 23,045 (305)
Adaptive Segmentation and Statistical Analysis for Multivariate Big Data Forecasting
Big Data and Cognitive ComputingForecasting high-volume, univariate, and multivariate longitudinal data streams is a critical challenge in Big Data systems, especially with constrained computational resources and pronounced data variability.
Desmond Fomo, Aki-Hiro Sato
doaj +1 more source
Annals of Gastroenterological Surgery, EarlyView.This retrospective study analyzed patients aged ≥ 85 years undergoing emergency abdominal surgery, focusing on short‐term outcomes and inpatient cost structure under the Japanese DPC system. Although major complications occurred in 19.4% of patients, more than 70% were discharged home.
Yuta Kobayashi +8 more
wiley +1 more source
Estimation of the Vector Autoregressive Model with the Multivariate Skew Normal Distribution for the Shocks: Application to Two Real-World Datasets [PDF]
فصلنامه پژوهشهای اقتصادی ایرانModeling plays a crucial role in economic and financial research, forming the foundation for analysis, decision-making, policy development, and planning.
Manijeh Mahmoodi +1 more
doaj +1 more source
Deep Learning‐Assisted Coherent Raman Scattering Microscopy
Advanced Intelligent Discovery, EarlyView.The analytical capabilities of coherent Raman scattering microscopy are augmented through deep learning integration. This synergistic paradigm improves fundamental performance via denoising, deconvolution, and hyperspectral unmixing. Concurrently, it enhances downstream image analysis including subcellular localization, virtual staining, and clinical ...
Jianlin Liu +4 more
wiley +1 more source
Forecasting Value-at-Risk under Different Distributional Assumptions
Econometrics, 2016 Financial asset returns are known to be conditionally heteroskedastic and generally non-normally distributed, fat-tailed and often skewed. These features must be taken into account to produce accurate forecasts of Value-at-Risk (VaR).
Manuela Braione, Nicolas K. Scholtes
doaj +1 more source
Explaining the Origin of Negative Poisson's Ratio in Amorphous Networks With Machine Learning
Advanced Intelligent Discovery, EarlyView.This review summarizes how machine learning (ML) breaks the “vicious cycle” in designing auxetic amorphous networks. By transitioning from traditional “black‐box” optimization to an interpretable “AI‐Physics” closed‐loop paradigm, ML is shown to not only discover highly optimized structures—such as all‐convex polygon networks—but also unveil hidden ...
Shengyu Lu, Xiangying Shen
wiley +1 more source
BMC Medical Imaging, 2022 Background To assess the value of whole-lesion apparent diffusion coefficient (ADC) histogram analysis in differentiating stage IA endometrial carcinoma (EC) from benign endometrial lesions (BELs) and characterizing histopathologic features of stage IA ...
Jieying Zhang +7 more
doaj +1 more source
On describing multivariate skewed distributions: A directional approach [PDF]
Canadian Journal of Statistics, 2006 AbstractMost multivariate measures of skewness in the literature measure the overall skewness of a distribution. These measures were designed for testing the hypothesis of distributional symmetry; their relevance for describing skewed distributions is less obvious.
Ferreira, José T. A. S. +1 more
openaire +2 more sources
An Autonomous Large Language Model‐Agent Framework for Transparent and Local Time Series Forecasting
Advanced Intelligent Discovery, EarlyView.Architecture of the proposed large language model (LLM)‐based agent framework for autonomous time series forecasting in thermal power generation systems. The framework operates through a vertical pipeline initiated by natural language queries from users, which are processed by the LLM Agent Core powered by Llama.cpp and a ReAct loop with persistent ...
William Gouvêa Buratto +5 more
wiley +1 more source
A Critical Assessment of Bonding Descriptors for Predicting Materials Properties
Advanced Intelligent Discovery, EarlyView.The impact of new bonding descriptors in machine learning models for predicting material properties is assessed. Improvements are validated using significance tests, and new, intuitive descriptors for screening lattice thermal conductivity and projected force constants are introduced.
Aakash Ashok Naik +6 more
wiley +1 more source