Results 101 to 110 of about 84,444 (264)

APPLICATION OF RANKING BASED ATTRIBUTE SELECTION FILTERS TO PERFORM AUTOMATED EVALUATION OF DESCRIPTIVE ANSWERS THROUGH SEQUENTIAL MINIMAL OPTIMIZATION MODELS [PDF]

ICTACT Journal on Soft Computing, 2014
In this paper, we study the performance of various models for automated evaluation of descriptive answers by using rank based feature selection filters for dimensionality reduction.
C. Sunil Kumar, R.J. Rama Sree
doaj  

dynoGP: Deep Gaussian Processes for Dynamic System Identification

International Journal of Adaptive Control and Signal Processing, EarlyView.
This work introduces a novel class of deep models for system identification, dynamical deep Gaussian processes, which combine the strengths of data‐driven methods, such as those based on neural network architectures, with the ability to output a probability distribution for uncertainty representation.
Alessio Benavoli, Marco Forgione, Dario Piga   +2 more
wiley   +1 more source

Based a machine learning approach to investigate the factors influencing nirmatrelvir/ritonavir exposure in human plasma: a multicenter, observational study

Frontiers in Cellular and Infection Microbiology
ObjectivesNirmatrelvir/ritonavir (N/R) is an effective antiviral for treating COVID-19. However, evidence supporting therapeutic drug monitoring (TDM) for N/R remains limited, potentially increasing the risk of adverse reactions and compromising efficacy.
Yue Zhang, Yue Zhang, Xiner Yang, Xiner Yang, Liang Sun, Liang Sun, Runcong Zhang, Runcong Zhang, Weibin Fan, Weibin Fan, Nengming Lin, Nengming Lin, Nengming Lin, Bin Lin, Bin Lin, Bin Lin   +15 more
doaj   +1 more source

Characterization of Defect Distribution in an Additively Manufactured AlSi10Mg as a Function of Processing Parameters and Correlations with Extreme Value Statistics

Advanced Engineering Materials, EarlyView.
Predicting extreme defects in additive manufacturing remains a key challenge limiting its structural reliability. This study proposes a statistical framework that integrates Extreme Value Theory with advanced process indicators to explore defect–process relationships and improve the estimation of critical defect sizes. The approach provides a basis for
Muhammad Muteeb Butt, Varma Kvvssn, Hossein Laieghi, Zia Uddin, Peyman Ansari, E. Kıvanç Sadak, Can Barış Toprak, Hüseyin Kızıl, Enrico Salvati   +8 more
wiley   +1 more source

An enhanced moth flame optimization extreme learning machines hybrid model for predicting CO2 emissions

Scientific Reports
This study introduces a novel hybrid model for accurate CO2 emissions prediction, supporting sustainable decision-making. The model integrates the Gaussian mutation and shrink mechanism-based moth flame optimization (GMSMFO) algorithm with an extreme ...
Ahmed Ramdan Almaqtouf Algwil, Wagdi M. S. Khalifa   +1 more
doaj   +1 more source

A Deep Learning Model Using Transformer Network and Expert Optimizer for an Hour Ahead Wind Power Forecasting

IEEE Access
The renewable energy platform cannot operate without an accurate wind power forecast. The power system can better manage its supply and guarantee grid reliability with an accurate wind power forecast. Accurate forecasting is difficult to achieve, though,
Anushalini Thiyagarajan, B. Sri Revathi, Vishnu Suresh   +2 more
doaj   +1 more source

Low‐Angle Grain Boundaries and Re‐Segregation in Single‐Crystalline Ni‐Base Superalloys

Advanced Engineering Materials, EarlyView.
This work demonstrates that Re‐segregation at low‐angle grain boundaries (LAGBs) in Ni‐base superalloys is influenced by misorientation angle. Advanced microscopy and atom probe tomography reveal that higher misorientation angles increases Re‐segregation.
Alireza B. Parsa, Aparna Saksena, Alesander Kostka, Pascal Thome, Felicitas Scholz, Alexandre Mussi, Christoph Somsen, Baptiste Gault, Jan Frenzel, Gunther Eggeler   +9 more
wiley   +1 more source

A Simplified Laminar Flow Model for the Pultrusion of Glass Fiber/Polyethylene Terephthalate Commingled Yarns

Advanced Engineering Materials, EarlyView.
A simplified thermoplastic pultrusion model is developed to predict thermal fields in glass fiber/polyethylene terephthalate (GF/PET) composites with reduced computational cost. By combining effective material homogenization, validation against literature data, and Gaussian‐process‐based optimization, the study reveals how heating limits, pulling speed,
Elder Soares, Nadège Bouchonneau, Christian Jochum, Donald Radford   +3 more
wiley   +1 more source

Karl Popper and the Mechanisms of Hydrogen Embrittlement

Advanced Engineering Materials, EarlyView.
Representation of the beginning of loss of ductility rather than embrittlement. Small concentrations of hydrogen in a diffusible form within iron are well‐established to harm the mechanical integrity of steels. There are theories that attempt to explain the pernicious role of hydrogen.
H. K. D. H. Bhadeshia
wiley   +1 more source

Multimodal Data‐Driven Microstructure Characterization

Advanced Engineering Materials, EarlyView.
A self‐consistent autonomous workflow for EBSP‐based microstructure segmentation by integrating PCA, GMM clustering, and cNMF with information‐theoretic parameter selection, requiring no user input. An optimal ROI size related to characteristic grain size is identified.
Qi Zhang, Santiago Benito, Özge Özgün, Sebastian Weber, Markus Stricker   +4 more
wiley   +1 more source