Results 51 to 60 of about 56,159 (288)

Yangians and Mickelsson Algebras I

, 2006
We study the composition of the functor from the category of modules over the Lie algebra gl_m to the category of modules over the degenerate affine Hecke algebra of GL_N introduced by I.
Khoroshkin, Sergey, Nazarov, Maxim
core   +3 more sources

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, Rujia Liu, Xinyan Zhang, Xun Chen, Shuhua Yue   +4 more
wiley   +1 more source

Skin Lesion Classification: A Deep Learning Approach with Local Interpretable Model-Agnostic Explanations (LIME) for Explainable Artificial Intelligence (XAI)

JOIV: International Journal on Informatics Visualization
The classification of skin cancer is crucial as the chance of survival increases significantly with timely and accurate treatment. Convolution Neural Networks (CNNs) have proven effective in classifying skin cancer. However, CNN models are often regarded
Sin Yi Hong, Lih Poh Lin
doaj   +1 more source

Demystifying XAI: Requirements for Understandable XAI Explanations

This paper establishes requirements for assessing the usability of Explainable Artificial Intelligence (XAI) methods, focusing on non-AI experts like healthcare professionals. Through a synthesis of literature and empirical findings, it emphasizes achieving optimal cognitive load, task performance, and task time in XAI explanations.
Jan, Stodt, Christoph, Reich, Martin, Knahl   +2 more
openaire   +2 more sources

Predict XAI

International Research Journal of Innovations in Engineering and Technology
Stroke predictors using Explainable Artificial Intelligence (XAI) aim to provide accurate and interpretable stroke risk predictions. This research integrates machine learning models such as Decision Trees, Random Forest, Logistic Regression, and Support Vector Machines, leveraging ensemble learning techniques like stacking and voting to enhance ...
Prof. Ankush Hutke, Kiran Sahu, Ameet Mishra, Aniruddha Sawant, Ruchitha Gowda   +4 more
openaire   +1 more source

Advances in Thermal Modeling and Simulation of Lithium‐Ion Batteries with Machine Learning Approaches

Advanced Intelligent Discovery, EarlyView.
Heat generation in lithium‐ion batteries affects performance, aging, and safety, requiring accurate thermal modeling. Traditional methods face efficiency and adaptability challenges. This article reviews machine learning‐based and hybrid modeling approaches, integrating data and physics to improve parameter estimation and temperature prediction ...
Qi Lin, Kang Fu, Junjie Ding, Kai Sun, Peng Tan   +4 more
wiley   +1 more source

Using statistical and machine learning approaches to describe estuarine tidal dynamics

Journal of Hydroinformatics
Estuaries are ecologically valuable regions where tidal forces move large volumes of water. To understand the ongoing physical processes in such dynamic systems, a series of estuarine monitoring stations is required.
Franziska Lauer, Frank Kösters
doaj   +1 more source

The Role of Normalization in the Belief Propagation Algorithm [PDF]

, 2011
An important part of problems in statistical physics and computer science can be expressed as the computation of marginal probabilities over a Markov Random Field.
Furtlehner, Cyril, Lasgouttes, Jean-Marc, Martin, Victorin   +2 more
core   +3 more sources

Excited Heavy Baryons and Their Symmetries I: Formalism

, 2000
This is the first of two papers to study a new emergent symmetry which connects orbitally excited heavy baryons to the ground states in the combined heavy quark and large $N_c$ limit.
't Hooft, Bodwin, Bohm, Carlson, Carlson, Carone, Chi-Keung Chow, Chow, Chow, Dashen, Falk, Georgi, Gervais, Gervais, Glozman, Guralnik, Harada, Hussain, Isgur, Isgur, Isgur, Jenkins, Jenkins, Lepage, Luke, Luty, Mannel, Min, Pirjol, Pirjol, Rho, Schechter, Thomas D. Cohen, Witten   +33 more
core   +1 more source

Toward Predictable Nanomedicine: Current Forecasting Frameworks for Nanoparticle–Biology Interactions

Advanced Intelligent Discovery, EarlyView.
Predictive models successfully screen nanoparticles for toxicity and cellular uptake. Yet, complex biological dynamics and sparse, nonstandardized data limit their accuracy. The field urgently needs integrated artificial intelligence/machine learning, systems biology, and open‐access data protocols to bridge the gap between materials science and safe ...
Mariya L. Ivanova, Michael Nicholls, Nicola Russo, Gueorgui Mihaylov, Konstantin Nikolic   +4 more
wiley   +1 more source