|
|
|
|
|
Gulsum Alicioglu and Bo Sun
Deep learning (DL) models have achieved state-of-the-art performance in many domains. The interpretation of their working mechanisms and decision-making process is essential because of their complex structure and black-box nature, especially for sensitiv...
ver más
|
|
|
|
|
|
|
|
|
|
|
SeyedehRoksana Mirzaei, Hua Mao, Raid Rafi Omar Al-Nima and Wai Lok Woo
Explainable Artificial Intelligence (XAI) evaluation has grown significantly due to its extensive adoption, and the catastrophic consequence of misinterpreting sensitive data, especially in the medical field. However, the multidisciplinary nature of XAI ...
ver más
|
|
|
|
|
|
|
|
|
|
|
Carlos Alejandro Perez Garcia, Marco Bovo, Daniele Torreggiani, Patrizia Tassinari and Stefano Benni
The escalating global population and climate change necessitate sustainable livestock production methods to meet rising food demand. Precision Livestock Farming (PLF) integrates information and communication technologies (ICT) to improve farming efficien...
ver más
|
|
|
|
|
|
|
|
|
|
|
Alexander Isaev, Tatiana Dobroserdova, Alexander Danilov and Sergey Simakov
This study introduces an innovative approach leveraging physics-informed neural networks (PINNs) for the efficient computation of blood flows at the boundaries of a four-vessel junction formed by a Fontan procedure. The methodology incorporates a 3D mesh...
ver más
|
|
|
|
|
|
|
|
|
|
|
Yiji Ma, Yuzhe Zhao, Jiahao Yu, Jingmiao Zhou and Haibo Kuang
Shipping companies and maritime organizations want to improve the energy efficiency of ships and reduce fuel costs through optimization measures; however, the accurate fuel consumption prediction of fuel consumption is a prerequisite for conducting optim...
ver más
|
|
|
|
|
|
|
|
|
|
|
Parisa Mahya and Johannes Fürnkranz
Recently, some effort went into explaining intransparent and black-box models, such as deep neural networks or random forests. So-called model-agnostic methods typically approximate the prediction of the intransparent black-box model with an interpretabl...
ver más
|
|
|
|
|
|
|
|
|
|
|
Bradley Walters, Sandra Ortega-Martorell, Ivan Olier and Paulo J. G. Lisboa
A lack of transparency in machine learning models can limit their application. We show that analysis of variance (ANOVA) methods extract interpretable predictive models from them. This is possible because ANOVA decompositions represent multivariate funct...
ver más
|
|
|
|
|
|
|
|
|
|
|
Abdulaziz AlMohimeed, Hager Saleh, Sherif Mostafa, Redhwan M. A. Saad and Amira Samy Talaat
Cervical cancer affects more than half a million women worldwide each year and causes over 300,000 deaths. The main goals of this paper are to study the effect of applying feature selection methods with stacking models for the prediction of cervical canc...
ver más
|
|
|
|
|
|
|
|
|
|
|
David Solís-Martín, Juan Galán-Páez and Joaquín Borrego-Díaz
The aim of predictive maintenance, within the field of prognostics and health management (PHM), is to identify and anticipate potential issues in the equipment before these become serious. The main challenge to be addressed is to assess the amount of tim...
ver más
|
|
|
|
|
|
|
|
|
|
|
Cédric Roussel and Klaus Böhm
Explainable Artificial Intelligence (XAI) has the potential to open up black-box machine learning models. XAI can be used to optimize machine learning models, to search for scientific findings, or to improve the understandability of the AI system for the...
ver más
|
|
|
|
|
|