Results 61 to 70 of about 24,617 (284)

Abduction-Based Explanations for Machine Learning Models

, 2018
The growing range of applications of Machine Learning (ML) in a multitude of settings motivates the ability of computing small explanations for predictions made. Small explanations are generally accepted as easier for human decision makers to understand.
Ignatiev, Alexey, Marques-Silva, Joao, Narodytska, Nina   +2 more
core   +1 more source

What to Make and How to Make It: Combining Machine Learning and Statistical Learning to Design New Materials

Advanced Intelligent Discovery, EarlyView.
Combining machine learning and probabilistic statistical learning is a powerful way to discover and design new materials. A variety of machine learning approaches can be used to identify promising candidates for target applications, and causal inference can help identify potential ways to make them a reality.
Jonathan Y. C. Ting, Amanda S. Barnard
wiley   +1 more source

Explainable AI Frameworks: Navigating the Present Challenges and Unveiling Innovative Applications

Algorithms
This study delves into the realm of Explainable Artificial Intelligence (XAI) frameworks, aiming to empower researchers and practitioners with a deeper understanding of these tools. We establish a comprehensive knowledge base by classifying and analyzing
Neeraj Anand Sharma, Rishal Ravikesh Chand, Zain Buksh, A. B. M. Shawkat Ali, Ambreen Hanif, Amin Beheshti   +5 more
doaj   +1 more source

Ameliorating Algorithmic Bias, or Why Explainable AI Needs Feminist Philosophy

Feminist Philosophy Quarterly, 2022
Artificial intelligence (AI) systems are increasingly adopted to make decisions in domains such as business, education, health care, and criminal justice.
Linus Ta-Lun Huang, Hsiang-Yun Chen, Ying-Tung Lin, Tsung-Ren Huang, Tzu-Wei Hung   +4 more
doaj  

A Solution for Exosome‐Based Analysis: Surface‐Enhanced Raman Spectroscopy and Artificial Intelligence

Advanced Intelligent Discovery, EarlyView.
Exosomes are emerging as powerful biomarkers for disease diagnosis and monitoring. This review highlights the integration of surface‐enhanced Raman spectroscopy with artificial intelligence to enhance molecular fingerprinting of exosomes. Machine learning and deep learning techniques improve spectral interpretation, enabling accurate classification of ...
Munevver Akdeniz, Zakarya Al‐Shaebi, Omer Aydin   +2 more
wiley   +1 more source

Explainable Artificial Intelligence for Resilient Security Applications in the Internet of Things

IEEE Open Journal of the Communications Society
The performance of Artificial Intelligence (AI) systems reaches or even exceeds that of humans in an increasing number of complicated tasks. Highly effective non-linear AI models are generally employed in a black-box form nested in their complex ...
Mohammed Tanvir Masud, Marwa Keshk, Nour Moustafa, Igor Linkov, Darren K. Emge   +4 more
doaj   +1 more source

Explainable Artificial Intelligence (XAI) in Healthcare

, 2023
Artificial Intelligence (AI) describes computer systems able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and language translation. Examples of AI techniques are machine learning, neural networks and deep learning.
openaire   +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, Rujia Liu, Xinyan Zhang, Xun Chen, Shuhua Yue   +4 more
wiley   +1 more source

The Tower of Babel in Explainable Artificial Intelligence (XAI)

, 2023
AbstractAs machine learning (ML) has emerged as the predominant technological paradigm for artificial intelligence (AI), complex black box models such as GPT-4 have gained widespread adoption. Concurrently, explainable AI (XAI) has risen in significance as a counterbalancing force.
David Schneeberger, Richard Röttger, Federico Cabitza, Andrea Campagner, Markus Plass, Heimo Müller, Andreas Holzinger   +6 more
openaire   +1 more source

Reliable Electricity Distribution Using a Digital Twin Based on Explainable Artificial Intelligence [PDF]

In this short paper we present the project Reliable Electricity Distribution utilizing a Digital Twin based on eXplainable Artificial Intelligence (ReDistXAI). Target of the project is to successfully apply methods of explainable Artificial Intelligence (
Madsen, Anders Læsø, Olsen, Rasmus Løvenstein, Schwefel, Hans-Peter   +2 more
core   +2 more sources