Results 61 to 70 of about 23,034 (235)

Predicting Postresection Colorectal Liver Metastases Recurrence Using Advanced Graph Neural Networks with Explainability and Causal Inference

Advanced Intelligent Systems, EarlyView.
This study introduces a framework that combines graph neural networks with causal inference to forecast recurrence and uncover the clinical and pathological factors driving it. It further provides interpretability, validates risk factors via counterfactual and interventional analyses, and offers evidence‐based insights for treatment planning ...
Jubair Ahmed, Md. Abdur Rahman, Mohaimenul Azam Khan Raiaan, Sami Azam   +3 more
wiley   +1 more source

Building an Intelligent Cardiovascular System Platform: Embedding Artificial Intelligence across All Facets of Cardiovascular Medicine

Advanced Intelligent Systems, EarlyView.
This paper presents an integrated AI‐driven cardiovascular platform unifying multimodal data, predictive analytics, and real‐time monitoring. It demonstrates how artificial intelligence—from deep learning to federated learning—enables early diagnosis, precision treatment, and personalized rehabilitation across the full disease lifecycle, promoting a ...
Mowei Kong, Xingwei Zhao, Qiuting Li, Yang Yu, Chunxiang Zhang   +4 more
wiley   +1 more source

Real‐time monitoring of tunnel structures using digital twin and artificial intelligence: A short overview

Deep Underground Science and Engineering, EarlyView.
How artificial intelligence (AI) and digital twin (DT) technologies are revolutionizing tunnel surveillance, offering proactive maintenance strategies and enhanced safety protocols. It explores AI's analytical power and DT's virtual replicas of infrastructure, emphasizing their role in optimizing maintenance and safety in tunnel management.
Mohammad Afrazi, Danial Jahed Armaghani, Hossein Afrazi, Hadi Fattahi, Pijush Samui   +4 more
wiley   +1 more source

Visualizations for an Explainable Planning Agent

, 2018
In this paper, we report on the visualization capabilities of an Explainable AI Planning (XAIP) agent that can support human in the loop decision making.
Bellamy, Rachel K. E., Chakraborti, Tathagata, Dholakia, Mishal, Fadnis, Kshitij P., Kephart, Jeffrey O., Srivastava, Biplav, Talamadupula, Kartik   +6 more
core   +1 more source

Strengthening urban resilience in China through underground infrastructures management: Addressing global climate challenges with technological solutions

Deep Underground Science and Engineering, EarlyView.
This paper explores how climate‐resilient technologies, such as smart grids, digital twins, and self‐healing materials, can enhance urban resilience. It highlights the urgent need for proactive planning, public‐private collaboration, and data‐driven innovation to future‐proof underground infrastructure amid accelerating climate and urban pressures ...
Kai Chen Goh, Tonni Agustiono Kurniawan, Christia Meidiana, Abdelkader Anouzla, Faissal Aziz, Imran Ali, Joan Cecilia C. Casila, M. Imran Khan, Dongdong Zhang, Meihui Jiang, Mohd Hafiz Dzarfan Othman, Md Munir Hayet Khan, Kasun Kumara Dissanayake   +12 more
wiley   +1 more source

AS‐XAI: Self‐Supervised Automatic Semantic Interpretation for CNN

Advanced Intelligent Systems
Explainable artificial intelligence (XAI) aims to develop transparent explanatory approaches for “black‐box” deep learning models. However, it remains difficult for existing methods to achieve the trade‐off of the three key criteria in interpretability ...
Changqi Sun, Hao Xu, Yuntian Chen, Dongxiao Zhang   +3 more
doaj   +1 more source

ExSS 2018: Workshop on explainable smart systems [PDF]

, 2018
Smart systems that apply complex reasoning to make decisions and plan behavior are often difficult for users to understand. While research to make systems more explainable and therefore more intelligible and transparent is gaining pace, there are ...
Lim, B., Smith, A., Stumpf, S.
core  

Artificial intelligence in preclinical epilepsy research: Current state, potential, and challenges

Epilepsia Open, EarlyView.
Abstract Preclinical translational epilepsy research uses animal models to better understand the mechanisms underlying epilepsy and its comorbidities, as well as to analyze and develop potential treatments that may mitigate this neurological disorder and its associated conditions. Artificial intelligence (AI) has emerged as a transformative tool across
Jesús Servando Medel‐Matus, Cesar Santana‐Gomez, Ruby G. Escalante, Dominique Duncan, Pedro F. Viana, Giulia Sofia Cereda, Naoto Kuroda, Aristea S. Galanopoulou   +7 more
wiley   +1 more source

Explainable machine learning in materials science

npj Computational Materials, 2022
Machine learning models are increasingly used in materials studies because of their exceptional accuracy. However, the most accurate machine learning models are usually difficult to explain.
Xiaoting Zhong, Brian Gallagher, Shusen Liu, Bhavya Kailkhura, Anna Hiszpanski, T. Yong-Jin Han   +5 more
doaj   +1 more source

Performance Monitoring of Photovoltaic Modules Using Machine‐Learning‐Based Solutions: A Survey of Current Trends

Energy Science &Engineering, EarlyView.
The graphical abstract presents the concept of applying machine‐learning algorithms to assess the performance of photovoltaic modules. Data from solar panels are fed to surrogates of intelligent models, to assess the following performance metrics: identifying faults, quantifying energy production and trend degradation over time. The combination of data
Nangamso Nathaniel Nyangiwe, Abraham Dimitri Kapim Kenfack, Nicolas Thantsha, Mandla Msimanga   +3 more
wiley   +1 more source