Results 31 to 40 of about 56,159 (288)
On the complexity of the chip-firing reachability problem [PDF]
, 2016 In this paper, we study the complexity of the chip-firing reachability problem. We show that for Eulerian digraphs, the reachability problem can be decided in strongly polynomial time, even if the digraph has multiple edges.
Hujter, Bálint +2 more
core +2 more sources
, 2012 We find the all solutions to the $sl_q(2)$-invariant multi-parametric Yang-Baxter equations (YBE) at $q=i$ defined on the cyclic (semi-cyclic, nilpotent) representations of the algebra.
Karakhanyan, D., Khachatryan, Sh.
core +1 more source
A Bayesian Framework for Collaborative Multi-Source Signal Detection [PDF]
, 2009 This paper introduces a Bayesian framework to detect multiple signals embedded in noisy observations from a sensor array. For various states of knowledge on the communication channel and the noise at the receiving sensors, a marginalization procedure ...
Couillet, Romain, Debbah, Merouane
core +3 more sources
Enabling Explainable Fusion in Deep Learning with Fuzzy Integral Neural Networks
, 2019 Information fusion is an essential part of numerous engineering systems and biological functions, e.g., human cognition. Fusion occurs at many levels, ranging from the low-level combination of signals to the high-level aggregation of heterogeneous ...
Anderson, Derek T. +5 more
core +1 more source
Explaining Machine Learning Solutions for Histopathology Images
Applied Medical Informatics, 2021 Machine Learning may have huge benefits for the medical practice. The problem with accepting the machine learning solutions is that it is difficult for the medical staff to understand their decision and because of this they are not able to trust them as ...
Cătălin Mihai PESECAN +1 more
doaj
Explainable artificial intelligence (XAI) in radiology and nuclear medicine: a literature review
Frontiers in Medicine, 2023 RationalDeep learning (DL) has demonstrated a remarkable performance in diagnostic imaging for various diseases and modalities and therefore has a high potential to be used as a clinical tool.
Bart M. de Vries +5 more
doaj +1 more source
Advanced Science, EarlyView.Identifying disease‐causing genes in neurocognitive disorders remains challenging due to variants of uncertain significance. CLinNET employs dual‐branch neural networks integrating Reactome pathways and Gene Ontology terms to provide pathway‐level interpretability of genomic alterations.
Ivan Bakhshayeshi +5 more
wiley +1 more source
Annals of Gastroenterological Surgery, EarlyView.Deep learning has shown promise in predicting postoperative complications, particularly when using image or time‐series data. However, on tabular clinical data such as the NCD, it often underperforms compared to conventional machine learning. Integrating multimodal data may enhance predictive accuracy and interpretability in surgical care.
Ryosuke Fukuyo +4 more
wiley +1 more source
Intelligent explainable optical sensing on Internet of nanorobots for disease detection
Nanotechnology ReviewsCombining deep learning (DL) with nanotechnology holds promise for transforming key facets of nanoscience and technology. This synergy could pave the way for groundbreaking advancements in the creation of novel materials, devices, and applications ...
Mesgaribarzi Niusha +4 more
doaj +1 more source
Evaluation of Similarity of Image Explanations Produced by SHAP, LIME and Grad-CAM
Кібернетика та комп'ютерні технологіїIntroduction. Convolutional neural networks (CNNs) are a subtype of neural networks developed specifically to work with images [1]. They have achieved great success both in research and in practical applications in recent years, however, one of the major
Vladyslav Yavtukhovskyi +1 more
doaj +1 more source