AIMC Topic: Algorithms

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Explainable AI-based feature importance analysis for ovarian cancer classification with ensemble methods.

Frontiers in public health
INTRODUCTION: Ovarian Cancer (OC) is one of the leading causes of cancer deaths among women. Despite recent advances in the medical field, such as surgery, chemotherapy, and radiotherapy interventions, there are only marginal improvements in the diag...

Leveraging Extended Windows in End-to-End Deep Learning for Improved Continuous Myoelectric Locomotion Prediction.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Current surface electromyography (sEMG) methods for locomotion mode prediction face limitations in anticipatory capability due to computation delays and constrained window lengths typically below 500 ms-a practice historically tied to stationarity re...

Non-invasive estimation of beat-by-beat aortic blood pressures from electrical impedance tomography data processed by machine learning.

Journal of clinical monitoring and computing
Hypotension in perioperative and intensive care settings is a significant risk factor associated with complications such as myocardial infarction and kidney injury thereby increasing perioperative complications and mortality. Continuous blood pressur...

Towards determining clinical factors influencing critical structure identification using Artificial Intelligence.

HPB : the official journal of the International Hepato Pancreato Biliary Association
BACKGROUND: Studys into factors influencing critical view of safety achievement depends on large volumes of video data and granular anatomical annotations, which are limited by the burden of inefficient manual work. Artificial intelligence (AI) has t...

Universality of reservoir systems with recurrent neural networks.

Neural networks : the official journal of the International Neural Network Society
Approximation capability of reservoir systems whose reservoir is a recurrent neural network (RNN) is discussed. We show what we call uniform strong universality of RNN reservoir systems for a certain class of dynamical systems. This means that, given...

AESeg: Affinity-enhanced segmenter using feature class mapping knowledge distillation for efficient RGB-D semantic segmentation of indoor scenes.

Neural networks : the official journal of the International Neural Network Society
Recent advances in deep learning for semantic segmentation models have introduced dynamic segmentation methods as opposed to static segmentation methods represented by full convolutional networks. Dynamic prediction methods replace static classifiers...

FE reduced-order model-informed neural operator for structural dynamic response prediction.

Neural networks : the official journal of the International Neural Network Society
Physics-Informed Neural Networks (PINN) have achieved remarkable advancements in recent years and have been extensively used in solving differential equations across various disciplines. However, when predicting structural dynamic responses, directly...

IBPL: Information Bottleneck-based Prompt Learning for graph out-of-distribution detection.

Neural networks : the official journal of the International Neural Network Society
When training and test graph samples follow different data distributions, graph out-of-distribution (OOD) detection becomes an indispensable component of constructing the reliable and safe graph learning systems. Motivated by the significant progress...

Fixed-time synchronization of proportional delay memristive complex-valued competitive neural networks.

Neural networks : the official journal of the International Neural Network Society
The fixed-time synchronization (FXS) is considered for memristive complex-valued competitive neural networks (MCVCNNs) with proportional delays. Two less conservative criteria supporting the FXS of MCVCNNs are founded by involving Lyapunov method and...

Using Optimal Feature Selection and Continuous Learning to Implement Efficient Model Arrays for Predicting Daily Clinical Radiology Workload.

Academic radiology
RATIONALE AND OBJECTIVE: Clinical workload can fluctuate daily in radiology practice. We sought to design, validate, and implement an efficient and sustainable machine learning model to forecast daily clinical image interpretation workload.