AIMC Topic: Algorithms

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Neighbor-aware calibration of segmentation networks with penalty-based constraints.

Medical image analysis
Ensuring reliable confidence scores from deep neural networks is of paramount significance in critical decision-making systems, particularly in real-world domains such as healthcare. Recent literature on calibrating deep segmentation networks has res...

Adaptable graph neural networks design to support generalizability for clinical event prediction.

Journal of biomedical informatics
OBJECTIVE: While many machine learning and deep learning-based models for clinical event prediction leverage various data elements from electronic healthcare records such as patient demographics and billing codes, such models face severe challenges w...

Enhanced heart failure mortality prediction through model-independent hybrid feature selection and explainable machine learning.

Journal of biomedical informatics
Heart failure (HF) remains a significant public health challenge with high mortality rates. Machine learning (ML) techniques offer a promising approach to predict HF mortality, potentially improving clinical outcomes. However, the effectiveness of th...

Machine learning modeling for predicting adherence to physical activity guideline.

Scientific reports
This study aims to create predictive models for PA guidelines by using ML and examine the critical determinants influencing adherence to the PA guidelines. 11,638 entries from the National Health and Nutrition Examination Survey were analyzed. Variab...

Reconfigurable security solution based on hopfield neural network for e-healthcare applications.

Scientific reports
In the healthcare sector, e-diagnosis through medical images is essential in a multi-speciality hospital; securing the medical images becomes crucial for preserving an individual's privacy in e-healthcare applications. So, this paper has proposed a n...

Prediction of thyroid malignancy risk using clinical and ultrasonography features and a machine learning approach.

European radiology
OBJECTIVE: This study aims to develop and validate a predictive model for thyroid nodule malignancy risks using clinical and ultrasonography features and a machine learning (ML) approach.

Don't fear peculiar activation functions: EUAF and beyond.

Neural networks : the official journal of the International Neural Network Society
In this paper, we propose a new super-expressive activation function called the Parametric Elementary Universal Activation Function (PEUAF). We demonstrate the effectiveness of PEUAF through systematic and comprehensive experiments on various industr...

Dual view graph transformer networks for multi-hop knowledge graph reasoning.

Neural networks : the official journal of the International Neural Network Society
To address the incompleteness of knowledge graphs, multi-hop reasoning aims to find the unknown information from existing data and enhance the comprehensive understanding. The presence of reasoning paths endows multi-hop reasoning with interpretabili...

Thermo-responsive and phase-separated hydrogels for cardiac arrhythmia diagnosis with deep learning algorithms.

Biosensors & bioelectronics
Adhesive epidermal hydrogel electrodes are essential for achieving robust signal transduction and cardiac arrhythmia diagnosis, but detachment of conventional adhesive dressings easily causes secondary damage to delicate wound tissues due to lack of ...