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Found 12 papers, 1 papers with code
Predicting the Risk of Ischemic Stroke in Patients with Atrial Fibrillation using Heterogeneous Drug-protein-disease Network-based Deep Learning
The model integrates drug-protein-disease pathways and real-world clinical data of AF patients to generate the IS risk and potential pathways for each patient.
Intrinsic Data Constraints and Upper Bounds in Binary Classification Performance
The structure of data organization is widely recognized as having a substantial influence on the efficacy of machine learning algorithms, particularly in binary classification tasks.
Data-driven Preference Learning Methods for Sorting Problems with Multiple Temporal Criteria
This study presents novel preference learning approaches to multiple criteria sorting problems in the presence of temporal criteria.
Viewpoint-Invariant Exercise Repetition Counting
We have also tested the performance in a variety of camera locations and concurrent motions with conveniently collected video with overall MAE 0. 06 and OBOA 0. 88.
Impacts of export restrictions on the global personal protective equipment trade network during COVID-19
The threshold model captures the shortage contagion on the global PPE trade network.
Physics and Society
An interpretable neural network model through piecewise linear approximation
To address these issues, we propose a hybrid interpretable model that combines a piecewise linear component and a nonlinear component.
Knowledge-Enhanced Attentive Learning for Answer Selection in Community Question Answering Systems
We propose the Knowledge-enhanced Attentive Answer Selection (KAAS) model, which enhances the performance through (a) considering both the expertise and the authority of the answerer; (b) utilizing the human-labeled tags, the taxonomy of the tags, and the votes as the domain knowledge to infer the expertise of the answer; (c) using matrix decomposition of the social network (formed by following-relationship) to infer the authority of the answerer and incorporating such information in the process of evaluating the similarity between segments.
Explainable Ordinal Factorization Model: Deciphering the Effects of Attributes by Piece-wise Linear Approximation
Ordinal regression predicts the objects' labels that exhibit a natural ordering, which is important to many managerial problems such as credit scoring and clinical diagnosis.
A hybrid machine learning framework for analyzing human decision making through learning preferences
To meet the decision maker's demand for more interpretable machine learning models, we propose a novel hybrid method, namely Neural Network-based Multiple Criteria Decision Aiding, which combines an additive value model and a fully-connected multilayer perceptron (MLP) to achieve good performance while capturing the explicit relationships between individual attributes and the prediction.
E-LSTM-D: A Deep Learning Framework for Dynamic Network Link Prediction
Predicting the potential relations between nodes in networks, known as link prediction, has long been a challenge in network science.
Evolution of the Chinese Guarantee Network under Financial Crisis and Stimulus Program
Our knowledge about the evolution of guarantee network in downturn period is limited due to the lack of comprehensive data of the whole credit system.
A Reference-Free Algorithm for Computational Normalization of Shotgun Sequencing Data
We here describe digital normalization, a single-pass computational algorithm that systematizes coverage in shotgun sequencing data sets, thereby decreasing sampling variation, discarding redundant data, and removing the majority of errors.
