Prescriptions

Fault Early Warning Model for High-Speed Railway Train Based on Feature Contribution and Causal Inference.

The demands for model accuracy and computing efficiency in fault warning scenarios are increasing as high-speed railway train technology continues to advance. The black box model is difficult to interpret, making it impossible for this technology to be widely adopted in the railway industry, which has strict safety regulations. This paper proposes a fault early warning machine learning model based...

Path sampling of recurrent neural networks by incorporating known physics.

Recurrent neural networks have seen widespread use in modeling dynamical systems in varied domains such as weather prediction, text prediction and several others. Often one wishes to supplement the experimentally observed dynamics with prior knowledge or intuition about the system. While the recurrent nature of these networks allows them to model arbitrarily long memories in the time series used i...

DrugnomeAI is an ensemble machine-learning framework for predicting druggability of candidate drug targets.

The druggability of targets is a crucial consideration in drug target selection. Here, we adopt a stochastic semi-supervised ML framework to develop D...

Machine Learning Techniques Outperform Conventional Statistical Methods in the Prediction of High Risk QTc Prolongation Related to a Drug-Drug Interaction.

In clinical practice, many drug therapies are associated with prolongation of the QT interval. In literature, estimation of the risk of prescribing dr...

A Generalized Robot Navigation Analysis Platform (RoNAP) with Visual Results Using Multiple Navigation Algorithms.

The robotic navigation task is to find a collision-free path among a mass of stationary or migratory obstacles. Various well-established algorithms ha...

Deep learning based approach for automated characterization of large marine microplastic particles.

The rapidly growing concern of marine microplastic pollution has drawn attentions globally. Microplastic particles are normally subjected to visual ch...

RGN: Residue-Based Graph Attention and Convolutional Network for Protein-Protein Interaction Site Prediction.

The prediction of a protein-protein interaction site (PPI site) plays a very important role in the biochemical process, and lots of computational meth...

A New Consensus Model Based on Trust Interactive Weights for Intuitionistic Group Decision Making in Social Networks.

A promising feature for group decision making (GDM) lies in the study of the interaction between individuals. In conventional GDM research, experts ar...

Adaptive-Constrained Impedance Control for Human-Robot Co-Transportation.

Human-robot co-transportation allows for a human and a robot to perform an object transportation task cooperatively on a shared environment. This rang...

Machine learning and ontology in eCoaching for personalized activity level monitoring and recommendation generation.

Leading a sedentary lifestyle may cause numerous health problems. Therefore, passive lifestyle changes should be given priority to avoid severe long-t...

A Graph Neural Network Approach for the Analysis of siRNA-Target Biological Networks.

Many biological systems are characterised by biological entities, as well as their relationships. These interaction networks can be modelled as graphs...

Gene-gene interaction detection with deep learning.

The extent to which genetic interactions affect observed phenotypes is generally unknown because current interaction detection approaches only conside...

DeepIDC: A Prediction Framework of Injectable Drug Combination Based on Heterogeneous Information and Deep Learning.

BACKGROUND AND OBJECTIVE: In clinical practice, injectable drug combination (IDC) usually provides good therapeutic effects for patients. Numerous cli...

Transit communication via Twitter during the COVID-19 pandemic.

Transit providers have used social media (e.g., Twitter) as a powerful platform to shape public perception and provide essential information, especial...

A systematic literature review for the prediction of anticancer drug response using various machine-learning and deep-learning techniques.

Computational methods have gained prominence in healthcare research. The accessibility of healthcare data has greatly incited academicians and researc...

Systems Drug Design for Muscle Invasive Bladder Cancer and Advanced Bladder Cancer by Genome-Wide Microarray Data and Deep Learning Method with Drug Design Specifications.

Bladder cancer is the 10th most common cancer worldwide. Due to the lack of understanding of the oncogenic mechanisms between muscle-invasive bladder ...

Continuous Estimation of Human Joint Angles From sEMG Using a Multi-Feature Temporal Convolutional Attention-Based Network.

Intention recognition based on surface electromyography (sEMG) signals is pivotal in human-machine interaction (HMI), where continuous motion estimati...

A robot for overground physical human-robot interaction experiments.

Many anticipated physical human-robot interaction (pHRI) applications in the near future are overground tasks such as walking assistance. For investig...

Historical evolution and new trends for soil-intruder interaction modeling.

Soil is a crucial resource for life on Earth. Every activity, whether natural or man-made, that interacts with the sub or deep soil can affect the lan...

Natural Language Processing Model for Identifying Critical Findings-A Multi-Institutional Study.

Improving detection and follow-up of recommendations made in radiology reports is a critical unmet need. The long and unstructured nature of radiology...