Machine Learning for Vehicular Networks: Recent Advances and Application Examples

2018 ◽  
Vol 13 (2) ◽  
pp. 94-101 ◽  
Author(s):  
Hao Ye ◽  
Le Liang ◽  
Geoffrey Ye Li ◽  
JoonBeom Kim ◽  
Lu Lu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Vehicular Networks ◽  
Recent Advances

scholarly journals Robust machine learning-enabled routing for highly mobile vehicular networks with PARRoT in ns-3

2021 ◽  
Author(s):  
Cedrik Schüler ◽  
Manuel Patchou ◽  
Benjamin Sliwa ◽  
Christian Wietfeld
Keyword(s):  
Machine Learning ◽  
Vehicular Networks

scholarly journals Latest Advances in Cardiac CT

2020 ◽  
Vol 15 ◽  
Author(s):  
Thomas D Heseltine ◽  
Scott W Murray ◽  
Balazs Ruzsics ◽  
Michael Fisher
Keyword(s):  
Machine Learning ◽  
Fractional Flow Reserve ◽  
Cardiac Ct ◽  
Disease Risk ◽  
Clinical Care ◽  
Plaque Burden ◽  
Extensive Literature ◽  
Fractional Flow ◽  
Flow Reserve ◽  
Recent Advances

Recent rapid technological advancements in cardiac CT have improved image quality and reduced radiation exposure to patients. Furthermore, key insights from large cohort trials have helped delineate cardiovascular disease risk as a function of overall coronary plaque burden and the morphological appearance of individual plaques. The advent of CT-derived fractional flow reserve promises to establish an anatomical and functional test within one modality. Recent data examining the short-term impact of CT-derived fractional flow reserve on downstream care and clinical outcomes have been published. In addition, machine learning is a concept that is being increasingly applied to diagnostic medicine. Over the coming decade, machine learning will begin to be integrated into cardiac CT, and will potentially make a tangible difference to how this modality evolves. The authors have performed an extensive literature review and comprehensive analysis of the recent advances in cardiac CT. They review how recent advances currently impact on clinical care and potential future directions for this imaging modality.

Recent Advances in the Machine Learning-Based Drug-Target Interaction Prediction

2019 ◽  
Vol 20 (3) ◽  
pp. 194-202 ◽  
Author(s):  
Wen Zhang ◽  
Weiran Lin ◽  
Ding Zhang ◽  
Siman Wang ◽  
Jingwen Shi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Computational Methods ◽  
Drug Target ◽  
Prediction Models ◽  
Prediction Methods ◽  
Crucial Issue ◽  
Target Interaction ◽  
Interaction Prediction ◽  
Recent Advances ◽  
Drug Similarity

Background:The identification of drug-target interactions is a crucial issue in drug discovery. In recent years, researchers have made great efforts on the drug-target interaction predictions, and developed databases, software and computational methods.Results:In the paper, we review the recent advances in machine learning-based drug-target interaction prediction. First, we briefly introduce the datasets and data, and summarize features for drugs and targets which can be extracted from different data. Since drug-drug similarity and target-target similarity are important for many machine learning prediction models, we introduce how to calculate similarities based on data or features. Different machine learningbased drug-target interaction prediction methods can be proposed by using different features or information. Thus, we summarize, analyze and compare different machine learning-based prediction methods.Conclusion:This study provides the guide to the development of computational methods for the drug-target interaction prediction.

scholarly journals Machine learning for digital try-on: Challenges and progress

2020 ◽  
Author(s):  
Junbang Liang ◽  
Ming C. Lin
Keyword(s):  
Machine Learning ◽  
State Of The Art ◽  
Economic Benefits ◽  
The Body ◽  
Future Directions ◽  
Recent Advances ◽  
Practical Constraints

Abstract Digital try-on systems for e-commerce have the potential to change people's lives and provide notable economic benefits. However, their development is limited by practical constraints, such as accurate sizing of the body and realism of demonstrations. We enumerate three open challenges remaining for a complete and easy-to-use try-on system that recent advances in machine learning make increasingly tractable. For each, we describe the problem, introduce state-of-the-art approaches, and provide future directions.

scholarly journals Recent advances in machine learning towards multiscale soft materials design

2019 ◽  
Vol 23 ◽  
pp. 106-114 ◽  
Author(s):  
Nicholas E Jackson ◽  
Michael A Webb ◽  
Juan J de Pablo
Keyword(s):  
Machine Learning ◽  
Soft Materials ◽  
Materials Design ◽  
Recent Advances
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