Machine learning for 5G security: Architecture, recent advances, and challenges

2021 ◽  
Vol 123 ◽  
pp. 102667
Author(s):  
Amir Afaq ◽  
Noman Haider ◽  
Muhammad Zeeshan Baig ◽  
Komal S. Khan ◽  
Muhammad Imran ◽  
...  
Keyword(s):  
Machine Learning ◽  
Security Architecture ◽  
Recent Advances ◽  
5G Security

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.

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

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

scholarly journals Proximal Methods for Plant Stress Detection Using Optical Sensors and Machine Learning

Biosensors ◽  
2020 ◽  
Vol 10 (12) ◽  
pp. 193
Author(s):  
Alanna V. Zubler ◽  
Jeong-Yeol Yoon
Keyword(s):  
Machine Learning ◽  
Optical Sensors ◽  
Near Infrared ◽  
Plant Stress ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Detection Methods ◽  
Stress Detection ◽  
Recent Advances ◽  
Wide Range

Plant stresses have been monitored using the imaging or spectrometry of plant leaves in the visible (red-green-blue or RGB), near-infrared (NIR), infrared (IR), and ultraviolet (UV) wavebands, often augmented by fluorescence imaging or fluorescence spectrometry. Imaging at multiple specific wavelengths (multi-spectral imaging) or across a wide range of wavelengths (hyperspectral imaging) can provide exceptional information on plant stress and subsequent diseases. Digital cameras, thermal cameras, and optical filters have become available at a low cost in recent years, while hyperspectral cameras have become increasingly more compact and portable. Furthermore, smartphone cameras have dramatically improved in quality, making them a viable option for rapid, on-site stress detection. Due to these developments in imaging technology, plant stresses can be monitored more easily using handheld and field-deployable methods. Recent advances in machine learning algorithms have allowed for images and spectra to be analyzed and classified in a fully automated and reproducible manner, without the need for complicated image or spectrum analysis methods. This review will highlight recent advances in portable (including smartphone-based) detection methods for biotic and abiotic stresses, discuss data processing and machine learning techniques that can produce results for stress identification and classification, and suggest future directions towards the successful translation of these methods into practical use.

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