scholarly journals Efficient machine learning approach for volunteer eye-blink detection in real-time using webcam

2021 ◽  
pp. 116073
Author(s):  
Paulo Augusto de Lima Medeiros ◽  
Gabriel Vinícius Souza da Silva ◽  
Felipe Ricardo dos Santos Fernandes ◽  
Ignacio Sánchez-Gendriz ◽  
Hertz Wilton Castro Lins ◽  
...  
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Learning Approach ◽  
Eye Blink ◽  
Machine Learning Approach ◽  
Blink Detection ◽  
Efficient Machine

A Machine Learning Approach for Predicting Real-time Risk of Intraoperative Hypotension in Traumatic Brain Injury

2021 ◽  
Vol Publish Ahead of Print ◽  
Author(s):  
Shara I. Feld ◽  
Daniel S. Hippe ◽  
Ljubomir Miljacic ◽  
Nayak L. Polissar ◽  
Shu-Fang Newman ◽  
...  
Keyword(s):  
Machine Learning ◽  
Traumatic Brain Injury ◽  
Brain Injury ◽  
Real Time ◽  
Learning Approach ◽  
Intraoperative Hypotension ◽  
Machine Learning Approach

EEG Feature Fusion for Person Identification Using Efficient Machine Learning Approach

2021 ◽  
Author(s):  
Zaid Abdi Alkareem Alyasseri ◽  
Mohammed Azmi Al-Betar ◽  
Mohammed A. Awadallah ◽  
Sharif Naser Makhadmeh ◽  
Osama Ahmad Alomari ◽  
...  
Keyword(s):  
Machine Learning ◽  
Feature Fusion ◽  
Learning Approach ◽  
Person Identification ◽  
Machine Learning Approach ◽  
Efficient Machine

Machine learning approach for multi-perspective volcanic eruption recognition using thermal infrared images

2020 ◽  
Author(s):  
Claudia Corradino ◽  
Gaetana Ganci ◽  
Giuseppe Bilotta ◽  
Annalisa Cappello ◽  
Ciro Del Negro
Keyword(s):  
Machine Learning ◽  
Decision Tree ◽  
Real Time ◽  
Mount Etna ◽  
Learning Approach ◽  
Bag Of Words ◽  
Infrared Images ◽  
Machine Learning Approach ◽  
Thermal Infrared Images ◽  
Thermal Cameras

<p>Detect, locate and characterize eruptions in real-time is fundamental to monitor volcanic activity. Here we present an automatic system able to discover and identify the main types of eruptive activities by exploiting infrared images acquired by the thermal cameras installed around Mount Etna volcano. The system, which employs the machine learning approach, is based on a decision tree tool and a bag of words-based classifier. The decision tree provides information on the visibility level of the monitored area, while the bag of words-based classifiers detects the onset of the eruptive activity and recognize the eruption type among either explosion and/or lava flow or plume. Thus, applied to each image of all thermal cameras over Etna in real-time, the proposed system provides two outputs, namely the visibility level and the recognized activity status. By merging the outcomes coming from each thermal camera, the monitored phenomena can be fully described from different perspectives getting deeper information in real-time and in an automatic way.   </p>

An efficient machine learning approach for indoor localization

2017 ◽  
Vol 14 (11) ◽  
pp. 141-150 ◽  
Author(s):  
Lingwen Zhang ◽  
Yishun Li ◽  
Yajun Gu ◽  
Wenkao Yang
Keyword(s):  
Machine Learning ◽  
Indoor Localization ◽  
Learning Approach ◽  
Machine Learning Approach ◽  
Efficient Machine

scholarly journals Machine Learning approach towards real time assessment of hand-arm vibration risk

2021 ◽  
Vol 54 (1) ◽  
pp. 1187-1192
Author(s):  
G. Aiello ◽  
A. Certa ◽  
Islam Abusohyon ◽  
Francesco Longo ◽  
Antonio Padovano
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Learning Approach ◽  
Machine Learning Approach ◽  
Time Assessment

scholarly journals A Machine Learning Approach to Calculate Electronic Couplings between Quasi-Diabatic Molecular Orbitals: The Case of DNA

2021 ◽  
Author(s):  
Xin Bai ◽  
Xin Guo ◽  
Linjun Wang
Keyword(s):  
Machine Learning ◽  
Molecular Dynamics ◽  
Complex Interaction ◽  
Learning Approach ◽  
Molecular Electronic ◽  
Base Pairs ◽  
Nonadiabatic Coupling ◽  
Representative Sequence ◽  
Machine Learning Approach ◽  
Efficient Machine

Diabatization of one-electron states in flexible molecular aggregates is a great challenge due to the presence of surface crossings between molecular orbital (MO) levels and the complex interaction between MOs of neighboring molecules. In this work, we present an efficient machine learning approach to calculate electronic couplings between quasi-diabatic MOs without the need of nonadiabatic coupling calculations. Using MOs of rigid molecules as references, the MOs that can be directly regarded to be quasi-diabatic in molecular dynamics are selected out, state tracked, and phase corrected. On the basis of this information, artificial neural networks are trained to characterize the structure-dependent onsite energies of quasi-diabatic MOs and the inter-molecular electronic couplings. A representative sequence of DNA is systematically studied as an illustration. Smooth time evolution of electronic couplings in all base pairs is obtained with quasi-diabatic MOs. Especially, our method can calculate electronic couplings between different quasi-diabatic MOs independently, and thus possesses unique advantages in many applications.

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