scholarly journals Efficient Machine Learning Approach for Optimizing Scientific Computing Applications on Emerging HPC Architectures

2017 ◽  
Author(s):  
Kamesh Arumugam
Keyword(s):  
Machine Learning ◽  
Scientific Computing ◽  
Learning Approach ◽  
Machine Learning Approach ◽  
Efficient Machine

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

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

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 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.

An Efficient Machine Learning Approach for Atmospheric Correction

2021 ◽  
Author(s):  
Prankur Rusia ◽  
Yatharath Bhateja ◽  
Indranil Misra ◽  
S. Manthira Moorthi ◽  
Debajyoti Dhar
Keyword(s):  
Machine Learning ◽  
Atmospheric Correction ◽  
Learning Approach ◽  
Machine Learning Approach ◽  
Efficient Machine

An efficient machine learning approach for diagnosis of paraquat-poisoned patients

2015 ◽  
Vol 59 ◽  
pp. 116-124 ◽  
Author(s):  
Lufeng Hu ◽  
Guangliang Hong ◽  
Jianshe Ma ◽  
Xianqin Wang ◽  
Huiling Chen
Keyword(s):  
Machine Learning ◽  
Learning Approach ◽  
Machine Learning Approach ◽  
Efficient Machine
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