Compressive sensing based machine learning strategy for characterizing the flow around a cylinder with limited pressure measurements

2013 ◽  
Vol 25 (12) ◽  
pp. 127102 ◽  
Author(s):  
Ido Bright ◽  
Guang Lin ◽  
J. Nathan Kutz
Keyword(s):  
Machine Learning ◽  
Compressive Sensing ◽  
Learning Strategy ◽  
Pressure Measurements ◽  
Flow Around A Cylinder

Machine learning strategy for predicting flutter performance of streamlined box girders

2021 ◽  
Vol 209 ◽  
pp. 104493
Author(s):  
Haili Liao ◽  
Hanyu Mei ◽  
Gang Hu ◽  
Bo Wu ◽  
Qi Wang
Keyword(s):  
Machine Learning ◽  
Learning Strategy ◽  
Box Girders

scholarly journals A transferable active-learning strategy for reactive molecular force fields

2021 ◽  
Author(s):  
Tom Young ◽  
Tristan Johnston-Wood ◽  
Volker L. Deringer ◽  
Fernanda Duarte
Keyword(s):  
Machine Learning ◽  
Active Learning ◽  
Learning Strategy ◽  
Force Fields ◽  
Molecular Simulations ◽  
Quantum Mechanical ◽  
Interatomic Potentials ◽  
Molecular Force ◽  
High Level ◽  
Active Learning Strategy

Predictive molecular simulations require fast, accurate and reactive interatomic potentials. Machine learning offers a promising approach to construct such potentials by fitting energies and forces to high-level quantum-mechanical data, but...

A machine learning strategy for protein analysis

2002 ◽  
Vol 17 (2) ◽  
pp. 28-35 ◽  
Author(s):  
P. Baldi ◽  
G. Pollastri
Keyword(s):  
Machine Learning ◽  
Learning Strategy ◽  
Protein Analysis

scholarly journals Teleconsultations between Patients and Healthcare Professionals in Primary Care in Catalonia: the Evaluation of Text Classification Algorithms Using Machine Learning

2019 ◽  
Author(s):  
Francesc López Seguí ◽  
Ricardo Ander Egg Aguilar ◽  
Gabriel de Maeztu ◽  
Anna García-Altés ◽  
Francesc García Cuyàs ◽  
...  
Keyword(s):  
Machine Learning ◽  
Primary Care ◽  
Text Classification ◽  
Learning Strategy ◽  
Care Service ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Face To Face ◽  
Classification Tool ◽  
The Impact

Background: the primary care service in Catalonia has operated an asynchronous teleconsulting service between GPs and patients since 2015 (eConsulta), which has generated some 500,000 messages. New developments in big data analysis tools, particularly those involving natural language, can be used to accurately and systematically evaluate the impact of the service. Objective: the study was intended to examine the predictive potential of eConsulta messages through different combinations of vector representation of text and machine learning algorithms and to evaluate their performance. Methodology: 20 machine learning algorithms (based on 5 types of algorithms and 4 text representation techniques)were trained using a sample of 3,559 messages (169,102 words) corresponding to 2,268 teleconsultations (1.57 messages per teleconsultation) in order to predict the three variables of interest (avoiding the need for a face-to-face visit, increased demand and type of use of the teleconsultation). The performance of the various combinations was measured in terms of precision, sensitivity, F-value and the ROC curve. Results: the best-trained algorithms are generally effective, proving themselves to be more robust when approximating the two binary variables "avoiding the need of a face-to-face visit" and "increased demand" (precision = 0.98 and 0.97, respectively) rather than the variable "type of query"(precision = 0.48). Conclusion: to the best of our knowledge, this study is the first to investigate a machine learning strategy for text classification using primary care teleconsultation datasets. The study illustrates the possible capacities of text analysis using artificial intelligence. The development of a robust text classification tool could be feasible by validating it with more data, making it potentially more useful for decision support for health professionals.

Machine learning improvements to compressive sensing for speech enhancement in hearing aid applications

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Hrishikesh B Vanjari ◽  
Mahesh T Kolte
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Hearing Loss ◽  
Compressive Sensing ◽  
Speech Enhancement ◽  
Hearing Aid ◽  
World Health ◽  
Content Type ◽  
L2 Norm ◽  
Reconstructed Signal

Purpose Speech is the primary means of communication for humans. A proper functioning auditory system is needed for accurate cognition of speech. Compressed sensing (CS) is a method for simultaneous compression and sampling of a given signal. It is a novel method increasingly being used in many speech processing applications. The paper aims to use Compressive sensing algorithm for hearing aid applications to reduce surrounding noise. Design/methodology/approach In this work, the authors propose a machine learning algorithm for improving the performance of compressive sensing using a neural network. Findings The proposed solution is able to reduce the signal reconstruction time by about 21.62% and root mean square error of 43% compared to default L2 norm minimization used in CS reconstruction. This work proposes an adaptive neural network–based algorithm to enhance the compressive sensing so that it is able to reconstruct the signal in a comparatively lower time and with minimal distortion to the quality. Research limitations/implications The use of compressive sensing for speech enhancement in a hearing aid is limited due to the delay in the reconstruction of the signal. Practical implications In many digital applications, the acquired raw signals are compressed to achieve smaller size so that it becomes effective for storage and transmission. In this process, even unnecessary signals are acquired and compressed leading to inefficiency. Social implications Hearing loss is the most common sensory deficit in humans today. Worldwide, it is the second leading cause for “Years lived with Disability” the first being depression. A recent study by World health organization estimates nearly 450 million people in the world had been disabled by hearing loss, and the prevalence of hearing impairment in India is around 6.3% (63 million people suffering from significant auditory loss). Originality/value The objective is to reduce the time taken for CS reconstruction with minimal degradation to the reconstructed signal. Also, the solution must be adaptive to different characteristics of the signal and in presence of different types of noises.

Variability-Aware Machine Learning Strategy for 3-D NAND Flash Memories

2020 ◽  
Vol 67 (4) ◽  
pp. 1575-1580 ◽  
Author(s):  
Kyul Ko ◽  
Jang Kyu Lee ◽  
Hyungcheol Shin
Keyword(s):  
Machine Learning ◽  
Learning Strategy ◽  
Nand Flash ◽  
Flash Memories

A Data-Driven, Flexible Machine Learning Strategy for the Classification of Biomedical Data

2007 ◽  
pp. 67-85 ◽  
Author(s):  
Rajmund L. Somorjai ◽  
Murray E. Alexander ◽  
Richard Baumgartner ◽  
Stephanie Booth ◽  
Christopher Bowman ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Strategy ◽  
Data Driven ◽  
Biomedical Data ◽  
Flexible Machine
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