Review of Signal Processing Techniques and Machine Learning Algorithms for Power Quality Analysis

2020 ◽  
Vol 3 (10) ◽  
pp. 2000118
Author(s):  
Rahul
Keyword(s):  
Machine Learning ◽  
Signal Processing ◽  
Power Quality ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Quality Analysis ◽  
Power Quality Analysis ◽  
Processing Techniques ◽  
Signal Processing Techniques

scholarly journals A Survey on EEG Signal Processing Techniques and Machine Learning: Applications to the Neurofeedback of Autobiographical Memory Deficits in Schizophrenia

Electronics ◽  
2021 ◽  
Vol 10 (23) ◽  
pp. 3037
Author(s):  
Miguel Luján ◽  
María Jimeno ◽  
Jorge Mateo Sotos ◽  
Jorge Ricarte ◽  
Alejandro Borja
Keyword(s):  
Machine Learning ◽  
Signal Processing ◽  
Autobiographical Memory ◽  
Memory Deficits ◽  
Research Field ◽  
Machine Learning Algorithms ◽  
Time Frequency ◽  
Machine Learning Classification ◽  
Processing Techniques ◽  
Signal Processing Techniques

In this paper, a general overview regarding neural recording, classical signal processing techniques and machine learning classification algorithms applied to monitor brain activity is presented. Currently, several approaches classified as electrical, magnetic, neuroimaging recordings and brain stimulations are available to obtain neural activity of the human brain. Among them, non-invasive methods like electroencephalography (EEG) are commonly employed, as they can provide a high degree of temporal resolution (on the order of milliseconds) and acceptable space resolution. In addition, it is simple, quick, and does not create any physical harm or stress to patients. Concerning signal processing, once the neural signals are acquired, different procedures can be applied for feature extraction. In particular, brain signals are normally processed in time, frequency, and/or space domains. The features extracted are then used for signal classification depending on its characteristics such us the mean, variance or band power. The role of machine learning in this regard has become of key importance during the last years due to its high capacity to analyze complex amounts of data. The algorithms employed are generally classified in supervised, unsupervised and reinforcement techniques. A deep review of the most used machine learning algorithms and the advantages/drawbacks of most used methods is presented. Finally, a study of these procedures utilized in a very specific and novel research field of electroencephalography, i.e., autobiographical memory deficits in schizophrenia, is outlined.

Wine Quality Analysis Using Machine Learning Algorithms

2020 ◽  
pp. 11-18
Author(s):  
Mahima ◽  
Ujjawal Gupta ◽  
Yatindra Patidar ◽  
Abhishek Agarwal ◽  
Kushall Pal Singh
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Quality Analysis ◽  
Wine Quality

New signal processing tools applied to power quality analysis

1999 ◽  
Vol 14 (2) ◽  
pp. 561-566 ◽  
Author(s):  
O. Poisson ◽  
P. Rioual ◽  
M. Meunier
Keyword(s):  
Signal Processing ◽  
Power Quality ◽  
Quality Analysis ◽  
Power Quality Analysis

scholarly journals Computerized Answer Grading

2021 ◽  
Vol 9 (VI) ◽  
pp. 618-619
Author(s):  
Anurag Langan
Keyword(s):  
Machine Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Computer Technology ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Grade Student ◽  
Processing Techniques

Grading student answers is a tedious and time-consuming task. A study had found that almost on average around 25% of a teacher's time is spent in scoring the answer sheets of students. This time could be utilized in much better ways if computer technology could be used to score answers. This system will aim to grade student answers using the various Natural Language processing techniques and Machine Learning algorithms available today.

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