scholarly journals Genre of Bangla Music: A Machine Classification Learning Approach

2019 ◽  
Vol 18 (2) ◽  
pp. 66-72
Author(s):  
Abhijit Bhowmik ◽  
AZM Ehtesham Chowdhury
Keyword(s):  
Classification Systems ◽  
Learning Approach ◽  
Large Set ◽  
Automated Classification ◽  
Classification Learning ◽  
Genre Classification ◽  
Machine Learning Approach ◽  
Music Databases ◽  
East Asian Language

The necessity for designing autonomous indexing tools to establish expressive and efficient means of describing musical media content is well recognized. Music genre classification systems are significant to manage and use music databases. This research paper proposes an enhanced method to automatically classify music into different genre using a machine learning approach and presents the insight and results of the application of the proposed scheme to the classification of a large set of The Bangla music content, a South-East Asian language rich with a variety of music genres developed over many centuries. Building upon musical feature extraction and decision-making techniques, we propose new features and procedures to achieve enhanced accuracy. We demonstrate the efficacy of the proposed method by extracting features from a dataset of hundreds of The Bangla music pieces and testing the automatic classification decisions. This is the first development of an automated classification technique applied specifically to the Bangla music to the best of our knowledge, while the superior accuracy of the method makes it universally applicable.

Automated Classification of Postural Control for Individuals With Parkinson’s Disease Using a Machine Learning Approach: A Preliminary Study

2020 ◽  
Vol 36 (5) ◽  
pp. 334-339
Author(s):  
Yumeng Li ◽  
Shuqi Zhang ◽  
Christina Odeh
Keyword(s):  
Machine Learning ◽  
Postural Control ◽  
Postural Sway ◽  
Center Of Pressure ◽  
Machine Learning Algorithms ◽  
Learning Approach ◽  
Automated Classification ◽  
Machine Learning Approach ◽  
Control Patterns

The purposes of the study were (1) to compare postural sway between participants with Parkinson’s disease (PD) and healthy controls and (2) to develop and validate an automated classification of PD postural control patterns using a machine learning approach. A total of 9 participants in the early stage of PD and 12 healthy controls were recruited. Participants were instructed to stand on a force plate and maintain stillness for 2 minutes with eyes open and eyes closed. The center of pressure data were collected at 50 Hz. Linear displacements, standard deviations, total distances, sway areas, and multiscale entropy of center of pressure were calculated and compared using mixed-model analysis of variance. Five supervised machine learning algorithms (ie, logistic regression, K-nearest neighbors, Naïve Bayes, decision trees, and random forest) were used to classify PD postural control patterns. Participants with PD exhibited greater center of pressure sway and variability compared with controls. The K-nearest neighbor method exhibited the best prediction performance with an accuracy rate of up to 0.86. In conclusion, participants with PD exhibited impaired postural stability and their postural sway features could be identified by machine learning algorithms.

scholarly journals A Machine Learning Approach to Study Glycosidase Activities from Bifidobacterium

2021 ◽  
Vol 9 (5) ◽  
pp. 1034
Author(s):  
Carlos Sabater ◽  
Lorena Ruiz ◽  
Abelardo Margolles
Keyword(s):  
Machine Learning ◽  
Supervised Classification ◽  
Machine Learning Algorithms ◽  
Learning Approach ◽  
Human Milk Oligosaccharides ◽  
Future Studies ◽  
High Fiber ◽  
Machine Learning Approach ◽  
Prebiotic Oligosaccharides

This study aimed to recover metagenome-assembled genomes (MAGs) from human fecal samples to characterize the glycosidase profiles of Bifidobacterium species exposed to different prebiotic oligosaccharides (galacto-oligosaccharides, fructo-oligosaccharides and human milk oligosaccharides, HMOs) as well as high-fiber diets. A total of 1806 MAGs were recovered from 487 infant and adult metagenomes. Unsupervised and supervised classification of glycosidases codified in MAGs using machine-learning algorithms allowed establishing characteristic hydrolytic profiles for B. adolescentis, B. bifidum, B. breve, B. longum and B. pseudocatenulatum, yielding classification rates above 90%. Glycosidase families GH5 44, GH32, and GH110 were characteristic of B. bifidum. The presence or absence of GH1, GH2, GH5 and GH20 was characteristic of B. adolescentis, B. breve and B. pseudocatenulatum, while families GH1 and GH30 were relevant in MAGs from B. longum. These characteristic profiles allowed discriminating bifidobacteria regardless of prebiotic exposure. Correlation analysis of glycosidase activities suggests strong associations between glycosidase families comprising HMOs-degrading enzymes, which are often found in MAGs from the same species. Mathematical models here proposed may contribute to a better understanding of the carbohydrate metabolism of some common bifidobacteria species and could be extrapolated to other microorganisms of interest in future studies.

scholarly journals A machine-learning approach for automatic classification of volcanic seismicity at La Soufrière Volcano, Guadeloupe

2020 ◽  
pp. 107151
Author(s):  
Alexis Falcin ◽  
Jean-Philippe Métaxian ◽  
Jérôme Mars ◽  
Éléonore Stutzmann ◽  
Jean-Christophe Komorowski ◽  
...  
Keyword(s):  
Machine Learning ◽  
Automatic Classification ◽  
Learning Approach ◽  
Machine Learning Approach ◽  
Volcanic Seismicity

The Classification of Skateboarding Tricks : A Transfer Learning and Machine Learning Approach

Mekatronika ◽  
2020 ◽  
Vol 2 (2) ◽  
pp. 1-12
Author(s):  
Muhammad Nur Aiman Shapiee ◽  
Muhammad Ar Rahim Ibrahim ◽  
Muhammad Amirul Abdullah ◽  
Rabiu Muazu Musa ◽  
Noor Azuan Abu Osman ◽  
...  
Keyword(s):  
Machine Learning ◽  
Classification Accuracy ◽  
Nearest Neighbor ◽  
Olympic Games ◽  
Learning Approach ◽  
K Nearest Neighbor ◽  
Test Dataset ◽  
Machine Learning Approach ◽  
Competitive Games

The skateboarding scene has arrived at new statures, particularly with its first appearance at the now delayed Tokyo Summer Olympic Games. Hence, attributable to the size of the game in such competitive games, progressed creative appraisal approaches have progressively increased due consideration by pertinent partners, particularly with the enthusiasm of a more goal-based assessment. This study purposes for classifying skateboarding tricks, specifically Frontside 180, Kickflip, Ollie, Nollie Front Shove-it, and Pop Shove-it over the integration of image processing, Trasnfer Learning (TL) to feature extraction enhanced with tradisional Machine Learning (ML) classifier. A male skateboarder performed five tricks every sort of trick consistently and the YI Action camera captured the movement by a range of 1.26 m. Then, the image dataset were features built and extricated by means of  three TL models, and afterward in this manner arranged to utilize by k-Nearest Neighbor (k-NN) classifier. The perception via the initial experiments showed, the MobileNet, NASNetMobile, and NASNetLarge coupled with optimized k-NN classifiers attain a classification accuracy (CA) of 95%, 92% and 90%, respectively on the test dataset. Besides, the result evident from the robustness evaluation showed the MobileNet+k-NN pipeline is more robust as it could provide a decent average CA than other pipelines. It would be demonstrated that the suggested study could characterize the skateboard tricks sufficiently and could, over the long haul, uphold judges decided for giving progressively objective-based decision.

scholarly journals A machine learning approach for identification and classification of symbiotic stars using 2MASS and WISE

2018 ◽  
Vol 483 (4) ◽  
pp. 5077-5104 ◽  
Author(s):  
Stavros Akras ◽  
Marcelo L Leal-Ferreira ◽  
Lizette Guzman-Ramirez ◽  
Gerardo Ramos-Larios
Keyword(s):  
Machine Learning ◽  
Learning Approach ◽  
Symbiotic Stars ◽  
Machine Learning Approach
Sign in / Sign up

Export Citation Format

Share Document