Classification of EEG Features Extracted from Classroom Experiment using Weighted K-Nearest Neighbors

Author(s):  
Areej Babiker ◽  
Eltaf Abdalsalam
Author(s):  
Sainesh Karan ◽  
Emily N Meese ◽  
Yu Yang ◽  
Hen-Geul Yeh ◽  
Christopher G Lowe ◽  
...  

Author(s):  
Ibrahim M Ali ◽  
Calvin J Lee ◽  
Hen-Geul Yeh ◽  
Sainesh Karan ◽  
Yu Yang ◽  
...  

2017 ◽  
Author(s):  
Bing Qiao ◽  
Chiyuan Li ◽  
Victoria W. Allen ◽  
Mimi M. Shirasu-Hiza ◽  
Sheyum Syed

AbstractDespite being pervasive, the control of programmed grooming is poorly understood. We have addressed this gap in knowledge by developing a high-throughput platform that allows long-term detection of grooming in the fruit fly Drosophila melanogaster. Automatic classification of daily behavior shows flies spend 30% of their active time grooming. We show that a large proportion of this behavior is driven by two major internal programs. One of these programs is the circadian clock that modulates rhythms in daily grooming. The second program depends on cycle and clock and regulates the amount of time flies spend grooming. This emerging dual control model of programmed grooming in which one regulator controls the timing and another controls the duration, resembles the well-established two-process regulatory model of fly sleep. Together, our quantitative approach in Drosophila has revealed that grooming is an important internally driven behavior under the control of two regulatory programs.


Data mining is currently being used in various applications; In research community it plays a vital role. This paper specify about data mining techniques for the preprocessing and classification of various disease in plants. Since various plants has different diseases based on that each of them has different data sets and different objectives for knowledge discovery. Data Mining Techniques applied on plants that it helps in segmentation and classification of diseased plants, it avoids Oral Inspection and helps to increase in crop productivity. This paper provides various classification techniques Such as K-Nearest Neighbors, Support Vector Machine, Principle component Analysis, Neural Network. Thus among various techniques neural network is effective for disease detection in plants.


Revista CERES ◽  
2021 ◽  
Vol 68 (5) ◽  
pp. 420-428
Author(s):  
Marciléia Santos Souza ◽  
Fábio Medeiros Ferreira ◽  
Rodrigo Barros Rocha ◽  
Maria Teresa Gomes Lopes ◽  
Leilane Nicolino Lamarão Oliveira

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