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Pandian, A.
- Controlling Artificial Limb Movement System using EEG Signals
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Authors
Affiliations
1 Department of Computer Science and Engineering, SRM University, Kattankulathur, Chennai - 603203, Tamil Nadu, IN
1 Department of Computer Science and Engineering, SRM University, Kattankulathur, Chennai - 603203, Tamil Nadu, IN
Source
Indian Journal of Science and Technology, Vol 9, No 47 (2016), Pagination:Abstract
Objectives: We mainly focus the application of machine learning for artificial limb movement system using Electroencephalogram (EEG) signals. Analysis: EEG signals depict the neuronal activity happening in brain, which will be used to control the artificial limb movement system. Findings: In this paper, four classes of EEG signals were recorded from healthy subjects while performing actions such as finger open (fopen), finger close (fclose), wrist clockwise (wcw) and wrist counterclockwise (wccw) movements. The main objective of this study is to extract the statistical features from EEG signals and identify the best possible features and classify them using J48 Decision Tree algorithm. Improvements: The EEG signals are complex in nature and machine-learning approach was used to study the same. To improve the classification accuracy better feature extraction techniques might be used.Keywords
Electroencephalogram (EEG) Signals, J48 Algorithm, Statistical Features.Full Text
- Authorship Identification for Tamil Classical Poem (Mukkoodar Pallu) using C4.5 Algorithm
Abstract Views :176 |
PDF Views:0
Authors
Affiliations
1 Department of Computer Science and Engineering, SRM University, Kattankulathur, Chennai - 603203, Tamil Nadu, IN
1 Department of Computer Science and Engineering, SRM University, Kattankulathur, Chennai - 603203, Tamil Nadu, IN
Source
Indian Journal of Science and Technology, Vol 9, No 47 (2016), Pagination:Abstract
Objectives: To training classifier based on the features extracted from the poems of Mukkoodar Pallu, authors for various unknown poems can be classified. Methods/Analysis: The classification accuracy by performing classification in the dataset using C4.5 algorithm is illustrated in this paper. Findings: The results of performing classification on dataset that consists of features extracted from the dataset are shown in this paper. Features like number of characters, number of sentences and the classification accuracy when C4.5 algorithm is used is illustrated. Novelty/Improvement: By doing this, authors of various other poems in Tamil language can be identified which will be helpful to the society. Also a generalized authorship identification tool for all regional languages can be achieved.Keywords
Authorship, Classification, Feature Selection, Tamil Articles.Full Text