ekspresi Mengidentifikasi Mood Mahasiswa Berdasarkan Ekspresi Wajah dengan Menggunakan Discrete Wavelet Transform dan Fuzzy K-Nearest Neighbor

2019 ◽  
Vol 11 (1) ◽  
pp. 34-38
Author(s):  
Nur Inzani Reski Amalia ◽  
Jayanti Yusmah Sari
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Nearest Neighbor ◽  
Discrete Wavelet ◽  
K Nearest Neighbor

Mood adalah keadaan emosional yang bersifat sementara. Mood biasanya memiliki nilai kualitas positif atau negatif. Kecerdasan emosional memiliki peran lebih dari 80% dalam mencapai kesuksesan hidup dan menjadi salah satu faktor yang mempengaruhi daya tangkap mahasiswa dalam proses perkuliahan. Dengan mengetahui emosi-emosi mahasiswa, kita dapat membantu daya tangkap mahasiswa saat proses perkuliahan, serta dibutuhkannya sistem yang dapat mengidentifikasi emosi-emosi yang terbentuk saat perkuliahan berlangsung. Sistem ini dibangun menggunakan  Discrete Wavelet Transform yang mentransformasikan citra menjadi 4 sub-image. Citra hasil Discrete Wavelet Transform tampak kasar atau membentuk wajah yang dapat membedakan ekspresi mahasiswa. Hasil pengolahan citra Discrete Wavelet Transform di klasifikasikan dengan menggunakan Fuzzy K-nearest neighbor. Pengklasifikasian dibagi kedalam tiga ekspresi yaitu : Marah, Senang dan Sedih dengan akurasi 77,49%

scholarly journals EEG Motor-Imagery BCI System Based on Maximum Overlap Discrete Wavelet Transform (MODWT) and Machine learning algorithm

2021 ◽  
Vol 17 (2) ◽  
pp. 38-45
Author(s):  
Samaa Abdulwahab ◽  
Hussain Khleaf ◽  
Manal Jassim
Keyword(s):  
Wavelet Transform ◽  
Decision Tree ◽  
Discrete Wavelet Transform ◽  
Nearest Neighbor ◽  
Learning Algorithm ◽  
Low Cost ◽  
Brain Diseases ◽  
Discrete Wavelet ◽  
K Nearest Neighbor ◽  
Brain Connection

The ability of the human brain to communicate with its environment has become a reality through the use of a Brain-Computer Interface (BCI)-based mechanism. Electroencephalography (EEG) has gained popularity as a non-invasive way of brain connection. Traditionally, the devices were used in clinical settings to detect various brain diseases. However, as technology advances, companies such as Emotiv and NeuroSky are developing low-cost, easily portable EEG-based consumer-grade devices that can be used in various application domains such as gaming, education. This article discusses the parts in which the EEG has been applied and how it has proven beneficial for those with severe motor disorders, rehabilitation, and as a form of communicating with the outside world. This article examines the use of the SVM, k-NN, and decision tree algorithms to classify EEG signals. To minimize the complexity of the data, maximum overlap discrete wavelet transform (MODWT) is used to extract EEG features. The mean inside each window sample is calculated using the Sliding Window Technique. The vector machine (SVM), k-Nearest Neighbor, and optimize decision tree load the feature vectors.

scholarly journals EMG Signal Based Pattern Recognition Of Grasping Movement Using MODWT And Weighted K- Nearest Neighbor

2019 ◽  
Vol 8 (10) ◽  
pp. 1759-1764
Keyword(s):  
Pattern Recognition ◽  
Wavelet Transform ◽  
Nearest Neighbor ◽  
Discrete Wavelet ◽  
K Nearest Neighbor ◽  
Signal Processing Technique ◽  
Semg Signal ◽  
Emg Signal ◽  
Semg Signals

This study purposed and evaluates a method based on weighted K-NN classification of surface Electromyogram (sEMG) signals. The sEMG signal classification plays the key role in designing a prosthetic for amputee persons. Wavelet transform is new signal processing technique, which provides better resolution in time and frequency domain simultaneously. Due to these wavelet properties, it can be effectively used in processing the sEMG signal to determine certain amplitude changes at certain frequencies. This paper propose a Maximal overlap Discrete Wavelet Transform (MODWT) approach for Weighted K-NN classifier for classification of sEMG signals based Grasping movements. At level 5 signal decomposition using MODWT, useful resolution component of the sEMG signal is obtained. In this paper Time-domain (TD) features set is used, which shows a decent performance. In WKNN, use a square-inverse weighted technique to improve the performance of the K-NN. Hence, a novel feature set obtained from decomposed signal using MODWT is used to improve the performance of sEMG for classification. MODWT was used for de-noising and time scale feature extraction of sEMG signals. Several WKNN classifiers are tested to optimize classification accuracy and computational problems. PCA is use to reduce the size of the level 5 decomposed data. WKNN performance evaluation on K=10 values with or without PCA. Six hand grasping movements have been classified, results indicate that this method allows the classification of hand pattern recognition with high precision.

scholarly journals BEAT CLASSIFICATION USING HYBRID WAVELET TRANSFORM BASED FEATURES AND SUPERVISED LEARNING APPROACH

2017 ◽  
Vol 13 (8) ◽  
pp. 6397-6405
Author(s):  
M. Sasireka ◽  
A. Senthilkumar
Keyword(s):  
Wavelet Transform ◽  
Nearest Neighbor ◽  
Discrete Wavelet ◽  
K Nearest Neighbor ◽  
Complex Wavelet Transform ◽  
Threshold Method ◽  
Hybrid Features ◽  
Qrs Complex Detection ◽  
Beat Classification ◽  
Learning Machine

This paper describes an automatic heartbeat recognition based on QRS detection, feature extraction and classification. In this paper five different type of ECG beats of MIT BIH arrhythmia database are automatically classified. The proposed method involves QRS complex detection based on the differences and approximation derivation, inversion and threshold method. The computation of combined Discrete Wavelet Transform (DWT) and Dual Tree Complex Wavelet Transform (DTCWT) of hybrid features coefficients are obtained from the QRS segmented beat from ECG signal which are then used as a feature vector. Then the feature vectors are given to Extreme Learning Machine (ELM) and k- Nearest Neighbor (kNN) classifier for automatic classification of heartbeat. The performance of the proposed system is measured by sensitivity, specificity and accuracy measures.

Improving Space Localization Properties of the Discrete Wavelet Transform

Informatica ◽  
2013 ◽  
Vol 24 (4) ◽  
pp. 657-675
Author(s):  
Jonas Valantinas ◽  
Deividas Kančelkis ◽  
Rokas Valantinas ◽  
Gintarė Viščiūtė
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Discrete Wavelet ◽  
Space Localization
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