Wavelet Based Machine Learning Technique to Classify the Different Shoulder Movement of Upper Limb Amputee

2017 ◽  
Vol 31 ◽  
pp. 32-43 ◽  
Author(s):  
Amanpreet Kaur ◽  
Amod Kumar ◽  
Ravinder Agarwal
Keyword(s):  
Machine Learning ◽  
Wavelet Transform ◽  
Random Forest ◽  
Upper Limb ◽  
Learning Algorithm ◽  
Discrete Wavelet ◽  
Semg Signal ◽  
Shoulder Movement ◽  
Fundamental Information

The wavelet transform is an accurate, efficient and efficacious method to improve the quality of the myoelectric signal. Classification of the signal from upper limb using Surface Electromyogram (SEMG) signal has been the matter of extensive research. Number of methods and algorithms have been described by researchers to classify biomedical signals. The main aim of this paper to extract the different coefficient values from the given SEMG data by using Discrete Wavelet Transform (DWT). Afterward, random forest machine learning algorithm was used to identify the different shoulder movement of an upper limb amputee. The combination of wavelet coefficients and random forest exhibited the best performance with 99.2% accuracy for the classification of different shoulder motions. It was found that the different motion can be identified accurately and provide the fundamental information to develop an efficient prosthetic device.

scholarly journals Classification of Diabetes using Random Forest with Feature Selection Algorithm

2019 ◽  
Vol 9 (1) ◽  
pp. 1295-1300 ◽  
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Random Forest ◽  
Electronic Health Records ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Selection Algorithm ◽  
Feature Selection Algorithm ◽  
Health Records

Diabetes has become a serious problem now a day. So there is a need to take serious precautions to eradicate this. To eradicate, we should know the level of occurrence. In this project we predict the level of occurrence of diabetes. We predict the level of occurrence of diabetes using Random Forest, a Machine Learning Algorithm. Using the patient’s Electronic Health Records (EHR) we can build accurate models that predict the presence of diabetes.

scholarly journals A Study of the Classification of Motor Imagery Signals using Machine Learning Tools

2021 ◽  
Author(s):  
Anam Hashmi ◽  
Bilal Alam Khan ◽  
Omar Farooq
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Wavelet Transform ◽  
Random Forest ◽  
Random Forest Algorithm ◽  
Eeg Signals ◽  
Relaxation State ◽  
Wavelet Transform Analysis ◽  
Imagined Movement

In this paper, we propose a system for the purpose of classifying Electroencephalography (EEG) signals associated with imagined movement of right hand and relaxation state using machine learning algorithm namely Random Forest Algorithm. The EEG dataset used in this research was created by the University of Tubingen, Germany. EEG signals associated with the imagined movement of right hand and relaxation state were processed using wavelet transform analysis with Daubechies orthogonal wavelet as the mother wavelet. After the wavelet transform analysis, eight features were extracted. Subsequently, a feature selection method based on Random Forest Algorithm was employed giving us the best features out of the eight proposed features. The feature selection stage was followed by classification stage in which eight different models combining the different features based on their importance were constructed. The optimum classification performance of 85.41% was achieved with the Random Forest classifier. This research shows that this system of classification of motor movements can be used in a Brain Computer Interface system (BCI) to mentally control a robotic device or an exoskeleton.

scholarly journals Classification of Phishing Email Using Random Forest Machine Learning Technique

2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
Andronicus A. Akinyelu ◽  
Aderemi O. Adewumi
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Learning Algorithm ◽  
False Negative ◽  
Machine Learning Algorithm ◽  
Detection Techniques ◽  
Phishing Attacks ◽  
Learning Technique ◽  
Phishing Detection

Phishing is one of the major challenges faced by the world of e-commerce today. Thanks to phishing attacks, billions of dollars have been lost by many companies and individuals. In 2012, an online report put the loss due to phishing attack at about $1.5 billion. This global impact of phishing attacks will continue to be on the increase and thus requires more efficient phishing detection techniques to curb the menace. This paper investigates and reports the use of random forest machine learning algorithm in classification of phishing attacks, with the major objective of developing an improved phishing email classifier with better prediction accuracy and fewer numbers of features. From a dataset consisting of 2000 phishing and ham emails, a set of prominent phishing email features (identified from the literature) were extracted and used by the machine learning algorithm with a resulting classification accuracy of 99.7% and low false negative (FN) and false positive (FP) rates.

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 Attribute-oriented Classification with Variable Importance using Random Forest Model

2019 ◽  
Vol 8 (2S3) ◽  
pp. 1630-1635
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Learning Algorithm ◽  
Large Data ◽  
Variable Importance ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Importance Measure ◽  
Variable Importance Measure

In the present century, various classification issues are raised with large data and most commonly used machine learning algorithms are failed in the classification process to get accurate results. Datamining techniques like ensemble, which is made up of individual classifiers for the classification process and to generate the new data as well. Random forest is one of the ensemble supervised machine learning technique and essentially used in numerous machine learning applications such as the classification of text and image data. It is popular since it collects more relevant features such as variable importance measure, Out-of-bag error etc. For the viable learning and classification of random forest, it is required to reduce the number of decision trees (Pruning) in the random forest. In this paper, we have presented systematic overview of random forest algorithm along with its application areas. In addition, we presented a brief review of machine learning algorithm proposed in the recent years. Animal classification is considered as an important problem and most of the recent studies are classifying the animals by taking the image dataset. But, very less work has been done on attribute-oriented animal classification and poses many challenges in the process of extracting the accurate features. We have taken a real-time dataset from the Kaggle to classify the animal by collecting the more relevant features with the help of variable importance measure metric and compared with the other popular machine learning models.

scholarly journals Application of Random Forest and Hidden Markov Models for Automatic and Fast Classification of Power Quality Signals

2020 ◽  
Vol 9 (4) ◽  
pp. 2856-2862
Keyword(s):  
Wavelet Transform ◽  
Random Forest ◽  
Wavelet Transforms ◽  
Markov Models ◽  
Hidden Markov ◽  
Recognition Rate ◽  
Discrete Wavelet ◽  
Second Generation Wavelet Transform ◽  
Quality Signals

In this paper, wavelet transform, namely the maximal overlap discrete Wavelet Transform (MODWT) and the second generation Wavelet Transform (SGWT) have been implemented. These wavelet transforms are applied to get selected features of the signals. Features are used as inputs to two types of classifiers namely, Hidden Markov Model (HMM) classifiers and the Random Forest (RF) classifier in the both absence and presence of Noise to evaluate the efficiency. The classification accuracy (CA) calculated using these classifiers clearly shows that the RF classifiers is a better classifier then the HMM classifier as it possess higher recognition rate at all levels of noise along with the pure PQ signals. Another important property of RF classifier is the proper classification of large number of class of both slow and the fast disturbances.

scholarly journals Bearing Fault Classification of Induction Motors Using Discrete Wavelet Transform and Ensemble Machine Learning Algorithms

2020 ◽  
Vol 10 (15) ◽  
pp. 5251 ◽  
Author(s):  
Rafia Nishat Toma ◽  
Jong-Myon Kim
Keyword(s):  
Machine Learning ◽  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Induction Motors ◽  
Fault Classification ◽  
Discrete Wavelet ◽  
Current Signal ◽  
Bearing Fault ◽  
Ensemble Machine Learning

Bearing fault diagnosis at early stage is very significant to ensure seamless operation of induction motors in industrial environment. The identification and classification of faults helps to undertook maintenance operation in an efficient manner. This paper presents an ensemble machine learning-based fault classification scheme for induction motors (IMs) utilizing the motor current signal that uses the discrete wavelet transform (DWT) for feature extraction. Three wavelets (db4, sym4, and Haar) are used to decompose the current signal, and several features are extracted from the decomposed coefficients. In the pre-processing stage, notch filtering is used to remove the line frequency component to improve classification performance. Finally, the two ensemble machine learning (ML) classifiers random forest (RF) and extreme gradient boosting (XGBoost) are trained and tested using the extracted feature set to classify the bearing fault condition. Both classifier models demonstrate very promising results in terms of accuracy and other accepted performance indicators. Our proposed method achieves an accuracy slightly greater than 99%, which is better than other models examined for the same dataset.

Sign in / Sign up

Export Citation Format

Share Document