scholarly journals Real Time Monitoring System of Pollution Waste on Musi River Using Support Vector Machine (SVM) Method

2016 ◽  
Vol 3 (1) ◽  
Author(s):  
Muhammad Fachrurrozi ◽  
Saparudin Saparudin ◽  
Erwin Erwin
Keyword(s):  
Support Vector Machine ◽  
Real Time ◽  
Monitoring System ◽  
Support Vector ◽  
Real Time Monitoring ◽  
Musi River

INVESTIGATION OF HAND KINEMATICS TOWARDS FRONT CRAWL SWIMMING USING NON-INVASIVE REAL-TIME MONITORING SYSTEM

2014 ◽  
Author(s):  
Rozaimi Ghazali ◽  
◽  
Asiah Mohd Pilus ◽  
Wan Mohd Bukhari Wan Daud ◽  
Mohd Juzaila Abd Latif ◽  
...  
Keyword(s):  
Real Time ◽  
Monitoring System ◽  
Real Time Monitoring ◽  
Front Crawl ◽  
Non Invasive ◽  
Hand Kinematics

Study on Slope Slide Real-Time Monitoring System in Guangdong Using GPS/CORS Integration Technique

2011 ◽  
Vol 13 (4) ◽  
pp. 562-570 ◽  
Author(s):  
Pingxin WEI ◽  
Chenggang LI ◽  
Feng XU ◽  
Xiaochun SHI
Keyword(s):  
Real Time ◽  
Monitoring System ◽  
Integration Technique ◽  
Real Time Monitoring

Selection and Evaluation of a Real Time Monitoring System for the Bigeye Bomb Fill/Close Production Facility. Phase 2

1989 ◽  
Author(s):  
Foy E. Ferguson ◽  
Michael W. Ellzy ◽  
Joseph W. Lovrich ◽  
Theodore L. Strozyk ◽  
Jr
Keyword(s):  
Real Time ◽  
Monitoring System ◽  
Phase 2 ◽  
Production Facility ◽  
Real Time Monitoring

Single Channel EEG signal for Automatic Detection of Absence Seizure using Convolutional Neural Network

2019 ◽  
Vol 13 ◽  
Author(s):  
Niha Kamal Basha ◽  
Aisha Banu Wahab
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Convolutional Neural Network ◽  
Monitoring System ◽  
Single Channel ◽  
Confusion Matrix ◽  
Sudden Change ◽  
Automatic Detection ◽  
Support Vector ◽  
Absence Seizure

: Absence seizure is a type of brain disorder in which subject get into sudden lapses in attention. Which means sudden change in brain stimulation. Most of this type of disorder is widely found in children’s (5-18 years). These Electroencephalogram (EEG) signals are captured with long term monitoring system and are analyzed individually. In this paper, a Convolutional Neural Network to extract single channel EEG seizure features like Power, log sum of wavelet transform, cross correlation, and mean phase variance of each frame in a windows are extracted after pre-processing and classify them into normal or absence seizure class, is proposed as an empowerment of monitoring system by automatic detection of absence seizure. The training data is collected from the normal and absence seizure subjects in the form of Electroencephalogram. The objective is to perform automatic detection of absence seizure using single channel electroencephalogram signal as input. Here the data is used to train the proposed Convolutional Neural Network to extract and classify absence seizure. The Convolutional Neural Network consist of three layers 1] convolutional layer – which extract the features in the form of vector 2] Pooling layer – the dimensionality of output from convolutional layer is reduced and 3] Fully connected layer–the activation function called soft-max is used to find the probability distribution of output class. This paper goes through the automatic detection of absence seizure in detail and provide the comparative analysis of classification between Support Vector Machine and Convolutional Neural Network. The proposed approach outperforms the performance of Support Vector Machine by 80% in automatic detection of absence seizure and validated using confusion matrix.

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