Convolutional Neural Network Algorithm based on Improved Support Vector Machine

: 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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A Convolutional Neural Network Algorithm Based on Rough Set Rule Extraction

2021 IEEE Asia-Pacific Conference on Image Processing, Electronics and Computers (IPEC) ◽

10.1109/ipec51340.2021.9421230 ◽

2021 ◽

Author(s):

Xuesi Ding

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Rough Set ◽

Rule Extraction ◽

Network Algorithm ◽

Neural Network Algorithm

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A visual terrain classification method for mobile robots’ navigation based on convolutional neural network and support vector machine

Transactions of the Institute of Measurement and Control ◽

10.1177/0142331220987917 ◽

2021 ◽

pp. 014233122098791

Author(s):

Wanli Wang ◽

Botao Zhang ◽

Kaiqi Wu ◽

Sergey A Chepinskiy ◽

Anton A Zhilenkov ◽

...

Keyword(s):

Neural Network ◽

Support Vector Machine ◽

Mobile Robots ◽

Convolutional Neural Network ◽

Hybrid Method ◽

Classification Accuracy ◽

Support Vector ◽

High Classification Accuracy ◽

Enhance Efficiency ◽

Multi Class Classification

In this paper, a hybrid method based on deep learning is proposed to visually classify terrains encountered by mobile robots. Considering the limited computing resource on mobile robots and the requirement for high classification accuracy, the proposed hybrid method combines a convolutional neural network with a support vector machine to keep a high classification accuracy while improve work efficiency. The key idea is that the convolutional neural network is used to finish a multi-class classification and simultaneously the support vector machine is used to make a two-class classification. The two-class classification performed by the support vector machine is aimed at one kind of terrain that users are mostly concerned with. Results of the two classifications will be consolidated to get the final classification result. The convolutional neural network used in this method is modified for the on-board usage of mobile robots. In order to enhance efficiency, the convolutional neural network has a simple architecture. The convolutional neural network and the support vector machine are trained and tested by using RGB images of six kinds of common terrains. Experimental results demonstrate that this method can help robots classify terrains accurately and efficiently. Therefore, the proposed method has a significant potential for being applied to the on-board usage of mobile robots.

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Research on Automated Defect Classification Based on Visual Sensing and Convolutional Neural Network-Support Vector Machine for GTA-Assisted Droplet Deposition Manufacturing Process

Metals ◽

10.3390/met11040639 ◽

2021 ◽

Vol 11 (4) ◽

pp. 639

Author(s):

Chen Ma ◽

Haifei Dang ◽

Jun Du ◽

Pengfei He ◽

Minbo Jiang ◽

...

Keyword(s):

Neural Network ◽

Support Vector Machine ◽

Convolutional Neural Network ◽

Manufacturing Process ◽

Support Vector ◽

Defect Classification ◽

Droplet Deposition ◽

Network Support ◽

Visual Sensing ◽

Low Efficiency

This paper proposes a novel metal additive manufacturing process, which is a composition of gas tungsten arc (GTA) and droplet deposition manufacturing (DDM). Due to complex physical metallurgical processes involved, such as droplet impact, spreading, surface pre-melting, etc., defects, including lack of fusion, overflow and discontinuity of deposited layers always occur. To assure the quality of GTA-assisted DDM-ed parts, online monitoring based on visual sensing has been implemented. The current study also focuses on automated defect classification to avoid low efficiency and bias of manual recognition by the way of convolutional neural network-support vector machine (CNN-SVM). The best accuracy of 98.9%, with an execution time of about 12 milliseconds to handle an image, proved our model can be enough to use in real-time feedback control of the process.

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