Comparison between Convolutional Neural Network and Convolutional Neural Network-Support Vector Machines as the classifier for Colon Cancer

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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Convolutional neural network - Support vector machine based approach for classification of cyanobacteria and chlorophyta microalgae groups

Algal Research ◽

10.1016/j.algal.2021.102568 ◽

2021 ◽

pp. 102568

Author(s):

Mesut Ersin Sonmez ◽

Numan Eczacıoglu ◽

Numan Emre Gumuş ◽

Muhammet Fatih Aslan ◽

Kadir Sabanci ◽

...

Keyword(s):

Neural Network ◽

Support Vector Machine ◽

Convolutional Neural Network ◽

Support Vector ◽

Network Support

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Design and implementation of an intelligent vision and sorting system

10.51415/10321/494 ◽

2009 ◽

Author(s):

◽

Zhi Li

Keyword(s):

Neural Network ◽

Fuzzy Logic ◽

Support Vector Machines ◽

Robot Control ◽

Support Vector ◽

Design And Implementation ◽

The Neural Network ◽

Vector Machines ◽

Control Computer ◽

Sorting System

This research focuses on the design and implementation of an intelligent machine vision and sorting system that can be used to sort objects in an industrial environment. Machine vision systems used for sorting are either geometry driven or are based on the textural components of an object’s image. The vision system proposed in this research is based on the textural analysis of pixel content and uses an artificial neural network to perform the recognition task. The neural network has been chosen over other methods such as fuzzy logic and support vector machines because of its relative simplicity. A Bluetooth communication link facilitates the communication between the main computer housing the intelligent recognition system and the remote robot control computer located in a plant environment. Digital images of the workpiece are first compressed before the feature vectors are extracted using principal component analysis. The compressed data containing the feature vectors is transmitted via the Bluetooth channel to the remote control computer for recognition by the neural network. The network performs the recognition function and transmits a control signal to the robot control computer which guides the robot arm to place the object in an allocated position. The performance of the proposed intelligent vision and sorting system is tested under different conditions and the most attractive aspect of the design is its simplicity. The ability of the system to remain relatively immune to noise, its capacity to generalize and its fault tolerance when faced with missing data made the neural network an attractive option over fuzzy logic and support vector machines.

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