Adaptive convolutional neural network using N-gram for spatial object recognition

2019 ◽  
Vol 12 (4) ◽  
pp. 525-540 ◽  
Author(s):  
J. Joshua Bapu ◽  
D. Jemi Florinabel ◽  
Y. Harold Robinson ◽  
E. Golden Julie ◽  
Raghvendra Kumar ◽  
...  
Keyword(s):  
Neural Network ◽  
Object Recognition ◽  
Convolutional Neural Network ◽  
Spatial Object ◽  
N Gram ◽  
Adaptive Convolutional Neural Network

scholarly journals Byte-Pair and N-Gram Convolutional Methods of Analysing Automatically Disseminated Content on Social Platforms

2020 ◽  
Author(s):  
Houjun Liu
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Text Processing ◽  
User Data ◽  
N Gram ◽  
Linguistic Content

In this experiment, an efficient and accurate network of detecting automatically disseminated (bot) content on social platforms is devised. Through the utilisation of parallel convolutional neural network (CNN) which processes variable n-grams of text 15, 20, and 25 tokens in length encoded by Byte Pair Encoding (BPE), the complexities of linguistic content on social platforms are effectively captured and analysed. With validation on two sets of previously unexposed data, the model was able to achieve an accuracy of around 96.6% and 97.4% respectively — meeting or exceeding the performance of other comparable supervised ML solutions to this problem. Through testing, it is concluded that this method of text processing and analysis proves to be an effective way of classifying potentially artificially synthesized user data — aiding the security and integrity of social platforms.

scholarly journals Stereo Vision-Based Object Recognition and Manipulation by Regions with Convolutional Neural Network

Electronics ◽  
2020 ◽  
Vol 9 (2) ◽  
pp. 210 ◽  
Author(s):  
Yi-Chun Du ◽  
Muslikhin Muslikhin ◽  
Tsung-Han Hsieh ◽  
Ming-Shyan Wang
Keyword(s):  
Neural Network ◽  
Object Recognition ◽  
Convolutional Neural Network ◽  
Stereo Vision ◽  
Fuzzy Inference ◽  
Target Object ◽  
Robot Arm ◽  
Segmentation Method ◽  
Inference System ◽  
Six Degree Of Freedom

This paper develops a hybrid algorithm of adaptive network-based fuzzy inference system (ANFIS) and regions with convolutional neural network (R-CNN) for stereo vision-based object recognition and manipulation. The stereo camera at an eye-to-hand configuration firstly captures the image of the target object. Then, the shape, features, and centroid of the object are estimated. Similar pixels are segmented by the image segmentation method, and similar regions are merged through selective search. The eye-to-hand calibration is based on ANFIS to reduce computing burden. A six-degree-of-freedom (6-DOF) robot arm with a gripper will conduct experiments to demonstrate the effectiveness of the proposed system.

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