The Use of Convolutional Neural Networks in Biomedical Data Processing

2017 ◽  
pp. 100-119 ◽  
Author(s):  
Miroslav Bursa ◽  
Lenka Lhotska
Keyword(s):  
Neural Networks ◽  
Data Processing ◽  
Convolutional Neural Networks ◽  
Biomedical Data

scholarly journals Model Architecture of CNN for Recognition the Pandava Mask

Jurnal INFORM ◽  
2020 ◽  
Vol 5 (2) ◽  
pp. 99
Author(s):  
Andi Sanjaya ◽  
Endang Setyati ◽  
Herman Budianto
Keyword(s):  
Neural Networks ◽  
Data Processing ◽  
Convolutional Neural Networks ◽  
Research Result ◽  
Trial Result ◽  
Trial Data ◽  
The Other ◽  
Architectural Model ◽  
Input Layer

This research was conducted to observe the use of architectural model Convolutional Neural Networks (CNN) LeNEt, which was suitable to use for Pandava mask objects. The Data processing in the research was 200 data for each class or similar with 1000 trial data. Architectural model CNN LeNET used input layer 32x32, 64x64, 128x128, 224x224 and 256x256. The trial result with the input layer 32x32 succeeded, showing a faster time compared to the other layer. The result of accuracy value and validation was not under fitted or overfit. However, when the activation of the second dense process as changed from the relu to sigmoid, the result was better in sigmoid, in the tem of time, and the possibility of overfitting was less. The research result had a mean accuracy value of 0.96.

scholarly journals An Economical Robotic Arm for Playing Chess Using Visual Servoing

2020 ◽  
Vol 2 (3) ◽  
pp. 141-146
Author(s):  
Dr. Ranganathan G.
Keyword(s):  
Neural Networks ◽  
Decision Making ◽  
Data Processing ◽  
Convolutional Neural Networks ◽  
Visual Servoing ◽  
Mechanical Design ◽  
Robotic Arm ◽  
Game Engine ◽  
Chess Game

The proposed paper outlines the design of an economical robotic arm which is used to visualize the chess board and play with the opponent using visual servoing system. We have used the FaBLab RUC's mechanical design prototype proposed and have further used Solidworks software to design the 4 jointed gripper. The proposed methodology involves detecting the squares on the corners of the chessboard and further segmenting the images. This is followed by using convolutional neural networks to train and recognize the image in order to determine the movement of the chess pieces. To trace the manipulator, Kanade-Lucas-Tomasi method is used in the visual servoing system. An Arduino uses Gcode commands to interact with the robotic arm. Game Decisions are taken with the help of chess game engine the pieces on the board are moved accordingly. Thus a didactic robotic arm is developed for decision making and data processing, serving to be a good opponent in playing chess.

CNN-based Classification of Degraded Images

2020 ◽  
Vol 2020 (10) ◽  
pp. 28-1-28-7 ◽  
Author(s):  
Kazuki Endo ◽  
Masayuki Tanaka ◽  
Masatoshi Okutomi
Keyword(s):  
Neural Networks ◽  
Image Restoration ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Deep Convolutional Neural Networks ◽  
Alternative Approach ◽  
Degraded Image ◽  
Degraded Images ◽  
Straightforward Approach

Classification of degraded images is very important in practice because images are usually degraded by compression, noise, blurring, etc. Nevertheless, most of the research in image classification only focuses on clean images without any degradation. Some papers have already proposed deep convolutional neural networks composed of an image restoration network and a classification network to classify degraded images. This paper proposes an alternative approach in which we use a degraded image and an additional degradation parameter for classification. The proposed classification network has two inputs which are the degraded image and the degradation parameter. The estimation network of degradation parameters is also incorporated if degradation parameters of degraded images are unknown. The experimental results showed that the proposed method outperforms a straightforward approach where the classification network is trained with degraded images only.

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