scholarly journals Grape disease detection using dual channel Convolution Neural Network method

SinkrOn ◽  
2021 ◽  
Vol 5 (2) ◽  
pp. 314-324
Author(s):  
Mawaddah Harahap ◽  
Valencia Angelina ◽  
Fenny Juliani ◽  
Celvin Celvin ◽  
Oscar Evander
Keyword(s):  
Neural Network ◽  
Gabor Filter ◽  
Grape Juice ◽  
Input Image ◽  
Filter Method ◽  
Neural Network Method ◽  
Dual Channel ◽  
Disease Identification ◽  
Grape Seed Oil ◽  
Network Method

Grapes are one type of fruit that is usually used to make grape juice, jelly, grapes, grape seed oil and raisins, or to be eaten directly. So far, checking for disease in grapes is still done manually, by checking the leaves of the grapes by experts. This method certainly takes a long time considering the extent of the vineyards that must be evaluated. To solve this problem, it is necessary to apply a method of detecting grape disease, so that it can help the common people to detect grape disease. This research will use the Dual-Channel Convolutional Neural Network method. The process of detecting grape disease using the DCCNN method will begin with the extraction of the leaves from the input image using the Gabor Filter method. After that, the Segmentation Based Fractal Co-Occurrence Texture Analysis method will be used to extract the features, color, and texture of the extracted leaves. The result is the number of datasets will affect the accuracy of the results of disease identification using the DCCNN method. However, more datasets will cause the execution process to take longer. Changes in the angle and frequency values in the Gabor method at the time of testing will reduce the accuracy of the test results. The conclusion of this study are the DCCNN method can be used to detect the type of leaf disease in grapes and the number of datasets will affect the accuracy of the results of disease identification using the DCCNN method.

scholarly journals Klasifikasi Usia Berdasarkan Citra Wajah Menggunakan Algoritma Artificial Neural Network dan Gabor Filter

2020 ◽  
Vol 2 (2) ◽  
pp. 60-67
Author(s):  
Sudirman Melangi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Gabor Filter ◽  
Confusion Matrix ◽  
Filter Method ◽  
Neural Network Method ◽  
Face Images ◽  
Network Method ◽  
Artificial Neural Network Method ◽  
Artificial Neural

Pengklasifikasian kelompok usia dibangun berdasarkan ciri-ciri dari fitur wajah. klasifikasi usia berdasarkan citra wajah perlu dilakukan dengan lebih akurat agar dapat berguna dalam sistem pengenalan usia manusia. Beberapa kesulitan dalam pengenalan wajah yang sering muncul karena variabilitas wajah seperti ekspresi, penuaan, variasi kumis dan sebagainya. Metode filter gabor dikenal sebagai detektor ciri yang sukses serta memiliki kemampuan mengeliminasi parameter variabilitas wajah yang pada metode lainnya sering menggangggu dalam proses pengenalan. Dengan menggunakan metode Gabor filter yang terbukti handal digunakan untuk memecahkan masalah agar pengenalan usia berdasarkan wajah dapat dilakukan dengan lebih akurat. Hasil penelitian menunjukkan bahwa penerapan metode Gabor Filter dan Artificial Neural Network pada masalah pengenalan usia berdasarkan citra wajah berhasil mendapatkan akurasi yaitu sebesar 83% dengan menggunakan pengujian Confusion Matrix. Dengan demikian penerapan metode Gabor Filter dan Artificial Neural Network pada masalah pengenalan usia berdasarkan citra wajah cukup akurat, dan dapat diimplementasikan. Kata kunci: Klasifikasi Usia, Wajah, ANN, Gabor Filter. Classification of age groups is built on the characteristics of facial features. Age classifications based on facial images need to be done more accurately in order to be useful in the human age recognition system. Some difficulties in facial recognition that often arise due to facial variability such as expression, aging, mustache variations and so on. Gabor filter method is known as a successful feature detector and has the ability to eliminate facial variability parameters which in other methods often interfere in the recognition process. By using the Gabor filter method which is proven to be reliable it is used to solve problems so that face recognition based on faces can be done more accurately. The results showed that the application of the Gabor Filter and Artificial Neural Network method on the problem of age recognition based on face images managed to get an accuracy of 83% using the Confusion Matrix test. Thus the application of the Gabor Filter and Artificial Neural Network method to the problem of age recognition based on face images is quite accurate, and can be implemented.Keywords: Age Classification, Face, ANN, Gabor Filter

Methods for evaluation the manufacturability of a vehicle

2020 ◽  
pp. 152-160
Keyword(s):  
Neural Network ◽  
Initial Data ◽  
Comparative Data ◽  
Training Algorithm ◽  
Neural Network Method ◽  
The Neural Network ◽  
Network Method ◽  
Calculation Results ◽  
Production And Operation

Methods for evaluation the manufacturability of a vehicle in the field of production and operation based on an energy indicator, expert estimates and usage of a neural network are stated. By using the neural network method the manufacturability of a car in a complex and for individual units is considered. The preparation of the initial data at usage a neural network for predicting the manufacturability of a vehicle is shown; the training algorithm and the architecture for calculating the manufacturability of the main units are given. According to the calculation results, comparative data on the manufacturability vehicles of various brands are given.

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