Proposed Model for an Expert System for Diagnosing Degenerative Diseases – Using Digital Image Processing with Neural Network

2017 ◽  
pp. 68-73
Author(s):  
Mittal N. Desai ◽  
Vishal Dahiya ◽  
A. K. Singh
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Expert System ◽  
Digital Image Processing ◽  
Digital Image ◽  
Degenerative Diseases ◽  
Proposed Model

scholarly journals APLIKASI PENGENALAN PLAT NOMOR KENDARAAN DI UNIVERSITAS RIAU

2019 ◽  
Vol 9 (3) ◽  
pp. 47-52
Author(s):  
Noprizal ◽  
Feri Candra
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Image Processing ◽  
Artificial Neural Network ◽  
Digital Image Processing ◽  
Digital Image ◽  
Training Data ◽  
License Plate ◽  
Backpropagation Method ◽  
Artificial Neural

Abstract Vehicle license plate recognition application has been found in shopping centers, university, and other agency buildings with various methods of recognition. Some examples of methods used such as digital image processing techniques, neural networks and so forth. This study makes an application for the introduction of license plates, especially for student vehicle license plates in the university area. This application is developed with Digital Image Processing Methods and Artificial Neural Networks. In this study, 900 training data are used, taken from 200 photo vehicle number plates, to train 36 characters that contain 26 alphabets and 10 decimal numbers. The training data is used to test 30 photos of vehicle license plates. Plate photos used as training and testing data are the Indonesian standard with black and white plates. Artificial Neural Network used to recognize vehicle license plate by using the Backpropagation method with parameters Epoch 1000, Hidden layer1 with node 60, Hidden layer2 with node 55, Goal 0.001. The final conclusion of this Study shows that the use of Artificial Neural Network Backpropagation method is very good, with the best testing accuracy obtained, namely 98% and 1.25 error. Keywords : digital image processing, artificial neural networks, vehicle license plate Abstrak Aplikasi pengenalan plat nomor kendaraan sudah banyak ditemukan di pusat perbelanjaan, universitas, dan gedung instansi dengan berbagai metode pengenalan. Beberapa contoh metode yang digunakan seperti teknik pengolahan citra digital, jaringan syaraf tiruan dan lain sebagainya. Disini penulis membuat sebuah aplikasi pengenalan plat nomor kendaraan khususnya untuk plat nomor kendaraan mahasiswa yang ada dilikungan Universitas Riau. Aplikasi ini dikembangkan dengan metode pengolahan citra digital dan jaringan syaraf tiruan. Pada penelitian ini, digunakan 700 data pelatihan yang diambil dari 200 foto plat nomor, untuk melatih 36 karakter. Data pelatihan tersebut kemudian digunakan untuk menguji 30 foto plat nomor kendaraan. Foto plat yang dijadikan untuk data pelatihan dan pengujian yaitu plat standar indonesia yang berwarna hitam dan putih. Jaringan syaraf tiruan yang digunakan untuk melakukan pengenalan yaitu dengan Metode Backpropagation dengan parameter Epoch 1000, Hidden layer1 dengan jumlah node 60, Hidden layer2 dengan jumlah node 55, Goal  0,001. Kesimpulan akhir dari penelitian ini yaitu menunjukan bahwa penggunaan Metode Backpropagation jaringan syaraf tiruan ini sangat bagus, dengan akurasi pengujian terbaik yang didapat yaitu 98% dengan eror 1,25. Kata kunci: pengolahan citra digital, jaringan syaraf tiruan, Backpropagation, plat nomor  

scholarly journals Waste Collector Roboboat Using Neural Network Method Based on Tensorflow Framework

2021 ◽  
Vol 328 ◽  
pp. 02003
Author(s):  
Fiqhi I Achmad ◽  
Riza Alfita ◽  
Vivin N Rosida ◽  
Aji W Kunto ◽  
Hanifudin Sukri ◽  
...  
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Digital Image Processing ◽  
Digital Image ◽  
Navigation System ◽  
Research Result ◽  
Average Accuracy ◽  
Network Method ◽  
The Right ◽  
Main Factor

Today, the waste problem has become more serious, because the waste can cause environmental pollution and bad smell pollution. The less awareness of cleanliness is the main factor, especially the less awareness of throwing waste at the right place. Based on the data of the Ministry of Environment and Forestry “Environmental Ignorance Behavior” in 2008 said that around 72 percent of Indonesian less be aware of the waste problem generally with plastic waste, this waste will flow to the sea and make pollution. This research objective is to design and accomplish previous roboboat research that still has some drawbacks. This research employs digital image processing and neural network based on the tensor flow framework method to overcome less accurate waste detection as well as autopilot navigation system. The research result shows by using 3600 dataset images, the model has the lowest loss 0.9 and 64.3% average accuracy with various samples and distance.

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