Distinct Sources of a Bovine Blastocyst Digital Image Do not Produce the Same Classification by a Previously Trained Software Using Artificial Neural Network

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

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AUTOMATIC IDENTIFYING LARYNGOPHARYNGEAL REFLUX USING ARTIFICIAL NEURAL NETWORK

Biomedical Engineering Applications Basis and Communications ◽

10.4015/s1016237212002949 ◽

2012 ◽

Vol 24 (01) ◽

pp. 47-56 ◽

Cited By ~ 2

Author(s):

Sheng-Fuu Lin ◽

Hsien-Tse Chen ◽

Tung-Lung Tsai

Keyword(s):

Neural Network ◽

Image Processing ◽

Artificial Neural Network ◽

Gastric Acid ◽

Digital Image Processing ◽

Digital Image ◽

Technical Assistance ◽

Laryngopharyngeal Reflux ◽

Precise Diagnosis ◽

Artificial Neural

Laryngopharyngeal Reflux mostly leads to burns of the pharynx and larynx by reflux from gastric acid and also leads to different degrees of burns in the esophagus and the stomach. This paper aims to develop a technique for analyzing pharyngeal and laryngeal images. With techniques of digital image processing, this paper can choose the suitable images from burns of the pharynx and larynx to obtain the feature zones of burns of the pharynx and larynx. Artificial neural network helps physicians to develop the diagnostic standard about the burns severity of Laryngopharyngeal Reflux. This paper divides the types of the complications into three degrees and compares with other ways (Hanson et al.5 and Ilgner et al.6). The results can be the technical assistance in helping physicians to diagnose the severity of Laryngopharyngeal Reflux and to make a more precise diagnosis.

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