scholarly journals PENGENALAN POLA HURUF HIJAIYAH KHAT KUFI DENGAN METODE DETEKSI TEPI SOBEL BERBASIS JARINGAN SYARAF TIRUAN BACKPROPAGATION

2018 ◽  
Vol 11 (1) ◽  
pp. 37-46
Author(s):  
Irvan Faturrahman
Keyword(s):  
Neural Network ◽  
Pattern Recognition ◽  
Artificial Neural Network ◽  
Edge Detection ◽  
Learning Rate ◽  
Canny Edge Detection ◽  
Canny Edge ◽  
Rate Test ◽  
Artificial Neural ◽  
Sobel Edge Detection

ABSTRAK Khat kufi memiliki bentuk huruf hijaiyah yang unik berbentuk kotak. Banyak penelitian yang membahas pengenalan huruf hijaiyah namun untuk spesifik khat belum ada. Pada penelitian ini penulis melakukan simulasi pengenalan pola huruf hijaiyah khat kufi menggunakan deteksi tepi sobel dan jaringan syaraf tiruan backpropagation dengan menggunakan parameter uji learning rate dan epoch. Simulasi dilakukan 28 target huruf hijaiyah dengan learning rate 0.01, 0.05, 0.1, 0.5, dan epoch 25, 1000, 3000, 5000, 10000. Akurasi terbaik didapatkan pada learning rate 0.01 dan epoch 10000 yaitu 100%. Penelitian ini dapat dikembangkan menggunakan deteksi tepi canny, prewitt, atau robert serta JST LVQ, ADALINE, atau RBF.   ABSTRACT Khat kufi has a unique hijaiyah shape that is square in shape. Much of the research that discusses the introduction of the hijaiyah letters but for the specifics khat does not yet exist. In this study, the author performs a simulation of hijaiyah khat kufi pattern recognition using sobel edge detection and artificial neural network backpropagation using learning rate test and epoch parameters. The simulation has been done on 28 target letters hijaiyah with learning rate 0.01, 0.05, 0.1, 0.5, and epoch 25, 1000, 3000, 5000, 10000. The best accuracy obtained at learning rate 0.01 and epoch 10000 is 100%. This research can be developed using canny edge detection, prewitt, or robert and also JST LVQ, ADALINE, or RBF. How To Cite : Faturrahman, I. Arini. Mintarsih, F. (2018). PENGENALAN POLA HURUF HIJAIYAH KHAT KUFI DENGAN METODE DETEKSI TEPI SOBEL BERBASIS JARINGAN SYARAF TIRUAN BACKPROPAGATION. Jurnal Teknik Informatika, 11(1), 37-46.  doi 10.15408/jti.v11i1.6262 Permalink/DOI: http://dx.doi.org/10.15408/jti.v11i1.6262  

scholarly journals Developing Arabic License Plate Recognition System Using Artificial Neural Network and Canny Edge Detection

2020 ◽  
Vol 17 (3) ◽  
pp. 0909
Author(s):  
Bydaa Ali Hussain ◽  
Mohammed Sadoon Hathal
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Edge Detection ◽  
Recognition System ◽  
Detection Algorithm ◽  
License Plate ◽  
License Plate Recognition ◽  
Canny Edge Detection ◽  
Canny Edge ◽  
Artificial Neural

            In recent years, there has been expanding development in the vehicular part and the number of vehicles moving on the roads in all the sections of the country. Arabic vehicle number plate identification based on image processing is a dynamic area of this work; this technique is used for security purposes such as tracking of stolen cars and access control to restricted areas. The License Plate Recognition System (LPRS) exploits a digital camera to capture vehicle plate numbers is used as input to the proposed recognition system. Basically, the proposed system consists of three phases, vehicle license plate localization, character segmentation, and character recognition, the License Plate (LP) detection is presented using canny edge detection algorithm, Connect Component Analysis (CCA) have been exploited for segmenting characters. Finally, a Multi-Layer Perceptron Artificial Neural Network (MLPANN) model is utilized to identify and detect the vehicle license plate characters, and hence the results are displayed as a text on GUI. The proposed system successfully detects LP and recognizes multi-style Arabic characters with rates of 96% and 97.872% respectively under different conditions.

Artificial neural network models for coronary artery disease

2020 ◽  
Vol 15 ◽  
Author(s):  
Elham Shamsara ◽  
Sara Saffar Soflaei ◽  
Mohammad Tajfard ◽  
Ivan Yamshchikov ◽  
Habibollah Esmaili ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Coronary Artery Disease ◽  
Pattern Recognition ◽  
Artificial Neural Network ◽  
Coronary Artery ◽  
Diagnostic Model ◽  
Early Prediction ◽  
Artificial Neural ◽  
Artery Disease

Background: Coronary artery disease (CAD) is an important cause of mortality and morbidity globally. Objective : The early prediction of the CAD would be valuable in identifying individuals at risk, and in focusing resources on its prevention. In this paper, we aimed to establish a diagnostic model to predict CAD by using three approaches of ANN (pattern recognition-ANN, LVQ-ANN, and competitive ANN). Methods: One promising method for early prediction of disease based on risk factors is machine learning. Among different machine learning algorithms, the artificial neural network (ANN) algo-rithms have been applied widely in medicine and a variety of real-world classifications. ANN is a non-linear computational model, that is inspired by the human brain to analyze and process complex datasets. Results: Different methods of ANN that are investigated in this paper indicates in both pattern recognition ANN and LVQ-ANN methods, the predictions of Angiography+ class have high accuracy. Moreover, in CNN the correlations between the individuals in cluster ”c” with the class of Angiography+ is strongly high. This accuracy indicates the significant difference among some of the input features in Angiography+ class and the other two output classes. A comparison among the chosen weights in these three methods in separating control class and Angiography+ shows that hs-CRP, FSG, and WBC are the most substantial excitatory weights in recognizing the Angiography+ individuals although, HDL-C and MCH are determined as inhibitory weights. Furthermore, the effect of decomposition of a multi-class problem to a set of binary classes and random sampling on the accuracy of the diagnostic model is investigated. Conclusion : This study confirms that pattern recognition-ANN had the most accuracy of performance among different methods of ANN. That’s due to the back-propagation procedure of the process in which the network classify input variables based on labeled classes. The results of binarization show that decomposition of the multi-class set to binary sets could achieve higher accuracy.

Pattern Recognition of Vertical Strabismus Using an Artificial Neural Network (StrabNet©)

Strabismus ◽  
2009 ◽  
Vol 17 (4) ◽  
pp. 131-138 ◽  
Author(s):  
Arvind Chandna ◽  
Anthony C. Fisher ◽  
Ian Cunningham ◽  
Deborah Stone ◽  
Maureen Mitchell
Keyword(s):  
Neural Network ◽  
Pattern Recognition ◽  
Artificial Neural Network ◽  
Artificial Neural

scholarly journals Pengaruh Cahaya dan Kualitas Citra dalam Klasifikasi Kematangan Pisang Cavendish Berdasarkan Ciri Warna Menggunakan Artificial Neural Network

2021 ◽  
Vol 21 (1) ◽  
pp. 215-228
Author(s):  
Aditya Dwi Putro ◽  
Arief Hermawan
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Learning Rate ◽  
Artificial Neural

Buah pisang merupakan komoditas yang memberikan kontribusi besar terhadap angka produksi buah nasional maupun internasional. Pemerintah melalui Badan Standarisasi Nasional menetapkan standar untuk buah pisang, menjaga mutu buah pisang. Tujuan dari penelitian ini adalah menganalisa pengaruh cahaya dan kualitas citra dalam mengklasifikasikan tingkat kematangan buah pisang berdasarkan ciri warna buah pisang di Kebun Pisang Cavendish kabupaten banyumas jawa tengah sesuai dengan SNI 7422:2009[1]. Pisang yang terdapat di Kebun Pisang Cavendish ini beraneka ragam kualitas, sebagai buah lokal yang memiliki nilai ekonomi tinggi dan memiliki potensi pasar yang masih terbuka luas, pisang menjadi salah satu komoditas buah-buahan yang dapat diandalkan. Permasalahan yang sering ditemukan selain resource dan ketelitian yakni kurang tepatnya dan kurang pengetahuannya karyawan dalam membedakan tingkat kematangan pisang terutama karyawan baru. Artificial Neural Network digunakan sebagai metode dalam proses pengklasifikasian. Dataset pada penelitian ini adalah 80 citra buah pisang yang diambil per tandan terdiri dari 40 tandan citra pisang Cavendish yang diambil di pagi hari dengan kualitas citra bagus 20 dan kualitas citra tidak bagus 20, 40 tandan citra pisang Cavendish yang diambil di sore hari dengan kualitas citra bagus 20 dan kualitas citra tidak bagus 20. Tingkat kematangan pisang pada penelitian ini yaitu mentah dan matang. pengujian menghasilkan Akurasi tertinggi dalam proses klasifikasi kategori buah pisang cavendish menggunakan epoch 5000, goal 0.0001 dan learning rate 0.1 dengan jumlah akurasi sebesar 100% dengan model trainlm dan waktu 1.6 detik.

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