scholarly journals Klasifikasi-PNN pada Citra Ikan Air Tawar dengan Sobel Edge Detection

KREA-TIF ◽  
2018 ◽  
Vol 6 (2) ◽  
pp. 66
Author(s):  
Puspa Eosina ◽  
Gibtha Fitri Laxmi ◽  
Fety Fatimah
Keyword(s):  
Neural Network ◽  
Edge Detection ◽  
Freshwater Fish ◽  
Probabilistic Neural Network ◽  
Data Display ◽  
Vector Data ◽  
Detection Techniques ◽  
Accuracy Result ◽  
Sobel Edge Detection ◽  
Shape Characteristics

<h1 align="center"><strong>Abstrak</strong></h1><p>Metode Sobel adalah salah satu teknik dalam edge detection (deteksi tepi) untuk mengekstraksi tepi dari citra ikan air tawar. Deteksi tepi adalah proses identifikasi keberadaan dan letak tepinya dengan diskontinuitas gambar yang tajam. Menggunakan data citra ikan sebanyak 200 gambar dari 10 jenis ikan air tawar, dilakukan pencarian model klasifikasi PNN sebagai model untuk identifikasi data ekstraksi citra ikan air tawar menggunakan metode Sobel.Ciri atau karakteristik yang digunakan dalam mengekstrak data ikan dalam penelitian ini adalah ciri bentuk, yang dapat dikenali melalui titik-titik yang membentuk tepi-tepi objek ikan. Kinerja algoritma Sobel dapat dinilai dari hasil tampilan data vektor yang menjadi ciri bentuk ikan, dimana estimasi nilai-nilai pixel dilakukan menggunakan operator konvolusi Sobel (convolution masks). Telah ditunjukkan bahwa algoritma ini bekerja dengan baik. Data hasil ekstraksi ini, untuk selanjutnya digunakan dalam mencari model klasifikasi PNN (Probabilistic Neural Network) untuk identifikasi ikan air tawar. Hasil perhitungan nilai akurasi dari model yang terbentuk, yaitu kurang dari 25%, menunjukkan model identifikasi yang diinginkan belum tercapai. Hasil ini dapat digunakan sebagai pembanding untuk membangun model identifikasi menggunakan metode klasifikasi yang lain pada penelitian selanjutnya.</p><p align="center"><strong><em>Abstract</em></strong><strong><em></em></strong></p><p><em>The Sobel method is one of the edge detection techniques to extract the edges of freshwater fish images. The Edge detection is the process of identifying the existence and position of the edge with a sharp discontinuity of images. Using 200 images of fish from 10 types of freshwater fish, the</em><em> </em><em>Probabilistic Neural Network</em><em> </em><em>(PNN) classification was performed on freshwater fish image extraction,  to obtain the model of identification.</em><em> </em><em>In this study, the Sobel method is used to extract images of the shape characteristics. The performance of the Sobel algorithm can be judged by the results of the vector data display which characterizes the shape of the fish, where the estimation of pixel values is performed using the convolution masks operator. It has been shown that this algorithm works well. The accuracy result of the obtain model, ie less than 25%, indicates the desired model of identification has not been achieved. This result can be used as a benchmark to construct an identification model using other classification methods in subsequent research.</em></p>

scholarly journals Identification of Virus in Microscopic Image Using Genetic Algorithm

2019 ◽  
Vol 8 (S2) ◽  
pp. 24-27
Author(s):  
N. Senthilkumaran ◽  
R. Preethi
Keyword(s):  
Genetic Algorithm ◽  
Image Segmentation ◽  
Edge Detection ◽  
Microscopic Image ◽  
Canny Edge Detection ◽  
Detection Techniques ◽  
Canny Edge ◽  
Sobel Edge Detection ◽  
The Many ◽  
Prewitt Edge Detection

In this paper describes a several techniques of effective edge detection by using image segmentation. The image segmentation provides various techniques to detect the edges on image. The paper mainly focused on edge detection using matlab parameters and solved the many problems. Edge detection techniques have a several type of techniques. We have taken microscopic image, which affects the human body by making diseases through viruses and bacteria’s. Now analyze only about the major techniques: a.) Roberts edge detection, b) sobel edge detection, c) prewitt edge detection, d) log (laplacian of gaussian) edge detection, e) genetic edge detection and f) canny edge detection. We have applied above five techniques which are used in edge detection and got a result on microscopic images. Hence, we scope this paper defines and compares the variety of techniques and demand assures the genetic algorithm provides a better performance on edge detection using microscopic image.

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  

Quantity Prediction of Mixed Freshwater Fish Based on Passive Underwater Acoustic Signals

2020 ◽  
Vol 63 (6) ◽  
pp. 1805-1811
Author(s):  
Qunzi Tu ◽  
Yongwen Yang ◽  
Hanying Huang ◽  
Lu Li ◽  
Shanbai Xiong ◽  
...  
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Linear Regression ◽  
Prediction Model ◽  
Freshwater Fish ◽  
Probabilistic Neural Network ◽  
Recognition Rate ◽  
Acoustic Signals ◽  
Support Vector ◽  
Underwater Acoustic

HighlightsThe use of passive underwater acoustic technology to estimate the species and quantity of freshwater fish provides a theoretical basis for effectively estimating the quantity of freshwater aquaculture.Mixed proportion recognition models for breams and crucians were built using probabilistic neural network (PNN) and support vector machine (SVM), and the influences of different super-parameters on the recognition rate were analyzed. The results showed that the classification model established with SVM after equiripple filtering was best.Mixed quantity prediction models for breams and crucians were constructed using multiple linear regression, and the effects of different filtering methods on the model performance were analyzed. The results showed that the best quantity prediction model was constructed with Butterworth filtering.Abstract. Acoustic signals of breams and crucians were collected at seven mixed proportions and 15 mixed quantitative gradients. After normalization and different filtering processes, the characteristics of the acoustic signals were extracted. Mixed proportion recognition models for breams and crucians were established using probabilistic neural network (PNN) and support vector machine (SVM). The results showed that the model established using SVM after equiripple filtering was best, and the recognition rate was 0.9583. A mixed quantity prediction model for breams and crucians was established by multiple linear regression based on ordinary least squares. The results showed that the model was best after Butterworth filtering, the adjusted decision coefficient of the model was 0.9514, and the relative analysis error was 4.7571. Keywords: Freshwater fish, Passive underwater acoustic signals, Pattern recognition, Regression analysis.

Using Neural Natural Network for Image Recognition in Bioinformatics

2021 ◽  
pp. 830-836
Author(s):  
Dina Kharicheva
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Edge Detection ◽  
Image Recognition ◽  
Detection Technique ◽  
Connected Component ◽  
Novel Technique ◽  
Automatic Image Recognition ◽  
Sobel Edge Detection ◽  
Artificial Neural Network Ann

Automatic image recognition is very useful in bioinformatics. This article presents a novel technique to recognize the characters in the number plate automatically by using connected component analysis (CCA), artificial neural network (ANN) and neural natural network (Triple N). The preprocessing steps, Sobel edge detection technique and CCA are applied to the captured image of the vehicle to obtain character images. ANN technique can be used over these images to recognize the characters of the image in bioinformatics. The preprocessing steps are used to remove the noise and to enhance the image for recognizing the characters effectively. After performing the preprocessing steps, the edge detection technique and CCA are carried out to separate the character images from the whole image which can be recognized using ANN. These text characters can be compared with database to find authentication of vehicle, identifying the owner of the vehicle, penalty bill generation, etc.

Using Neural Natural Network for Image Recognition in Bioinformatics

2019 ◽  
Vol 9 (2) ◽  
pp. 35-41
Author(s):  
Dina Kharicheva
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Edge Detection ◽  
Image Recognition ◽  
Detection Technique ◽  
Connected Component ◽  
Novel Technique ◽  
Automatic Image Recognition ◽  
Sobel Edge Detection ◽  
Artificial Neural Network Ann

Automatic image recognition is very useful in bioinformatics. This article presents a novel technique to recognize the characters in the number plate automatically by using connected component analysis (CCA), artificial neural network (ANN) and neural natural network (Triple N). The preprocessing steps, Sobel edge detection technique and CCA are applied to the captured image of the vehicle to obtain character images. ANN technique can be used over these images to recognize the characters of the image in bioinformatics. The preprocessing steps are used to remove the noise and to enhance the image for recognizing the characters effectively. After performing the preprocessing steps, the edge detection technique and CCA are carried out to separate the character images from the whole image which can be recognized using ANN. These text characters can be compared with database to find authentication of vehicle, identifying the owner of the vehicle, penalty bill generation, etc.

Classification and Identification of Power System Disturbances Using Wavalet and Artificial Neural Network Technique

2005 ◽  
Vol 2 (2) ◽  
pp. 25
Author(s):  
Noraliza Hamzah ◽  
Wan Nor Ainin Wan Abdullah ◽  
Pauziah Mohd Arsad
Keyword(s):  
Neural Network ◽  
Power Quality ◽  
Probabilistic Neural Network ◽  
Microsoft Excel ◽  
Waveform Data ◽  
The Neural Network ◽  
Harmonic Voltage ◽  
Power Quality Disturbances ◽  
Electronic Load ◽  
Wavelet Technique

Power Quality disturbances problems have gained widespread interest worldwide due to the proliferation of power electronic load such as adjustable speed drives, computer, industrial drives, communication and medical equipments. This paper presents a technique based on wavelet and probabilistic neural network to detect and classify power quality disturbances, which are harmonic, voltage sag, swell and oscillatory transient. The power quality disturbances are obtained from the waveform data collected from premises, which include the UiTM Sarawak, Faculty of Science Computer in Shah Alam, Jati College, Menara UiTM, PP Seksyen 18 and Putra LRT. Reliable Power Meter is used for data monitoring and the data is further processed using the Microsoft Excel software. From the processed data, power quality disturbances are detected using the wavelet technique. After the disturbances being detected, it is then classified using the Probabilistic Neural Network. Sixty data has been chosen for the training of the Probabilistic Neural Network and ten data has been used for the testing of the neural network. The results are further interfaced using matlab script code.  Results from the research have been very promising which proved that the wavelet technique and Probabilistic Neural Network is capable to be used for power quality disturbances detection and classification.

Research on Improved Gamma Transform Face Image Preprocessing Fusion Algorithm under Complex Lighting Conditions

2020 ◽  
Vol 13 ◽  
Author(s):  
Xiaolin Tang ◽  
Xiaogang Wang ◽  
Jin Hou ◽  
Huafeng Wu ◽  
Ping He
Keyword(s):  
Edge Detection ◽  
Face Image ◽  
Light Sources ◽  
Fusion Algorithm ◽  
Edge Information ◽  
Image Preprocessing ◽  
Lighting Conditions ◽  
Weighted Fusion ◽  
The Face ◽  
Sobel Edge Detection

Introduction: Under complex illumination conditions such as poor light sources and light changes rapidly, there are two disadvantages of current gamma transform in preprocessing face image: one is that the parameters of transformation need to be set based on experience; the other is the details of the transformed image are not obvious enough. Objective: Improve the current gamma transform. Methods: This paper proposes a weighted fusion algorithm of adaptive gamma transform and edge feature extraction. First, this paper proposes an adaptive gamma transform algorithm for face image preprocessing, that is, the parameter of transformation generated by calculation according to the specific gray value of the input face image. Secondly, this paper uses Sobel edge detection operator to extract the edge information of the transformed image to get the edge detection image. Finally, this paper uses the adaptively transformed image and the edge detection image to obtain the final processing result through a weighted fusion algorithm. Results: The contrast of the face image after preprocessing is appropriate, and the details of the image are obvious. Conclusion: The method proposed in this paper can enhance the face image while retaining more face details, without human-computer interaction, and has lower computational complexity degree.

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