scholarly journals IDENTIFIKASI SPESIES IKAN BERDASARKAN KONTUR OTOLITH MENGGUNAKAN METODE OTSU DAN BACK PROPAGATION NEURAL NETWORK

JOUTICA ◽  
2020 ◽  
Vol 5 (2) ◽  
pp. 389
Author(s):  
Heri Susanto ◽  
Jamal Jamal
Keyword(s):  
Neural Network ◽  
Discriminant Analysis ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Backpropagation Neural Network ◽  
Image Thresholding

Otolith merupakan organ yang sangat penting, karena melalui otolith dapat diketahui jenis ikan, pertumbuhan, lingkungan, serta sejarah kehidupannya, misalnya, umur, reproduksi, dan migrasi. Dengan semakin canggihnya komputer dan pengolahan di bidang citra, diharapkan kemampuan mengidenifikasi jenis ikan yang dimiliki oleh manusia bisa diadopsi dan diterapkan pada perangkat komputer. Threshold adalah sebuah teknik penting dalam aplikasi segmentasi citra. Hal mendasar dari threshold adalah menentukan nilai batas optimal dari citra keabuan, untuk memisahkan antara obyek dengan latar belakang. Metode Backpropagation Neural Network, merupakan metode klasifikasi yang handal untuk perhitungan yang rumit dengan waktu komputasi lebih sedikit, dan mampu memberikan nilai akurasi yang tinggi. Untuk keperluan segmentasi citra menggunakan metode Otsu karena metode ini merupakan metode paling berhasil untuk image thresholding. Proses klasifikasi untuk pengenalan spesies ikan berdasar otolith menggunakan metode Backpropagation Neural Network. Hasil eksperimen diperoleh akurasi sebesar 95% lebih tinggi dibanding metode Discriminant Analysis yang memiliki akurasi sebesar 92%.

scholarly journals Tracking Facila Expressions by using Stereoscopy Video and Back Propagation Neural Network

1970 ◽  
Vol 6 (1) ◽  
pp. 11-24
Author(s):  
R Romero-Herrera ◽  
FJ Gallagos-Funes ◽  
AG Juarez-Gracia ◽  
J López-Bonilla
Keyword(s):  
Neural Network ◽  
Facial Expressions ◽  
Back Propagation ◽  
Correlation Method ◽  
Back Propagation Neural Network ◽  
Feature Points ◽  
Stereoscopic Video ◽  
Backpropagation Neural Network ◽  
Engineering And Technology ◽  
Different Types

In this paper we propose a method to tracking facial expressions. A system with two cameras is used tocapture stereoscopic video sequences. The frames are acquired and analyzed by matching twostereoscopic frames through a correlation method that performs image processing to obtain a resultingframe, and then it is processed to recognize a human face by using the Viola and Jones (VJ) method. Theface is located via the Nitzberg operator and it provides the feature points of the eyes, eyebrows, noseand mouth, which are introduced into a Backpropagation neural network that is capable of learning andclassifying different types of facial expressions that make a person, feel such as: surprised, scared,unhappy, sad, mad and happy. Finally, the result of this process is recognition of facial expressions.Keywords: Facial expression; Backpropagation neural networks; VJ method; Nitzberg algorithm.DOI: 10.3126/kuset.v6i1.3303Kathmandu University Journal of Science, Engineering and Technology Vol.6(1) 2010, pp11-24

scholarly journals OPTIMASI CONJUGATE GRADIENT PADA BACKPROPAGATION NEURAL NETWORK UNTUK PREDIKSI HASIL TANGKAP IKAN

SAINTEKBU ◽  
2020 ◽  
Vol 12 (2) ◽  
pp. 29-39
Author(s):  
A. Aviv Mahmudi
Keyword(s):  
Neural Network ◽  
Conjugate Gradient ◽  
Back Propagation ◽  
Accurate Method ◽  
Back Propagation Neural Network ◽  
Market Process ◽  
Backpropagation Neural Network ◽  
Neural Network Method ◽  
Average Value ◽  
Network Method

The need for fish catch by a company or fisherman in Rembang Regency affects market process and also welfare. The catch made by the fishermen is not on target, due to the weather and type of fishing gear. An accurate method is needed in making predictions and a correlation between catch and weather so that fisherman can get maximum predictions results, so that price adjustment can be made. The research was conducted using an experimental method, to determine the accuracy of the effect of the Conjugate Gradient on the Back Propagation Neural Network in obtaining the best value. Based on the results of the Cycle training test with the Conjugate Gradient Backpropagation Neural Network method, the smallest average value is obtained at the 400th Epoch compared to the Epoch Gradient Descent With Momentum method at Epoch 800.Thus it is proven that using the Conjugate Gradient Backpropagation Neural Network method is better with an average value of- MSE average 0.2223 in three stages of testing Training Cycle, Learning Rate and Hidden Layer.

Economic benefit of shale gas exploitation based on back propagation neural network

2020 ◽  
Vol 39 (6) ◽  
pp. 8823-8830
Author(s):  
Jiafeng Li ◽  
Hui Hu ◽  
Xiang Li ◽  
Qian Jin ◽  
Tianhao Huang
Keyword(s):  
Neural Network ◽  
Shale Gas ◽  
Bp Neural Network ◽  
Large Scale ◽  
Linear Prediction ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Economic Benefits ◽  
Gas Well ◽  
Well Production

Under the influence of COVID-19, the economic benefits of shale gas development are greatly affected. With the large-scale development and utilization of shale gas in China, it is increasingly important to assess the economic impact of shale gas development. Therefore, this paper proposes a method for predicting the production of shale gas reservoirs, and uses back propagation (BP) neural network to nonlinearly fit reservoir reconstruction data to obtain shale gas well production forecasting models. Experiments show that compared with the traditional BP neural network, the proposed method can effectively improve the accuracy and stability of the prediction. There is a nonlinear correlation between reservoir reconstruction data and gas well production, which does not apply to traditional linear prediction methods

An Anatomy of a Hybrid Color Descriptor with a Neural Network Model to Enhance the Retrieval Accuracy of an Image Retrieval System

2019 ◽  
Vol 13 ◽  
Author(s):  
Shikha Bhardwaj ◽  
Gitanjali Pandove ◽  
Pawan Kumar Dahiya
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Image Retrieval ◽  
Hybrid System ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Retrieval Accuracy ◽  
Color Descriptor ◽  
Benchmark Datasets ◽  
Color Moment

Background: In order to retrieve a particular image from vast repository of images, an efficient system is required and such an eminent system is well-known by the name Content-based image retrieval (CBIR) system. Color is indeed an important attribute of an image and the proposed system consist of a hybrid color descriptor which is used for color feature extraction. Deep learning, has gained a prominent importance in the current era. So, the performance of this fusion based color descriptor is also analyzed in the presence of Deep learning classifiers. Method: This paper describes a comparative experimental analysis on various color descriptors and the best two are chosen to form an efficient color based hybrid system denoted as combined color moment-color autocorrelogram (Co-CMCAC). Then, to increase the retrieval accuracy of the hybrid system, a Cascade forward back propagation neural network (CFBPNN) is used. The classification accuracy obtained by using CFBPNN is also compared to Patternnet neural network. Results: The results of the hybrid color descriptor depict that the proposed system has superior results of the order of 95.4%, 88.2%, 84.4% and 96.05% on Corel-1K, Corel-5K, Corel-10K and Oxford flower benchmark datasets respectively as compared to many state-of-the-art related techniques. Conclusion: This paper depict an experimental and analytical analysis on different color feature descriptors namely, Color moment (CM), Color auto-correlogram (CAC), Color histogram (CH), Color coherence vector (CCV) and Dominant color descriptor (DCD). The proposed hybrid color descriptor (Co-CMCAC) is utilized for the withdrawal of color features with Cascade forward back propagation neural network (CFBPNN) is used as a classifier on four benchmark datasets namely Corel-1K, Corel-5K and Corel-10K and Oxford flower.

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