scholarly journals Banten Batik Classification with Backpropagation Neural Network

2018 ◽  
Vol 218 ◽  
pp. 03012
Author(s):  
Rian Fahrizal ◽  
Ridwan Prasetya Parlindungan Siahaan ◽  
Romi Wiryadinata
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Public Awareness ◽  
The Other ◽  
Gray Level ◽  
Backpropagation Neural Network ◽  
Gray Scale ◽  
Indigenous Cultures ◽  
Artificial Neural ◽  
Occurrence Matrix

Batik Cloth is Indonesia heritage of the indigenous cultures. However, the lack of public awareness could made the other nations has claimed that Batik cloth as their heritage. Therefore, We will need serious attention to take the problems. The classification Banten pattern batik cloth had been made to protect the Batik pattern. The classification had been made with Artificial Neural Network with Lavender-Marquant methods. Gray-Level Co-Occurrence Matrix (GLCM) has been used to preprocessing the pattern from Gray scale images. The results showed that the Batik patterns had 97% accuracy and 2% error.

scholarly journals Deteksi Api Pada Video Menggunakan Metode Artificial Neural Network

2021 ◽  
Vol 3 (1) ◽  
pp. 96-107
Author(s):  
Budiman Rabbani
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Feature Extraction ◽  
Extraction Method ◽  
Gray Level ◽  
Backpropagation Neural Network ◽  
Validation Process ◽  
Feature Extraction Method ◽  
Time Value ◽  
Occurrence Matrix

Abstract The camera is one of the tools used to collect images. Cameras are often used for the safety of homes, highways and others. Then in this study camera captures are used to support fire objects because fire is one of the causes of safety that can be controlled. Therefore, by utilizing a capture camera will see the best model of backpropagation neural network by combining the local binary patern (LBP) feature extraction method and the Gray Level Co-occurrence Matrix (GLCM) to access the fire. Then to evaluate the performance of the model created using three parameters that contain accuracy, recall, precision. The data in this study consisted of videos with variations of fire and non-fire videos. Based on the final results of the study, accuracy, remember, the best precision obtained simultaneously 96%, 97%, 97%. Then the validation process was done using 30 videos with a variation of 15 fire videos and 15 non-fire videos and obtained an accuracy of 86.6% with an average time value of 6.029 minutes.

scholarly journals CLASSIFICATION OF MEAT IMAGERY USING ARTIFICIAL NEURAL NETWORK METHOD AND TEXTURE FEATURE EXTRACTION BY GRAY LEVEL CO-OCCURRENCE MATRIX METHOD

2021 ◽  
Vol 2 (1) ◽  
pp. 1-8
Author(s):  
Chairul Imam ◽  
Eka Wahyu Hidayat ◽  
Neng Ika Kurniati
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Texture Feature ◽  
General Term ◽  
Gray Level ◽  
Artificial Neural Network Method ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Occurrence Matrix

Lately, there is often a mixture of beef and pork done by traders to the general public as buyers. This is due to the unconsciousness of the buyer on how to recognize the type of meat purchased. The effect of this meat mix can certainly be detrimental to buyers, especially Muslims. Image processing is a general term for various methods in which it is used to manipulate and modify images in various ways. Classification is a method of grouping some information and ensuring it is listed in a class.. Classification of beef and pork differentiator in this application using Artificial Neural Network (ANN) method while for texture extraction using Gray Level Co-occurrence Matrix (GLCM) method. The information used in the examination was 30 images of fresh meat divided into 15 images of fresh beef and 15 images of fresh pork. The data used is data Classification of Beef and Pork Image based on Color and Texture Characteristics. The result of classification accuracy obtained in this application is 80%.

scholarly journals Artificial Neural Network and Gray Level Co-Occurrence Matrix Based Automated Corrosion Detection

2019 ◽  
Vol 8 (6) ◽  
pp. 4499-4502
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Artificial Neural Network ◽  
Crack Detection ◽  
Gaussian Mixture ◽  
Image Processing Technique ◽  
Gray Level ◽  
Corrosion Detection ◽  
Artificial Neural ◽  
Occurrence Matrix

In this paper, we show an image processing algorithm with its capabilities in detecting the corrosion. This algorithm is programmed and requires no parameter modification and no previous knowledge of image acquisition process because function evaluates their parameters. Digital image processing technique proposed to avoid such incident occurrences. Combining Poisson-Gaussian- Mixture distribution with a Fuzzy segmentation framework an algorithm is developed to clutch image information. Artificial neural network and gray level co-occurrence matrix (GLCM) utilized to recognize the corrosion. The developed algorithm can be used in the ROV to detect the corrosion spots. The algorithm results exhibit the sufficiency in perceives corroded spots. Using image processing the corrosion detection process can be automated with a monitoring software setup which can generate an alert based on corrosion severity. Using image processing the infrastructure’s corrosion evaluation effort will be minimized, and presenting the result statistics is easier. In application point of view, we can extend the algorithm capabilities to the fatigue crack detection.

scholarly journals Identification of monogeneans parasite using gray level co-occurrence matrix and artificial neural network

2020 ◽  
Vol 1641 ◽  
pp. 012095
Author(s):  
Hikmatulloh ◽  
Dwiza Riana ◽  
Jamal Maulana Hudin ◽  
Susilawati ◽  
Dede Wintana ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Gray Level ◽  
Artificial Neural ◽  
Occurrence Matrix

scholarly journals Design of an OCR System and its Hardware Implementation

2021 ◽  
Vol 9 (12) ◽  
pp. 159-174
Author(s):  
Gulfeshan Parween
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Character Recognition ◽  
Hardware Implementation ◽  
Gray Level ◽  
Document Images ◽  
English Alphabet ◽  
Barcode Recognition ◽  
Printed Text ◽  
Artificial Neural

Abstract: In this paper, we present a scheme to develop to complete OCR system for printed text English Alphabet of Uppercase of different font and of different sizes so that we can use this system in Banking, Corporate, Legal industry and so on. OCR system consists of different modules like preprocessing, segmentation, feature extraction and recognition. In preprocessing step it is expected to include image gray level conversion, binary conversion etc. After finding out the feature of the segmented characters artificial neural network and can be used for Character Recognition purpose. Efforts have been made to improve the performance of character recognition using artificial neural network techniques. The proposed OCR system is capable of accepting printed document images from a file and implemented using MATLAB R2014a version. Key words: OCR, Printed text, Barcode recognition

scholarly journals Analysis of Crowd Flow Parameters Using Artificial Neural Network

2018 ◽  
Vol 19 (4) ◽  
pp. 335-345
Author(s):  
Poojari Yugendar ◽  
K.V.R. Ravishankar
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Absolute Error ◽  
The Other ◽  
Ann Model ◽  
Deterministic Models ◽  
Accuracy Measurement ◽  
Flow Parameters ◽  
Non Linear ◽  
Artificial Neural

Abstract Research scientists have been developing mathematical tools to detect objects, recognize objects and actions, and discover behaviours and events to human abilities. In all these efforts, the understanding of human actions is of a special interest for both application and research purposes. In this study, crowd flow parameters are analysed by considering linear and non linear relationships between stream flow parameters using conventional and soft computing approach. Deterministics models like Greenshield and Underwood were applied in the study to describe flow characteristics. A non-linear model based on Artificial Neural Network (ANN) approach is also used to build a relationship between different crowd flow parameters and compared with the other deterministic models. ANN model gave good results based on accuracy measurement to deterministic models because of their self-processing and intelligent behaviour. Mean absolute error (MAE) and root mean square error (RMSE) values for the best fitted ANN model are less than those for the other models. ANN model gives better performance in fitness of model and future prediction of flow parameters.

scholarly journals Smart Routing Protocol Algorithm Using Fuzzy Artificial Neural Network OSPF

2021 ◽  
pp. 155-160
Author(s):  
Sami Hasan ◽  
Abdulhakeem Amer
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Routing Protocol ◽  
Minimum Weight ◽  
The Other ◽  
Transmission Time ◽  
Fuzzy Membership ◽  
Optimum Path ◽  
Artificial Neural ◽  
Network Overlap

The OSPF cost is proportionally indicated the transmitting packet overhead through a certain interface and inversely proportional to the interface bandwidth. Thus, this cost may minimized by direct packet transmitting to the other side via various probable paths simultaneously. Logically, the minimum weight path is the optimum path. This paper propose a novel Fuzzy Artificial Neural Network to create Smart Routing Protocol Algorithm. Consequently, the Fuzzy Artificial Neural Network Overlap has been reduced from (0.883 ms) to (0.602 ms) at fuzzy membership 1.5 to 4.5 respectively. This indicated the transmission time is two-fold faster than the standard overlapping time (1.3 ms).

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