scholarly journals CNN for Image Identification of Hiragana Based on Pattern Recognition using CNN

2021 ◽  
Vol 6 (2) ◽  
pp. 62-71
Author(s):  
Chaerul Umam ◽  
Andi Danang Krismawan ◽  
Rabei Raad Ali
Keyword(s):  
Neural Network ◽  
Pattern Recognition ◽  
Convolutional Neural Network ◽  
Training Data ◽  
Training Process ◽  
Image Identification ◽  
Training Stage ◽  
Testing Stage ◽  
Testing Data ◽  
Network Method

Hiragana is one of the letters in Japanese. In this study, CNN (Convolutional Neural Network) method used as identication method, while he preprocessing used thresholding. Then carry out the normalization stage and the filtering stage to remove noise in the image. At the training stage use maxpooling and danse methods as a liaison in the training process, wherea in testing stage using the Adam Optimizer method. Here, we use 1000 images from 50 hiragana characters with a ratio of 950: 50, 950 as training data and 50 data as testing data. Our experiment yield accuracy in 95%.

Improving Pattern Recognition Rate by Gaussian Hopfield Neural Network

2011 ◽  
Vol 189-193 ◽  
pp. 2042-2045 ◽  
Author(s):  
Shang Jen Chuang ◽  
Chiung Hsing Chen ◽  
Chien Chih Kao ◽  
Fang Tsung Liu
Keyword(s):  
Neural Network ◽  
Pattern Recognition ◽  
Gaussian Distribution ◽  
Test Pattern ◽  
Recognition Rate ◽  
Hopfield Neural Network ◽  
Training Data ◽  
New Method ◽  
Gaussian Filter ◽  
Testing Data

English letters cannot be recognized by the Hopfield Neural Network if it contains noise over 50%. This paper proposes a new method to improve recognition rate of the Hopfield Neural Network. To advance it, we add the Gaussian distribution feature to the Hopfield Neural Network. The Gaussian filter was added to eliminate noise and improve Hopfield Neural Network’s recognition rate. We use English letters from ‘A’ to ‘Z’ as training data. The noises from 0% to 100% were generated randomly for testing data. Initially, we use the Gaussian filter to eliminate noise and then to recognize test pattern by Hopfield Neural Network. The results are we found that if letters contain noise between 50% and 53% will become reverse phenomenon or unable recognition [6]. In this paper, we propose to uses multiple filters to improve recognition rate when letters contain noise between 50% and 53%.

scholarly journals Batik pattern recognition using convolutional neural network

2020 ◽  
Vol 9 (4) ◽  
pp. 1430-1437
Author(s):  
Mohammad Arif Rasyidi ◽  
Taufiqotul Bariyah
Keyword(s):  
Neural Network ◽  
Pattern Recognition ◽  
Convolutional Neural Network ◽  
Test Data ◽  
Network Architecture ◽  
Experimental Results ◽  
Training Data ◽  
Excellent Performance ◽  
Artistic Value

Batik is one of Indonesia's cultures that is well-known worldwide. Batik is a fabric that is painted using canting and liquid wax so that it forms patterns of high artistic value. In this study, we applied the convolutional neural network (CNN) to identify six batik patterns, namely Banji, Ceplok, Kawung, Mega Mendung, Parang, and Sekar Jagad. 994 images from the 6 categories were collected and then divided into training and test data with a ratio of 8:2. Image augmentation was also done to provide variations in training data as well as to prevent overfitting. Experimental results on the test data showed that CNN produced an excellent performance as indicated by accuracy of 94% and top-2 accuracy of 99% which was obtained using the DenseNet network architecture.

scholarly journals The Artificial Power System Networks Stability Control Using the Technology of Neural Network

2019 ◽  
Vol 124 ◽  
pp. 05002
Author(s):  
A M-N Alzakkar ◽  
I.M. Valeev ◽  
N.P. Mestnikov ◽  
E.G. Nurullin
Keyword(s):  
Neural Network ◽  
Stability Control ◽  
Training Data ◽  
Electric Voltage ◽  
Normal Loading ◽  
Training Stage ◽  
Testing Stage ◽  
On Line ◽  
Two Stages ◽  
Speed And Accuracy

In the present work, the electric voltage stability at Muharda station in Syria was studied during the normal and up to normal loading states. The results were obtained using artificial neural network, which consists of three layers (input-hidden-output). This network is characterized by the speed and accuracy in processing before failure and supply turn-off, which may lead to economical problems. This study was carried out using two different generating schemes in this station (single double). The performance of this network consists of two stages: training stage (off-line) and testing stage (on-line), and a comparison between these stages is carried out, which leads to optimization the load in testing cases depending on the training data.

scholarly journals KLASIFIKASI CITRA DIGITAL BUMBU DAN REMPAH DENGAN ALGORITMA CONVOLUTIONAL NEURAL NETWORK (CNN)

2020 ◽  
Vol 9 (3) ◽  
pp. 273-282
Author(s):  
Isna Wulandari ◽  
Hasbi Yasin ◽  
Tatik Widiharih
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Activation Function ◽  
Training Data ◽  
Young Generation ◽  
Testing Data ◽  
Filter Size ◽  
Hidden Layer ◽  
Herbs And Spices

The recognition of herbs and spices among young generation is still low. Based on research in SMK 9 Bandung, showed that there are 47% of students that did not recognize herbs and spices. The method that can be used to overcome this problem is automatic digital sorting of herbs and spices using Convolutional Neural Network (CNN) algorithm. In this study, there are 300 images of herbs and spices that will be classified into 3 categories. It’s ginseng, ginger and galangal. Data in each category is divided into two, training data and testing data with a ratio of 80%: 20%. CNN model used in classification of digital images of herbs and spices is a model with 2 convolutional layers, where the first convolutional layer has 10 filters and the second convolutional layer has 20 filters. Each filter has a kernel matrix with a size of 3x3. The filter size at the pooling layer is 3x3 and the number of neurons in the hidden layer is 10. The activation function at the convolutional layer and hidden layer is tanh, and the activation function at the output layer is softmax. In this model, the accuracy of training data is 0.9875 and the loss value is 0.0769. The accuracy of testing data is 0.85 and the loss value is 0.4773. Meanwhile, testing new data with 3 images for each category produces an accuracy of 88.89%. Keywords: image classification, herbs and spices, CNN. 

scholarly journals Indonesian news classification using convolutional neural network

2020 ◽  
Vol 19 (2) ◽  
pp. 1000
Author(s):  
Muhammad Ali Ramdhani ◽  
Dian Sa’adillah Maylawati ◽  
Teddy Mantoro
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Language Processing ◽  
Research Area ◽  
Training Data ◽  
Text Data ◽  
Testing Data ◽  
Headline News ◽  
Language Characteristics ◽  
Area Data

<span>Every language has unique characteristics, structures, and grammar. Thus, different styles will have different processes and result in processed in Natural Language Processing (NLP) research area. In the current NLP research area, Data Mining (DM) or Machine Learning (ML) technique is popular, especially for Deep Learning (DL) method. This research aims to classify text data in the Indonesian language using Convolutional Neural Network (CNN) as one of the DL algorithms. The CNN algorithm used modified following the Indonesian language characteristics. Thereby, in the text pre-processing phase, stopword removal and stemming are particularly suitable for the Indonesian language. The experiment conducted using 472 Indonesian News text data from various sources with four categories: ‘hiburan’ (entertainment), ‘olahraga’ (sport), ‘tajuk utama’ (headline news), and ‘teknologi’ (technology). Based on the experiment and evaluation using 377 training data and 95 testing data, producing five models with ten epoch for each model, CNN has the best percentage of accuracy around 90,74% and loss value around 29,05% for 300 hidden layers in classifying the Indonesian News data.</span>

scholarly journals Skin Cancer Detection using CNN Algorithm

2020 ◽  
Vol 9 (6) ◽  
pp. 45-49
Keyword(s):  
Neural Network ◽  
Skin Cancer ◽  
Convolutional Neural Network ◽  
Well Being ◽  
Training Data ◽  
Data Sets ◽  
Data Set ◽  
Sequential Model ◽  
Pigmented Lesions ◽  
Testing Data

The project “Disease Prediction Model” focuses on predicting the type of skin cancer. It deals with constructing a Convolutional Neural Network(CNN) sequential model in order to find the type of a skin cancer which takes a huge troll on mankind well-being. Since development of programmed methods increases the accuracy at high scale for identifying the type of skin cancer, we use Convolutional Neural Network, CNN algorithm in order to build our model . For this we make use of a sequential model. The data set that we have considered for this project is collected from NCBI, which is well known as HAM10000 dataset, it consists of massive amounts of information regarding several dermatoscopic images of most trivial pigmented lesions of skin which are collected from different sufferers. Once the dataset is collected, cleaned, it is split into training and testing data sets. We used CNN to build our model and using the training data we trained the model , later using the testing data we tested the model. Once the model is implemented over the testing data, plots are made in order to analyze the relation between the echos and loss function. It is also used to analyse accuracy and echos for both training and testing data.

scholarly journals Detecting the Ripeness of Harvest-Ready Dragon Fruit using Smaller VGGNet-Like Network

2020 ◽  
Vol 3 (2) ◽  
pp. 35 ◽  
Author(s):  
I Made Wismadi ◽  
Duman Care Khrisne ◽  
I Made Arsa Suyadnya
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Confusion Matrix ◽  
Learning Approach ◽  
Training Process ◽  
Dragon Fruit ◽  
Network Method

This study has a purpose to develop an application to detect the ripeness of the dragon fruit with the deep learning approach using the Smaller VGGNet-like Network method. In this study, the dragon fruit are classified into two classes: ripe or ready for harvest and still raw, by using the Convolutional Neural Network (CNN). The training process utilize the hard packages in python with the backend tensorflow. The model in this research is tested using the confusion matrix and ROC method with the condition that 100 new data are tested. Based on the test conducted, the level of accuracy in classifying the ripeness of the dragon fruit is 91%, and the test using 20 epoch, 50 epoch, 100 epoch, and 500 epoch produced an AUROC value of 0,95.

scholarly journals DETEKSI SITUS PORNOGRAFI BERDASARKAN GAMBAR MENGGUNAKAN METODE CONVOLUTIONAL NEURAL NETWORK

2019 ◽  
Vol 12 (2) ◽  
pp. 137-148
Author(s):  
Ahmad Hunaepi ◽  
Makhsun Makhsun ◽  
Sarwani Sarwani
Keyword(s):  
Neural Network ◽  
Information Technology ◽  
Convolutional Neural Network ◽  
Service Providers ◽  
Test Results ◽  
Training Process ◽  
Accuracy Rate ◽  
Internet Service ◽  
Internet Users ◽  
Network Method

Today, the development of information technology is very fast. It is also followed by the growth of internet sites that are very fast, no exception the growth of sites contains pornographic. The Association of Indonesian Internet Service Providers (APJII), in 2018, reports as many as 55.9% of Indonesian internet users had experienced of appearance pornographic content suddenly. This study aims to detect pornographic sites based on images using the convolutional neural network method. To find out the level of accuracy in detection of pornographic sites, a training process with a dataset of 300 images is required. From the test results, an accuracy rate of 85% in the detection of pornographic sites is obtained.

scholarly journals Pemodelan Generator Uap Berbasis Jaringan Saraf Tiruan dengan Algoritme Pelatihan BPGD-ALAM

2016 ◽  
Vol 6 (1) ◽  
pp. 1
Author(s):  
Fadhlia Annisa ◽  
Agfianto Eko Putra
Keyword(s):  
Neural Network ◽  
Steam Generator ◽  
Multiple Input Multiple Output ◽  
Network Models ◽  
Training Data ◽  
Training Process ◽  
Training Simulator ◽  
Neural Network Models ◽  
Validation Data ◽  
Testing Data

Steam generator is unit plant which has nonlinear and complex system with multiple-input-multiple-output (MIMO) configuration which is hard to be modeled. Whereas, steam generator model is very useful to create simulation such as operator training simulator (OTS). The purpose of this research is to obtain model of steam generator which has 8 output parameters and 9 input parameters based neural network (NN) with BPGD-ALAM training algorithm. Data had been taken from steam generator of PT. Chevron Pacific Indonesia, Duri and it is divided into three types, i.e training data, validation data and testing data. Training data was used to obtain model for each ouput through training process. Verification model is also done for each epoch using validation data to monitor training process whether overfitting occurs or not. Eight NN model of each output which is obtained from training and verification, is tested using testing data for getting its performance. From the reseach results, architecture of neural network models are obtained with various configuration for each output with RMSE value under 9.71 %. It shows that model which has been obtained, close with steam generator real system.

scholarly journals Maturity classification of cacao through spectrogram and convolutional neural network

2020 ◽  
Vol 8 (3) ◽  
pp. 228-233
Author(s):  
Gilbert E. Bueno ◽  
Kristine A. Valenzuela ◽  
Edwin R. Arboleda
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Poor Quality ◽  
Training Data ◽  
Training Process ◽  
Validation Data ◽  
Sound File ◽  
Audio Data ◽  
Mel Frequency Cepstral Coefficient

Cacao pod's ideal harvesting time is when it is about to be ripe. Immature harvest would result in hard cacao beans not suitable for fermentation, while overripe cacao pods lead to fungal-infected, defective, and poor-quality yields. The demand for high-quality cacao products is expected to rise due to advancing technology in the present. Pre-harvesting needs to provide optimal identification of which amongst the pods are ripened enough and ready for the next stage of the cacao process. This paper recommends a technique to determine the ripeness of cacao. Nine hundred thirty-three cacao samples were used to collect thumping audio data at five different pod's exocarp locations. Each sound file is 1 second long, creating 4665 cacao sound file datasets at 16kHz sample rate and 16-bit audio bit depth. The process of the Mel-Frequency Cepstral Coefficient Spectogram was then applied to extract recognizable features for the training process. The deep learning method integrated was a convolutional neural network (CNN) to classify the cacao sound successfully. The experimental design model's output exhibits an accuracy of 97.50 % for the training data and 97.13 % for the validation data. While the overall accuracy mean of the classification system is 97.46 %, whether the cacao is unripe or ripe.

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