scholarly journals Klasifikasi Topeng Cirebon menggunakan Metode Convolutional Neural Network

2021 ◽  
Vol 8 (1) ◽  
pp. 163-169
Author(s):  
Felix Indra Kurniadi
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Cultural Heritage ◽  
Convolutional Neural Network ◽  
Automatic Identification ◽  
Intangible Cultural Heritage ◽  
Image Processing Techniques ◽  
The Third ◽  
The Government ◽  
Processing Techniques

Cirebon mask is one of the intangible cultural heritage in Indonesia. It is one of the prominent cultural assets from Cirebon and becoming one of the identity Cirebon culture. However, the current condition people tend to forget the cultural asset and lack of help from the government makes the Cirebon mask become the third-rate assets. Our concern lays on the extinction of this Mask. We want to implement digitation and automatic identification using image processing techniques. In this paper, we applied the Convolutional Neural Network for Cirebon Mask classification.

scholarly journals APLICAÇÃO DE REDES NEURAIS CONVOLUCIONAIS NO RECONHECIMENTO DE CARACTERES EM PLACAS INFORMATIVAS JAPONESAS

2021 ◽  
Vol 13 (2) ◽  
pp. 12-24
Author(s):  
Rafael Yuji Hirata Furusho ◽  
Francisco Assis da Silva ◽  
Leandro Luiz de Almeida ◽  
Danillo Roberto Pereira ◽  
Mário Augusto Pazoti ◽  
...  
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Edge Detection ◽  
Convolutional Neural Network ◽  
Mathematical Morphology ◽  
Digital Image Processing ◽  
Digital Image ◽  
Image Processing Techniques ◽  
Processing Techniques

Unlike most Western countries, which have a Latin-derived base alphabet, Japan has two syllabic alphabets called Hiragana and Katakana, and a Chinese alphabet, called Kanji. The vast differences in the writing of these Eastern alphabets to Western alphabets, Western alphabet-based OCR algorithms tend not to efficiently detect Japanese characters. This work contributes to a methodology applying digital image processing techniques, such as color range-based segmentation, edge detection and mathematical morphology techniques, to detect Japanese traffic informationalplates correctly the perspective and segment the characters contained in it. A convolutional neural network wasused to perform the classification of Hiragana characters contained in the segmented plates, withaccuracyof 94.37%.

scholarly journals Brain Tumor Segmentation using K-Means Clustering and Detection using Convolutional Neural Network

2020 ◽  
Vol 9 (5) ◽  
pp. 1452-1455
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Brain Tumor ◽  
Convolutional Neural Network ◽  
Clustering Algorithm ◽  
Tumor Segmentation ◽  
Brain Tumor Segmentation ◽  
Mri Scans ◽  
Two Phases ◽  
Processing Techniques

This paper presents brain tumor detection and segmentation using image processing techniques. Convolutional neural networks can be applied for medical research in brain tumor analysis. The tumor in the MRI scans is segmented using the K-means clustering algorithm which is applied of every scan and the feed it to the convolutional neural network for training and testing. In our CNN we propose to use ReLU and Sigmoid activation functions to determine our end result. The training is done only using the CPU power and no GPU is used. The research is done in two phases, image processing and applying neural network.

scholarly journals Machine Learning and Fuzzy Logic based Detection, Classification and Grading of Leaf Disease in Plants

2020 ◽  
Vol 9 (7S) ◽  
pp. 38-42
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Image Processing ◽  
Fuzzy Logic ◽  
Learning Algorithm ◽  
Expert Advice ◽  
Support Vector ◽  
Machine Learning Algorithm ◽  
Image Processing Techniques ◽  
Processing Techniques

India is an agricultural country where most of people are depends on the agriculture. When Plants are infected by the virus, fungus and bacteria, they are mostly seen on leaves and stems of the plants. Because of that, plants production is decreased also economy of the country is decreased. The farmer has to identify the disease and decide which pesticide will be used to control the disease in plants. To finding out which disease affect the plants, the farmer contacts the expert for the solution. The expert gives the advice based on its knowledge and information but sometimes seeking the expert advice is time consuming, expensive and may be not accurate. So, to solve this problem, the image processing techniques and Machine Learning algorithm like Neural Network, Fuzzy Logic and Support Vector Machine gives the better, accurate and affordable solution to control the plants disease than manual method.

scholarly journals Devanagari Handwritten Character Recognition Using Deep Learning

2022 ◽  
Vol 10 (1) ◽  
pp. 102-105
Author(s):  
Kannuru Padmaja
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Character Recognition ◽  
Automation System ◽  
Handwritten Character Recognition ◽  
Image Processing Techniques ◽  
Handwritten Character ◽  
Processing Techniques ◽  
Deep Learning Model

Abstract: In this paper, we present the implementation of Devanagari handwritten character recognition using deep learning. Hand written character recognition gaining more importance due to its major contribution in automation system. Devanagari script is one of various languages script in India. It consists of 12 vowels and 36 consonants. Here we implemented the deep learning model to recognize the characters. The character recognition mainly five steps: pre-processing, segmentation, feature extraction, prediction, post-processing. The model will use convolutional neural network to train the model and image processing techniques to use the character recognition and predict the accuracy of rcognition. Keywords: convolutional neural network, character recognition, Devanagari script, deep learning.

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