scholarly journals Implementation of Machine Learning Using the Convolution Neural Network Method for Aglaonema Interest Classification

2021 ◽  
Vol 5 (1) ◽  
pp. 21-30
Author(s):  
Rachmat Rasyid ◽  
Abdul Ibrahim
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Ornamental Plants ◽  
Convolution Neural Network ◽  
Neural Network Method ◽  
Study Results ◽  
Network Method ◽  
The Many

One of the wealth of the Indonesian nation is the many types of ornamental plants. Ornamental plants, for example, the Aglaonema flower, which is much favored by hobbyists of ornamental plants, from homemakers, is a problem to distinguish between types of aglaonema ornamental plants with other ornamental plants. So the authors try to research with the latest technology using a deep learning convolutional neural network method. It is for calcifying aglaonema interest. This research is based on having fascinating leaves and colors. With the study results using the CNN method, the products of aglaonema flowers of Adelia, Legacy, Widuri, RedKochin, Tiara with moderate accuracy value are 56%. In contrast, the aglaonema type Sumatra, RedRuby, has the most accuracy a high of 61%.

Retinal blood vessels detection for diabetic retinopathy with Ridgelet transform and convolution neural network

2020 ◽  
Vol 18 (06) ◽  
pp. 2050048
Author(s):  
Nirmal Yadav
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Diabetic Retinopathy ◽  
Deep Learning ◽  
Blood Vessels ◽  
Machine Learning Algorithms ◽  
Convolution Neural Network ◽  
Transform Method ◽  
Ridgelet Transform ◽  
Study Results

Applying machine learning in life sciences, especially diagnostics, has become a key area of focus for researchers. Combining machine learning with traditional algorithms provides a unique opportunity of providing better solutions for the patients. In this paper, we present study results of applying the Ridgelet Transform method on retina images to enhance the blood vessels, then using machine learning algorithms to identify cases of Diabetic Retinopathy (DR). The Ridgelet transform provides better results for line singularity of image function and, thus, helps to reduce artefacts along the edges of the image. The Ridgelet Transform method, when compared with earlier known methods of image enhancement, such as Wavelet Transform and Contourlet Transform, provided satisfactory results. The transformed image using the Ridgelet Transform method with pre-processing quantifies the amount of information in the dataset. It efficiently enhances the generation of features vectors in the convolution neural network (CNN). In this study, a sample of fundus photographs was processed, which was obtained from a publicly available dataset. In pre-processing, first, CLAHE was applied, followed by filtering and application of Ridgelet transform on the patches to improve the quality of the image. Then, this processed image was used for statistical feature detection and classified by deep learning method to detect DR images from the dataset. The successful classification ratio was 98.61%. This result concludes that the transformed image of fundus using the Ridgelet Transform enables better detection by leveraging a transform-based algorithm and the deep learning.

scholarly journals Implementasi Speech Recognition Pada Aplikasi E-Prescribing Menggunakan Algoritme Convolutional Neural Network

2021 ◽  
Vol 5 (2) ◽  
pp. 460
Author(s):  
Nur Azis ◽  
Herwanto Herwanto ◽  
Fathurrahman Ramadhani
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Speech Recognition ◽  
Convolutional Neural Network ◽  
Open Source ◽  
Basic Method ◽  
Neural Network Method ◽  
The Neural Network ◽  
Network Method

The process of manually prescribing drugs by doctors can cause several problems, including doctors not knowing what drugs are available and it takes time to find out what drugs are available in the pharmacy. Speech recognition is now widely used in various ways, which can help facilitate work. The application of speech recognition can be done in the e-prescribing application with the neural network method using the Convolutional Neural Network (CNN) algorithm, which is the basic method of deep learning. This study aims to facilitate physicians in filling out drug data in e-prescribing applications using speech recognition. The data used in this study were obtained from the open source dataset provided by Google and collected independent datasets. From the results of experiments that have been carried out, the accuracy achieved with 40 epochs and 40 direct impressions with different words is 90%. Where words are successfully recognized 36 words out of 40 words

Onions classification automation using deep learning with convolutional neural network method

2021 ◽  
Author(s):  
Rinandha Puspadhani ◽  
Tuti Purwaningsih ◽  
Ayundyah Kesumawati ◽  
Arum Handini P. ◽  
R. B. Fajriya Hakim
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Neural Network Method ◽  
Network Method

scholarly journals Classification Of Rice Plant Diseases Using the Convolutional Neural Network Method

2021 ◽  
Vol 12 (2) ◽  
pp. 123
Author(s):  
A A JE Veggy Priyangka ◽  
I Made Surya Kumara
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Convolutional Neural Network ◽  
Rice Plant ◽  
Data Augmentation ◽  
Rice Production ◽  
Plant Diseases ◽  
Test Accuracy ◽  
Neural Network Method ◽  
Network Method

Indonesia is one of the countries with the population majority of farming. The agricultural sector in Indonesia is supported by fertile land and a tropical climate. Rice is one of the agricultural sectors in Indonesia. Rice production in Indonesia has decreased every year. Thus, rice production factors are very significant. Rice disease is one of the factors causing the decline in rice production in Indonesia. Technological developments have made it easier to recognize the types of rice plant diseases. Machine learning is one of the technologies used to identify types of rice diseases. The classification system of rice plant disease used the Convolutional Neural Network method. Convolutional Neural Network (CNN) is a machine learning method used in object recognition. This method applies to the VGG19 architecture, which has features to improve results. The image used as training and test data consists of 105 images, divided into training and test images. Parameter testing using epoch variations and data augmentation. The research results obtained a test accuracy of 95.24%.

scholarly journals An Overview of Convolutional Neural Network: Its Architecture and Applications

2018 ◽  
Author(s):  
Shadman Sakib ◽  
Nazib Ahmed ◽  
Ahmed Jawad Kabir ◽  
Hridon Ahmed
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Artificial Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Learning Strategies ◽  
Learning Tasks ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
The Many

With the increase of the Artificial Neural Network (ANN), machine learning has taken a forceful twist in recent times. One of the most spectacular kinds of ANN design is the Convolutional Neural Network (CNN). The Convolutional Neural Network (CNN) is a technology that mixes artificial neural networks and up to date deep learning strategies. In deep learning, Convolutional Neural Network is at the center of spectacular advances. This artificial neural network has been applied to several image recognition tasks for decades and attracted the eye of the researchers of the many countries in recent years as the CNN has shown promising performances in several computer vision and machine learning tasks. This paper describes the underlying architecture and various applications of Convolutional Neural Network.

scholarly journals An Overview of Convolutional Neural Network: Its Architecture and Applications

2019 ◽  
Author(s):  
Shadman Sakib ◽  
Nazib Ahmed ◽  
Ahmed Jawad Kabir ◽  
Hridon Ahmed
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Artificial Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Learning Strategies ◽  
Learning Tasks ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
The Many

With the increase of the Artificial Neural Network (ANN), machine learning has taken a forceful twist in recent times. One of the most spectacular kinds of ANN design is the Convolutional Neural Network (CNN). The Convolutional Neural Network (CNN) is a technology that mixes artificial neural networks and up to date deep learning strategies. In deep learning, Convolutional Neural Network is at the center of spectacular advances. This artificial neural network has been applied to several image recognition tasks for decades and attracted the eye of the researchers of the many countries in recent years as the CNN has shown promising performances in several computer vision and machine learning tasks. This paper describes the underlying architecture and various applications of Convolutional Neural Network.

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