Convolutional Neural Network (CNN) to determine the character of wayang kulit

Indonesia is a country with diverse cultures. One of which is Wayang Kulit, which has been recognized by UNESCO. Wayang kulit has a variety of names and personalities, however most younger generations are not familiar with the characters of these shadow puppets. With today's rapid technological advancements, people could use this technology to detect objects using cameras. Convolutional Neural Network (CNN) is one method that can be used. CNN is a learning process that is included in the Deep Learning section and is used to find the best representation. The CNN is commonly used for object detection, would be used to classify good and bad characters. The data used consists of 100 black and white puppet images that were downloaded one at a time. The data was obtained through a training process that uses the CNN method and Google Colab to help speed up the training process. After that, a new model is created to test the puppet images. The result obtained a 92 percent accuracy rate, means that CNN can differentiate the Wayang Kulit character

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Convolutional Neural Network for Iris Recognition

10.21203/rs.3.rs-244624/v1 ◽

2021 ◽

Author(s):

Wael Alnahari

Keyword(s):

Neural Network ◽

Machine Learning ◽

Neural Networks ◽

Deep Learning ◽

Convolutional Neural Network ◽

Iris Recognition ◽

Recognition System ◽

High Accuracy ◽

Accuracy Rate

Abstract In this paper, I proposed an iris recognition system by using deep learning via neural networks (CNN). Although CNN is used for machine learning, the recognition is achieved by building a non-trained CNN network with multiple layers. The main objective of the code the test pictures’ category (aka person name) with a high accuracy rate after having extracted enough features from training pictures of the same category which are obtained from a that I added to the code. I used IITD iris which included 10 iris pictures for 223 people.

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Optimasi Akurasi Metode Convolutional Neural Network untuk Identifikasi Jenis Sampah

Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) ◽

10.29207/resti.v5i2.2754 ◽

2021 ◽

Vol 5 (2) ◽

pp. 312-318

Author(s):

Rima Dias Ramadhani ◽

Afandi Nur Aziz Thohari ◽

Condro Kartiko ◽

Apri Junaidi ◽

Tri Ginanjar Laksana ◽

...

Keyword(s):

Neural Network ◽

Neural Networks ◽

Convolutional Neural Network ◽

Computer Science ◽

No Value ◽

Convolutional Neural Networks ◽

Training Process ◽

Indonesian Government ◽

Speed Up ◽

Model Training

Waste is goods / materials that have no value in the scope of production, where in some cases the waste is disposed of carelessly and can damage the environment. The Indonesian government in 2019 recorded waste reaching 66-67 million tons, which is higher than the previous year, which was 64 million tons. Waste is differentiated based on its type, namely organic and anorganic waste. In the field of computer science, the process of sensing the type waste can be done using a camera and the Convolutional Neural Networks (CNN) method, which is a type of neural network that works by receiving input in the form of images. The input will be trained using CNN architecture so that it will produce output that can recognize the object being inputted. This study optimizes the use of the CNN method to obtain accurate results in identifying types of waste. Optimization is done by adding several hyperparameters to the CNN architecture. By adding hyperparameters, the accuracy value is 91.2%. Meanwhile, if the hyperparameter is not used, the accuracy value is only 67.6%. There are three hyperparameters used to increase the accuracy value of the model. They are dropout, padding, and stride. 20% increase in dropout to increase training overfit. Whereas padding and stride are used to speed up the model training process.

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Implementation of Deep Learning for Slump Optimization Based on Concrete Quality Using Convolutional Neural Network in PT. SELO PROGO SAKTI

Jurnal AKSI (Akuntansi dan Sistem Informasi) ◽

10.32486/aksi.v5i2.598 ◽

2020 ◽

Vol 5 (2) ◽

pp. 83-88

Author(s):

Hedi Pandowo

Keyword(s):

Neural Network ◽

Machine Learning ◽

Deep Learning ◽

Experimental Design ◽

Convolutional Neural Network ◽

Processing Unit ◽

Training Process ◽

Average Accuracy ◽

Graphical Processing ◽

Intelligence Science

Deep Learning is part of the scientific field of Machine Learning and Machine Learning is part of Artificial Intelligence science. Deep Learning has extraordinary capabilities by using a hardware Graphical Processing Unit (GPU) so that the artificial requirement network can run faster than using a Personal Computer Unit (CPU). Especially in terms of object classification in images using existing methods in the Convolutional Neural Network (CNN). The method used in this research is Preprocessing and Processing of Input Data, Training Process in which CNN is trained to obtain high accuracy from the classification carried out and the Testing Process which is a classification process using weights and bias from the results of the training process. This type of research is a pre experimental design (pre experimental design). The results of the object image classification test with different levels of confusion in the Concrete database with the Mix Design K-125, K-150, K-250 and K-300 produce an average accuracy value. This is also relevant to measuring the failure rate of concrete or slump

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Fish Detection Using Deep Learning

Applied Computational Intelligence and Soft Computing ◽

10.1155/2020/3738108 ◽

2020 ◽

Vol 2020 ◽

pp. 1-13 ◽

Cited By ~ 3

Author(s):

Suxia Cui ◽

Yu Zhou ◽

Yonghui Wang ◽

Lujun Zhai

Keyword(s):

Neural Network ◽

Deep Learning ◽

Data Augmentation ◽

Autonomous Underwater Vehicle ◽

Training Data ◽

Training Process ◽

Data Set ◽

Speed Up ◽

Fish Detection ◽

Process Speed

Recently, human being’s curiosity has been expanded from the land to the sky and the sea. Besides sending people to explore the ocean and outer space, robots are designed for some tasks dangerous for living creatures. Take the ocean exploration for an example. There are many projects or competitions on the design of Autonomous Underwater Vehicle (AUV) which attracted many interests. Authors of this article have learned the necessity of platform upgrade from a previous AUV design project, and would like to share the experience of one task extension in the area of fish detection. Because most of the embedded systems have been improved by fast growing computing and sensing technologies, which makes them possible to incorporate more and more complicated algorithms. In an AUV, after acquiring surrounding information from sensors, how to perceive and analyse corresponding information for better judgement is one of the challenges. The processing procedure can mimic human being’s learning routines. An advanced system with more computing power can facilitate deep learning feature, which exploit many neural network algorithms to simulate human brains. In this paper, a convolutional neural network (CNN) based fish detection method was proposed. The training data set was collected from the Gulf of Mexico by a digital camera. To fit into this unique need, three optimization approaches were applied to the CNN: data augmentation, network simplification, and training process speed up. Data augmentation transformation provided more learning samples; the network was simplified to accommodate the artificial neural network; the training process speed up is introduced to make the training process more time efficient. Experimental results showed that the proposed model is promising, and has the potential to be extended to other underwear objects.

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Deep Learning Image Feature Recognition Algorithm for Judgment on the Rationality of Landscape Planning and Design

Complexity ◽

10.1155/2021/9921095 ◽

2021 ◽

Vol 2021 ◽

pp. 1-15

Author(s):

Bin Hu

Keyword(s):

Neural Network ◽

Deep Learning ◽

Convolutional Neural Network ◽

Feature Recognition ◽

Semantic Segmentation ◽

Landscape Patterns ◽

Image Feature ◽

Accuracy Rate ◽

Landscape Elements ◽

Landscape Element

This paper uses an improved deep learning algorithm to judge the rationality of the design of landscape image feature recognition. The preprocessing of the image is proposed to enhance the data. The deficiencies in landscape feature extraction are further addressed based on the new model. Then, the two-stage training method of the model is used to solve the problems of long training time and convergence difficulties in deep learning. Innovative methods for zoning and segmentation training of landscape pattern features are proposed, which makes model training faster and generates more creative landscape patterns. Because of the impact of too many types of landscape elements in landscape images, traditional convolutional neural networks can no longer effectively solve this problem. On this basis, a fully convolutional neural network model is designed to perform semantic segmentation of landscape elements in landscape images. Through the method of deconvolution, the pixel-level semantic segmentation is realized. Compared with the 65% accuracy rate of the convolutional neural network, the fully convolutional neural network has an accuracy rate of 90.3% for the recognition of landscape elements. The method is effective, accurate, and intelligent for the classification of landscape element design, which better improves the accuracy of classification, greatly reduces the cost of landscape element design classification, and ensures that the technical method is feasible. This paper classifies landscape behavior based on this model for full convolutional neural network landscape images and demonstrates the effectiveness of using the model. In terms of landscape image processing, the image evaluation provides a certain basis.

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

Journal of Electrical Electronics and Informatics ◽

10.24843/jeei.2019.v03.i02.p01 ◽

2020 ◽

Vol 3 (2) ◽

pp. 35 ◽

Cited By ~ 2

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.

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An Accuracy vs. Complexity Comparison of Deep Learning Architectures for the Detection of COVID-19 Disease

Computation ◽

10.3390/computation9010003 ◽

2021 ◽

Vol 9 (1) ◽

pp. 3

Author(s):

Sima Sarv Ahrabi ◽

Michele Scarpiniti ◽

Enzo Baccarelli ◽

Alireza Momenzadeh

Keyword(s):

Neural Network ◽

Deep Learning ◽

Convolutional Neural Network ◽

Medical Research ◽

Binary Classification ◽

Lung Infection ◽

X Rays ◽

Training Process ◽

Learning Architectures

In parallel with the vast medical research on clinical treatment of COVID-19, an important action to have the disease completely under control is to carefully monitor the patients. What the detection of COVID-19 relies on most is the viral tests, however, the study of X-rays is helpful due to the ease of availability. There are various studies that employ Deep Learning (DL) paradigms, aiming at reinforcing the radiography-based recognition of lung infection by COVID-19. In this regard, we make a comparison of the noteworthy approaches devoted to the binary classification of infected images by using DL techniques, then we also propose a variant of a convolutional neural network (CNN) with optimized parameters, which performs very well on a recent dataset of COVID-19. The proposed model’s effectiveness is demonstrated to be of considerable importance due to its uncomplicated design, in contrast to other presented models. In our approach, we randomly put several images of the utilized dataset aside as a hold out set; the model detects most of the COVID-19 X-rays correctly, with an excellent overall accuracy of 99.8%. In addition, the significance of the results obtained by testing different datasets of diverse characteristics (which, more specifically, are not used in the training process) demonstrates the effectiveness of the proposed approach in terms of an accuracy up to 93%.

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Application of Deep Learning Networks to Segmentation of Surface of Railway Tracks

Sensors ◽

10.3390/s21124065 ◽

2021 ◽

Vol 21 (12) ◽

pp. 4065

Author(s):

Piotr Bojarczak ◽

Waldemar Nowakowski

Keyword(s):

Neural Network ◽

Deep Learning ◽

Convolutional Neural Network ◽

Vision System ◽

Gaussian Function ◽

Railway Track ◽

Learning Networks ◽

Improve Performance ◽

Speed Up ◽

Visual Geometry

The article presents a vision system for detecting elements of railway track. Four types of fasteners, wooden and concrete sleepers, rails, and turnouts can be recognized by our system. In addition, it is possible to determine the degree of sleeper ballast coverage. Our system is also able to work when the track is moderately covered by snow. We used a Fully Convolutional Neural Network with 8 times upsampling (FCN-8) to detect railway track elements. In order to speed up training and improve performance of the model, a pre-trained deep convolutional neural network developed by Oxford’s Visual Geometry Group (VGG16) was used as a framework for our system. We also verified the invariance of our system to changes in brightness. To do this, we artificially varied the brightness of images. We performed two types of tests. In the first test, we changed the brightness by a constant value for the whole analyzed image. In the second test, we changed the brightness according to a predefined distribution corresponding to Gaussian function.

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DETEKSI SITUS PORNOGRAFI BERDASARKAN GAMBAR MENGGUNAKAN METODE CONVOLUTIONAL NEURAL NETWORK

JURNAL TEKNIK INFORMATIKA ◽

10.15408/jti.v12i2.12488 ◽

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.

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Image-Based Animal Detection and Breed Identification Using Neural Networks

Issue 4 - Journal of Science and Technology ◽

10.46243/jst.2020.v5.i5.pp130-134 ◽

2020 ◽

pp. 130-134

Keyword(s):

Neural Network ◽

Deep Learning ◽

Convolutional Neural Network ◽

High Volume ◽

Manual Task ◽

In The Wild ◽

Research Findings ◽

Speed Up ◽

Animal Images ◽

Animal Detection

Having accurate, detailed, and up-to-date information about the behaviour of animals in the wild world would improve our ability to study and conserve ecosystems. We investigate the ability to automatically, accurately, and inexpensively collect such data through various sources, which could help catalyse the transformation of many fields of ecology, wildlife biology, zoology, conservation biology, animal behaviour into “big data” sciences and many more. So extracting information from the pictures remains an expensive, time-consuming, and manual task for us. We demonstrate that such information can be automatically extracted by deep learning and convolutional neural network. Leveraging on recent advances in deep learning techniques in computer vision, we propose in this project a framework to build automated animal recognition in the wild, aiming at an automated wildlife monitoring system. In particular, we use a single-labelled dataset done by citizen scientists, and the state-of-the-art deep convolutional neural network architectures, face biometrics, to train a computational system capable of filtering animal images and identifying species automatically and counting the number of species. Our results suggest that deep learning could enable the inexpensive, unobtrusive, high-volume, and even real-time collection of a wealth of information about vast numbers of animals in the wild and this, in turn, can, therefore, speed up research findings, construct more efficient citizen science-based monitoring systems and subsequent management decisions, having the potential to make significant impacts to the world of ecology and trap camera images analysis .

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