scholarly journals LITERATURE REVIEW: PENGENALAN WAJAH MENGGUNAKAN ALGORITMA CONVOLUTIONAL NEURAL NETWORK

2020 ◽  
Vol 8 (2) ◽  
Author(s):  
Sriyati Sriyati ◽  
Arief Setyanto ◽  
Emha Emha Luthfi
Keyword(s):  
Neural Network ◽  
Literature Review ◽  
Convolutional Neural Network ◽  
School Environment ◽  
Service System ◽  
Facial Recognition ◽  
The Self ◽  
Hybrid Approaches ◽  
Network Methods ◽  
Self Service

Facial recognition to detect the identity of the gallon user's face in honesty in the school environment has many methods such as local, global, and hybrid approaches. The main problem of using the gallon of honesty is that the program uses the Self-service system, which is a self-service system, where the buyer serves itself unattended. The water charging activity is still found by users who are dishonest, such as taking water but not putting money into the place that has been provided, the thing that should be when the user fills the water then the user must also enter Money into the box provided. Because of the absence of supervision in this program of honesty then it is difficult to know who is dishonest in order to be able to do prevention for the dishonesty that has occurred when using the gallon of honesty program. Facial recognition using the Convolutional Neural Network (CNN) method to classify images. A literature review is used to analyse and focus on techniques in conducting facial recognition on the use of gallons of honesty. Keywords: facial recognition, convolutional neural network methods, a gallon of honesty

Pencarian Question-Answer Menggunakan Convolutional Neural Network Pada Topik Agama Berbahasa Indonesia

2018 ◽  
Vol 10 (1) ◽  
pp. 57-64 ◽  
Author(s):  
Rizqa Raaiqa Bintana ◽  
Chastine Fatichah ◽  
Diana Purwitasari
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Question Answering ◽  
Mean Average Precision ◽  
Average Precision ◽  
Community Based ◽  
Network Methods ◽  
The Mean ◽  
Index Terms ◽  
Search Information

Community-based question answering (CQA) is formed to help people who search information that they need through a community. One condition that may occurs in CQA is when people cannot obtain the information that they need, thus they will post a new question. This condition can cause CQA archive increased because of duplicated questions. Therefore, it becomes important problems to find semantically similar questions from CQA archive towards a new question. In this study, we use convolutional neural network methods for semantic modeling of sentence to obtain words that they represent the content of documents and new question. The result for the process of finding the same question semantically to a new question (query) from the question-answer documents archive using the convolutional neural network method, obtained the mean average precision value is 0,422. Whereas by using vector space model, as a comparison, obtained mean average precision value is 0,282. Index Terms—community-based question answering, convolutional neural network, question retrieval

Convolutional Neural Network (CNN) on Realization of Facial Recognition Devices using Multi Embedded Computers

2021 ◽  
Author(s):  
Muhammad Fidy Nursyahrul ◽  
Agung Nugroho Jati ◽  
Casi Setianingsih ◽  
Gagah Ghaniswara Khaesarrahman ◽  
Muhammad Riefky Hadid ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Facial Recognition

Bearing fault diagnostics using EEMD processing and convolutional neural network methods

2020 ◽  
Vol 107 (9-10) ◽  
pp. 4077-4095
Author(s):  
Iskander Imed Eddine Amarouayache ◽  
Mohamed Nacer Saadi ◽  
Noureddine Guersi ◽  
Nadir Boutasseta
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Fault Diagnostics ◽  
Bearing Fault ◽  
Network Methods

scholarly journals SF-CNN: Signal Filtering Convolutional Neural Network for Precipitation Intensity Estimation

Sensors ◽  
2022 ◽  
Vol 22 (2) ◽  
pp. 551
Author(s):  
Chih-Wei Lin ◽  
Xiuping Huang ◽  
Mengxiang Lin ◽  
Sidi Hong
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Weather Conditions ◽  
Critical Issue ◽  
Activation Function ◽  
Precipitation Intensity ◽  
The Self ◽  
Intensity Estimation ◽  
Signal Filtering ◽  
Complex Models

Precipitation intensity estimation is a critical issue in the analysis of weather conditions. Most existing approaches focus on building complex models to extract rain streaks. However, an efficient approach to estimate the precipitation intensity from surveillance cameras is still challenging. This study proposes a convolutional neural network known as the signal filtering convolutional neural network (SF-CNN) to handle precipitation intensity using surveillance-based images. The SF-CNN has two main blocks, the signal filtering block (SF block) and the gradually decreasing dimension block (GDD block), to extract features for the precipitation intensity estimation. The SF block with the filtering operation is constructed in different parts of the SF-CNN to remove the noise from the features containing rain streak information. The GDD block continuously takes the pair of the convolutional operation with the activation function to reduce the dimension of features. Our main contributions are (1) an SF block considering the signal filtering process and effectively removing the useless signals and (2) a procedure of gradually decreasing the dimension of the feature able to learn and reserve the information of features. Experiments on the self-collected dataset, consisting of 9394 raining images with six precipitation intensity levels, demonstrate the proposed approach’s effectiveness against the popular convolutional neural networks. To the best of our knowledge, the self-collected dataset is the largest dataset for monitoring infrared images of precipitation intensity.

scholarly journals 3D Face Modeling Algorithm for Film and Television Animation Based on Lightweight Convolutional Neural Network

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Cheng Di ◽  
Jing Peng ◽  
Yihua Di ◽  
Siwei Wu
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Convolutional Neural Network ◽  
Facial Recognition ◽  
Qr Decomposition ◽  
Small Sample ◽  
Feature Extraction Algorithm ◽  
Extraction Algorithm ◽  
The Difference ◽  
Difference Criterion

Through the analysis of facial feature extraction technology, this paper designs a lightweight convolutional neural network (LW-CNN). The LW-CNN model adopts a separable convolution structure, which can propose more accurate features with fewer parameters and can extract 3D feature points of a human face. In order to enhance the accuracy of feature extraction, a face detection method based on the inverted triangle structure is used to detect the face frame of the images in the training set before the model extracts the features. Aiming at the problem that the feature extraction algorithm based on the difference criterion cannot effectively extract the discriminative information, the Generalized Multiple Maximum Dispersion Difference Criterion (GMMSD) and the corresponding feature extraction algorithm are proposed. The algorithm uses the difference criterion instead of the entropy criterion to avoid the “small sample” problem, and the use of QR decomposition can extract more effective discriminative features for facial recognition, while also reducing the computational complexity of feature extraction. Compared with traditional feature extraction methods, GMMSD avoids the problem of “small samples” and does not require preprocessing steps on the samples; it uses QR decomposition to extract features from the original samples and retains the distribution characteristics of the original samples. According to different change matrices, GMMSD can evolve into different feature extraction algorithms, which shows the generalized characteristics of GMMSD. Experiments show that GMMSD can effectively extract facial identification features and improve the accuracy of facial recognition.

The self‐service system in Toensberg Public Library, Norway

VINE ◽  
1997 ◽  
Vol 27 (1) ◽  
pp. 33-35 ◽  
Author(s):  
Vigdis Gjelstad Jakobsen
Keyword(s):  
Service System ◽  
Public Library ◽  
The Self ◽  
Self Service
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