scholarly journals PENENTUAN EMOSI PADA VIDEO DENGAN CONVOLUTIONAL NEURAL NETWORK

2020 ◽  
Vol 5 (1) ◽  
pp. 23
Author(s):  
Daru Prasetyawan ◽  
Shofwatul 'Uyun
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Confusion Matrix ◽  
Video Model

Emosi seseorang dapat ditunjukan melalui ekspresi wajah. Ekspresi wajah manusia dapat berubah-ubah secara dinamis tanpa disadari oleh orang tersebut. Penelitian ini melakukan penentuan emosi dengan melakukan pengenalan ekspresi wajah manusia dan melakukan perekaman untuk setiap perubahan ekspresi wajah tersebut. Metode dalam penelitian ini adalah dengan melakukan klasifikasi terhadap 6 ekspresi dasar wajah manusia ditambah ekspresi netral dengan Convolutional Neural Network (CNN). Pemerataan distribusi data dilakukan untuk meningkatkan kinerja model. Dari pemodelan tersebut, dihasilkan model klasifikasi yang dapat diterapkan pada sebuah video. Model tersebut diuji menggunakan data yang terpisah dari data latih dan dievaluasi menggunakan confusion matrix. Sebagai hasil evaluasi, diperoleh akurasi 74%, rata-rata presisi 75,05%, dan rata-rata recall 74%. Di akhir penelitian ini, peneliti melakukan percobaan dengan menerapkan model klasifikasi tersebut pada beberapa video yang mewakili ekspresi seseorang di dalam video tersebut. Setiap perubahan ekspresi akan direkam dan dianalisis sehingga ditemukan emosi yang paling dominan.

Single Channel EEG signal for Automatic Detection of Absence Seizure using Convolutional Neural Network

2019 ◽  
Vol 13 ◽  
Author(s):  
Niha Kamal Basha ◽  
Aisha Banu Wahab
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Convolutional Neural Network ◽  
Monitoring System ◽  
Single Channel ◽  
Confusion Matrix ◽  
Sudden Change ◽  
Automatic Detection ◽  
Support Vector ◽  
Absence Seizure

: Absence seizure is a type of brain disorder in which subject get into sudden lapses in attention. Which means sudden change in brain stimulation. Most of this type of disorder is widely found in children’s (5-18 years). These Electroencephalogram (EEG) signals are captured with long term monitoring system and are analyzed individually. In this paper, a Convolutional Neural Network to extract single channel EEG seizure features like Power, log sum of wavelet transform, cross correlation, and mean phase variance of each frame in a windows are extracted after pre-processing and classify them into normal or absence seizure class, is proposed as an empowerment of monitoring system by automatic detection of absence seizure. The training data is collected from the normal and absence seizure subjects in the form of Electroencephalogram. The objective is to perform automatic detection of absence seizure using single channel electroencephalogram signal as input. Here the data is used to train the proposed Convolutional Neural Network to extract and classify absence seizure. The Convolutional Neural Network consist of three layers 1] convolutional layer – which extract the features in the form of vector 2] Pooling layer – the dimensionality of output from convolutional layer is reduced and 3] Fully connected layer–the activation function called soft-max is used to find the probability distribution of output class. This paper goes through the automatic detection of absence seizure in detail and provide the comparative analysis of classification between Support Vector Machine and Convolutional Neural Network. The proposed approach outperforms the performance of Support Vector Machine by 80% in automatic detection of absence seizure and validated using confusion matrix.

scholarly journals AlexNet convolutional neural network to classify the types of Indonesian coffee beans

2021 ◽  
Vol 905 (1) ◽  
pp. 012059
Author(s):  
Y Hendrawan ◽  
B Rohmatulloh ◽  
F I Ilmi ◽  
M R Fauzy ◽  
R Damayanti ◽  
...  
Keyword(s):  
Neural Network ◽  
Computer Vision ◽  
Sensitivity Analysis ◽  
Convolutional Neural Network ◽  
Confusion Matrix ◽  
Learning Rate ◽  
Agricultural Products ◽  
Coffee Bean ◽  
Coffee Beans ◽  
Non Destructive

Abstract Various types of Indonesian coffee are already popular internationally. Recently, there are still not many methods to classify the types of typical Indonesian coffee. Computer vision is a non-destructive method for classifying agricultural products. This study aimed to classify three types of Indonesian Arabica coffee beans, i.e., Gayo Aceh, Kintamani Bali, and Toraja Tongkonan, using computer vision. The classification method used was the AlexNet convolutional neural network with sensitivity analysis using several variations of the optimizer such as SGDm, Adam, and RMSProp and the learning rate of 0.00005 and 0.0001. Each type of coffee used 500 data for training and validation with the distribution of 70% training and 30% validation. The results showed that all AlexNet models achieved a perfect validation accuracy value of 100% in 1,040 iterations. This study also used 100 testing-set data on each type of coffee bean. In the testing confusion matrix, the accuracy reached 99.6%.

scholarly journals OIL PATTERN IDENTIFICATION ANALYSIS USING SEMANTIC DEEP LEARNING METHOD FROM PLEIADES-1B SATELIITE IMAGERY WITH ARCGIS PRO SOFTWARE (Case Study: Village “A”)

2021 ◽  
Vol 936 (1) ◽  
pp. 012021
Author(s):  
Novi Anita ◽  
Bangun Muljo Sukojo ◽  
Sondy Hardian Meisajiwa ◽  
Muhammad Alfian Romadhon
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
High Resolution ◽  
Land Cover ◽  
Convolutional Neural Network ◽  
Local Community ◽  
Confusion Matrix ◽  
Test Method ◽  
Oil Refinery ◽  
Thermal Data

Abstract There are many petroleum mining activities scattered in developing countries, such as Indonesia. Indonesia is one of the largest oil-producing countries in Southeast Asia with the 23rd ranking. Since the Dutch era, Indonesia has produced a very large amount of petroleum. One of the oil producing areas is “A” Village. There is an old well that produces petroleum oil which is still active with an age of more than 100 years, for now the oil well is still used by the local community as the main source of livelihood. With this activity, resulting in an oil pattern around the old oil refinery, which over time will absorb into the ground. This study aims to analyze and identify the oil pattern around the old oil refinery in the “A” area. The data used is in the form of High-Resolution Satellite Imagery (CSRT), namely Pleiades-1B with a spatial resolution of 1.5 meters. Data were identified using the Deep Learning Semantic method. For the limitation of this research is the administrative limit of XX Regency with a scale of 1: 25,000 as supporting data when cutting the image. The method used is the Deep Learning Convolutional Neural Network series. This research is based on how to wait for the method of the former oil spill which is the consideration of the consideration used. This study produced a land cover map that was classified into 3 categories, namely oil patterns area, area not affected by oil and vegetation. As a supporting value to show the accuracy of the classification results, an accuracy test method is used with the confusion matrix method. To show the accuracy of this study using thermal data taken from the field. Thermal data used in the form of numbers that show the temperature of each land cover. Based on the above reference, a research related to the analysis of very high-resolution image data (Pleiades-1B) will be conducted to examine the oil pattern. This research uses the deep learning series convolutional neural network (CNN) method. With this research, it is hoped that it can help agencies in knowing the right method to identify oil in mainland areas.

scholarly journals Blister Defect Detection Based on Convolutional Neural Network for Polymer Lithium-Ion Battery

2019 ◽  
Vol 9 (6) ◽  
pp. 1085 ◽  
Author(s):  
Liyong Ma ◽  
Wei Xie ◽  
Yong Zhang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Lithium Ion Battery ◽  
Defect Detection ◽  
Confusion Matrix ◽  
Cell Sheet ◽  
Lithium Ion ◽  
Classification Performance ◽  
Field Application ◽  
Cell Sheets

To ensure the quality and reliability of polymer lithium-ion battery (PLB), automatic blister defect detection instead of manual detection is developed in the production of PLB cell sheets. A convolutional neural network (CNN) based detection method is proposed to detect blister in cell sheets employing cell sheet images. An improved architecture for dense block and a learning method based on optimization of learning rate are discussed. The proposed method was superior to other machine learning based methods when the classification performance and confusion matrix were compared in experiments. The proposed CNN method had the best defect detection performance and real-time performance for industry field application.

scholarly journals Facial Expression Recognition using CNN

2020 ◽  
pp. 179-183
Author(s):  
S. Mary Hima Preethi ◽  
P. Sobha ◽  
P. Rajalakshmi Kamalini ◽  
K. Gowri Raghavendra Narayan
Keyword(s):  
Neural Network ◽  
Facial Expression ◽  
Convolutional Neural Network ◽  
Facial Expression Recognition ◽  
Confusion Matrix ◽  
Neural System ◽  
Expression Recognition ◽  
The Face ◽  
Human Faces ◽  
The Ideal

People have consistently been able to perceive and recognize faces and their feelings. Presently PCs can do likewise. We propose a model which recognizes human faces and classifies the emotion on the face as happy, angry, sad, neutral, surprise, disgust or fear. It is developed utilizing a convolutional neural network(CNN) and involves various stages. All these are carried out using a dataset available on the Kaggle repository named fer2013. Precision and execution of the neural system can be assessed utilizing a confusion matrix. We applied cross-approval to decide the ideal hyper-parameters and assessed the presentation of the created models by looking at their training histories.

scholarly journals Deep Learning dalam Mengindetifikasi Jenis Bangunan Heritage dengan Algoritma Convolutional Neural Network

2021 ◽  
Vol 5 (3) ◽  
pp. 831
Author(s):  
Sri Winiarti ◽  
Mochammad Yulianto Andi Saputro ◽  
Sunardi Sunardi
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Confusion Matrix ◽  
Architectural Style ◽  
The Public ◽  
Heritage Buildings ◽  
Heritage Building ◽  
History Of ◽  
Digital Documentation ◽  
New Buildings

A heritage building is a building that has a distinctive style or tradition from a culture whose activities are carried out continuously until now and are used as a characteristic of that culture. The problems that occur in the community are the lack of knowledge to recognize the types of heritage buildings and the lack of digital documentation. Another problem that occurs in identifying heritage buildings is that there are similarities between heritage buildings and new buildings that imitate the architectural style of heritage buildings from ornaments. This can raise doubts in the information related to the original history of heritage buildings for the public or visitors. This study aims to apply the Convolutional Neural Network (CNN) to identify the types of heritage buildings. The benefits of this research can be found in the characteristics of a building based on ornaments so that it can be used to obtain information about the types of heritage buildings in Indonesia. A dataset of 7184 images of ornaments from heritage buildings were used which were taken directly at the Yogyakarta location, namely; Mataram Grand Mosque, Taqwa Wonokromo Mosque, Kalang House, Joglo KH Ahmad Dahlan and Ketandan. It is necessary to identify the heritage building because the object of the building can become extinct at any time, so to maintain it, documentation is needed as an effort to preserve culture and for education. Based on the evaluation of the performance of the tests carried out using the confusion matrix method from 391 ornamental images, the results obtained are 98% accuracy

Performance of a convolutional neural network algorithm for tooth detection and numbering on periapical radiographs

2021 ◽  
Author(s):  
Cansu Görürgöz ◽  
Kaan Orhan ◽  
Ibrahim Sevki Bayrakdar ◽  
Özer Çelik ◽  
Elif Bilgir ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Confusion Matrix ◽  
True Positive Rate ◽  
Turnaround Time ◽  
Classification Model ◽  
Data Sets ◽  
Data Set ◽  
Region Detection ◽  
Positive Rate

Objectives: The present study aimed to evaluate the performance of a Faster Region-based Convolutional Neural Network (R-CNN) algorithm for tooth detection and numbering on periapical images. Methods: The data sets of 1686 randomly selected periapical radiographs of patients were collected retrospectively. A pre-trained model (GoogLeNet Inception v3 CNN) was employed for pre-processing, and transfer learning techniques were applied for data set training. The algorithm consisted of: (1) the Jaw classification model, (2) Region detection models, and (3) the Final algorithm using all models. Finally, an analysis of the latest model has been integrated alongside the others. The sensitivity, precision, true-positive rate, and false-positive/negative rate were computed to analyze the performance of the algorithm using a confusion matrix. Results: An artificial intelligence algorithm (CranioCatch, Eskisehir-Turkey) was designed based on R-CNN inception architecture to automatically detect and number the teeth on periapical images. Of 864 teeth in 156 periapical radiographs, 668 were correctly numbered in the test data set. The F1 score, precision, and sensitivity were 0.8720, 0.7812, and 0.9867, respectively. Conclusion: The study demonstrated the potential accuracy and efficiency of the CNN algorithm for detecting and numbering teeth. The deep learning-based methods can help clinicians reduce workloads, improve dental records, and reduce turnaround time for urgent cases. This architecture might also contribute to forensic science.

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.

Surface fatigue crack identification in steel box girder of bridges by a deep fusion convolutional neural network based on consumer-grade camera images

2018 ◽  
Vol 18 (3) ◽  
pp. 653-674 ◽  
Author(s):  
Yang Xu ◽  
Yuequan Bao ◽  
Jiahui Chen ◽  
Wangmeng Zuo ◽  
Hui Li
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Network Structure ◽  
Network Architecture ◽  
Confusion Matrix ◽  
Super Resolution ◽  
Identification Accuracy ◽  
Crack Identification ◽  
Threshold Method ◽  
Neural Network Structure

This study conducts crack identification from real-world images containing complicated disturbance information (cracks, handwriting scripts, and background) inside steel box girders of bridges. Considering the multilevel and multi-scale features of the input images, a modified fusion convolutional neural network architecture is proposed. As input, 350 raw images are taken with a consumer-grade camera and divided into sub-images with resolution of 64 × 64 pixels (67,200 in total). A regular convolutional neural network structure is employed as baseline to demonstrate the accuracy benefits from the proposed fusion convolutional neural network structure. The confusion matrix is defined for prediction performance evaluation on the test set. A total of six additional entire raw images are used to investigate the robustness and feasibility of the proposed approach. A binary conversion process based on the optimal entropy threshold method is applied and closely followed to identify the crack pixels in the sub-images. The effect of the super-resolution inputs on accuracy is investigated. Results show that the trained modified fusion convolutional neural network can automatically detect the cracks, handwriting, and background from the raw images. The recognition errors of the fusion convolutional neural network in both the training and validation processes are smaller than those of the regular convolutional neural network. The super-resolution process hurts the general identification accuracy.

scholarly journals Accuracy Assessment in Convolutional Neural Network-Based Deep Learning Remote Sensing Studies—Part 1: Literature Review

2021 ◽  
Vol 13 (13) ◽  
pp. 2450
Author(s):  
Aaron E. Maxwell ◽  
Timothy A. Warner ◽  
Luis Andrés Guillén
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Accuracy Assessment ◽  
Confusion Matrix ◽  
Area Under The Curve ◽  
Average Precision ◽  
Wide Range ◽  
New Sensors

Convolutional neural network (CNN)-based deep learning (DL) is a powerful, recently developed image classification approach. With origins in the computer vision and image processing communities, the accuracy assessment methods developed for CNN-based DL use a wide range of metrics that may be unfamiliar to the remote sensing (RS) community. To explore the differences between traditional RS and DL RS methods, we surveyed a random selection of 100 papers from the RS DL literature. The results show that RS DL studies have largely abandoned traditional RS accuracy assessment terminology, though some of the accuracy measures typically used in DL papers, most notably precision and recall, have direct equivalents in traditional RS terminology. Some of the DL accuracy terms have multiple names, or are equivalent to another measure. In our sample, DL studies only rarely reported a complete confusion matrix, and when they did so, it was even more rare that the confusion matrix estimated population properties. On the other hand, some DL studies are increasingly paying attention to the role of class prevalence in designing accuracy assessment approaches. DL studies that evaluate the decision boundary threshold over a range of values tend to use the precision-recall (P-R) curve, the associated area under the curve (AUC) measures of average precision (AP) and mean average precision (mAP), rather than the traditional receiver operating characteristic (ROC) curve and its AUC. DL studies are also notable for testing the generalization of their models on entirely new datasets, including data from new areas, new acquisition times, or even new sensors.

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