scholarly journals SISTEM PRESENSI MAHASISWA OTOMATIS PADA ZOOM MEETING MENGGUNAKAN FACE RECOGNITION DENGAN METODE CONVULITIONAL NEURAL NETWORK BERBASIS WEB

2021 ◽  
Vol 5 (2) ◽  
pp. 785-793
Author(s):  
Sujud Satwikayana ◽  
Suryo Adi Wibowo ◽  
Nurlaily Vendyansyah
Keyword(s):  
Neural Network ◽  
Face Recognition ◽  
Convolutional Neural Network ◽  
Virus Disease ◽  
Training Data ◽  
Corona Virus

Dalam rangka pencegahan perkembangan dan penyebaran Corona Virus Disease (COVID-19), Kementerian Pendidikan dan Kebudayaan mengeluarkan SE Mendikbud Tahun 2020 tentang Pembelajaran secara Daring dan Bekerja dari Rumah dalam rangka Pencegahan Penyebaran COVID-19. Pembelajaran secara daring dan bekerja dari rumah bagi para tenaga pendidik merupakan perubahan yang harus dilakukan untuk tetap mengajar mahasiswa. Ketika melakukan pembelajaran secara daring tentunya memerlukan media sebagai sarananya. Survei terbaru yang dilakukan oleh Lembaga Arus Survei Indonesia (ASI) terkait penggunaan media video call dalam pembelajaran daring, mayoritas publik menggunakan aplikasi Zoom (57,2 %), disusul Google Meet (18,5 %), Cisco Webex (8,3 %), U Meet Me (5,0 %), Microsoft Teams (2,0 %), dan lainnya (2,2 %). Sisanya 6,9 % mengaku tidak tahu atau tidak jawab. Presensi sangat penting untuk mengetahui dan mengontrol kehadiran peserta didik dalam proses belajar mengajar. Saat ini presensi dalam perkuliahan daring masih dilakukan secara manual. Untuk itu perlu dibuat sistem pencatatan kehadiran berbasis face recognition secara otomatis. Dalam penelitian ini metode yang digunakan untuk face recognition adalah Convolutional Neural Network (CNN). Metode diimplementasikan dengan bantuan library Keras untuk proses training data. Hasil dari penelitian ini adalah sistem berbasis web yang dapat mendeteksi wajah mahasiswa yang berpartisipasi dalam ruang Zoom meeting. Pengujian yang dilakukan kepada 10 orang relawan munggunakan model hasil training data metode  CNN dari total 150 kali uji coba, total benar sebanyak 138 kali dan total salah sebanyak 12 kali, menunjukkan kinerja pengenalan wajah meraih rata-rata tingkat akurasi benar sebesar 92,00 % dan salah sebesar 8,00 % yang berarti sudah menghasilkan kecocokan yang baik.

scholarly journals Face Biometrics as a Potential Predictor for COVID-19 Susceptibility

2021 ◽  
Author(s):  
Oleg Kit
Keyword(s):  
Neural Network ◽  
Face Recognition ◽  
Convolutional Neural Network ◽  
Disease Severity ◽  
Virus Disease ◽  
Binary Classification ◽  
Severe Form ◽  
K Nearest Neighbors ◽  
Face Descriptor ◽  
The Individual

Fighting against the COVID-19 pandemic caused by the SARS-CoV-2 virus is one of the most critical challenges facing the global health system today. The possibility to identify the group of persons in the cohort of people under 50 years old, who are sensitive to the COVID-disease by non-invasive methods, is a very perspective approach for estimating the epidemiological state of the human population. The study aimed to identify the features of people's faces with COVID-19 that the most correlate with disease severity could serve as one of these approaches. For this aim, 525 photos of patients' faces with different outcomes of COVID-19 disease were analyzed using the Dlib face recognition convolutional neural network pre-trained for face recognition. Face descriptor vectors were obtained using the convolutional neural network. Facial features were found that predict a person's sensitivity to the SARS-CoV-2 virus (disease severity), and the contribution of each of the features to the risk of developing a severe form of COVID in a person was found. The accuracy of the binary classification of the individual severity of the COVID-19 course using the k-nearest neighbors algorithm on the test dataset was accuracy - 84%, AUC - 0.90.

scholarly journals Performance Evaluation of Deep CNN-Based Crack Detection and Localization Techniques for Concrete Structures

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1688
Author(s):  
Luqman Ali ◽  
Fady Alnajjar ◽  
Hamad Al Jassmi ◽  
Munkhjargal Gochoo ◽  
Wasif Khan ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Crack Detection ◽  
Concrete Structures ◽  
Model Performance ◽  
Training Data ◽  
Computational Time ◽  
Data Heterogeneity ◽  
Public Datasets ◽  
Detection And Localization

This paper proposes a customized convolutional neural network for crack detection in concrete structures. The proposed method is compared to four existing deep learning methods based on training data size, data heterogeneity, network complexity, and the number of epochs. The performance of the proposed convolutional neural network (CNN) model is evaluated and compared to pretrained networks, i.e., the VGG-16, VGG-19, ResNet-50, and Inception V3 models, on eight datasets of different sizes, created from two public datasets. For each model, the evaluation considered computational time, crack localization results, and classification measures, e.g., accuracy, precision, recall, and F1-score. Experimental results demonstrated that training data size and heterogeneity among data samples significantly affect model performance. All models demonstrated promising performance on a limited number of diverse training data; however, increasing the training data size and reducing diversity reduced generalization performance, and led to overfitting. The proposed customized CNN and VGG-16 models outperformed the other methods in terms of classification, localization, and computational time on a small amount of data, and the results indicate that these two models demonstrate superior crack detection and localization for concrete structures.

scholarly journals Evaluation of Power Insulator Detection Efficiency with the Use of Limited Training Dataset

2020 ◽  
Vol 10 (6) ◽  
pp. 2104
Author(s):  
Michał Tomaszewski ◽  
Paweł Michalski ◽  
Jakub Osuchowski
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Object Detection ◽  
Convolutional Neural Network ◽  
Deep Neural Networks ◽  
Detection Efficiency ◽  
Training Data ◽  
Training Dataset ◽  
Training Set ◽  
Convolutional Network

This article presents an analysis of the effectiveness of object detection in digital images with the application of a limited quantity of input. The possibility of using a limited set of learning data was achieved by developing a detailed scenario of the task, which strictly defined the conditions of detector operation in the considered case of a convolutional neural network. The described solution utilizes known architectures of deep neural networks in the process of learning and object detection. The article presents comparisons of results from detecting the most popular deep neural networks while maintaining a limited training set composed of a specific number of selected images from diagnostic video. The analyzed input material was recorded during an inspection flight conducted along high-voltage lines. The object detector was built for a power insulator. The main contribution of the presented papier is the evidence that a limited training set (in our case, just 60 training frames) could be used for object detection, assuming an outdoor scenario with low variability of environmental conditions. The decision of which network will generate the best result for such a limited training set is not a trivial task. Conducted research suggests that the deep neural networks will achieve different levels of effectiveness depending on the amount of training data. The most beneficial results were obtained for two convolutional neural networks: the faster region-convolutional neural network (faster R-CNN) and the region-based fully convolutional network (R-FCN). Faster R-CNN reached the highest AP (average precision) at a level of 0.8 for 60 frames. The R-FCN model gained a worse AP result; however, it can be noted that the relationship between the number of input samples and the obtained results has a significantly lower influence than in the case of other CNN models, which, in the authors’ assessment, is a desired feature in the case of a limited training set.

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