Detection of Facial Mask Using Deep Learning Classification Algorithm

Abstract

Machine Learning is increasingly popular among the public because of the many devices that are made using the capabilities of Machine Learning algorithms. Machine Learning's ability to process and analyze data quickly and accurately, as well as generate useful and relevant information for users, is the main reason for its popularity. Many machine learning algorithms have been used to solve various problems in society, including Deep Learning (DL). Deep Learning is an algorithm that works by representing data in layers of learning layers so that the representation becomes more meaningful. "Deep" in Deep Learning means that Deep Learning begins layers of sequential representation. This study aims to provide a reference on how to create a system and analyze the results of identifying face masks using deep learning algorithms. From the results of research conducted, it is known that this model can recognize faces well, both those who wear masks and those who do not use masks. This is evident from the average specificity and precision of 96.00%, and the average sensitivity or recall value of 93.47%. In addition, this model has also proven to be quite accurate in conducting overall classification with an average accuracy of 94.73%.

Research on Cable Tension Prediction Based on Neural Network

“…The dendritic neural model (DNM) is better able to handle uncertainty in data and may, in this way, improve the memory function of the model [23]. At the same time, the algorithms used now are also improved and optimized, either through distributed parallel cooperative co-evolution particle swarm optimization (DPCCPSO) [24], where the inertia weights and learning factors are adjusted during the evolutionary process, or through deep learning, eliminating the requirement for manual feature engineering [25]. The purpose of analyzing the cable is achieved by converting the measured data into images using window-based convolutional neural network (CNN), integrated recurrent neural network (RNN), and autoen- In Figure 15, it can be observed that the results predicted by the neural network model have a higher accuracy than the results calculated using the formulas mentioned in the literature.…”

A Reduced Complexity Acoustic-Based 3D DoA Estimation with Zero Cyclic Sum

“…The precision and accuracy of an acoustic-based DoA estimation are essential across a spectrum of industries, spanning vital applications in both civilian and military sectors. Encompassing critical domains such as defense, law enforcement, security [1], and surveillance [2], reliable and precise DoA estimation ensures safety, strategic decision-making [3], and operational effectiveness. Applications such as gunshot DoA estimation [4], drone DoA estimation [5], and automotive angle estimation [6] require highly accurate estimates for optimal functionality.…”