scholarly journals Face Recognition Attendance System using Raspberry Pi

2021 ◽  
Vol 9 (8) ◽  
pp. 1145-1149
Author(s):  
Tresnor Menezes
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Face Recognition ◽  
Network Models ◽  
Raspberry Pi ◽  
Biometric Authentication ◽  
Neural Network Models ◽  
Social Media Networks ◽  
Non Invasive ◽  
Security Authentication

Abstract: Face recognition is used for security, authentication, identification, and has got many advantages over conventional methods. It is being used in many sectors since it is contactless and non-invasive. Billions of images have been uploaded on social media networks and are crawled by search engines over many years. These images may include many different faces. The increase in computing capability and collected data has helped in creating more powerful neural network models. [1] This project thesis aims to create an attendance system which uses face recognition biometric authentication as the currently used manual attendance system is cumbersome to maintain and time consuming. Face recognition prohibits the chance of students marking attendance for their peers (proxy attendance). Keywords: Face Recognition, Face embeddings, Face Detection, Image Processing, Raspberry Pi automation.

scholarly journals Gut microbiome identifies risk for colorectal polyps

2019 ◽  
Vol 6 (1) ◽  
pp. e000297 ◽  
Author(s):  
Ezzat Dadkhah ◽  
Masoumeh Sikaroodi ◽  
Louis Korman ◽  
Robert Hardi ◽  
Jeffrey Baybick ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Gut Microbiome ◽  
Rectal Swab ◽  
Naive Bayes ◽  
Network Models ◽  
Naïve Bayes ◽  
Neural Network Models ◽  
Non Invasive ◽  
Stool Samples

ObjectiveTo characterise the gut microbiome in subjects with and without polyps and evaluate the potential of the microbiome as a non-invasive biomarker to screen for risk of colorectal cancer (CRC).DesignPresurgery rectal swab, home collected stool, and sigmoid biopsy samples were obtained from 231 subjects undergoing screening or surveillance colonoscopy. 16S rRNA analysis was performed on 552 samples (231 rectal swab, 183 stool, 138 biopsy) and operational taxonomic units (OTU) were identified using UPARSE. Non-parametric statistical methods were used to identify OTUs that were significantly different between subjects with and without polyps. These informative OTUs were then used to build classifiers to predict the presence of polyps using advanced machine learning models.ResultsWe obtained clinical data on 218 subjects (87 females, 131 males) of which 193 were White, 21 African-American, and 4 Asian-American. Colonoscopy detected polyps in 56% of subjects. Modelling of the non-invasive home stool samples resulted in a classification accuracy >75% for Naïve Bayes and Neural Network models using informative OTUs. A naïve holdout analysis performed on home stool samples resulted in an average false negative rate of 11.5% for the Naïve Bayes and Neural Network models, which was reduced to 5% when the two models were combined.ConclusionGut microbiome analysis combined with advanced machine learning represents a promising approach to screen patients for the presence of polyps, with the potential to optimise the use of colonoscopy, reduce morbidity and mortality associated with CRC, and reduce associated healthcare costs.

Method of complex copper-zinc ore typification using neural network models

2020 ◽  
Vol 5 ◽  
pp. 140-147 ◽  
Author(s):  
T.N. Aleksandrova ◽  
◽  
E.K. Ushakov ◽  
A.V. Orlova ◽  
◽  
...  
Keyword(s):  
Neural Network ◽  
Network Models ◽  
Neural Network Models ◽  
Copper Zinc ◽  
Complex Copper

Digital twin of equipment as a basis for the consumer in digital production

2020 ◽  
Keyword(s):  
Neural Network ◽  
Tool Wear ◽  
Chip Formation ◽  
Network Models ◽  
Machining Accuracy ◽  
Neural Network Models ◽  
Digital Twin ◽  
The Neural Network ◽  
Digital Production ◽  
Cyberphysical System

The neural network models series used in the development of an aggregated digital twin of equipment as a cyber-physical system are presented. The twins of machining accuracy, chip formation and tool wear are examined in detail. On their basis, systems for stabilization of the chip formation process during cutting and diagnose of the cutting too wear are developed. Keywords cyberphysical system; neural network model of equipment; big data, digital twin of the chip formation; digital twin of the tool wear; digital twin of nanostructured coating choice

scholarly journals Prediction of Stress in Power Transformer Winding Conductors Using Artificial Neural Networks: Hyperparameter Analysis

Energies ◽  
2021 ◽  
Vol 14 (14) ◽  
pp. 4242
Author(s):  
Fausto Valencia ◽  
Hugo Arcos ◽  
Franklin Quilumba
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Finite Element ◽  
Power Transformer ◽  
Network Models ◽  
Activation Function ◽  
Neural Network Models ◽  
Transformer Winding ◽  
Artificial Neural ◽  
Femm Software

The purpose of this research is the evaluation of artificial neural network models in the prediction of stresses in a 400 MVA power transformer winding conductor caused by the circulation of fault currents. The models were compared considering the training, validation, and test data errors’ behavior. Different combinations of hyperparameters were analyzed based on the variation of architectures, optimizers, and activation functions. The data for the process was created from finite element simulations performed in the FEMM software. The design of the Artificial Neural Network was performed using the Keras framework. As a result, a model with one hidden layer was the best suited architecture for the problem at hand, with the optimizer Adam and the activation function ReLU. The final Artificial Neural Network model predictions were compared with the Finite Element Method results, showing good agreement but with a much shorter solution time.

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