Facial Photo Recognition Using Deep Learning in Archival Record Management System

2019 ◽  
pp. 376-380
Author(s):  
Gantur Togtokh ◽  
Kyung Chang Kim ◽  
Kang Woo Lee
Keyword(s):  
Deep Learning ◽  
Management System ◽  
Record Management ◽  
Archival Record

Real-time Worker Safety Management System Using Deep Learning-based Video Analysis Algorithm

2020 ◽  
Vol 9 (3) ◽  
pp. 25-30
Author(s):  
So Yeon Jeon ◽  
Jong Hwa Park ◽  
Sang Byung Youn ◽  
Young Soo Kim ◽  
Yong Sung Lee ◽  
...  
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Video Analysis ◽  
Management System ◽  
Safety Management ◽  
Worker Safety ◽  
Safety Management System ◽  
Analysis Algorithm

scholarly journals The efficient consensus algorithm for land record management system

2021 ◽  
Vol 1022 ◽  
pp. 012090
Author(s):  
A S Yadav ◽  
S Shikha ◽  
S Gupta ◽  
D S Kushwaha
Keyword(s):  
Management System ◽  
Consensus Algorithm ◽  
Record Management

Examining the Association Between Massage Parlors and Neighborhood Crime

2018 ◽  
Vol 65 (11) ◽  
pp. 1537-1569 ◽  
Author(s):  
Jessica Huff ◽  
Danielle Wallace ◽  
Courtney Riggs ◽  
Charles M. Katz ◽  
David Choate
Keyword(s):  
Management System ◽  
Autoregressive Models ◽  
Physical Disorder ◽  
Record Management ◽  
Social Disorder ◽  
Neighborhood Crime ◽  
Spatial Autoregressive ◽  
Spatial Autoregressive Models ◽  
The Impact ◽  
User Review

Although massage parlors have been associated with illicit activities including prostitution, less is known about their association with neighborhood crime. Employing the Computer Automated Dispatch/Record Management System (CAD/RMS), online user review, licensing, Census, and zoning data, we examine the impact of massage parlors on crime in their surrounding neighborhoods. Using spatial autoregressive models, our results indicate the total number of massage parlors was associated with increased social disorder. The presence of illicit massage parlors in adjacent neighborhoods was associated with crime and physical disorder in the focal neighborhoods. This study has consequences for how police address crime associated with massage parlors. Specifically, the use of online user review forums could be an effective way to identify illicit massage parlors. Recommendations for policing and code enforcement are discussed.

scholarly journals Automatic Attendance Management System under Unconstrained Video using Face Recognition

2020 ◽  
Vol 9 (10) ◽  
pp. 229-232
Keyword(s):  
Deep Learning ◽  
Face Recognition ◽  
Management System ◽  
Traditional Method ◽  
Surveillance Camera ◽  
Recognition Systems ◽  
End To End ◽  
Learning By Using

Attendance Management System under unconstrained video using face recognition technology has made a great variation from the traditional method of attendance marking system. This attendance management system has been developed under the domain of Deep Learning by using Face recognition. Automatic Attendance Management under unconstrained video using face recognition systems which automatically mark attendance by detecting end to end face from the frames obtained from live stream video of surveillance camera which placed in center of the classroom. From the recognized faces, it will be compared with stored images in database, then the attendance report will be generated and it also provides attendance reports to parents of the absentee’s student.

scholarly journals Smart Attendance Management System Using Face Recognition

2021 ◽  
pp. 706-711
Author(s):  
K. V. Prasad Reddy ◽  
R. Chaitanya Latha ◽  
M. Lohitha ◽  
R. Sonia ◽  
A. B. Usha
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Face Recognition ◽  
Conventional Method ◽  
Management System ◽  
Reading And Writing ◽  
Manual Method

To Maintain the attendance record with day to day activities is a challenging task. The conventional method of calling name of each student is time consuming and there is always a chance of proxy attendance. The smart attendance management will replace the manual method, which takes a lot of time consuming and difficult to maintain. There are many biometric processes, in that face recognition is the best method. Here we are using the computer vision which is a field of deep learning that is used for the camera reading and writing and using TkInter to create a GUI application.

scholarly journals A Novel Deep Learning-Based State-of-Charge Estimation for Renewable Energy Management System in Hybrid Electric Vehicles

Mathematics ◽  
2022 ◽  
Vol 10 (2) ◽  
pp. 260
Author(s):  
Mahendiran T. Vellingiri ◽  
Ibrahim M. Mehedi ◽  
Thangam Palaniswamy
Keyword(s):  
Renewable Energy ◽  
Deep Learning ◽  
Energy Management ◽  
Electric Vehicles ◽  
Management System ◽  
Hybrid Electric Vehicles ◽  
State Of Charge ◽  
Accurate Estimation ◽  
Soc Estimation ◽  
Hybrid Electric

In recent years, alternative engine technologies are necessary to resolve the problems related to conventional vehicles. Electric vehicles (EVs) and hybrid electric vehicles (HEVs) are effective solutions to decarbonize the transportation sector. It also becomes important to shift from traditional houses to smart houses and from classical vehicles to EVs or HEVs. It is needed to combine renewable energy sources (RESs) such as solar photovoltaics, wind energy systems, and various forms of bio-energies. Among various HEV technologies, an effective battery management system (BMS) still remains a crucial issue that is majorly used for indicating the battery state of charge (SOC). Since over-charging and over-discharging result in inevitable impairment to the batteries, accurate SOC estimation desires to be presented by the BMS. Although several SOC estimation techniques exist to regulate the SOC of the battery cell, it is needed to improvise the SOC estimation performance on HEVs. In this view, this paper focuses on the design of a novel deep learning (DL) with SOC estimation model for secure renewable energy management (DLSOC-REM) technique for HEVs. The presented model employs a hybrid convolution neural network and long short-term memory (HCNN-LSTM) model for the accurate estimation of SOC. In order to improve the SOC estimation outcomes of the HCNN-LSTM model, the barnacles mating optimizer (BMO) is applied for the hyperpower tuning process. The utilization of the HCNN-LSTM model makes the modeling process easier and offers a precise depiction of the input–output relationship of the battery model. The design of BMO based HCNN-LSTM model for SOC estimation shows the novelty of the work. An extensive experimental analysis highlighted the supremacy of the proposed model over other existing methods in terms of different aspects.

ATTENDANCE MANAGEMENT SYSTEM USING FACE RECOGNITION AND DEEP LEARNING

2021 ◽  
Author(s):  
Filip Marenčić ◽  
Zlatko Stapić ◽  
Petra Grd
Keyword(s):  
Deep Learning ◽  
Face Recognition ◽  
Management System
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