Erişen, Serdar

Profile Picture
Profile URL
https://hdl.handle.net/20.500.14411/8377
Name Variants
Erişen, Serdar
S.,Erisen
E., Serdar
Erisen,S.
S., Erisen
Serdar, Erişen
E.,Serdar
Serdar, Erisen
Erisen, Serdar
S.,Erişen
Erişen,S.
Job Title
Doktor Öğretim Üyesi
Email Address
serdar.erisen@atilim.edu.tr
Main Affiliation
Department of Architecture
Status
Former Staff
Website
ORCID ID
Scopus Author ID
Turkish CoHE Profile ID
Google Scholar ID
WoS Researcher ID

Sustainable Development Goals

NO POVERTY1
NO POVERTY
0
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ZERO HUNGER2
ZERO HUNGER
0
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GOOD HEALTH AND WELL-BEING3
GOOD HEALTH AND WELL-BEING
0
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QUALITY EDUCATION4
QUALITY EDUCATION
1
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GENDER EQUALITY5
GENDER EQUALITY
0
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CLEAN WATER AND SANITATION6
CLEAN WATER AND SANITATION
0
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AFFORDABLE AND CLEAN ENERGY7
AFFORDABLE AND CLEAN ENERGY
0
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DECENT WORK AND ECONOMIC GROWTH8
DECENT WORK AND ECONOMIC GROWTH
0
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INDUSTRY, INNOVATION AND INFRASTRUCTURE9
INDUSTRY, INNOVATION AND INFRASTRUCTURE
1
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REDUCED INEQUALITIES10
REDUCED INEQUALITIES
0
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SUSTAINABLE CITIES AND COMMUNITIES11
SUSTAINABLE CITIES AND COMMUNITIES
0
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RESPONSIBLE CONSUMPTION AND PRODUCTION12
RESPONSIBLE CONSUMPTION AND PRODUCTION
0
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CLIMATE ACTION13
CLIMATE ACTION
0
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LIFE BELOW WATER14
LIFE BELOW WATER
0
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LIFE ON LAND15
LIFE ON LAND
0
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PEACE, JUSTICE AND STRONG INSTITUTIONS16
PEACE, JUSTICE AND STRONG INSTITUTIONS
0
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PARTNERSHIPS FOR THE GOALS17
PARTNERSHIPS FOR THE GOALS
1
Research Products
This researcher does not have a Scopus ID.
This researcher does not have a WoS ID.
Scholarly Output

3

Articles

3

Views / Downloads

4/49

Supervised MSc Theses

0

Supervised PhD Theses

0

WoS Citation Count

19

Scopus Citation Count

24

Patents

0

Projects

0

WoS Citations per Publication

6.33

Scopus Citations per Publication

8.00

Open Access Source

3

Supervised Theses

0

JournalCount
Buildings1
Sensors1
Sustainability1
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End date: 2023Subject: big data

Scholarly Output Search Results

Now showing 1 - 1 of 1
  • Thumbnail Image
    Article
    Citation - WoS: 4
    Citation - Scopus: 8
    Real-Time Learning and Monitoring System in Fighting Against Sars-Cov in a Private Indoor Environment
    (Mdpi, 2022) Erisen, Serdar
    The SARS-CoV-2 virus has posed formidable challenges that must be tackled through scientific and technological investigations on each environmental scale. This research aims to learn and report about the current state of user activities, in real-time, in a specially designed private indoor environment with sensors in infection transmission control of SARS-CoV-2. Thus, a real-time learning system that evolves and updates with each incoming piece of data from the environment is developed to predict user activities categorized for remote monitoring. Accordingly, various experiments are conducted in the private indoor space. Multiple sensors, with their inputs, are analyzed through the experiments. The experiment environment, installed with microgrids and Internet of Things (IoT) devices, has provided correlating data of various sensors from that special care context during the pandemic. The data is applied to classify user activities and develop a real-time learning and monitoring system to predict the IoT data. The microgrids were operated with the real-time learning system developed by comprehensive experiments on classification learning, regression learning, Error-Correcting Output Codes (ECOC), and deep learning models. With the help of machine learning experiments, data optimization, and the multilayered-tandem organization of the developed neural networks, the efficiency of this real-time monitoring system increases in learning the activity of users and predicting their actions, which are reported as feedback on the monitoring interfaces. The developed learning system predicts the real-time IoT data, accurately, in less than 5 milliseconds and generates big data that can be deployed for different usages in larger-scale facilities, networks, and e-health services.