The study’s objective was to assess the Indoor Environmental Quality (IEQ) of 32 naturally ventilated classrooms using spider monkey metaheuristic regression in Bangalore, India. These classrooms, which had a total seating capacity of 805 students, were selected from various educational institutions throughout southern India. The researchers evaluated PM2.5 levels, carbon dioxide levels, water vapor levels, and ozone levels as factors. They used the spider monkey approach for multilinear regression analysis. The findings revealed that individuals are willing to tolerate higher temperatures for better Indoor Air Quality (IAQ). However, it is crucial to consider all aspects of IEQ, as dissatisfaction with a single component may not lead to widespread discomfort unless it is a significant issue. By identifying the most critical building controls, designers can ensure their clients’ comfort.
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5 June 2025
INTERNATIONAL CONFERENCE ON MODELLING STRATEGIES IN MATHEMATICS: ICMSM 2024
22–23 October 2024
Coimbatore, India
Research Article|
June 05 2025
Deep learning on binary classification to identify concrete strength through cube, cylinder, and beam parameters Available to Purchase
D. S. Vijayan;
D. S. Vijayan
a)
1
Department of Civil Engineering, Aarupadai Veedu Institute of Technology, Vinayaka Missions Research Foundation (VMRF)
, Chennai, Tamil Nadu, India
a)Corresponding author: [email protected]
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D. Parthiban;
D. Parthiban
1
Department of Civil Engineering, Aarupadai Veedu Institute of Technology, Vinayaka Missions Research Foundation (VMRF)
, Chennai, Tamil Nadu, India
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Naveen Sankaran;
Naveen Sankaran
2
DACK Consulting Solutions, Inc.
, White Plains, New York, USA
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Sangsangra C. H. Sangma;
Sangsangra C. H. Sangma
3
College of Military Engineering, Jawaharlal Nehru University (JNU)
, New Delhi, India
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Arvindan Sivasuriyan
Arvindan Sivasuriyan
4
Institute of Civil Engineering, Warsaw University of Life Sciences—SGGW
, Warsaw, Poland
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D. S. Vijayan
1,a)
D. Parthiban
1
Naveen Sankaran
2
Sangsangra C. H. Sangma
3
Arvindan Sivasuriyan
4
1
Department of Civil Engineering, Aarupadai Veedu Institute of Technology, Vinayaka Missions Research Foundation (VMRF)
, Chennai, Tamil Nadu, India
2
DACK Consulting Solutions, Inc.
, White Plains, New York, USA
3
College of Military Engineering, Jawaharlal Nehru University (JNU)
, New Delhi, India
4
Institute of Civil Engineering, Warsaw University of Life Sciences—SGGW
, Warsaw, Poland
a)Corresponding author: [email protected]
AIP Conf. Proc. 3306, 060027 (2025)
Citation
D. S. Vijayan, D. Parthiban, Naveen Sankaran, Sangsangra C. H. Sangma, Arvindan Sivasuriyan; Deep learning on binary classification to identify concrete strength through cube, cylinder, and beam parameters. AIP Conf. Proc. 5 June 2025; 3306 (1): 060027. https://doi.org/10.1063/5.0275760
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