Skip to main content
SpringerLink
Log in

An Efficient Indoor Occupancy Detection System Using Artificial Neural Network

  • Conference paper
  • First Online:
Proceedings of International Ethical Hacking Conference 2018

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 811))

  • 856 Accesses

Abstract

Accurate occupancy information in a room helps to provide different valuable applications like security, dynamic seat allocation, energy management etc. This paper represents the detection of human in a room on the basis of some identical features which has been done by using the artificial neural network with three data sets of training and testing with the help of a suitable algorithm from which 97% accuracy for detecting occupancy is being calculated.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Cali, D., Matthes, P., Huchtemann, K., Streblow, R., Müller, D.: CO2 based occupancy detection algorithm: experimental analysis and validation for office and residential buildings. Build. Environ. 86, 39–49 (2015)

    Article  Google Scholar 

  2. Pedersen, T.H., Nielsen, K.U., Petersen, S.: Method for room occupancy detection based on trajectory of indoor climate sensor data. Build. Environ. 115, 147–156 (2017)

    Article  Google Scholar 

  3. Candanedo, L.M., Feldheim, V.: Accurate occupancy detection of an office room from light, temperature, humidity and CO2 measurements using statistical learning models. Energy Build. 112, 28–39 (2016)

    Article  Google Scholar 

  4. Chatterjee, S., Hore, S., Dey, N., Chakraborty, S., Ashour, A.S.: Dengue fever classification using gene expression data: a PSO based artificial neural network approach. In: Proceedings of the 5th International Conference on Frontiers in Intelligent Computing: Theory and Applications (pp. 331–341). Springer, Singapore (2017)

    Google Scholar 

  5. Chatterjee, S., Dutta, B., Sen, S., Dey, N., Debnath, N.C.: Rainfall prediction using hybrid neural network approach. In: 2nd International Conference on Recent Advances in Signal Processing, Telecommunications & Computing (SigTelCom)—2018, Vietnam (In press)

    Google Scholar 

  6. Chatterjee, S., Sarkar, S., Hore, S., Dey, N., Ashour, A.S., Shi, F., Le, D.N.: Structural failure classification for reinforced concrete buildings using trained neural network based multi-objective genetic algorithm. Struct. Eng. Mech. 63(4), 429–438 (2017)

    Google Scholar 

  7. Chatterjee, S., Dey, N., Shi, F., Ashour, A.S., Fong, S.J., Sen, S.: Clinical application of modified bag-of-features coupled with hybrid neural-based classifier in dengue fever classification using gene expression data. Med. Biol. Eng. Comput. 1–12 (2017)

    Google Scholar 

  8. Chatterjee, S., Sarkar, S., Dey, N., Ashour, A.S., Sen, S., Hassanien, A.E.: Application of cuckoo search in water quality prediction using artificial neural network. Int. J. Comput. Intell. Stud. 6(2–3), 229–244 (2017)

    Article  Google Scholar 

  9. Chatterjee, S., Banerjee, S., Mazumdar, K.G., Bose, S., Sen, S.: Non-dominated sorting genetic algorithm—II supported neural network in classifying forest types. In: 2017 1st International Conference on Electronics, Materials Engineering and Nano-Technology (IEMENTech) (pp. 1–6). IEEE, April 2017

    Google Scholar 

  10. Chatterjee, S., Banerjee, S., Basu, P., Debnath, M., Sen, S.: Cuckoo search coupled artificial neural network in detection of chronic kidney disease. In: 2017 1st International Conference on Electronics, Materials Engineering and Nano-Technology (IEMENTech) (pp. 1–4). IEEE, April 2017

    Google Scholar 

  11. Chatterjee, S., Dey, N., Ashour, A.S., Drugarin, C.V.A.: Electrical energy output prediction using cuckoo search based artificial neural network. In: Smart Trends in Systems, Security and Sustainability (pp. 277–285). Springer, Singapore (2018)

    Google Scholar 

  12. Chakraborty, S., Dey, N., Chatterjee, S., Ashour, A.S.: Gradient Approximation in Retinal Blood Vessel Segmentation

    Google Scholar 

  13. Chatterjee, S., Sarkar, S., Dey, N., Ashour, A.S., Sen, S.: Hybrid non-dominated sorting genetic algorithm: II-neural network approach. Adv. Appl. Metaheuristic Comput. 264 (2017)

    Google Scholar 

  14. Chatterjee, S., Sarkar, S., Hore, S., Dey, N., Ashour, A.S., Balas, V.E.: Particle swarm optimization trained neural network for structural failure prediction of multistoried RC buildings. Neural Comput. Appl. 28(8), 2005–2016 (2017)

    Article  Google Scholar 

  15. Chatterjee, S., Ghosh, S., Dawn, S., Hore, S., Dey, N.: Forest type classification: a hybrid NN-GA model based approach. In: Information Systems Design and Intelligent Applications (pp. 227–236). Springer, New Delhi (2016)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Application, University of Engineering and Management, Kolkata, India

    Suseta Datta

  2. Department of Computer Science and Engineering, University of Engineering and Management, Kolkata, India

    Sankhadeep Chatterjee

Authors
  1. Suseta Datta
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sankhadeep Chatterjee
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sankhadeep Chatterjee .

Editor information

Editors and Affiliations

  1. Department of Information Technology, Institute of Engineering and Management, Kolkata, India

    Mohuya Chakraborty

  2. Institute of Engineering and Management, Kolkata, India

    Satyajit Chakrabarti

  3. Department of Automation and Applied Informatics, Aurel Vlaicu University of Arad, Arad, Romania

    Valentina Emilia Balas

  4. Department of Computer Science and Engineering, University of Kalyani, Kalyani, West Bengal, India

    J. K. Mandal

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Datta, S., Chatterjee, S. (2019). An Efficient Indoor Occupancy Detection System Using Artificial Neural Network. In: Chakraborty, M., Chakrabarti, S., Balas, V., Mandal, J. (eds) Proceedings of International Ethical Hacking Conference 2018. Advances in Intelligent Systems and Computing, vol 811. Springer, Singapore. https://doi.org/10.1007/978-981-13-1544-2_26

Download citation

  • DOI: https://doi.org/10.1007/978-981-13-1544-2_26

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-1543-5

  • Online ISBN: 978-981-13-1544-2

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation