An Efficient Indoor Occupancy Detection System Using Artificial Neural Network

  • Suseta Datta
  • Sankhadeep ChatterjeeEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 811)


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.


Occupancy detection Artificial neural network Security 


  1. 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)CrossRefGoogle Scholar
  2. 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)CrossRefGoogle Scholar
  3. 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)CrossRefGoogle Scholar
  4. 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. 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. 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. 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. 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)CrossRefGoogle Scholar
  9. 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 2017Google Scholar
  10. 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 2017Google Scholar
  11. 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. 12.
    Chakraborty, S., Dey, N., Chatterjee, S., Ashour, A.S.: Gradient Approximation in Retinal Blood Vessel SegmentationGoogle Scholar
  13. 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. 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)CrossRefGoogle Scholar
  15. 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

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Department of Computer ApplicationUniversity of Engineering and ManagementKolkataIndia
  2. 2.Department of Computer Science and EngineeringUniversity of Engineering and ManagementKolkataIndia

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