OccupancySense: Context-based indoor occupancy detection & prediction using CatBoost model

[1]  F. Haghighat,et al.  Impact of occupancy prediction models on building HVAC control system performance: Application of machine learning techniques , 2021, Energy and Buildings.

[2]  Gang Liu,et al.  A framework for occupancy prediction based on image information fusion and machine learning , 2021, Building and Environment.

[3]  Sarbani Roy,et al.  IndoorSense: context based indoor pollutant prediction using SARIMAX model , 2021, Multimedia Tools and Applications.

[4]  Sarbani Roy,et al.  Indoor Air Pollutant Prediction Using Time Series Forecasting Models , 2021, Advances in Intelligent Systems and Computing.

[5]  Limao Zhang,et al.  Data-driven estimation of building energy consumption with multi-source heterogeneous data , 2020 .

[6]  Bernd Bischl,et al.  Benchmark for filter methods for feature selection in high-dimensional classification data , 2020, Comput. Stat. Data Anal..

[7]  Tanmoy Maitra,et al.  ES3B: Enhanced Security System for Smart Building Using IoT , 2018, 2018 IEEE International Conference on Smart Cloud (SmartCloud).

[8]  Robert Weigel,et al.  Fusion of Nonintrusive Environmental Sensors for Occupancy Detection in Smart Homes , 2018, IEEE Internet of Things Journal.

[9]  Kevin Weekly,et al.  Occupancy Detection via Environmental Sensing , 2018, IEEE Transactions on Automation Science and Engineering.

[10]  Anna Veronika Dorogush,et al.  CatBoost: unbiased boosting with categorical features , 2017, NeurIPS.

[11]  Indrajit Banerjee,et al.  IoT-Based Sensor Data Fusion for Occupancy Sensing Using Dempster–Shafer Evidence Theory for Smart Buildings , 2017, IEEE Internet of Things Journal.

[12]  Chandreyee Chowdhury,et al.  Towards Smart City: Sensing Air Quality in City based on Opportunistic Crowd-sensing , 2017, ICDCN.

[13]  Sarbani Roy,et al.  IoT-fog-cloud based architecture for smart city: Prototype of a smart building , 2017, 2017 7th International Conference on Cloud Computing, Data Science & Engineering - Confluence.

[14]  Agnieszka Wyłomańska,et al.  Detection of occupancy profile based on carbon dioxide concentration pattern matching , 2016 .

[15]  Stéphane Ploix,et al.  Estimating Occupancy In Heterogeneous Sensor Environment , 2016 .

[16]  Hyeun Jun Moon,et al.  Development of an occupancy prediction model using indoor environmental data based on machine learning techniques , 2016 .

[17]  Chandreyee Chowdhury,et al.  AirSense: Opportunistic crowd-sensing based air quality monitoring system for smart city , 2016, 2016 IEEE SENSORS.

[18]  Dimitrios Tzovaras,et al.  Conditional Random Fields - based approach for real-time building occupancy estimation with multi-sensory networks , 2016 .

[19]  Hua Li,et al.  Indoor occupancy estimation from carbon dioxide concentration , 2016, ArXiv.

[20]  A. Kankaria,et al.  Indoor Air Pollution in India: Implications on Health and its Control , 2014, Indian journal of community medicine : official publication of Indian Association of Preventive & Social Medicine.

[21]  Federico Castanedo,et al.  A Review of Data Fusion Techniques , 2013, TheScientificWorldJournal.