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Mohammad Khishe ORCID

The following bibliography contains all publications indexed in this database that are linked with this name as either author, editor or any other kind of contributor.

  1. Wang, Qiong / Chen, Gang / Khishe, Mohammad / Ibrahim, Banar Fareed / Rashidi, Shima (2023): Multi-objective optimization of IoT-based green building energy system using binary metaheuristic algorithms. In: Journal of Building Engineering, v. 68 (June 2023).

    https://doi.org/10.1016/j.jobe.2023.106031

  2. Mahmoodzadeh, Arsalan / Nejati, Hamid Reza / Mohammadi, Mokhtar / Hashim Ibrahim, Hawkar / Khishe, Mohammad / Rashidi, Shima / Hussein Mohammed, Adil (2022): Developing six hybrid machine learning models based on gaussian process regression and meta-heuristic optimization algorithms for prediction of duration and cost of road tunnels construction. In: Tunnelling and Underground Space Technology, v. 130 (December 2022).

    https://doi.org/10.1016/j.tust.2022.104759

  3. Wang, Le / Khishe, Mohammad / Mohammadi, Mokhtar / Mahmoodzadeh, Arsalan (2022): Extreme learning machine evolved by fuzzified hunger games search for energy and individual thermal comfort optimization. In: Journal of Building Engineering, v. 60 (November 2022).

    https://doi.org/10.1016/j.jobe.2022.105187

  4. Mahmoodzadeh, Arsalan / Mohammadi, Mokhtar / Hashim Ibrahim, Hawkar / Nariman Abdulhamid, Sazan / Farid Hama Ali, Hunar / Mohammed Hasan, Ahmed / Khishe, Mohammad / Mahmud, Hoger (2021): Machine learning forecasting models of disc cutters life of tunnel boring machine. In: Automation in Construction, v. 128 (August 2021).

    https://doi.org/10.1016/j.autcon.2021.103779

  5. Mahmoodzadeh, Arsalan / Mohammadi, Mokhtar / M Gharrib Noori, Krikar / Khishe, Mohammad / Hashim Ibrahim, Hawkar / Farid Hama Ali, Hunar / Nariman Abdulhamid, Sazan (2021): Presenting the best prediction model of water inflow into drill and blast tunnels among several machine learning techniques. In: Automation in Construction, v. 127 (July 2021).

    https://doi.org/10.1016/j.autcon.2021.103719

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