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Indoor air quality pollutants predicting approach using unified labelling process-based multi-criteria decision making and machine learning techniques

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Abstract

Indoor air quality (IAQ) refers to the conditions found within buildings that can impact respiratory health. Good IAQ conditions for hospital facilities are essential, especially for patients and medical staff. Recently, several concerns have been outlined and require an urgent solution in identifying IAQ pollutants and related thresholds and ways to provide a knowledge-based method for labelling pollution levels. To this end, a systematic review should be conducted first to construct new taxonomy research on internet of things-based IAQ sensory technology in hospital facilities to identify a research gap. Thus, the present study aims to develop an IAQ methodology that includes the recommended nine pollutants for hospitals and facilities: Carbon monoxide, Carbon dioxide, Nitrogen Dioxide, Ozone, Formaldehyde, Volatile organic compound, particulate matter (PM) and air humidity and temperature. The developed methodology utilised actual and simulated IAQ pollutant datasets to predict the pollution levels within hospital facilities in three distinct phases. In the first phase, two IAQ datasets (real and large-scale simulated datasets) are identified. The second phase includes the following: First is utilising the Interval type 2 trapezoidal-fuzzy weighted with zero inconsistency (IT2TR-FWZIC) method from the Multi-Criteria Decision Making theory for providing the required weights to the nine pollutants. Second is developing a new method, the Unified Process for Labelling Pollutants Dataset (UPLPD), consisting of six processes based on the IT2TR-FWZIC method. The UPLPD can classify the pollution levels into four levels and assign the required labels within the two datasets. Third is applying the labelled datasets to the developed machine learning model using eight algorithms. The third phase includes the model evaluation using five metrics in terms of accuracy, Area under the Curve, F1-score, precision and recall. For the actual dataset, the best three algorithms' results are Support Vector Machine, Logistic Regression and Decision Tree (DT), which achieved the highest accuracy of 99.813, 99.259 and 98.182%, respectively, with performance metrics. The simulated dataset, the Random Forest, DT and AdaBoost achieved the highest accuracy of 90.094, 88.964 and 87.735%, respectively, with performance metrics. The results satisfied the challenges and overcame the issues, and experimental results confirmed the efficacy of the predictive model.

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References

  1. Lasomsri, P., Yanbuaban, P., Kerdpoca, O., & Ouypornkochagorn, T. (2019). A development of low-cost devices for monitoring indoor air quality in a large-scale hospital. In: ECTI-CON 2018 - 15th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology, pp. 282–285. Doi: https://doi.org/10.1109/ECTICon.2018.8619934.

  2. U. S. E. P. Agency and I. Division (2014). Air Quality Index (AQI). Encycl. Qual. Life Well-Being Res., no. February, pp. 120–120. Doi: https://doi.org/10.1007/978-94-007-0753-5_100115.

  3. Pitarma, R., Marques, G., & Ferreira, B. R. (2017). Monitoring indoor air quality for enhanced occupational health. Journal of Medical Systems, 41(2), 23. https://doi.org/10.1007/s10916-016-0667-2

    Article  Google Scholar 

  4. Thomas, N. M., et al. (2019). Investigation of indoor air quality determinants in a field study using three different data streams. Building and Environment, 154, 281–295. https://doi.org/10.1016/j.buildenv.2019.03.022

    Article  Google Scholar 

  5. Rastogi, K., Barthwal, A., &. Lohani, D., (2019). AQCI: An IoT Based Air Quality and Thermal Comfort Model using Fuzzy Inference. Int. Symp. Adv. Networks Telecommun. Syst. ANTS, vol. 2019-Decem, pp. 1–6. Doi: https://doi.org/10.1109/ANTS47819.2019.9118026.

  6. Hapsari, A. A., Hajamydeen, A. I., Vresdian, D. J., Manfaluthy, M., Prameswono, L., & Yusuf, E. (2019). Real time indoor air quality monitoring system based on IoT using MQTT and wireless sensor network. ICETAS 2019–2019 IEEE 6th International Conference on Engineering Technologies and Applied Sciences (ICETAS). https://doi.org/10.1109/ICETAS48360.2019.9117518

    Article  Google Scholar 

  7. Yogalakshmi, K. P., Sudha, R., & Selvam, C., (2015). Design and prototype implementation of indoor air quality monitoring using LonWorks technology. In: 2015 IEEE Technological Innovation in ICT for Agriculture and Rural Development (TIAR), pp. 193–196. Doi: https://doi.org/10.1109/TIAR.2015.7358556.

  8. OSACH, (2003). Indoor Air Quality (IAQ) in Healthcare Facilities, vol. 4386. Springer

  9. Dong, Q., et al. (2020). A cloud-connected NO2and ozone sensor system for personalized pediatric asthma research and management. IEEE Sensors Journal, 20(24), 15143–15153. https://doi.org/10.1109/JSEN.2020.3009911

    Article  Google Scholar 

  10. Bang C. S. et al. (2018). Ambient air pollution in gastrointestinal endoscopy unit; rationale and design of a prospective study. Med. (United States), 97(49). Doi: https://doi.org/10.1097/MD.0000000000013600.

  11. Asif, A., Zeeshan, M., & Jahanzaib, M. (2018). Indoor temperature, relative humidity and CO2 levels assessment in academic buildings with different heating, ventilation and air-conditioning systems. Building and Environment, 133, 83–90. https://doi.org/10.1016/j.buildenv.2018.01.042

    Article  Google Scholar 

  12. Cornelius, K., Kumar, N. K., Pradhan, S., Patel, P., & Vinay, N. (2020). An efficient tracking system for air and sound pollution using IoT. In 2020 6th International Conference on Advanced Computing and Communication Systems, ICACCS 2020, pp. 22–25. Doi: https://doi.org/10.1109/ICACCS48705.2020.9074301.

  13. Gryech, I., Ben-Aboud, Y., Guermah, B., Sbihi, N., Ghogho, M., & Kobbane, A. (2020). Moreair: A low-cost urban air pollution monitoring system. Sensors (Switzerland), 20(4). Doi: https://doi.org/10.3390/s20040998.

  14. Divya, A., Kiruthika, R., & Gayathri, D. (2019). Detecting and analysing the quality of air using low cost sensors to reduce air pollution in urban areas. In: 2019 IEEE International Conference on System, Computation, Automation and Networking (ICSCAN), pp. 1–5. Doi: https://doi.org/10.1109/ICSCAN.2019.8878780.

  15. Kumar, A., Kumari, M., & Gupta, H. (2020). Design and analysis of IoT based air quality monitoring system. In: 2020 International Conference on Power Electronics and IoT Applications in Renewable Energy and its Control (PARC), pp. 242–245. Doi: https://doi.org/10.1109/PARC49193.2020.236600.

  16. Gnanaraj, V. V., Ranjana, P., & Thenmozhi, P. (2019). Patient monitoring and control system using internet of thing. The International Journal of Innovative Technology and Exploring Engineering., 8(6), 120–123.

    Google Scholar 

  17. Saad Baqer, N., Mohammed, H. A., Albahri, A. S., Zaidan, A. A., Al-qaysi, Z. T., & Albahri, O. S. (2022). Development of the Internet of Things sensory technology for ensuring proper indoor air quality in hospital facilities: Taxonomy analysis, challenges, motivations, open issues and recommended solution. Measurement: Journal of the International Measurement Confederation, vol. 192. Elsevier, p. 110920. Doi: https://doi.org/10.1016/j.measurement.2022.110920.

  18. Bhangar, S., et al. (2016). Pilot study of sources and concentrations of size-resolved airborne particles in a neonatal intensive care unit. Building and Environment, 106, 10–19. https://doi.org/10.1016/j.buildenv.2016.06.020

    Article  Google Scholar 

  19. Stamp, S., Burman, E., Shrubsole, C., Chatzidiakou, L., Mumovic, D., & Davies, M. (2020). Long-term, continuous air quality monitoring in a cross-sectional study of three UK non-domestic buildings. Build. Environ., vol. 180. Doi: https://doi.org/10.1016/j.buildenv.2020.107071.

  20. Lee, H. J., Lee, K. H., & Kim, D. K. (2020). Evaluation and comparison of the indoor air quality in different areas of the hospital. Med. (United States), 99(52), e23942. https://doi.org/10.1097/MD.0000000000023942

    Article  Google Scholar 

  21. Jaimini, U., Banerjee, T., Romine, W., Thirunarayan, K., Sheth, A., & Kalra, M. (2017). Investigation of an indoor air quality sensor for asthma management in children. IEEE Sensors Letters., 1(2), 1–4. https://doi.org/10.1109/lsens.2017.2691677

    Article  Google Scholar 

  22. Marques, G., C. Roque Ferreira, C., and Pitarma, R. (2018). A system based on the internet of things for real-time particle monitoring in buildings. International Journal of Environmental Research and Public Health, 15(4). Doi: https://doi.org/10.3390/ijerph15040821.

  23. Busso, I. T., Herrera, F., Tames, M. F., Gasquez, I. G., Camisassa, L. N., & Carreras, H. A. (2020). QuEChER method for air microbiological monitoring in hospital environments. Journal of Infection in Developing Countries, 14(1), 66–73. https://doi.org/10.3855/jidc.11563

    Article  Google Scholar 

  24. Choi, N., Yamanaka, T., Kobayashi, T., Ihama, T., & Wakasa, M. (2020). Influence of vertical airflow along walls on temperature and contaminant concentration distributions in a displacement-ventilated four-bed hospital ward. Building and Environment, 183, 107181. https://doi.org/10.1016/j.buildenv.2020.107181

    Article  Google Scholar 

  25. Mumtaz, R., et al. (2021). Internet of things (Iot) based indoor air quality sensing and predictive analytic—a covid-19 perspective. Electron., 10(2), 1–26. https://doi.org/10.3390/electronics10020184

    Article  Google Scholar 

  26. World Health Organization (2010). WHO guidelines for indoor air quality: selected pollutants. Bonn, Ger. puncto druck+ Medien GmbH, p. 484.

  27. United States Environmental Protection Agency (2021). Criteria Air Pollutants | US EPA. Epa.

  28. ISO (2021). Sterilization and Disinfection in General Including Sterilization Methods, Air Quality of Surgery Rooms, Etc.

  29. Baqer, N. S., Mohammed, H. A., & Albahri, A. S. (2022). Development of a real-time monitoring and detection indoor air quality system for intensive care unit and emergency department. Signa Vitae, 1, 16. https://doi.org/10.22514/sv.2022.013

    Article  Google Scholar 

  30. Bennett, D. A. (2001). How can I deal with missing data in my study? Australian and New Zealand Journal of Public Health, 25(5), 464–469. https://doi.org/10.1111/j.1467-842X.2001.tb00294.x

    Article  Google Scholar 

  31. García-Laencina, P. J., Sancho-Gómez, J. L., & Figueiras-Vidal, A. R. (2010). Pattern classification with missing data: A review. Neural Computing and Applications, 19(2), 263–282. https://doi.org/10.1007/s00521-009-0295-6

    Article  Google Scholar 

  32. Jiang, W. (2020). Time series classification: Nearest neighbor versus deep learning models. SN Appl. Sci., 2(4), 1–17. https://doi.org/10.1007/s42452-020-2506-9

    Article  Google Scholar 

  33. Rao, X. S., Song, J. J.¸ Yang, X. B., Liu, K. Y.& Wang, P. X. (2019). Neighborhood Classifier for Label Noise. In Proceedings - International Conference on Machine Learning and Cybernetics, vol. 2019, pp. 1–6. Doi: https://doi.org/10.1109/ICMLC48188.2019.8949200.

  34. Mohammed, K. I., et al. (2020). A Uniform intelligent prioritisation for solving diverse and big data generated from multiple chronic diseases patients based on hybrid decision-making and voting method. IEEE Access, 8, 91521–91530.

    Article  Google Scholar 

  35. Ibrahim, N. K., et al. (2019). Multi-criteria evaluation and benchmarking for young learners’ english language mobile applications in terms of LSRW skills. IEEE Access, 7, 146620–146651. https://doi.org/10.1109/ACCESS.2019.2941640

    Article  Google Scholar 

  36. Alsalem, M. A., et al. (2019). Multiclass benchmarking framework for automated acute leukaemia detection and classification based on BWM and Group-VIKOR. Journal of Medical Systems, 43(7), 212. https://doi.org/10.1007/s10916-019-1338-x

    Article  Google Scholar 

  37. Albahri A. S. et al., (2020). multi-biological laboratory examination framework for the prioritization of patients with COVID-19 based on integrated AHP and group VIKOR methods. International Journal of Information Technology and Decision 19(5) Doi: https://doi.org/10.1142/S0219622020500285.

  38. Alaa, M., Albakri, I., Singh, C., (2019). Assessment and Ranking Framework for the English Skills of Pre-Service Teachers Based on Fuzzy Delphi and TOPSIS Methods. ieeexplore.ieee.org, Accessed: Oct. 22, 2019. [Online]. Available: https://ieeexplore.ieee.org/abstract/document/8809695/.

  39. Albahri, O. S. et al., (2020). Helping doctors hasten COVID-19 treatment: Towards a rescue framework for the transfusion of best convalescent plasma to the most critical patients based on biological requirements via ML and novel MCDM methods. Comput. Methods Programs Biomed., p. 105617.

  40. Albahri, A. S. et al., (2019). Based multiple heterogeneous wearable sensors : A smart real-time health-monitoring structured for hospitals distributor. IEEE Access, pp. 1–1. Doi: https://doi.org/10.1109/ACCESS.2019.2898214.

  41. Mohammed, K. I., et al. (2019). Real-time remote-health monitoring systems: A review on patients prioritisation for multiple-chronic diseases, taxonomy analysis, concerns and solution procedure. Journal of Medical Systems, 43(7), 223. https://doi.org/10.1007/s10916-019-1362-x

    Article  Google Scholar 

  42. Albahri, O. S., et al. (2019). Fault-tolerant mHealth framework in the context of IoT-based real-time wearable health data sensors. IEEE Access, 7, 50052–50080. https://doi.org/10.1109/ACCESS.2019.2910411

    Article  Google Scholar 

  43. Alsalem, M. A., et al. (2018). Systematic review of an automated multiclass detection and classification system for acute leukaemia in terms of evaluation and benchmarking, open challenges, issues and methodological aspects. Journal of Medical Systems, 42(11), 204. https://doi.org/10.1007/s10916-018-1064-9

    Article  Google Scholar 

  44. Zaidan, A. A., et al. (2018). A review on smartphone skin cancer diagnosis apps in evaluation and benchmarking: Coherent taxonomy, open issues and recommendation pathway solution. Health Technol. (Berl), 8(4), 223–238. https://doi.org/10.1007/s12553-018-0223-9

    Article  Google Scholar 

  45. Albahri, A. S., Zaidan, A. A., Albahri, O. S., Zaidan, B. B. & Alsalem, M. A. (2018). Real-time fault-tolerant mhealth system: Comprehensive review of healthcare services, opens issues, challenges and methodological aspects. Journal of Medical Systems, 42(8), 137. Springer US, Aug. 23, 2018. Doi: https://doi.org/10.1007/s10916-018-0983-9.

  46. Abdulkareem, K. H. et al., A new standardisation and selection framework for real-time image dehazing algorithms from multi-foggy scenes based on fuzzy Delphi and hybrid multi-criteria decision analysis methods

  47. Albahri, O. S., Zaidan, A. A., Zaidan, B. B., Hashim, M., Albahri, A. S., & Alsalem, M. A. (2018). Real-time remote health-monitoring systems in a medical centre: A review of the provision of healthcare services-based body sensor information, open challenges and methodological aspects. Journal of Medical Systems, 42(9), 164. https://doi.org/10.1007/s10916-018-1006-6

    Article  Google Scholar 

  48. Albahri, et al. (2020) Systematic review of artificial intelligence techniques in the detection and classification of COVID-19 medical images in terms of evaluation and benchmarking: Taxonomy analysis, challenges, future solutions and methodological aspects. Journal of Infection and Public Health.

  49. Mohammed, K. I., et al. (2020). Novel technique for reorganisation of opinion order to interval levels for solving several instances representing prioritisation in patients with multiple chronic diseases. Computer Methods and Programs in Biomedicine, 185, 105151. https://doi.org/10.1016/j.cmpb.2019.105151

    Article  Google Scholar 

  50. Khatari, M., Zaidan, A. A., Zaidan, B. B., Albahri, O. S., & Alsalem, M. A. (2019). Multi-criteria evaluation and benchmarking for active queue management methods: Open issues, challenges and recommended pathway solutions. International Journal of Information Technology and Decision Making, 18(04), 1187–1242. https://doi.org/10.1142/S0219622019300039

    Article  Google Scholar 

  51. Abdulkareem, K. H. et al., (2020). a novel multi-perspective benchmarking framework for selecting image dehazing intelligent algorithms based on BWM and group VIKOR techniques. International Journal of Information Technology and Decision, pp. 1–49.

  52. Albahri, A. S., Hamid, R. A., Albahri, O. S., & Zaidan, A. A. (2020). Detection-based prioritisation: Framework of multi-laboratory characteristics for asymptomatic COVID-19 carriers based on integrated Entropy–TOPSIS methods. Artificial Intelligence in Medicine, 111, 101983.

    Article  Google Scholar 

  53. Zaidan, A. A., Zaidan, B. B., Alsalem, M. A., Albahri, O. S., Albahri, A. S. & Qahtan, M. Y. (2019). Multi-agent learning neural network and Bayesian model for real-time IoT skin detectors: a new evaluation and benchmarking methodology. Neural Computing and Applications, pp. 1–52. Doi: https://doi.org/10.1007/s00521-019-04325-3.

  54. Almahdi, E. M., Zaidan, A. A., Zaidan, B. B., Alsalem, M. A., Albahri, O. S., & Albahri, A. S. (2019). Mobile-based patient monitoring systems: A prioritisation framework using multi-criteria decision-making techniques. Journal of Medical Systems, 43(7), 219. https://doi.org/10.1007/s10916-019-1339-9

    Article  Google Scholar 

  55. Almahdi, E. M., Zaidan, A. A., Zaidan, B. B., Alsalem, M. A., Albahri, O. S., & Albahri, A. S. (2019). Mobile patient monitoring systems from a benchmarking aspect: Challenges, open issues and recommended solutions. Journal of Medical Systems, 43(7), 207.

    Article  Google Scholar 

  56. Krishnan, E., et al. (2021). Interval type 2 trapezoidal-fuzzy weighted with zero inconsistency combined with VIKOR for evaluating smart e-tourism applications. International Journal of Intelligent Systems, 36(9), 4723–4774. https://doi.org/10.1002/int.22489

    Article  Google Scholar 

  57. Alamleh, A. et al., (2022). Federated learning for IoMT applications: A standardisation and benchmarking framework of intrusion detection systems. IEEE Journal of Biomedical and Health Informatics.

  58. Al-Humairi, S. et al., (2022). Towards sustainable transportation: A pavement strategy selection based on the extension of dual-hesitant fuzzy multi-criteria decision-making methods. IEEE Transactions on Fuzzy Systems.

  59. Khatari, M., Zaidan, A. A., Zaidan, B. B., Albahri, O. S., Alsalem, M. A., & Albahri, A. S. (2021). Multidimensional benchmarking framework for AQMs of network congestion control based on AHP and Group-TOPSIS. International Journal of Information Technology and Decision Making, 20(05), 1409–1446.

    Article  Google Scholar 

  60. Zughoul, O., Zaidan, A. A., Zaidan, B. B., & Faiez, M. (2020). Novel triplex procedure for ranking the ability of software engineering students based on two levels of AHP and Group TOPSIS techniques. International Journal of Information Technology and Decision Making.

  61. Albahri, O. S., et al. (2021). New mHealth hospital selection framework supporting decentralised telemedicine architecture for outpatient cardiovascular disease-based integrated techniques: Haversine-GPS and AHP-VIKOR. Journal of Ambient Intelligence and Humanized Computing. https://doi.org/10.1007/s12652-021-02897-4

    Article  Google Scholar 

  62. Mohammed, T. J., et al. (2021). Convalescent-plasma-transfusion intelligent framework for rescuing COVID-19 patients across centralised/decentralised telemedicine hospitals based on AHP-group TOPSIS and matching component. Applied Intelligence. https://doi.org/10.1007/s10489-020-02169-2

    Article  Google Scholar 

  63. Albahri, O. S. et al., (2020). Multidimensional benchmarking of the active queue management methods of network congestion control based on extension of fuzzy decision by opinion score method International Journal of Intelligent Systems.

  64. Albahri, O. S. et al., (2022). Combination of Fuzzy-weighted zero-inconsistency and Fuzzy decision by opinion score methods in pythagorean m-polar fuzzy environment: A case study of sing language recognition systems. International Journal of Information Technology and Decision Making, pp. 1–29.

  65. Alamoodi, A. H. et al., (2022). Based on neutrosophic fuzzy environment: a new development of FWZIC and FDOSM for benchmarking smart e-tourism applications. Complex and Intelligent Systems, pp. 1–25.

  66. Alsalem, M. A. et al., (2022). Multi-criteria decision-making for coronavirus disease 2019 applications: a theoretical analysis review. Artificial Intelligence Review, pp. 1–84.

  67. Albahri, A. S., et al. (2021). Development of IoT-based mhealth framework for various cases of heart disease patients. Health Technol. (Berl), 11(5), 1013–1033.

    Article  Google Scholar 

  68. Alsalem, M. A. et al., Rise of multiattribute decision-making in combating COVID-19: A systematic review of the state-of-the-art literature. International Journal of Intelligent Systems Doi: https://doi.org/10.1002/int.22699.

  69. Hamid, R. A., Albahri, A. S., Albahri, O. S., & Zaidan, A. A. (2021). Dempster–Shafer theory for classification and hybridised models of multi-criteria decision analysis for prioritisation: a telemedicine framework for patients with heart diseases. Journal of Ambient Intelligence and Humanized Computing, pp. 1–35.

  70. Malik, R. Q. et al., (2021). Novel roadside unit positioning framework in the context of the vehicle-to-infrastructure communication system based on AHP—Entropy for weighting and borda—VIKOR for uniform ranking. International Journal of Information Technology and Decision Making, pp. 1–34.

  71. Salih, M. M., Albahri, O. S., Zaidan, A. A., Zaidan, B. B., Jumaah, F. M., & Albahri, A. S. (2021). Benchmarking of AQM methods of network congestion control based on extension of interval type-2 trapezoidal fuzzy decision by opinion score method. Telecommunication Systems, 77(3), 493–522.

    Article  Google Scholar 

  72. Zhu, G. N., Hu, J., Qi, J., Gu, C. C., & Peng, Y. H. (2015). An integrated AHP and VIKOR for design concept evaluation based on rough number. Advance Engineering in Informatics, 29(3), 408–418. https://doi.org/10.1016/j.aei.2015.01.010

    Article  Google Scholar 

  73. Qahtan, S. et al., (2022). Novel Multi security and privacy benchmarking framework for blockchain-based IoT healthcare industry 4.0 systems. IEEE Transactions on Industrial Informatics.

  74. Alamoodi, A. H. et al., (2022). New extension of fuzzy-weighted zero-inconsistency and fuzzy decision by opinion score method based on cubic pythagorean fuzzy environment: A benchmarking case study of sign language recognition systems. International Journal of Fuzzy Systems, pp. 1–18.

  75. Al-Samarraay, M. S. et al., (2022). A new extension of FDOSM based on Pythagorean fuzzy environment for evaluating and benchmarking sign language recognition systems. Neural Computing and Applications, pp. 1–19.

  76. Alsalem, M. A. et al., (2022). Rescuing emergency cases of COVID-19 patients: An intelligent real-time MSC transfusion framework based on multicriteria decision-making methods. Applied Intelligence, pp. 1–25.

  77. Al-Samarraay, M. S., et al. (2022). Extension of interval-valued Pythagorean FDOSM for evaluating and benchmarking real-time SLRSs based on multidimensional criteria of hand gesture recognition and sensor glove perspectives. Applied Soft Computing, 116, 108284.

    Article  Google Scholar 

  78. Albahri, O. S., et al. (2021). Novel dynamic fuzzy Decision-Making framework for COVID-19 vaccine dose recipients. Journal of Advanced Research. https://doi.org/10.1016/j.jare.2021.08.009

    Article  Google Scholar 

  79. Mohammed, R. T., et al. (2021). Determining importance of many-objective optimisation competitive algorithms evaluation criteria based on a novel Fuzzy-weighted zero-inconsistency method. International Journal of Information Technology and Decision Making. https://doi.org/10.1142/S0219622021500140

    Article  Google Scholar 

  80. Alsalem, M. A., et al. (2021). Based on T-spherical fuzzy environment: A combination of FWZIC and FDOSM for prioritising COVID-19 vaccine dose recipients. Journal of Infection and Public Health, 14(10), 1513–1559. https://doi.org/10.1016/j.jiph.2021.08.026

    Article  Google Scholar 

  81. Albahri, A. S., et al. (2022). Integration of fuzzy-weighted zero-inconsistency and fuzzy decision by opinion score methods under a q-rung orthopair environment: A distribution case study of COVID-19 vaccine doses. Comput. Stand. Interfaces, 80, 103572. https://doi.org/10.1016/j.csi.2021.103572

    Article  Google Scholar 

  82. Matell, M. S., & Jacoby, J. (1971). Is there an optimal number of alternatives for likert scale items? study 1: Reliability and validity. Educational and Psychological Measurement, 31(3), 657–674. https://doi.org/10.1177/001316447103100307

    Article  Google Scholar 

  83. Salih, M. M., Zaidan, B. B., & Zaidan, A. A. (2020). Fuzzy decision by opinion score method. Applied Soft Computing, 96, 106595.

    Article  Google Scholar 

  84. Keshavarz Ghorabaee, M. (2016). Developing an MCDM method for robot selection with interval type-2 fuzzy sets. Robotics and Computer-Integrated Manufacturing, 37, 221–232. Doi: https://doi.org/10.1016/j.rcim.2015.04.007.

  85. Ghorabaee, M. K., Amiri, M., Sadaghiani, J. S., & Zavadskas, E. K. (2015). Multi-criteria project selection using an extended VIKOR method with interval type-2 fuzzy sets. International Journal of Information Technology and Decision Making, 14(5), 993–1016. https://doi.org/10.1142/S0219622015500212

    Article  Google Scholar 

  86. Septiawan, W. M., & Endah, S. N. (2018). Suitable recurrent neural network for air quality prediction with backpropagation through time. The 2nd International Conference on Informatics and Computational Sciences ICICoS, 2018, 196–201. https://doi.org/10.1109/ICICOS.2018.8621720

    Article  Google Scholar 

  87. Getting Started with MokaFive Getting Started with MokaFive, pp. 3–4, (2011).

  88. Smith, S. M. (1993). Accuracy and precision. Teaching Statistics, 15(1), 31–31. https://doi.org/10.1111/j.1467-9639.1993.tb00258.x

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Informatics Institute for Postgraduate Studies (IIPS), Iraqi Commission for Computers and Informatics (ICCI), Baghdad, Iraq

    Noor S. Baqer, A. S. Albahri & Hussein A. Mohammed

  2. Ministry of Education, Baghdad, Iraq

    Noor S. Baqer

  3. University of Information Technology and Communications (UOITC), Baghdad, Iraq

    Hussein A. Mohammed, Rula A. Amjed & Abbas M. Al-Bakry

  4. Faculty of Engineering and IT, The British University in Dubia, Dubai, United Arab Emirates

    A. A. Zaidan

  5. Computer Techniques Engineering, Department, Mazaya University College Thi-Qar, Nassiriya, Iraq

    O. S. Albahri

  6. Department of Computing, Faculty of Arts, Computing and Creative Industry, Universiti Pendidikan Sultan Idris, 35900, Tanjung Malim, Malaysia

    O. S. Albahri & A. H. Alamoodi

  7. Department of Business Administration, College of Administrative Science, The University of Mashreq, 10021, Baghdad, Iraq

    H. A. Alsattar

  8. Southern Technical University, Basrah, Iraq

    Alhamzah Alnoor

  9. Future Technology Research Center, National Yunlin University of Science and Technology, 123 University Road, Section 3, Douliou, Yunlin, 64002, Taiwan

    B. B. Zaidan

  10. Medical Instrumentation Techniques Engineering Department, Al-Mustaqbal University College, Babylon, Iraq

    R. Q. Malik & Z. H. Kareem

Authors
  1. Noor S. Baqer
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  2. A. S. Albahri
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  3. Hussein A. Mohammed
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  4. A. A. Zaidan
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  5. Rula A. Amjed
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  6. Abbas M. Al-Bakry
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  7. O. S. Albahri
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  8. H. A. Alsattar
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  9. Alhamzah Alnoor
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  10. A. H. Alamoodi
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  11. B. B. Zaidan
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  12. R. Q. Malik
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  13. Z. H. Kareem
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Corresponding author

Correspondence to A. A. Zaidan.

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Baqer, N.S., Albahri, A.S., Mohammed, H.A. et al. Indoor air quality pollutants predicting approach using unified labelling process-based multi-criteria decision making and machine learning techniques. Telecommun Syst 81, 591–613 (2022). https://doi.org/10.1007/s11235-022-00959-2

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