International Journal of Neutrosophic Science

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https://doi.org/10.54216/IJNS

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2690-6805ISSN (Online) 2692-6148ISSN (Print)

Volume 21 , Issue 3 , PP: 56-63, 2023 | Cite this article as | XML | Html | PDF | Full Length Article

Neutrosophic Multinominal Logistic Regression Technique for Optimizing Adaptive Reuse of Historical Castles

Rama Hussen Omar 1 * , Mohamed Najeb Kayali 2 , Mohamed Bisher Zeina 3

  • 1 Faculty of Architectural Engineering, University of Aleppo, Aleppo, Syria - (rama.hseen.omar@gmail.com)
  • 2 Department of Mathematical Statistics, University of Aleppo, Aleppo, Syria - (mohamed.najebkayali@gmail.com)
  • 3 Department of Mathematical Statistics, University of Aleppo, Aleppo, Syria - (bisher.zeina@gmail.com)
  • Doi: https://doi.org/10.54216/IJNS.210305

    Received: January 23, 2023 Revised: April 22, 2023 Accepted: June 18, 2023
    Abstract

    Defining and utilizing Neutrosophic Multinomial Logistic Regression (NMLR) is significant in architecture because it introduces a novel approach to prioritizing the optimal alternative for adaptively reusing a historic building. This is particularly crucial in post-conflict recovery. NMLR presents an intelligent classification system and decision-making tool that optimizes the evaluation process for adaptive reuse projects, even under conditions of uncertainty. The integration of Neutrosophic sets and digital technologies provides decision-makers with a more accurate and reliable tool to make rational decisions regarding functional spaces reuse. The effectiveness of this approach is demonstrated through a case study of the Castle of Aleppo. The study determined that the most suitable alternative for the castle is a multi-purpose facility that caters to tourism. This approach can be adapted to various restoration projects, and the assessment of proposed alternatives can be customized according to the weight of the criteria by creating desktop application which contributes to the sustainability and improvement of post-disaster reconstruction efforts.

    Keywords :

    Multi Criteria Decision Making , Neutrosophic , Multi-Nominal Logistic Regression , Adaptive Reuse , Historic Building , Machine learning , Castles , Building Information Modelling

    References

    [1]    A. Oppio, M. Bottero, V. Ferretti, U. Fratesi, D. Ponzini, and V. Pracchi, “Giving space to multicriteria analysis for complex cultural heritage systems: The case of the castles in Valle D’Aosta Region, Italy,” J Cult Herit, vol. 16, no. 6, pp. 779–789, Nov. 2015, doi: 10.1016/j.culher.2015.03.003.

    [2]    A. Kamari, C. Laustsen, S. Peterson, and P. H. Kirkegaard, “A BIM-based decision support system for the evaluation of holistic renovation scenarios” 2018. Available: http://www.itcon.org/2018/18

    [3]    Khyati Chaudhary,Gopal Chaudhary, A Decision Support System for Credit Risk Assessment using Business Intelligence and Machine Learning Techniques, Journal of American Journal of Business and Operations Research, Vol. 10 , No. 2 , (2023) : 32-38 (Doi   :  https://doi.org/10.54216/AJBOR.100204)

    [4]    Nesrine M. Roumieh,Sonia Ahmed, Adopting Risk Management Professional Methodologies as an Effective Strategy to Protect Heritage Sites in Syria, Journal of International Journal of BIM and Engineering Science, Vol. 5 , No. 1 , (2022) : 61-72 (Doi   :  https://doi.org/10.54216/IJBES.050104)

    [5]    R. R. Nadkarni and B. Puthuvayi, “A comprehensive literature review of Multi-Criteria Decision Making methods in heritage buildings,” Journal of Building Engineering, vol. 32. Elsevier Ltd, Nov. 01, 2020. doi: 10.1016/j.jobe.2020.101814.

    [6]    Hrudaya Kumar Tripathy,Sunday Adeola Ajagbe,El-Sayed M. El-Kenawy, Sustainable Management for the Architectural Heritage in Intelligent Cities using MCDM methods, Journal of Journal of Intelligent Systems and Internet of Things, Vol. 6 , No. 1 , (2022) : 41-58 (Doi   :  https://doi.org/10.54216/JISIoT.060104)

    [7]    W. Slany, P. Klement, ? ? Christian, and E. P. Klement, “Fuzzy Logic in Artificial Intelligence Fuzzy Logic in Artiicial Intelligence Christian Doppler Laboratory for Expert Systems Technische Universitt at Wien Institut ff ur Informationssysteme Abteilung ff ur Datenbanken und Expertensysteme Fuzzy Logic in Artiicial Intelligence,” 1997.

    [8]    Lootsma, Freerk A. Fuzzy logic for planning and decision making. Vol. 8. Springer Science & Business Media, 2013.

    [9]    F. Smarandache, D. Stanujkic, D. Karabasevic, and G. Popovic, “A novel approach for assessing the reliability of data contained in A novel approach for assessing the reliability of data contained in a single valued neutrosophic number and its application in a single valued neutrosophic number and its application in multiple criteria decision making multiple criteria decision making A Novel Approach for Assessing the Reliability of Data Contained in a Single Valued Neutrosophic Number and”, doi: 10.5281/zenodo.4030337.

    [10]  M. B. Zeina and Y. Karmouta, “Introduction to Neutrosophic Stochastic Processes,” Neutrosophic Sets and Systems, vol. 54, 2023.

    [11]  F. Smarandache, “Introduction to Neutrosophic Measure, Neutrosophic Integral, and Neutrosophic Probability,” ArXiv, 2013.

    [12]  F. Smarandache, “Introduction to Neutrosophic Statistics,” Branch Mathematics and Statistics Faculty and Staff Publications, Jan. 2014, Accessed: Feb. 21, 2023. Available: https://digitalrepository.unm.edu/math_fsp/33

    [13]  M. B. Zeina, “Neutrosophic Event-Based Queueing Model,” International Journal of Neutrosophic Science, vol. 6, no. 1, 2020, doi: 10.5281/zenodo.3840771.

    [14]  M. B. Zeina, “Erlang Service Queueing Model with Neutrosophic Parameters,” International Journal of Neutrosophic Science, vol. 6, no. 2, pp. 106–112, 2020, doi: 10.54216/IJNS.060202.

    [15]  “Solving Thermal System Problem Via Fuzzy Mohand Transform under Neutrosophic Initial Conditions”, doi: 10.54216/IJNS.2.

    [16]  M. Alqarni, A. H. Samak, S. S. I. Ismail, R. M. Abd El-Aziz, and A. I. Taloba, “Utilizing a Neutrosophic Fuzzy Logic System with ANN for Short-Term Estimation of Solar Energy,” International Journal of Neutrosophic Science, vol. 20, no. 4, pp. 240–259, 2023, doi: 10.54216/IJNS.200422.

    [17]  Z. Khan, M. Gulistan, N. Kausar, and C. Park, “Neutrosophic Rayleigh Model with Some Basic Characteristics and Engineering Applications,” IEEE Access, vol. 9, pp. 71277–71283, 2021, doi: 10.1109/ACCESS.2021.3078150.

    [18]  M. Kanan, N. Omer, S. S. I. Ismail, R. M. Abd El-Aziz, and A. I. Taloba, “Neutrosophic Fuzzy Neural Network Modelling and Current-Voltage Analysis for Forecasting Post-Surgery Risks,” International Journal of Neutrosophic Science, vol. 20, no. 4, pp. 232–239, 2023, doi: 10.54216/IJNS.200421.

    [19]  M. B. Zeina and M. Abobala, “A novel approach of neutrosophic continuous probability distributions using AH-isometry with applications in medicine,” Cognitive Intelligence with Neutrosophic Statistics in Bioinformatics, pp. 267–286, Jan. 2023, doi: 10.1016/B978-0-323-99456-9.00014-3.

    [20]  N. Moussa, B. Yousef, S. A. Naby, and S. Al Qudah, “Neutrosophic Environment and E-Learning: An Investigation into Student Satisfaction and Attitudes in the College of Engineering,” International Journal of Neutrosophic Science, vol. 20, no. 4, pp. 164–180, 2023, doi: 10.54216/IJNS.200413.

    [21]  N. M. Alnaqbi and W. Fouda, “Exploring the Role of ChatGPT and social media in Enhancing Student Evaluation of Teaching Styles in Higher Education Using Neutrosophic Sets,” International Journal of Neutrosophic Science, vol. 20, no. 4, pp. 181–190, 2023, doi: 10.54216/IJNS.200414.

    [22]  N. Gayathri, M. Helen, and P. Mounika, “Utilization of Jaccard Index Measures on Multiple Attribute Group Decision Making under Neutrosophic Environment,” International Journal of Neutrosophic Science (IJNS), vol. 3, no. 2, pp. 67–77, 2020, doi: 10.5281/zenodo.3742151.

    [23]  I. Deli and Y. Şubaş, “A ranking method of single valued neutrosophic numbers and its applications to multi-attribute decision making problems,” International Journal of Machine Learning and Cybernetics, vol. 8, no. 4, pp. 1309–1322, Aug. 2017, doi: 10.1007/s13042-016-0505-3.

    [24]  A. H. Alamoodi et al., “Based on neutrosophic fuzzy environment: a new development of FWZIC and FDOSM for benchmarking smart e-tourism applications,” Complex and Intelligent Systems, vol. 8, no. 4, 2022, doi: 10.1007/s40747-022-00689-7.

    [25]  D. Zhang, Y. Ma, H. Zhao, and X. Yang, “Neutrosophic Clustering Algorithm Based on Sparse Regular Term Constraint,” Complexity, vol. 2021, 2021, doi: 10.1155/2021/6657849.

    [26]  M. Abdel-Basset, M. El-hoseny, A. Gamal, and F. Smarandache, “A novel model for evaluation Hospital medical care systems based on plithogenic sets,” Artif Intell Med, vol. 100, Sep. 2019, doi: 10.1016/J.ARTMED.2019.101710.

    [27]  M. Miari, M. T. Anan, and M. B. Zeina, “Neutrosophic Two Way ANOVA,” International Journal of Neutrosophic Science, vol. 18, no. 3, pp. 72–83, 2022, doi: 10.54216/IJNS.180306.

    [28]  S. Hossain, S. E. Ahmed, and H. A. Howlader, “Model selection and parameter estimation of a multinomial logistic regression model,” J Stat Comput Simul, vol. 84, no. 7, pp. 1412–1426, 2014, doi: 10.1080/00949655.2012.746347.

    [29]  H. A. E.-S. A. A. H. R. Abdalla, “Fuzzy multinomial logistic regression analysis: A multi-objective programming approach,” AIP Conf Proc, pp. 10–6, 2023.

    [30]  Y. Hong and F. Chen, “Evaluating the adaptive reuse potential of buildings in conservation areas,” Facilities, vol. 35, no. 3–4, pp. 202–219, 2017, doi: 10.1108/F-10-2015-0077.

    [31]  D. Misirlisoy and K. Günçe, “Assessment of the adaptive reuse of castles as museums: Case of Cyprus,” International Journal of Sustainable Development and Planning, vol. 11, no. 2, pp. 147–159, 2016, doi: 10.2495/SDP-V11-N2-147-159.

    [32]  S. Conejos, C. Langston, and J. Smith, “Designing for better building adaptability: A comparison of adaptSTAR and ARP models,” Habitat Int, vol. 41, pp. 85–91, 2014, doi: 10.1016/j.habitatint.2013.07.002.

    [33]  A. Sharifi, “Most appropriate time for the adaptive reuse of historic buildings using ARP model,” Property Management, vol. 38, no. 1, pp. 109–123, Jan. 2020, doi: 10.1108/PM-07-2019-0039.

    [34]  M. Dell’ovo, F. Dell’anna, R. Simonelli, and L. Sdino, “Enhancing the cultural heritage through adaptive reuse. A multicriteria approach to evaluate the Castello Visconteo in Cusago (Italy),” Sustainability (Switzerland), vol. 13, no. 8, Apr. 2021, doi: 10.3390/su13084440.

    [35]  A. Yaghi, “Restoration Protocol In Conflict Zones ‘Practical Insights From The Old City Of Aleppo,’” 2017. Available: https://archnet.org/collections/54/sites/6414

    [36]  T. Aga Khan, Aleppo Citadel The of Aleppo Citadel Opposite: A detail of a relief above the Gate. 2008. Available: www.akdn.org

    [37]  S. Ibrahim, “Decision-making methodology between revitalisation and rehabilitation of world heritage city centers. Case study: The ancient City of Aleppo (Syria),” in International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, International Society for Photogrammetry and Remote Sensing, Jul. 2020, pp. 255–262. doi: 10.5194/isprs-archives-XLIV-M-1-2020-255-2020.

    Cite This Article As :
    Hussen, Rama. , Najeb, Mohamed. , Bisher, Mohamed. Neutrosophic Multinominal Logistic Regression Technique for Optimizing Adaptive Reuse of Historical Castles. Journal of International Journal of Neutrosophic Science, vol. 21, no. 3, 2023, pp. 56-63. DOI: https://doi.org/10.54216/IJNS.210305
    Hussen, R. Najeb, M. Bisher, M. (2023). Neutrosophic Multinominal Logistic Regression Technique for Optimizing Adaptive Reuse of Historical Castles. Journal of International Journal of Neutrosophic Science, 21( 3), 56-63. DOI: https://doi.org/10.54216/IJNS.210305
    Hussen, Rama. Najeb, Mohamed. Bisher, Mohamed. Neutrosophic Multinominal Logistic Regression Technique for Optimizing Adaptive Reuse of Historical Castles. Journal of International Journal of Neutrosophic Science 21, no. 3 (2023): 56-63. DOI: https://doi.org/10.54216/IJNS.210305
    Hussen, R. , Najeb, M. , Bisher, M. (2023) . Neutrosophic Multinominal Logistic Regression Technique for Optimizing Adaptive Reuse of Historical Castles. Journal of International Journal of Neutrosophic Science , 21( 3) , 56-63 . DOI: https://doi.org/10.54216/IJNS.210305
    Hussen R. , Najeb M. , Bisher M. [2023]. Neutrosophic Multinominal Logistic Regression Technique for Optimizing Adaptive Reuse of Historical Castles. Journal of International Journal of Neutrosophic Science. 21( 3): 56-63. DOI: https://doi.org/10.54216/IJNS.210305
    Hussen, R. Najeb, M. Bisher, M. "Neutrosophic Multinominal Logistic Regression Technique for Optimizing Adaptive Reuse of Historical Castles," Journal of International Journal of Neutrosophic Science, vol. 21, no. 3, pp. 56-63, 2023. DOI: https://doi.org/10.54216/IJNS.210305