Enhancing learning environments with IoT: a novel decision-making approach using probabilistic linguistic T-spherical fuzzy set
The Internet of Things is a system of networked devices that can gather, process, and share data through the Internet. The Internet of Things has vast potential to spread widely across various aspects of our lives. The educational process model is undergoing a transformation in which the learning re...
- Autores:
-
Shafiq, Aqsa
Naz, Sumera
Butt, Shariq Aziz
Piñeres Espitia, Gabriel
- Tipo de recurso:
- Article of investigation
- Fecha de publicación:
- 2024
- Institución:
- Corporación Universidad de la Costa
- Repositorio:
- REDICUC - Repositorio CUC
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.cuc.edu.co:11323/13786
- Acceso en línea:
- https://hdl.handle.net/11323/13786
https://repositorio.cuc.edu.co/
- Palabra clave:
- AHP method
Internet of Things in enhancing learning environments
Probabilistic linguistic T-spherical fuzzy set
Weighted power average operator
Weighted power geometric operator
- Rights
- embargoedAccess
- License
- Atribución 4.0 Internacional (CC BY 4.0)
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Enhancing learning environments with IoT: a novel decision-making approach using probabilistic linguistic T-spherical fuzzy set |
| title |
Enhancing learning environments with IoT: a novel decision-making approach using probabilistic linguistic T-spherical fuzzy set |
| spellingShingle |
Enhancing learning environments with IoT: a novel decision-making approach using probabilistic linguistic T-spherical fuzzy set AHP method Internet of Things in enhancing learning environments Probabilistic linguistic T-spherical fuzzy set Weighted power average operator Weighted power geometric operator |
| title_short |
Enhancing learning environments with IoT: a novel decision-making approach using probabilistic linguistic T-spherical fuzzy set |
| title_full |
Enhancing learning environments with IoT: a novel decision-making approach using probabilistic linguistic T-spherical fuzzy set |
| title_fullStr |
Enhancing learning environments with IoT: a novel decision-making approach using probabilistic linguistic T-spherical fuzzy set |
| title_full_unstemmed |
Enhancing learning environments with IoT: a novel decision-making approach using probabilistic linguistic T-spherical fuzzy set |
| title_sort |
Enhancing learning environments with IoT: a novel decision-making approach using probabilistic linguistic T-spherical fuzzy set |
| dc.creator.fl_str_mv |
Shafiq, Aqsa Naz, Sumera Butt, Shariq Aziz Piñeres Espitia, Gabriel |
| dc.contributor.author.none.fl_str_mv |
Shafiq, Aqsa Naz, Sumera Butt, Shariq Aziz Piñeres Espitia, Gabriel |
| dc.subject.proposal.eng.fl_str_mv |
AHP method Internet of Things in enhancing learning environments Probabilistic linguistic T-spherical fuzzy set Weighted power average operator Weighted power geometric operator |
| topic |
AHP method Internet of Things in enhancing learning environments Probabilistic linguistic T-spherical fuzzy set Weighted power average operator Weighted power geometric operator |
| description |
The Internet of Things is a system of networked devices that can gather, process, and share data through the Internet. The Internet of Things has vast potential to spread widely across various aspects of our lives. The educational process model is undergoing a transformation in which the learning requirements for different students must be fulfilled in various ways. Our study presents a novel multi-attribute group decision-making strategy that examines how the Internet of Things can help to provide the best learning environment while also making the educational process more effective. The probabilistic linguistic T-spherical fuzzy set (PLT-SFS) is a modification of the T-spherical fuzzy set in which the degrees of membership, abstinence, and non-membership are characterized by probabilistic linguistic terms. Then two new aggregation operators, the PLT-SF weighted power average (PLT-SFWPA) operator and the PLT-SF weighted power geometric (PLT-SFWPG) operator, are introduced. After that, an approach to multi-attribute group decision-making based on the analytic hierarchy process is constructed in which the data are aggregated by the PLT-SFWPA operator. To illustrate the validity of the proposed approach, a case study of nine Internet of Things applications for enhancing learning environments is provided. |
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2024 |
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2024-11-22T12:08:36Z |
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2024-11-22T12:08:36Z 2025-04-29 |
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2024-04-29 |
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Artículo de revista |
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Shafiq, A., Naz, S., Butt, S.A. et al. Enhancing learning environments with IoT: a novel decision-making approach using probabilistic linguistic T-spherical fuzzy set. J Supercomput 80, 17524–17574 (2024). https://doi.org/10.1007/s11227-024-06129-2. |
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0920-8542 |
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https://hdl.handle.net/11323/13786 |
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10.1007/s11227-024-06129-2 |
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1573-0484 |
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Corporación Universidad de la Costa |
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REDICUC - Repositorio CUC |
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https://repositorio.cuc.edu.co/ |
| identifier_str_mv |
Shafiq, A., Naz, S., Butt, S.A. et al. Enhancing learning environments with IoT: a novel decision-making approach using probabilistic linguistic T-spherical fuzzy set. J Supercomput 80, 17524–17574 (2024). https://doi.org/10.1007/s11227-024-06129-2. 0920-8542 10.1007/s11227-024-06129-2 1573-0484 Corporación Universidad de la Costa REDICUC - Repositorio CUC |
| url |
https://hdl.handle.net/11323/13786 https://repositorio.cuc.edu.co/ |
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eng |
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eng |
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Journal of Supercomputing |
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Zadeh, L.A. Fuzzy sets (1965) Inf Control, 8 (3), pp. 338-353. Yager, R.R. Generalized orthopair fuzzy sets (2016) IEEE Trans Fuzzy Syst, 25 (5), pp. 1222-1230. Atanassov, K.T. Intuitionistic fuzzy sets (1986) Fuzzy Sets Syst, 20 (1), pp. 87-96. 937346 Yager, R.R. Pythagorean membership grades in multicriteria decision making (2013) IEEE Trans Fuzzy Syst, 22 (4), pp. 958-965. Herrera, F., Herrera-Viedma, E. Linguistic decision analysis: steps for solving decision problems under linguistic information (2000) Fuzzy Sets Syst, 115 (1), pp. 67-82. 1776304 Vashishth, T.K., Sharma, V., Sharma, K.K., Kumar, B., Chaudhary, S., Panwar, R. AIoT in education transforming learning environments and educational technology (2024) Artificial Intelligence of Things (AIoT) for Productivity and Organizational Transition, pp. 72-107. In:., https:// Meylani, R. Transforming education with the Internet of Things: a journey into smarter learning environments (2024) Int J Res Educ Sci, 10 (1), pp. 161-178 Dhir, S., Maheshwari, A., Sharma, S. Improving teaching-learning through smart classes using IoT (2024) Enhancing Education with Intelligent Systems and Data-Driven Instruction, pp. 107- 131. In:., https:// Yinka, K.R., Chidinma, A.E. (2024) The role and applications of Internet of Things (IoT) in higher education: uses and ways IoT affects students’ learning, 5, pp. 243-249. Nyaga, J.M. IoT-enhanced adaptive learning environments: personalized online education for the digital age (2023) Afr J Comput Inf Syst (AJCIS), 7, pp. 1-14. Tuǧrul, F. Evaluation of papers according to offset print quality: the intuitionistic fuzzy based multi criteria decision making mechanism (2024) Pigment Resin Technol, 53 (1), pp. 122-129 Jiafu, S., Wang, D., Xu, B., Zhang, F., Zhang, N. An interval-valued intuitionistic fuzzy group decision-making method for evaluating online knowledge payment products (2024) Appl Soft Comput, 150. Naz, S., Shafiq, A., Butt, S.A., Mazhar, S., Martinez, D.J., De la Hoz Franco, E. Decision analysis with IDOCRIW-QUALIFLEX approach in the 2TLq-ROF environment: an application of accident prediction models in Pakistan (2024) Heliyon, 10 (2024). Naz, S., Shafiq, A., Abbas, M. An approach for 2-tuple linguistic q-rung orthopair fuzzy MAGDM for the evaluation of historical sites with power Heronian mean (2024) J Supercomput, 80 (5), pp. 6435-6485. Naz, S., Akram, M., Shafiq, A., Akhtar, K. Optimal airport selection utilizing power muirhead mean based group decision model with 2-tuple linguistic q-rung orthopair fuzzy information (2024) Int J Mach Learn Cybern, 15 (2), pp. 303-340. Naz, S., Mehreen, R., Abbas, T., Piñeres-Espitia, G., Butt, S.A. An extended COPRAS method based on complex q-rung orthopair fuzzy 2-tuple linguistic Maclaurin symmetric mean aggregation operators (2024) J Ambient Intell Humanized Comput, 15 (4), pp. 2119-2142. Naz, S., Shafiq, A., Butt, S.A., Ijaz, R. A new approach to sentiment analysis algorithms: extended SWARA-MABAC method with 2-tuple linguistic q-rung orthopair fuzzy information (2023) Eng Appl Artif Intell, 126. Naz, S., Fatima, S.S., Butt, S.A., Tabassum, N. A MAGDM model based on 2-tuple linguistic variables and power Hamacher aggregation operators for optimal selection of digital marketing strategies (2023) Granul Comput, 8 (6), pp. 1955-1990. Naz, S., Saeed, M.R., Butt, S.A. Multi-attribute group decision-making based on 2-tuple linguistic cubic q-rung orthopair fuzzy DEMATEL analysis (2024) Granu Comput, 9 (1), p. 12. Zhang, S., Hu, L., Ma, Z., Liu, X. Two-rank multi-attribute group decision-making with linguistic distribution assessments: an optimization-based integrated approach (2023) Eng Appl Artif Intell, 121, pp. 106-170. Wang, Y.M., Jia, X., Song, H.H., Martinez, L. Improving consistency based on regret theory: a multi-attribute group decision making method with linguistic distribution assessments (2023) Expert Syst Appl, 221, pp. 119-748. Pang, Q., Wang, H., Xu, Z. Probabilistic linguistic term sets in multi-attribute group decision making (2016) Inf Sci, 369, pp. 128-143. Xie, M., Liu, J., Chen, S., Xu, G., Lin, M. Primary node election based on probabilistic linguistic term set with confidence interval in the PBFT consensus mechanism for blockchain (2023) Complex Intell Syst, 9 (2), pp. 1507-1524. Darko, A.P., Liang, D. Modeling customer satisfaction through online reviews: a FlowSort group decision model under probabilistic linguistic settings (2022) Expert Syst Appl, 195, pp. 116-649. Teng, F., Du, C., Shen, M., Liu, P. A dynamic large-scale multiple attribute group decision-making method with probabilistic linguistic term sets based on trust relationship and opinion correlation (2022) Inf Sci, 612, pp. 257-295. Han, X., Zhan, J. A sequential three-way decision-based group consensus method under probabilistic linguistic term sets (2023) Inf Sci, 624, pp. 567-589. Jin, F., Cai, Y., Zhou, L., Ding, T. Regret-rejoice two-stage multiplicative DEA models-driven cross-efficiency evaluation with probabilistic linguistic information (2023) Omega, 117, pp. 102-839. Mahmood, T., Ullah, K., Khan, Q., Jan, N. An approach toward decision-making and medical diagnosis problems using the concept of spherical fuzzy sets (2019) Neural Comput Appl, 31, pp. 7041-7053. Akram, M., Naz, S., Feng, F., Shafiq, A. Assessment of hydropower plants in Pakistan: Muirhead mean-based 2-tuple linguistic T-spherical fuzzy model combining SWARA with COPRAS (2023) Arab J Sci Eng, 48 (5), pp. 5859-5888. Khan, M.R., Ullah, K., Khan, Q. Multi-attribute decision-making using Archimedean aggregation operator in Tspherical fuzzy environment (2023) Rep Mech Eng, 4 (1), pp. 18-38. Saad, M., Rafiq, A. Correlation coefficients for T-spherical fuzzy sets and their applications in pattern analysis and multi-attribute decision-making (2023) Granul Comput, 8 (4), pp. 851-862. Asif, K., Jamil, M.K., Karamti, H., Azeem, M., Ullah, K. Randic energies for T-spherical fuzzy Hamacher graphs and their applications in decision making for business plans (2023) Comput Appl Math, 42 (3), p. 106. 4557393 Wang, H., Mahmood, T., Ullah, K. Improved CoCoSo method based on frank softmax aggregation operators for Tspherical fuzzy multiple attribute group decision-making (2023) Int J Fuzzy Syst, 25 (3), pp. 1275-1310. Yager, R.R. The power average operator (2001) IEEE Trans Syst Man Cybern Part A Syst Hum, 31 (6), pp. 724-731. Kumar, K., Chen, S.M. Group decision making based on q-rung orthopair fuzzy weighted averaging aggregation operator of q-rung orthopair fuzzy numbers (2022) Inf Sci, 598, pp. 1-18. Jana, C., Garg, H., Pal, M. Multi-attribute decision making for power Dombi operators under Pythagorean fuzzy information with MABAC method (2023) J Ambient Intell Humaniz Comput, 14, pp. 10761-10778. Xu, Z., Yager, R.R. Power-geometric operators and their use in group decision making (2009) IEEE Trans Fuzzy Syst, 18 (1), pp. 94-105. Saaty, T.L. (1988) What is the analytic hierarchy process?, pp. 109-121. Berlin Heidelberg, Springer Kostić-Ljubisavljević, A., Samčović, A. Selection of available GIS software for education of students of telecommunications engineering by AHP methodology (2024) Educ Inf Technol, 29 (4), pp. 5001-5015. Kinay, A.O., Tezel, B.T. Modification of the fuzzy analytic hierarchy process via different ranking methods (2022) Int J Intell Syst, 37 (1), pp. 336-364. Cheng, Z., Lu, G., Wu, M., Li, Z., Deng, Y., Wu, J., Hu, B.X. Quantification and visualization of groundwater contamination prevention regionalization based on analytic hierarchy process method (AHP) in Guangdong– Hong Kong–Macao Greater Bay Area, South China (2024) J Hydrol, 628. He, S., Xu, H., Zhang, J., Xue, P. Risk assessment of oil and gas pipelines hot work based on AHP-FCE (2023) Petroleum, 9 (1), pp. 94-100. Roy, P.K., Shaw, K. A credit scoring model for SMEs using AHP and TOPSIS (2023) Int J Financ Econ, 28 (1), pp. 372-391. Moslem, S., Saraji, M.K., Mardani, A., Alkharabsheh, A., Duleba, S., Esztergar-Kiss, D. A systematic review of analytic hierarchy process applications to solve transportation problems: from 2003 to 2019 (2023) IEEE Access, 11, pp. 11973-11990. Deretarla, O., Erdebilli, B., Gundogan, M. An integrated analytic hierarchy process and complex proportional assessment for vendor selection in supply chain management (2023) Decis Anal J, 6, pp. 100-155. Nguyen, C.C., Vo, P., Doan, V.L., Nguyen, Q.B., Nguyen, T.C., Nguyen, Q.D. Assessment of the effects of rainfall frequency on landslide susceptibility mapping using AHP method: A case study for a mountainous region in central Vietnam (2023) Progress in Landslide Research and Technology, 1 (2), pp. 87-98. . Springer, Cham Gyani, J., Ahmed, A., Haq, M.A. MCDM and various prioritization methods in AHP for CSS: a comprehensive review (2022) IEEE Access, 10, pp. 33492-33511. Nazim, M., Mohammad, C.W., Sadiq, M. A comparison between fuzzy AHP and fuzzy TOPSIS methods to software requirements selection (2022) Alex Eng J, 61 (12), pp. 10851-10870. Nie, R.X., Wang, J.Q. Prospect theory-based consistency recovery strategies with multiplicative probabilistic linguistic preference relations in managing group decision making (2020) Arab J Sci Eng, 45 (3), pp. 2113-2130. Xu, Z. Deviation measures of linguistic preference relations in group decision making (2005) Omega, 33 (3), pp. 249-254. Gou, X., Xu, Z., Liao, H. Multiple criteria decision making based on Bonferroni means with hesitant fuzzy linguistic information (2017) Soft Comput, 21, pp. 6515-6529. Liu, P., Zhu, B., Wang, P., Shen, M. An approach based on linguistic spherical fuzzy sets for public evaluation of shared bicycles in China (2020) Eng Appl Artif Intell, 87, pp. 103-295. Liu, D., Huang, A. Consensus reaching process for fuzzy behavioral TOPSIS method with probabilistic linguistic q-rung orthopair fuzzy set based on correlation measure (2020) Int J Intell Syst, 35 (3), pp. 494-528. Ranjan, M.J., Kumar, B.P., Bhavani, T.D., Padmavathi, A.V., Bakka, V. Probabilistic linguistic q-rung orthopair fuzzy Archimedean aggregation operators for group decision-making (2023) Decis Mak Appl Manag Eng, 6 (2), pp. 639-667. Wei, G., Wei, C., Wu, J., Wang, H. Supplier selection of medical consumption products with a probabilistic linguistic MABAC method (2019) Int J Environ Res Public Health, 16 (24), pp. 50-82. Chen, L., Gou, X. The application of probabilistic linguistic CODAS method based on new score function in multi-criteria decision-making (2022) Comput Appl Math, 41, pp. 1-25. 4350303 Kayvanfar, V., Elomri, A., Kerbache, L., Vandchali, H.R., El Omri, A. A review of decision support systems in the Internet of Things and supply chain and logistics using web content mining (2024) Supply Chain Analyt, Li, L., Han, F., Li, J., An, S., Shi, K., Zhang, S., Zhangzhong, L. The development of variable system-based Internet of Things for the solar greenhouse and its application in lettuce (2024) Front Plant Sci, 15, p. 1292719. Hajlaoui, R., Moulahi, T., Zidi, S., El Khediri, S., Alaya, B., Sherali, Z. Towards smarter cyberthreats detection model for industrial Internet of Things (IIoT) 4.0 (2024) J Ind Inf Integr, Solanki, A., Sarkar, D., Shah, D. Evaluation of factors affecting the effective implementation of Internet of Things and cloud computing in the construction industry through WASPAS and TOPSIS methods (2024) Int J Constr Manag, 24 (2), pp. 226-239. |
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Atribución 4.0 Internacional (CC BY 4.0)© 2024 The Authors.https://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/embargoedAccesshttp://purl.org/coar/access_right/c_f1cfShafiq, AqsaNaz, SumeraButt, Shariq AzizPiñeres Espitia, Gabriel2024-11-22T12:08:36Z2025-04-292024-11-22T12:08:36Z2024-04-29Shafiq, A., Naz, S., Butt, S.A. et al. Enhancing learning environments with IoT: a novel decision-making approach using probabilistic linguistic T-spherical fuzzy set. J Supercomput 80, 17524–17574 (2024). https://doi.org/10.1007/s11227-024-06129-2.0920-8542https://hdl.handle.net/11323/1378610.1007/s11227-024-06129-21573-0484Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/The Internet of Things is a system of networked devices that can gather, process, and share data through the Internet. The Internet of Things has vast potential to spread widely across various aspects of our lives. The educational process model is undergoing a transformation in which the learning requirements for different students must be fulfilled in various ways. Our study presents a novel multi-attribute group decision-making strategy that examines how the Internet of Things can help to provide the best learning environment while also making the educational process more effective. The probabilistic linguistic T-spherical fuzzy set (PLT-SFS) is a modification of the T-spherical fuzzy set in which the degrees of membership, abstinence, and non-membership are characterized by probabilistic linguistic terms. Then two new aggregation operators, the PLT-SF weighted power average (PLT-SFWPA) operator and the PLT-SF weighted power geometric (PLT-SFWPG) operator, are introduced. After that, an approach to multi-attribute group decision-making based on the analytic hierarchy process is constructed in which the data are aggregated by the PLT-SFWPA operator. To illustrate the validity of the proposed approach, a case study of nine Internet of Things applications for enhancing learning environments is provided.7 páginasapplication/pdfengSpringer NetherlandsNetherlandshttps://link.springer.com/article/10.1007/s11227-024-06129-2Enhancing learning environments with IoT: a novel decision-making approach using probabilistic linguistic T-spherical fuzzy setArtículo de revistahttp://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/drafthttp://purl.org/coar/version/c_b1a7d7d4d402bcceJournal of SupercomputingZadeh, L.A. Fuzzy sets (1965) Inf Control, 8 (3), pp. 338-353.Yager, R.R. Generalized orthopair fuzzy sets (2016) IEEE Trans Fuzzy Syst, 25 (5), pp. 1222-1230.Atanassov, K.T. Intuitionistic fuzzy sets (1986) Fuzzy Sets Syst, 20 (1), pp. 87-96. 937346Yager, R.R. Pythagorean membership grades in multicriteria decision making (2013) IEEE Trans Fuzzy Syst, 22 (4), pp. 958-965.Herrera, F., Herrera-Viedma, E. Linguistic decision analysis: steps for solving decision problems under linguistic information (2000) Fuzzy Sets Syst, 115 (1), pp. 67-82. 1776304Vashishth, T.K., Sharma, V., Sharma, K.K., Kumar, B., Chaudhary, S., Panwar, R. AIoT in education transforming learning environments and educational technology (2024) Artificial Intelligence of Things (AIoT) for Productivity and Organizational Transition, pp. 72-107. In:., https://Meylani, R. Transforming education with the Internet of Things: a journey into smarter learning environments (2024) Int J Res Educ Sci, 10 (1), pp. 161-178Dhir, S., Maheshwari, A., Sharma, S. Improving teaching-learning through smart classes using IoT (2024) Enhancing Education with Intelligent Systems and Data-Driven Instruction, pp. 107- 131. In:., https://Yinka, K.R., Chidinma, A.E. (2024) The role and applications of Internet of Things (IoT) in higher education: uses and ways IoT affects students’ learning, 5, pp. 243-249.Nyaga, J.M. IoT-enhanced adaptive learning environments: personalized online education for the digital age (2023) Afr J Comput Inf Syst (AJCIS), 7, pp. 1-14.Tuǧrul, F. Evaluation of papers according to offset print quality: the intuitionistic fuzzy based multi criteria decision making mechanism (2024) Pigment Resin Technol, 53 (1), pp. 122-129Jiafu, S., Wang, D., Xu, B., Zhang, F., Zhang, N. An interval-valued intuitionistic fuzzy group decision-making method for evaluating online knowledge payment products (2024) Appl Soft Comput, 150.Naz, S., Shafiq, A., Butt, S.A., Mazhar, S., Martinez, D.J., De la Hoz Franco, E. Decision analysis with IDOCRIW-QUALIFLEX approach in the 2TLq-ROF environment: an application of accident prediction models in Pakistan (2024) Heliyon, 10 (2024).Naz, S., Shafiq, A., Abbas, M. An approach for 2-tuple linguistic q-rung orthopair fuzzy MAGDM for the evaluation of historical sites with power Heronian mean (2024) J Supercomput, 80 (5), pp. 6435-6485.Naz, S., Akram, M., Shafiq, A., Akhtar, K. Optimal airport selection utilizing power muirhead mean based group decision model with 2-tuple linguistic q-rung orthopair fuzzy information (2024) Int J Mach Learn Cybern, 15 (2), pp. 303-340.Naz, S., Mehreen, R., Abbas, T., Piñeres-Espitia, G., Butt, S.A. An extended COPRAS method based on complex q-rung orthopair fuzzy 2-tuple linguistic Maclaurin symmetric mean aggregation operators (2024) J Ambient Intell Humanized Comput, 15 (4), pp. 2119-2142.Naz, S., Shafiq, A., Butt, S.A., Ijaz, R. A new approach to sentiment analysis algorithms: extended SWARA-MABAC method with 2-tuple linguistic q-rung orthopair fuzzy information (2023) Eng Appl Artif Intell, 126.Naz, S., Fatima, S.S., Butt, S.A., Tabassum, N. 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Evaluation of factors affecting the effective implementation of Internet of Things and cloud computing in the construction industry through WASPAS and TOPSIS methods (2024) Int J Constr Manag, 24 (2), pp. 226-239.17574175241280AHP methodInternet of Things in enhancing learning environmentsProbabilistic linguistic T-spherical fuzzy setWeighted power average operatorWeighted power geometric operatorPublicationORIGINALEnhancing learning environments with IoT a novel decision-making approach using probabilistic linguistic T-spherical fuzzy set.pdfEnhancing learning environments with IoT a novel decision-making approach using probabilistic linguistic T-spherical fuzzy set.pdfapplication/pdf239114https://repositorio.cuc.edu.co/bitstreams/cbbb32a8-0af2-48d9-938e-860fe373c2d9/download5c5bfe9f74c5ec85c4299ab3405e1583MD51LICENSElicense.txtlicense.txttext/plain; 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ara ejercer estos derechos sobre la Obra tal y como se indica a continuación:</p>
    <ol type="a">
      <li>Reproducir la Obra, incorporar la Obra en una o más Obras Colectivas, y reproducir la Obra incorporada en las Obras Colectivas.</li>
      <li>Distribuir copias o fonogramas de las Obras, exhibirlas públicamente, ejecutarlas públicamente y/o ponerlas a disposición pública, incluyéndolas como incorporadas en Obras Colectivas, según corresponda.</li>
      <li>Distribuir copias de las Obras Derivadas que se generen, exhibirlas públicamente, ejecutarlas públicamente y/o ponerlas a disposición pública.</li>
    </ol>
    <p>Los derechos mencionados anteriormente pueden ser ejercidos en todos los medios y formatos, actualmente conocidos o que se inventen en el futuro. Los derechos antes mencionados incluyen el derecho a realizar dichas modificaciones en la medida que sean técnicamente necesarias para ejercer los derechos en otro medio o formatos, pero de otra manera usted no está autorizado para realizar obras derivadas. Todos los derechos no otorgados expresamente por el Licenciante quedan por este medio reservados, incluyendo pero sin limitarse a aquellos que se mencionan en las secciones 4(d) y 4(e).</p>
  </li>
  <br/>
  <li>
    Restricciones.
    <p>La licencia otorgada en la anterior Sección 3 está expresamente sujeta y limitada por las siguientes restricciones:</p>
    <ol type="a">
      <li>Usted puede distribuir, exhibir públicamente, ejecutar públicamente, o poner a disposición pública la Obra sólo bajo las condiciones de esta Licencia, y Usted debe incluir una copia de esta licencia o del Identificador Universal de Recursos de la misma con cada copia de la Obra que distribuya, exhiba públicamente, ejecute públicamente o ponga a disposición pública. No es posible ofrecer o imponer ninguna condición sobre la Obra que altere o limite las condiciones de esta Licencia o el ejercicio de los derechos de los destinatarios otorgados en este documento. No es posible sublicenciar la Obra. Usted debe mantener intactos todos los avisos que hagan referencia a esta Licencia y a la cláusula de limitación de garantías. Usted no puede distribuir, exhibir públicamente, ejecutar públicamente, o poner a disposición pública la Obra con alguna medida tecnológica que controle el acceso o la utilización de ella de una forma que sea inconsistente con las condiciones de esta Licencia. Lo anterior se aplica a la Obra incorporada a una Obra Colectiva, pero esto no exige que la Obra Colectiva aparte de la obra misma quede sujeta a las condiciones de esta Licencia. Si Usted crea una Obra Colectiva, previo aviso de cualquier Licenciante debe, en la medida de lo posible, eliminar de la Obra Colectiva cualquier referencia a dicho Licenciante o al Autor Original, según lo solicitado por el Licenciante y conforme lo exige la cláusula 4(c).</li>
      <li>Usted no puede ejercer ninguno de los derechos que le han sido otorgados en la Sección 3 precedente de modo que estén principalmente destinados o directamente dirigidos a conseguir un provecho comercial o una compensación monetaria privada. El intercambio de la Obra por otras obras protegidas por derechos de autor, ya sea a través de un sistema para compartir archivos digitales (digital file-sharing) o de cualquier otra manera no será considerado como estar destinado principalmente o dirigido directamente a conseguir un provecho comercial o una compensación monetaria privada, siempre que no se realice un pago mediante una compensación monetaria en relación con el intercambio de obras protegidas por el derecho de autor.</li>
      <li>Si usted distribuye, exhibe públicamente, ejecuta públicamente o ejecuta públicamente en forma digital la Obra o cualquier Obra Derivada u Obra Colectiva, Usted debe mantener intacta toda la información de derecho de autor de la Obra y proporcionar, de forma razonable según el medio o manera que Usted esté utilizando: (i) el nombre del Autor Original si está provisto (o seudónimo, si fuere aplicable), y/o (ii) el nombre de la parte o las partes que el Autor Original y/o el Licenciante hubieren designado para la atribución (v.g., un instituto patrocinador, editorial, publicación) en la información de los derechos de autor del Licenciante, términos de servicios o de otras formas razonables; el título de la Obra si está provisto; en la medida de lo razonablemente factible y, si está provisto, el Identificador Uniforme de Recursos (Uniform Resource Identifier) que el Licenciante especifica para ser asociado con la Obra, salvo que tal URI no se refiera a la nota sobre los derechos de autor o a la información sobre el licenciamiento de la Obra; y en el caso de una Obra Derivada, atribuir el crédito identificando el uso de la Obra en la Obra Derivada (v.g., "Traducción Francesa de la Obra del Autor Original," o "Guión Cinematográfico basado en la Obra original del Autor Original"). Tal crédito puede ser implementado de cualquier forma razonable; en el caso, sin embargo, de Obras Derivadas u Obras Colectivas, tal crédito aparecerá, como mínimo, donde aparece el crédito de cualquier otro autor comparable y de una manera, al menos, tan destacada como el crédito de otro autor comparable.</li>
      <li>
        Para evitar toda confusión, el Licenciante aclara que, cuando la obra es una composición musical:
        <ol type="i">
          <li>Regalías por interpretación y ejecución bajo licencias generales. El Licenciante se reserva el derecho exclusivo de autorizar la ejecución pública o la ejecución pública digital de la obra y de recolectar, sea individualmente o a través de una sociedad de gestión colectiva de derechos de autor y derechos conexos (por ejemplo, SAYCO), las regalías por la ejecución pública o por la ejecución pública digital de la obra (por ejemplo Webcast) licenciada bajo licencias generales, si la interpretación o ejecución de la obra está primordialmente orientada por o dirigida a la obtención de una ventaja comercial o una compensación monetaria privada.</li>
          <li>Regalías por Fonogramas. El Licenciante se reserva el derecho exclusivo de recolectar, individualmente o a través de una sociedad de gestión colectiva de derechos de autor y derechos conexos (por ejemplo, los consagrados por la SAYCO), una agencia de derechos musicales o algún agente designado, las regalías por cualquier fonograma que Usted cree a partir de la obra (“versión cover”) y distribuya, en los términos del régimen de derechos de autor, si la creación o distribución de esa versión cover está primordialmente destinada o dirigida a obtener una ventaja comercial o una compensación monetaria privada.</li>
        </ol>
      </li>
      <li>Gestión de Derechos de Autor sobre Interpretaciones y Ejecuciones Digitales (WebCasting). Para evitar toda confusión, el Licenciante aclara que, cuando la obra sea un fonograma, el Licenciante se reserva el derecho exclusivo de autorizar la ejecución pública digital de la obra (por ejemplo, webcast) y de recolectar, individualmente o a través de una sociedad de gestión colectiva de derechos de autor y derechos conexos (por ejemplo, ACINPRO), las regalías por la ejecución pública digital de la obra (por ejemplo, webcast), sujeta a las disposiciones aplicables del régimen de Derecho de Autor, si esta ejecución pública digital está primordialmente dirigida a obtener una ventaja comercial o una compensación monetaria privada.</li>
    </ol>
  </li>
  <br/>
  <li>
    Representaciones, Garantías y Limitaciones de Responsabilidad.
    <p>A MENOS QUE LAS PARTES LO ACORDARAN DE OTRA FORMA POR ESCRITO, EL LICENCIANTE OFRECE LA OBRA (EN EL ESTADO EN EL QUE SE ENCUENTRA) “TAL CUAL”, SIN BRINDAR GARANTÍAS DE CLASE ALGUNA RESPECTO DE LA OBRA, YA SEA EXPRESA, IMPLÍCITA, LEGAL O CUALQUIERA OTRA, INCLUYENDO, SIN LIMITARSE A ELLAS, GARANTÍAS DE TITULARIDAD, COMERCIABILIDAD, ADAPTABILIDAD O ADECUACIÓN A PROPÓSITO DETERMINADO, AUSENCIA DE INFRACCIÓN, DE AUSENCIA DE DEFECTOS LATENTES O DE OTRO TIPO, O LA PRESENCIA O AUSENCIA DE ERRORES, SEAN O NO DESCUBRIBLES (PUEDAN O NO SER ESTOS DESCUBIERTOS). ALGUNAS JURISDICCIONES NO PERMITEN LA EXCLUSIÓN DE GARANTÍAS IMPLÍCITAS, EN CUYO CASO ESTA EXCLUSIÓN PUEDE NO APLICARSE A USTED.</p>
  </li>
  <br/>
  <li>
    Limitación de responsabilidad.
    <p>A MENOS QUE LO EXIJA EXPRESAMENTE LA LEY APLICABLE, EL LICENCIANTE NO SERÁ RESPONSABLE ANTE USTED POR DAÑO ALGUNO, SEA POR RESPONSABILIDAD EXTRACONTRACTUAL, PRECONTRACTUAL O CONTRACTUAL, OBJETIVA O SUBJETIVA, SE TRATE DE DAÑOS MORALES O PATRIMONIALES, DIRECTOS O INDIRECTOS, PREVISTOS O IMPREVISTOS PRODUCIDOS POR EL USO DE ESTA LICENCIA O DE LA OBRA, AUN CUANDO EL LICENCIANTE HAYA SIDO ADVERTIDO DE LA POSIBILIDAD DE DICHOS DAÑOS. ALGUNAS LEYES NO PERMITEN LA EXCLUSIÓN DE CIERTA RESPONSABILIDAD, EN CUYO CASO ESTA EXCLUSIÓN PUEDE NO APLICARSE A USTED.</p>
  </li>
  <br/>
  <li>
    Término.
    <ol type="a">
      <li>Esta Licencia y los derechos otorgados en virtud de ella terminarán automáticamente si Usted infringe alguna condición establecida en ella. Sin embargo, los individuos o entidades que han recibido Obras Derivadas o Colectivas de Usted de conformidad con esta Licencia, no verán terminadas sus licencias, siempre que estos individuos o entidades sigan cumpliendo íntegramente las condiciones de estas licencias. Las Secciones 1, 2, 5, 6, 7, y 8 subsistirán a cualquier terminación de esta Licencia.</li>
      <li>Sujeta a las condiciones y términos anteriores, la licencia otorgada aquí es perpetua (durante el período de vigencia de los derechos de autor de la obra). No obstante lo anterior, el Licenciante se reserva el derecho a publicar y/o estrenar la Obra bajo condiciones de licencia diferentes o a dejar de distribuirla en los términos de esta Licencia en cualquier momento; en el entendido, sin embargo, que esa elección no servirá para revocar esta licencia o que deba ser otorgada , bajo los términos de esta licencia), y esta licencia continuará en pleno vigor y efecto a menos que sea terminada como se expresa atrás. La Licencia revocada continuará siendo plenamente vigente y efectiva si no se le da término en las condiciones indicadas anteriormente.</li>
    </ol>
  </li>
  <br/>
  <li>
    Varios.
    <ol type="a">
      <li>Cada vez que Usted distribuya o ponga a disposición pública la Obra o una Obra Colectiva, el Licenciante ofrecerá al destinatario una licencia en los mismos términos y condiciones que la licencia otorgada a Usted bajo esta Licencia.</li>
      <li>Si alguna disposición de esta Licencia resulta invalidada o no exigible, según la legislación vigente, esto no afectará ni la validez ni la aplicabilidad del resto de condiciones de esta Licencia y, sin acción adicional por parte de los sujetos de este acuerdo, aquélla se entenderá reformada lo mínimo necesario para hacer que dicha disposición sea válida y exigible.</li>
      <li>Ningún término o disposición de esta Licencia se estimará renunciada y ninguna violación de ella será consentida a menos que esa renuncia o consentimiento sea otorgado por escrito y firmado por la parte que renuncie o consienta.</li>
      <li>Esta Licencia refleja el acuerdo pleno entre las partes respecto a la Obra aquí licenciada. No hay arreglos, acuerdos o declaraciones respecto a la Obra que no estén especificados en este documento. El Licenciante no se verá limitado por ninguna disposición adicional que pueda surgir en alguna comunicación emanada de Usted. Esta Licencia no puede ser modificada sin el consentimiento mutuo por escrito del Licenciante y Usted.</li>
    </ol>
  </li>
  <br/>
</ol>
 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