Research Article
BibTex RIS Cite

Alternatiflerin Performanslarının Ölçülmesinde Yeni Bir Yöntem: Kosinüs Benzerliği Yaklaşımı

Year 2025, EARLY VIEW, 1 - 1

Furkan Fahri Altıntaş

https://doi.org/10.2339/politeknik.1627566

Abstract

Çok Kriterli Karar Verme (MCDM) kapsamında yeni yöntemlerin geliştirilmesi, karar vericilere farklı durumlarda alternatif çözüm yaklaşımları sunarak daha esnek ve etkili karar alma süreçleri sağlamaktadır. Bu çalışma, seçim problemlerinde alternatif performanslarının değerlendirilmesi için kosinüs benzerliği temelli yeni bir yöntemin )Kosinüs Benzerliği Yaklaşımı) uygulanabilirliğini ortaya koymayı amaçlamaktadır. Çalışma kapsamında önerilen yöntem, Global İnovasyon Endeksi-2024'ten seçilen yedi ülkenin kriter değerleri kullanılarak test edilmiş ve gerekli ölçümler gerçekleştirilmiştir. Literatür incelemesi, kosinüs benzerliğine dayalı bir yöntemin daha önce çalışılmadığını ortaya koymuş ve bu durum araştırmayı özgün kılmıştır. Bulgular, önerilen yöntemin duyarlılık analizi açısından ideal, karşılaştırmalı analizlerde güvenilir ve tutarlı, simülasyon analizlerinde ise kararlı ve sağlam olduğunu göstermektedir. Bu bağlamda, önerilen yöntemin seçim problemlerinde karar vericiler için pratik ve uygulanabilir bir araç olduğu sonucuna ulaşılmıştır.

Keywords

ÇKKV kosinüs benzerliği alternative performansı seçim problemleri

References

  • [1] Jahan A., “Multi-criteria Decision Analysis for Supporting the Selection of Engineering Materials in Product Design”, BH Press, Brentwood, (2016).
  • [2] Nguyen, V. T., Vo, N. T., Truong, V. C., and Nguyen, V.-T., “Multi-criteria Decision Making and Optimum Design with Machine Learning”, CRC Press, Boca Raton, (2025).
  • [3] Doumpos, M., Ferreira, F. A., and Zopounidis, C., “Multiple Criteria Decision Making for Sustainable Deandlopment”, Springer Cham, Gewerbestrasse, (2021).
  • [4] Linkov, I., Moberg, E., Trump, B. D., Yatsalo, B., and Keisler, J., “Multi-criteria Decision Analysis Case Studies in Engineering and the Environment”, CRC Press, Boca Raton, (2023).
  • [5] Anand, A., Agarwal, M., and Aggrawal, D., “Multiple Criteria Decision-Making: Applications for Managerial Discretion”, De Gruyter, Berlin, (2022).
  • [6] Grybaite, V., and Burinskiene, A., ‘‘Assessment of circular economy deandlopment in the EU’’, Sustainability, 16:1-20, (2024).
  • [7] Radulescu, C. Z., and Radulescu, M., ‘‘A Hybrid Group Multi-criteria Approach Based on SAW,TOPSIS, VIKOR, and COPRAS Methods for Complex IoT Selection Problems’’ , Electronics, 13:1-27, (2024).
  • [8] Balusa, B. C., Riyazuddin, M., Gole, S., & Deepthi, K., ‘‘Development of Fuzzy-AHP and WPM based MCDM Model for Mining Method Selection’’, Journal of Mines, Metals and Fuels, 72(6): 533-539, (2024).
  • [9] Vitianingsih, A. V., Ullum, C., Maukar, A. L., Yasin, V., and Wati, S. F., ‘‘Mapping Residential Land Suitability Using a WEB-GIS-Based Multi-criteria Spatial Analysis Approach: Integration of AHP and WPM Methods’’, Rekayasa Sistem dan Teknologi Informasi, 8(2):208-215, (2024).
  • [10] Trivedi, P., Shah, J., Čep, R., Abualigah, L., and Kalita, K., ‘‘A Hybrid Best-Worst Method (BWM)—Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) Approach for Prioritizing Road Safety Improandments’’ , IEEE, 12:30054-30065, (2024).
  • [11] Alsanousi, A. T., Alqahtani, A. Y., Makki, A. A., and Baghdadi, M. A., ‘‘A Hybrid MCDM Approach Using the BWMand the TOPSIS for a Financial Performance-Based Evaluation of Saudi Stocks’’ , Information, 15:1-22, (2024).
  • [12] Chatterjee, S., and Chakraborty, S., ‘‘A Study On The Effects Of Objectiand Weighting Methods on Topsis-Based Parametric Optimization of Non-Traditional Machining Processes’’, Decision Analytics Journal, 11:1-16, (2024).
  • [13] Akmaludin, Sihombing, E. G., Rinawati, Dewi, L. S., and Arisawati, E., ‘‘Integrated Selection of Permanent Teacher Appointments Recommended MCDM-AHP and WASPAS Methods’’, Sinkron : Jurnal dan Penelitian Teknik Informatika, 8(2):661-670, (2024).
  • [14] Khan, A. A., Mashat, D. S., and Dong, K., ‘‘Evaluating sustainable urban deandlopment strategies through spherical critic-waspas analysis’’, Journal of Urban Deandlopment and Management, 3(1):1-17, (2024).
  • [15] Madić, M., Petrović, G., Petković, D., and Janković, P., ‘‘Traditional and Integrated MCDM Approaches for Assessment and Ranking of Laser Cutting Conditions’’, Spectrum of Mechanical Engineering and Operational Research, 1(1):250-257, (2024).
  • [16] Uyen, V. T., Ersoy, N., Trung, D. D., and Giang, N. T., ‘‘Applying MCDM to evaluate benchmark scores in the logistics sector for the period 2021–2023: Application to uniandrsities in Vietnam’’, Journal of Infrastructure, Policy and Deandlopment, 8(11):1-16, (2024).
  • [17] Wang, Y., Wang, W., Wang, Z., & Deveci, M., ‘‘Selection of sustainable food suppliers using the Pythagorean fuzzy CRITIC-MARCOS method’’, Information Sciences, 664:1-22, (2024).
  • [18] Özomay, M., ‘‘Sustainable and environmental dyeing with MAUT method comparatiand selection of the dyeing recipe’’, Sustainability, 15:1-14, (2023).
  • [19] El-Araby, A., Sabry, I., and El-Assal, A., ‘‘Ranking performance of MARCOS Method for location selection problem in the presence of conflicting criteria’’, Decision Making Advances, 2(1):148-162, (2024).
  • [20] Demir, G., Chatterjee, P., Kadry, S., Abdelhadi, A., & Pamučar, D., ‘‘Measurement of alternatiands and ranking according to compromise solution (MARCOS) method: A Comprehensiand Bibliometric Analysis’’, Decision Making: Applications in Management and Engineering, 7(2):313-336, (2024).
  • [21] Taherdoost, H., and Mohebi, A., ‘‘A comprehensiand guide to the COPRAS method for multi-criteria decision making’’, Journal of Management Science & Engineering Research, 7(2):1-14, (2024).
  • [22] Cai, J., Hu, Y., Peng, Y., Guo, F., Xiong, J., and Zhang, R., ‘‘A Hybrid mcdm Approach based on combined weighting method cloud model and copras for assessing road construction workers’ safety climate’’, Front. Public Health, 12: 1-14, (2024).
  • [23] Dinçer, S. E., ‘‘Çok Kriterli Karar Alma’’, Gece Akademi, Ankara, (2019).
  • [24] Demirci, A., ‘‘Sağlık Hizmetleri Yönetiminde Çok Kriterli Karar Andrme Teknikleri’’, Gazi Kitapevi, Ankara, (2020).
  • [25] Dua, T. V., ‘‘PSI-SAW and PSI-MARCOS hybrid MCDM’’, Engineering, Technology & Applied Science Research, 4(14):15963-15968, (2024).
  • [26] Kawecka, E., Perec, A., and Radomska-Zalas, A., ‘‘Use of the simple multicriteria decision-making (MCDM) method for optimization of the high-alloy steel cutting process by the abrasiand water jet’’, Spectrum of Mechanical Engineering and Operational Research, 1(1):111-120, (2024).
  • [27] Demir, G., Özyalçın, A. T., and Bircan, H., ‘‘Çok Kriterli Karar Andrme Yöntemleri ve ÇKKV Yazılımı ile Problem Çözümü’’, Nobel, Ankara, (2021).
  • [28] Keleş, N., ‘‘Uygulamalarla Klasik and Güncel Karar Verme Yöntemleri’’, Nobel, Ankara, (2023).
  • [29] AlAli, A. M., Salih, A., and Hassaballa, A., ‘‘Geospatial-based analytical hierarchy process (AHP) and Weighted product model (WPM) techniques for mapping andassessing flood susceptibility in theWadi Hanifah Drainage basin, Riyadh Region, Saudi Arabia’’, Water, 15: 1-30, (2023).
  • [30] Chinnasamy, S., Ramachandran, M., Rajkumar, S., and Sivaji, C., ‘‘A surandy on Transportation System Using the WPM method’’, Building Materials and Engineering Structures, 1(7):37-43, (2023).
  • [31] Ayçin, E., ‘‘Çok Kriterli Karar Alma’’, Nobel Yayın, Ankara, (2019).
  • [32] Ecer, F., ‘‘Çok Kriterli Karar Alma’’, Seçkin Yayın, Ankara, (2020).
  • [33] Yaşar, M., ‘‘Determination of the best transport alternatiands by entropy based waspas method: A Comparatiand study on cross-competitiand routes’’, Journal of Management, Marketing and Logistics (JMML), 10(3):132-142, (2023).
  • [34] Stanujkić, D., and Karabašević, D., ‘‘An extension of the decision making prob lems with ıntuitionistic fuzzy numbers: A case of website evaluation,’’ Operational Research in Engineering Sciences: Theory and Applications, 1(1):29-39, (2018).
  • [35] Özbek, A., ‘‘Çok Kriterli Karar Verrme Yöntemleri and Excel ile Problem Çözümü Kavram-Teori-Uygulama’’, Seçkin Yayıncılık, Ankara, (2019).
  • [36] Çelikbilek, Y., ‘‘Çok Kriterli Karar Verrme Yöntemleri’’, Nobel Akademik Yayıncılık, Ankara, (2018).
  • [37] Kaya, İ., and Karaşan, A., ‘‘Çok Kriterli Karar Verrme Yöntemleri’’, Umuttepe Yayınları, İstanbul, (2020).
  • [38] S. Tepe, ‘‘Örnek Uygulamalarla Çok Kriterli Karar Andrme Yöntemleri’’, Akademisyen Kitapevi, Ankara, (2021).
  • [39] Karakış, E., ‘‘Machine selection for a textile company with critic and maut methods’’, European Journal of Science and Technology, (27):821-848, (2021).
  • [40] Atan, A., and Altan, Ş., ‘‘Örnek Uygulamalarla Çok Kriterli Karar Verrme Yöntemleri’’, Gazi Kitapevi, Ankara, (2020).
  • [41] Öztel, A., and Yavuz, H. (2019)., ‘‘CRITIC Tabanlı MAUT Yöntemi ile Ölçek Bazında Finansal Performans Analizi: Tekstil Sektöründe Bir Uygulama’’, 5. Uluslararası Ekonomi Yönetim ve Pazar Araştırmaları Kongeresi, İstanbul, (2019).
  • [42] Gelmez, E., Güleş, H. K., and Zerenler, M., ‘‘Evaluation of logistics performance of G20 countries using SD-based copras and saw methods’’, Journal of Turkish Operation Management, 8(2): 339-353, (2024).
  • [43] Mishra, A. R., Alrasheedi, M., and Lakshmi, J., ‘‘Multi-criteria decision analysis model using the q-rung orthopair fuzzy similarity measures and the copras method for electric andhicle charging station site selection,’’ Granul. Comput., 9(23):1-10, (2024).
  • [44] Paksoy, S., ‘‘Çok Kriterli Karar Almada Güncel Yaklaşımlar’’, Karahan Kitapevi, Adana, (2017).
  • [45] Uludağ, A, S., and Doğan, D., ‘‘Üretim Yönetiminde Çok Kriterli Karar Alma’’, Nobel, Ankara, (2021).
  • [46] Nurtiyanto, W. A., Rosyani, P., Ikasari, I. H., Noverick, M. S., Permana, G. S., and Wicaksono, B., ‘‘Decision support system for performance assessment of honoray personnel applying mabac, moora, and aras method with a combination of roc weighthing’’, International Journal of Integratiand Sciences (IJIS), 2(12):2067-2086, (2023).
  • [47] Kurşunmaden, F, İ., ‘‘Measuring corporate goandrnance maturity leandl with critic-based aras method’’, Sosyal Bilimler Meslek Yüksekokulu Dergisi, 7(2):766-774, (2024).
  • [48] Binh, V., Tuyen, V. T., Thanh, D. V., Duong, V., Dung, N. T., and Thao, L. P., ‘‘Application of marcos method for determining best process factor for edm using graphite electrodes’’, Journal of Harbin Engineering Uniandrsity, 45(2):329-334, (2024).
  • [49] Mastilo, Z., Štilić, A., Gligović, D., and Puška, A., ‘‘Assessing the banking sector of Bosnia and Herzegovina an analysis of financial ındicators through the merec and marcos methods’’, Journal of Central Banking Theory and Practice, 1:167-197, (2024).
  • [50] Li, Z., Xing, Y., and Dong, P., ‘‘A noandl q rung orthopair fuzzy best worst method, shannon entropy and marcos method for mobile medical app service quality evaluation’’, Applied Soft Computing, 155:1-15, (2024).
  • [51] Huskanović, E., Bjelić, D., and Novarlić, B., ‘‘Optimising the efficiency of municipal utility andhicle fleets using dea-critic-marcos: A sustainable waste management approach’’, Journal of Intelligent Management Decision, 3(3):139-149, (2024).
  • [52] Al-Hagery, M. A., ‘‘Google search filter using cosine similarity measure to find all relevant documents of a specific research topic’’, International Journal of Education And Information Technologies, 10:1-15, (2016).
  • [53] Saptono, R., Prasetyo, H., and Irawan, A., ‘‘Combination of cosine similarity method and conditional probability for plagiarism detection in the thesis documents andctor space model’’, Journal of Telecommunication, Electronic and Computer Engineering, 10(1):139-143, (2018).
  • [54] Chinram, R., Mahmood, T., Rehman, U., Ali, Z., and Iampan, A., ‘‘Some noandl cosine similarity measures based on complex hesitant fuzzy sets and their applications’’, Hindawi-Journal of Mathematics,, 1-20., (2021).
  • [55] Januzaj, Y, and Luma, A., ‘‘Cosine similarity – a computing approach to match similarity between higher education programs and jobmarket demands based on maximum number of common words’’, International Journal of Emerging Technologies in Learning (iJET), 17(12):258–268, (2022).
  • [56] Bhanuse, R, S., and Yenurkar, G., ‘‘Recommendation of branch of engineering using machine learning’’, International Research Journal of Engineering and Technology (IRJET), 7(3):5189-5192, (2020).
  • [57] Li, B, and Han, L., ‘‘Distance weighted cosine similarity measure for text classification’', 14th International Conference on Intelligent Data Engineering and Automated Learning, Hefei, (2023).
  • [58] Iriananda, S. W., Muslim, M. A., and Dachlan, H. S., ‘‘Measure the similarity of complaint document using cosine similarity based on class-based ındexing’’, International Journal of Computer Applications Technology and Research, 7(8):292-296, (2018).
  • [59] Oduntan, O., Adeyanju, I., A.s, F., and Obe, O., ‘‘A comparatiand analysis of euclidean distance and cosine similarity measure for automated essay-type grading’’, ARPN Journal of Engineering and Applied Sciences, 13(11):4198-4204, (2018).
  • [60] Karabiber, F., ‘‘Cosine similarity,’’ 2024. [Online]. Available: https://www.learndatasci.com/ glossary/cosine-Similarity/. [Retrieved on 12/01/2024.].
  • [61] Ye, J., ‘‘Cosine similarity measures for ıntuitionistic fuzzy sets and their applications’’, Mathematical and Computer Modelling, 53: 91–97, (2011).
  • [62] Han, Y., Shao, X.-F., Tsai,, S.-B., and Fan, D., ‘‘E-goandrnment and foreign direct ınandstment: evidence from chinese cities’’, Journal of Global Information Management, 29(6), 1-17, (2021).
  • [63] Park, K., Hong, J. S., and Kim, W., ‘‘A methodology combining cosine similarity with classifier’’, Applied Artificial Intelligence, 34(5):396–411, (2020).
  • [64] Jun, Y., ‘‘A multicriteria decision-making method using aggregation operators for simplified neutrosophic sets’’, Journal of Intelligent & Fuzzy Systems, 26(5):2459-2466, (2014).
  • [65] Wojke, N., and Bewley, A., ‘‘Deep cosine metric learning for person re-identification’’, In: 2018 IEEE Winter Conference on Applications of Computer Vision (WACV), Lake Tahoe, (2018).
  • [66] Elfakir, Y., Khaissidi, G., Mrabti, M., Chenouni, D., and Boualam, M., ‘‘Combined cosine-linear regression model similarity with application to handwritten word spotting’’, International Journal of Electrical and Computer Engineering (IJECE), 10(3):2367~2374, (2020).
  • [67] Huang, Y., Wang, Y., Tai, Y., Liu, X., Shen, P., Li, S., . . . Huang, F., ‘‘CurricularFace: adaptiand curriculum learning loss for deep face recognition’’, Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 5901-5910, (2020).
  • [68] Singh, R. H., Maurya, S., Tripathi, T., Narula, T., and Srivastav, G., ‘‘Movie recommendation system using cosine similarity and knn’’, International Journal of Engineering and Advanced Technology (IJEAT), 9(5):556-559, (2020).
  • [69] Yu, P., and Allan, J., ‘‘A study of neural matching models for cross lingual ir’’, SIGIR '20: Proceedings of the 43rd International ACM SIGIR Conference on Research and Deandlopment in Information Retrieval, 1637 - 1640, (2020).
  • [70] Zhang, X., Wei, Y., Yang, Y., and Huang, T. S., ‘‘SG-One: similarity guidance network for one-shot semantic segmentation’’, IEEE Transactions on Cybernetics, 50(9):3855-3865, (2020).
  • [71] Sattler, F., Müller, K.-R., and Samek, W., ‘‘Clustered federated learning: model-agnostic distributed multitask optimization under privacy constraints’’, IEEE Transactions On Neural Networks And Learning Systems, 32(8):3710-3722, (2021).
  • [72] Duan, M., Liu, D., Ji, X., Liu, R., Liang, L., Chen, X., & Tan, Y., ‘‘FedGroup: Efficient federated learning via decomposed similarity-based clustering’’, 2021 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking (ISPA/BDCloud/SocialCom/SustainCom), New York, (2021).
  • [73] Huang, C., Shen, J., Hu, B., Haqqani, M. A., Lee, T., Kooner, K., . . . Zhang, J., ‘‘Semantic and visual attention-driandn multi-lstm network for automated clinical report generation’’, In AI for Health Equity and Fairness Studies in Computational Intelligence. In: Shaban-Nejad, A., Michalowski, M., Bianco, S. (eds) AI for Health Equity and Fairness, Springer. Cham, Gewerbestrasse, pp. 25-40, (2024).
  • [74] WIPO. ‘‘Global Innovation Index 2024: Unlocking the Promise of Social’’, WIPO, Geneva, (2024).
  • [75] Munier, N., ‘‘Strategic Approach in Multi-Criteria Decision Making’’, Springer International Publishing AG, Gewerbestrasse, (2024).
  • [76] Lopez, L. M., Ishizaka, A., and Qin, J., ‘‘Multi-criteria Decision Making Sorting Methods: Applications to Real World Problems’’, Academic Press, London, (2023).
  • [77] Tzeng, G.-H., and Huang, J.-J, ‘‘Multiple Attribute Decision Making Methods and Applications’’, Chapman & Hall, London, (2011).
  • [78] Zavadskas, E, K., and Kaklauskas, A., ‘‘Systemotechnical evaluation of buildings (Pastatų-sistemotechninis įandrtinimas)’’, Vilnius: Technika, 280, (in Lithuanian), (1996).
  • [79] Silva, M. d., Gavião, L. O., Gomes, C. F., and Lima, G. B., ‘‘Global innovation indicators analysed by multicriteria decision’’, Brazilian Journal of Operations & Production Management, 17(4):1-17, (2022).
  • [80] Stojanović, I., Puška, A., and Selaković, M. ‘‘A multi-criteria approach to the comparative analysis of the global innovation index on the example of the Western Balkan countries,’’ Sciendo-Economics, 9(26):9-26., (2020).
  • [81] Demir, D, and Arslan, R., ‘‘Sensitivity analysis in multi-criteria decision-making problems,’’ Ankara Hacı Bayram Veli Üniversitesi İktisadi and İdari Bilimler Fakültesi Dergisi, 24(3):1025-1056, (2022).
  • [82] Keshavarz-Ghorabaee, M., Amiri, M., Zavadskas, E. K., Turskis, Z., and Antucheviciene, J., ‘‘Determination of objectiand weights using a new method based on the removal effects of criteria (MEREC),’’ Symmetry, 13:1-21, (2021).

A New Method for Measuring the Performance of Alternatives: Cosine Similarty Approach

Year 2025, EARLY VIEW, 1 - 1

Furkan Fahri Altıntaş

https://doi.org/10.2339/politeknik.1627566

Abstract

The development of new methods within the scope of Multi-Criteria Decision Making (MCDM) provides decision-makers with alternative solution approaches in various scenarios, enabling more flexible and effective decision-making processes. This study aims to demonstrate the applicability of a newly proposed method based on cosine Similarty (Cosine Similarty Approach) for evaluating the performance of alternatives in selection problems. The proposed method was tested using the criterion values of seven selected countries from the Global Innovation Index-2024, and the necessary measurements were conducted accordingly. A review of the literature revealed that no prior studies have been conducted based on cosine Similarty, establishing the originality of this research. The findings indicate that the proposed method is ideal in terms of sensitivity analysis, reliable and consistent in comparative analysis, and robust and stable in simulation analysis. In this context, the proposed method is concluded to be a practical and applicable tool for decision-makers in solving selection problems.

Keywords

MCDM cosine similarty alternative performance selection problems

References

  • [1] Jahan A., “Multi-criteria Decision Analysis for Supporting the Selection of Engineering Materials in Product Design”, BH Press, Brentwood, (2016).
  • [2] Nguyen, V. T., Vo, N. T., Truong, V. C., and Nguyen, V.-T., “Multi-criteria Decision Making and Optimum Design with Machine Learning”, CRC Press, Boca Raton, (2025).
  • [3] Doumpos, M., Ferreira, F. A., and Zopounidis, C., “Multiple Criteria Decision Making for Sustainable Deandlopment”, Springer Cham, Gewerbestrasse, (2021).
  • [4] Linkov, I., Moberg, E., Trump, B. D., Yatsalo, B., and Keisler, J., “Multi-criteria Decision Analysis Case Studies in Engineering and the Environment”, CRC Press, Boca Raton, (2023).
  • [5] Anand, A., Agarwal, M., and Aggrawal, D., “Multiple Criteria Decision-Making: Applications for Managerial Discretion”, De Gruyter, Berlin, (2022).
  • [6] Grybaite, V., and Burinskiene, A., ‘‘Assessment of circular economy deandlopment in the EU’’, Sustainability, 16:1-20, (2024).
  • [7] Radulescu, C. Z., and Radulescu, M., ‘‘A Hybrid Group Multi-criteria Approach Based on SAW,TOPSIS, VIKOR, and COPRAS Methods for Complex IoT Selection Problems’’ , Electronics, 13:1-27, (2024).
  • [8] Balusa, B. C., Riyazuddin, M., Gole, S., & Deepthi, K., ‘‘Development of Fuzzy-AHP and WPM based MCDM Model for Mining Method Selection’’, Journal of Mines, Metals and Fuels, 72(6): 533-539, (2024).
  • [9] Vitianingsih, A. V., Ullum, C., Maukar, A. L., Yasin, V., and Wati, S. F., ‘‘Mapping Residential Land Suitability Using a WEB-GIS-Based Multi-criteria Spatial Analysis Approach: Integration of AHP and WPM Methods’’, Rekayasa Sistem dan Teknologi Informasi, 8(2):208-215, (2024).
  • [10] Trivedi, P., Shah, J., Čep, R., Abualigah, L., and Kalita, K., ‘‘A Hybrid Best-Worst Method (BWM)—Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) Approach for Prioritizing Road Safety Improandments’’ , IEEE, 12:30054-30065, (2024).
  • [11] Alsanousi, A. T., Alqahtani, A. Y., Makki, A. A., and Baghdadi, M. A., ‘‘A Hybrid MCDM Approach Using the BWMand the TOPSIS for a Financial Performance-Based Evaluation of Saudi Stocks’’ , Information, 15:1-22, (2024).
  • [12] Chatterjee, S., and Chakraborty, S., ‘‘A Study On The Effects Of Objectiand Weighting Methods on Topsis-Based Parametric Optimization of Non-Traditional Machining Processes’’, Decision Analytics Journal, 11:1-16, (2024).
  • [13] Akmaludin, Sihombing, E. G., Rinawati, Dewi, L. S., and Arisawati, E., ‘‘Integrated Selection of Permanent Teacher Appointments Recommended MCDM-AHP and WASPAS Methods’’, Sinkron : Jurnal dan Penelitian Teknik Informatika, 8(2):661-670, (2024).
  • [14] Khan, A. A., Mashat, D. S., and Dong, K., ‘‘Evaluating sustainable urban deandlopment strategies through spherical critic-waspas analysis’’, Journal of Urban Deandlopment and Management, 3(1):1-17, (2024).
  • [15] Madić, M., Petrović, G., Petković, D., and Janković, P., ‘‘Traditional and Integrated MCDM Approaches for Assessment and Ranking of Laser Cutting Conditions’’, Spectrum of Mechanical Engineering and Operational Research, 1(1):250-257, (2024).
  • [16] Uyen, V. T., Ersoy, N., Trung, D. D., and Giang, N. T., ‘‘Applying MCDM to evaluate benchmark scores in the logistics sector for the period 2021–2023: Application to uniandrsities in Vietnam’’, Journal of Infrastructure, Policy and Deandlopment, 8(11):1-16, (2024).
  • [17] Wang, Y., Wang, W., Wang, Z., & Deveci, M., ‘‘Selection of sustainable food suppliers using the Pythagorean fuzzy CRITIC-MARCOS method’’, Information Sciences, 664:1-22, (2024).
  • [18] Özomay, M., ‘‘Sustainable and environmental dyeing with MAUT method comparatiand selection of the dyeing recipe’’, Sustainability, 15:1-14, (2023).
  • [19] El-Araby, A., Sabry, I., and El-Assal, A., ‘‘Ranking performance of MARCOS Method for location selection problem in the presence of conflicting criteria’’, Decision Making Advances, 2(1):148-162, (2024).
  • [20] Demir, G., Chatterjee, P., Kadry, S., Abdelhadi, A., & Pamučar, D., ‘‘Measurement of alternatiands and ranking according to compromise solution (MARCOS) method: A Comprehensiand Bibliometric Analysis’’, Decision Making: Applications in Management and Engineering, 7(2):313-336, (2024).
  • [21] Taherdoost, H., and Mohebi, A., ‘‘A comprehensiand guide to the COPRAS method for multi-criteria decision making’’, Journal of Management Science & Engineering Research, 7(2):1-14, (2024).
  • [22] Cai, J., Hu, Y., Peng, Y., Guo, F., Xiong, J., and Zhang, R., ‘‘A Hybrid mcdm Approach based on combined weighting method cloud model and copras for assessing road construction workers’ safety climate’’, Front. Public Health, 12: 1-14, (2024).
  • [23] Dinçer, S. E., ‘‘Çok Kriterli Karar Alma’’, Gece Akademi, Ankara, (2019).
  • [24] Demirci, A., ‘‘Sağlık Hizmetleri Yönetiminde Çok Kriterli Karar Andrme Teknikleri’’, Gazi Kitapevi, Ankara, (2020).
  • [25] Dua, T. V., ‘‘PSI-SAW and PSI-MARCOS hybrid MCDM’’, Engineering, Technology & Applied Science Research, 4(14):15963-15968, (2024).
  • [26] Kawecka, E., Perec, A., and Radomska-Zalas, A., ‘‘Use of the simple multicriteria decision-making (MCDM) method for optimization of the high-alloy steel cutting process by the abrasiand water jet’’, Spectrum of Mechanical Engineering and Operational Research, 1(1):111-120, (2024).
  • [27] Demir, G., Özyalçın, A. T., and Bircan, H., ‘‘Çok Kriterli Karar Andrme Yöntemleri ve ÇKKV Yazılımı ile Problem Çözümü’’, Nobel, Ankara, (2021).
  • [28] Keleş, N., ‘‘Uygulamalarla Klasik and Güncel Karar Verme Yöntemleri’’, Nobel, Ankara, (2023).
  • [29] AlAli, A. M., Salih, A., and Hassaballa, A., ‘‘Geospatial-based analytical hierarchy process (AHP) and Weighted product model (WPM) techniques for mapping andassessing flood susceptibility in theWadi Hanifah Drainage basin, Riyadh Region, Saudi Arabia’’, Water, 15: 1-30, (2023).
  • [30] Chinnasamy, S., Ramachandran, M., Rajkumar, S., and Sivaji, C., ‘‘A surandy on Transportation System Using the WPM method’’, Building Materials and Engineering Structures, 1(7):37-43, (2023).
  • [31] Ayçin, E., ‘‘Çok Kriterli Karar Alma’’, Nobel Yayın, Ankara, (2019).
  • [32] Ecer, F., ‘‘Çok Kriterli Karar Alma’’, Seçkin Yayın, Ankara, (2020).
  • [33] Yaşar, M., ‘‘Determination of the best transport alternatiands by entropy based waspas method: A Comparatiand study on cross-competitiand routes’’, Journal of Management, Marketing and Logistics (JMML), 10(3):132-142, (2023).
  • [34] Stanujkić, D., and Karabašević, D., ‘‘An extension of the decision making prob lems with ıntuitionistic fuzzy numbers: A case of website evaluation,’’ Operational Research in Engineering Sciences: Theory and Applications, 1(1):29-39, (2018).
  • [35] Özbek, A., ‘‘Çok Kriterli Karar Verrme Yöntemleri and Excel ile Problem Çözümü Kavram-Teori-Uygulama’’, Seçkin Yayıncılık, Ankara, (2019).
  • [36] Çelikbilek, Y., ‘‘Çok Kriterli Karar Verrme Yöntemleri’’, Nobel Akademik Yayıncılık, Ankara, (2018).
  • [37] Kaya, İ., and Karaşan, A., ‘‘Çok Kriterli Karar Verrme Yöntemleri’’, Umuttepe Yayınları, İstanbul, (2020).
  • [38] S. Tepe, ‘‘Örnek Uygulamalarla Çok Kriterli Karar Andrme Yöntemleri’’, Akademisyen Kitapevi, Ankara, (2021).
  • [39] Karakış, E., ‘‘Machine selection for a textile company with critic and maut methods’’, European Journal of Science and Technology, (27):821-848, (2021).
  • [40] Atan, A., and Altan, Ş., ‘‘Örnek Uygulamalarla Çok Kriterli Karar Verrme Yöntemleri’’, Gazi Kitapevi, Ankara, (2020).
  • [41] Öztel, A., and Yavuz, H. (2019)., ‘‘CRITIC Tabanlı MAUT Yöntemi ile Ölçek Bazında Finansal Performans Analizi: Tekstil Sektöründe Bir Uygulama’’, 5. Uluslararası Ekonomi Yönetim ve Pazar Araştırmaları Kongeresi, İstanbul, (2019).
  • [42] Gelmez, E., Güleş, H. K., and Zerenler, M., ‘‘Evaluation of logistics performance of G20 countries using SD-based copras and saw methods’’, Journal of Turkish Operation Management, 8(2): 339-353, (2024).
  • [43] Mishra, A. R., Alrasheedi, M., and Lakshmi, J., ‘‘Multi-criteria decision analysis model using the q-rung orthopair fuzzy similarity measures and the copras method for electric andhicle charging station site selection,’’ Granul. Comput., 9(23):1-10, (2024).
  • [44] Paksoy, S., ‘‘Çok Kriterli Karar Almada Güncel Yaklaşımlar’’, Karahan Kitapevi, Adana, (2017).
  • [45] Uludağ, A, S., and Doğan, D., ‘‘Üretim Yönetiminde Çok Kriterli Karar Alma’’, Nobel, Ankara, (2021).
  • [46] Nurtiyanto, W. A., Rosyani, P., Ikasari, I. H., Noverick, M. S., Permana, G. S., and Wicaksono, B., ‘‘Decision support system for performance assessment of honoray personnel applying mabac, moora, and aras method with a combination of roc weighthing’’, International Journal of Integratiand Sciences (IJIS), 2(12):2067-2086, (2023).
  • [47] Kurşunmaden, F, İ., ‘‘Measuring corporate goandrnance maturity leandl with critic-based aras method’’, Sosyal Bilimler Meslek Yüksekokulu Dergisi, 7(2):766-774, (2024).
  • [48] Binh, V., Tuyen, V. T., Thanh, D. V., Duong, V., Dung, N. T., and Thao, L. P., ‘‘Application of marcos method for determining best process factor for edm using graphite electrodes’’, Journal of Harbin Engineering Uniandrsity, 45(2):329-334, (2024).
  • [49] Mastilo, Z., Štilić, A., Gligović, D., and Puška, A., ‘‘Assessing the banking sector of Bosnia and Herzegovina an analysis of financial ındicators through the merec and marcos methods’’, Journal of Central Banking Theory and Practice, 1:167-197, (2024).
  • [50] Li, Z., Xing, Y., and Dong, P., ‘‘A noandl q rung orthopair fuzzy best worst method, shannon entropy and marcos method for mobile medical app service quality evaluation’’, Applied Soft Computing, 155:1-15, (2024).
  • [51] Huskanović, E., Bjelić, D., and Novarlić, B., ‘‘Optimising the efficiency of municipal utility andhicle fleets using dea-critic-marcos: A sustainable waste management approach’’, Journal of Intelligent Management Decision, 3(3):139-149, (2024).
  • [52] Al-Hagery, M. A., ‘‘Google search filter using cosine similarity measure to find all relevant documents of a specific research topic’’, International Journal of Education And Information Technologies, 10:1-15, (2016).
  • [53] Saptono, R., Prasetyo, H., and Irawan, A., ‘‘Combination of cosine similarity method and conditional probability for plagiarism detection in the thesis documents andctor space model’’, Journal of Telecommunication, Electronic and Computer Engineering, 10(1):139-143, (2018).
  • [54] Chinram, R., Mahmood, T., Rehman, U., Ali, Z., and Iampan, A., ‘‘Some noandl cosine similarity measures based on complex hesitant fuzzy sets and their applications’’, Hindawi-Journal of Mathematics,, 1-20., (2021).
  • [55] Januzaj, Y, and Luma, A., ‘‘Cosine similarity – a computing approach to match similarity between higher education programs and jobmarket demands based on maximum number of common words’’, International Journal of Emerging Technologies in Learning (iJET), 17(12):258–268, (2022).
  • [56] Bhanuse, R, S., and Yenurkar, G., ‘‘Recommendation of branch of engineering using machine learning’’, International Research Journal of Engineering and Technology (IRJET), 7(3):5189-5192, (2020).
  • [57] Li, B, and Han, L., ‘‘Distance weighted cosine similarity measure for text classification’', 14th International Conference on Intelligent Data Engineering and Automated Learning, Hefei, (2023).
  • [58] Iriananda, S. W., Muslim, M. A., and Dachlan, H. S., ‘‘Measure the similarity of complaint document using cosine similarity based on class-based ındexing’’, International Journal of Computer Applications Technology and Research, 7(8):292-296, (2018).
  • [59] Oduntan, O., Adeyanju, I., A.s, F., and Obe, O., ‘‘A comparatiand analysis of euclidean distance and cosine similarity measure for automated essay-type grading’’, ARPN Journal of Engineering and Applied Sciences, 13(11):4198-4204, (2018).
  • [60] Karabiber, F., ‘‘Cosine similarity,’’ 2024. [Online]. Available: https://www.learndatasci.com/ glossary/cosine-Similarity/. [Retrieved on 12/01/2024.].
  • [61] Ye, J., ‘‘Cosine similarity measures for ıntuitionistic fuzzy sets and their applications’’, Mathematical and Computer Modelling, 53: 91–97, (2011).
  • [62] Han, Y., Shao, X.-F., Tsai,, S.-B., and Fan, D., ‘‘E-goandrnment and foreign direct ınandstment: evidence from chinese cities’’, Journal of Global Information Management, 29(6), 1-17, (2021).
  • [63] Park, K., Hong, J. S., and Kim, W., ‘‘A methodology combining cosine similarity with classifier’’, Applied Artificial Intelligence, 34(5):396–411, (2020).
  • [64] Jun, Y., ‘‘A multicriteria decision-making method using aggregation operators for simplified neutrosophic sets’’, Journal of Intelligent & Fuzzy Systems, 26(5):2459-2466, (2014).
  • [65] Wojke, N., and Bewley, A., ‘‘Deep cosine metric learning for person re-identification’’, In: 2018 IEEE Winter Conference on Applications of Computer Vision (WACV), Lake Tahoe, (2018).
  • [66] Elfakir, Y., Khaissidi, G., Mrabti, M., Chenouni, D., and Boualam, M., ‘‘Combined cosine-linear regression model similarity with application to handwritten word spotting’’, International Journal of Electrical and Computer Engineering (IJECE), 10(3):2367~2374, (2020).
  • [67] Huang, Y., Wang, Y., Tai, Y., Liu, X., Shen, P., Li, S., . . . Huang, F., ‘‘CurricularFace: adaptiand curriculum learning loss for deep face recognition’’, Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 5901-5910, (2020).
  • [68] Singh, R. H., Maurya, S., Tripathi, T., Narula, T., and Srivastav, G., ‘‘Movie recommendation system using cosine similarity and knn’’, International Journal of Engineering and Advanced Technology (IJEAT), 9(5):556-559, (2020).
  • [69] Yu, P., and Allan, J., ‘‘A study of neural matching models for cross lingual ir’’, SIGIR '20: Proceedings of the 43rd International ACM SIGIR Conference on Research and Deandlopment in Information Retrieval, 1637 - 1640, (2020).
  • [70] Zhang, X., Wei, Y., Yang, Y., and Huang, T. S., ‘‘SG-One: similarity guidance network for one-shot semantic segmentation’’, IEEE Transactions on Cybernetics, 50(9):3855-3865, (2020).
  • [71] Sattler, F., Müller, K.-R., and Samek, W., ‘‘Clustered federated learning: model-agnostic distributed multitask optimization under privacy constraints’’, IEEE Transactions On Neural Networks And Learning Systems, 32(8):3710-3722, (2021).
  • [72] Duan, M., Liu, D., Ji, X., Liu, R., Liang, L., Chen, X., & Tan, Y., ‘‘FedGroup: Efficient federated learning via decomposed similarity-based clustering’’, 2021 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking (ISPA/BDCloud/SocialCom/SustainCom), New York, (2021).
  • [73] Huang, C., Shen, J., Hu, B., Haqqani, M. A., Lee, T., Kooner, K., . . . Zhang, J., ‘‘Semantic and visual attention-driandn multi-lstm network for automated clinical report generation’’, In AI for Health Equity and Fairness Studies in Computational Intelligence. In: Shaban-Nejad, A., Michalowski, M., Bianco, S. (eds) AI for Health Equity and Fairness, Springer. Cham, Gewerbestrasse, pp. 25-40, (2024).
  • [74] WIPO. ‘‘Global Innovation Index 2024: Unlocking the Promise of Social’’, WIPO, Geneva, (2024).
  • [75] Munier, N., ‘‘Strategic Approach in Multi-Criteria Decision Making’’, Springer International Publishing AG, Gewerbestrasse, (2024).
  • [76] Lopez, L. M., Ishizaka, A., and Qin, J., ‘‘Multi-criteria Decision Making Sorting Methods: Applications to Real World Problems’’, Academic Press, London, (2023).
  • [77] Tzeng, G.-H., and Huang, J.-J, ‘‘Multiple Attribute Decision Making Methods and Applications’’, Chapman & Hall, London, (2011).
  • [78] Zavadskas, E, K., and Kaklauskas, A., ‘‘Systemotechnical evaluation of buildings (Pastatų-sistemotechninis įandrtinimas)’’, Vilnius: Technika, 280, (in Lithuanian), (1996).
  • [79] Silva, M. d., Gavião, L. O., Gomes, C. F., and Lima, G. B., ‘‘Global innovation indicators analysed by multicriteria decision’’, Brazilian Journal of Operations & Production Management, 17(4):1-17, (2022).
  • [80] Stojanović, I., Puška, A., and Selaković, M. ‘‘A multi-criteria approach to the comparative analysis of the global innovation index on the example of the Western Balkan countries,’’ Sciendo-Economics, 9(26):9-26., (2020).
  • [81] Demir, D, and Arslan, R., ‘‘Sensitivity analysis in multi-criteria decision-making problems,’’ Ankara Hacı Bayram Veli Üniversitesi İktisadi and İdari Bilimler Fakültesi Dergisi, 24(3):1025-1056, (2022).
  • [82] Keshavarz-Ghorabaee, M., Amiri, M., Zavadskas, E. K., Turskis, Z., and Antucheviciene, J., ‘‘Determination of objectiand weights using a new method based on the removal effects of criteria (MEREC),’’ Symmetry, 13:1-21, (2021).
There are 82 citations in total.

Details

Primary Language English
Subjects Multiple Criteria Decision Making
Journal Section Research Article
Authors

Furkan Fahri Altıntaş 0000-0002-0161-5862

Early Pub Date April 10, 2025
Publication Date
Submission Date January 27, 2025
Acceptance Date April 6, 2025
Published in Issue Year 2025 EARLY VIEW

Cite

APA Altıntaş, F. F. (2025). A New Method for Measuring the Performance of Alternatives: Cosine Similarty Approach. Politeknik Dergisi1-1. https://doi.org/10.2339/politeknik.1627566
AMA Altıntaş FF. A New Method for Measuring the Performance of Alternatives: Cosine Similarty Approach. Politeknik Dergisi. Published online April 1, 2025:1-1. doi:10.2339/politeknik.1627566
Chicago Altıntaş, Furkan Fahri. “A New Method for Measuring the Performance of Alternatives: Cosine Similarty Approach”. Politeknik Dergisi, April (April 2025), 1-1. https://doi.org/10.2339/politeknik.1627566.
EndNote Altıntaş FF (April 1, 2025) A New Method for Measuring the Performance of Alternatives: Cosine Similarty Approach. Politeknik Dergisi 1–1.
IEEE F. F. Altıntaş, “A New Method for Measuring the Performance of Alternatives: Cosine Similarty Approach”, Politeknik Dergisi, pp. 1–1, April 2025, doi: 10.2339/politeknik.1627566.
ISNAD Altıntaş, Furkan Fahri. “A New Method for Measuring the Performance of Alternatives: Cosine Similarty Approach”. Politeknik Dergisi. April 2025. 1-1. https://doi.org/10.2339/politeknik.1627566.
JAMA Altıntaş FF. A New Method for Measuring the Performance of Alternatives: Cosine Similarty Approach. Politeknik Dergisi. 2025;:1–1.
MLA Altıntaş, Furkan Fahri. “A New Method for Measuring the Performance of Alternatives: Cosine Similarty Approach”. Politeknik Dergisi, 2025, pp. 1-1, doi:10.2339/politeknik.1627566.
Vancouver Altıntaş FF. A New Method for Measuring the Performance of Alternatives: Cosine Similarty Approach. Politeknik Dergisi. 2025:1-.

 download This work is licensed under Creative Commons Attribution-ShareAlike 4.0 International.