PREDICTING THE THERMAL INSULATION PROPERTIES OF TWILL WOVEN COTTON FABRIC BY USING ANN AND ANFIS

Mahmuda Akter; Elias Khalil; Shah Md. Maruf Hasan; Md. Kamrul Hassan Chowdhury

doi:10.7216/teksmuh.1587503

Araştırma Makalesi

BibTex

RIS

Kaynak Göster

PREDICTING THE THERMAL INSULATION PROPERTIES OF TWILL WOVEN COTTON FABRIC BY USING ANN AND ANFIS

Yıl 2025, Cilt: 32 Sayı: 138, 128 - 145, 30.06.2025

Mahmuda Akter , Elias Khalil , Shah Md. Maruf Hasan Md. Kamrul Hassan Chowdhury

https://doi.org/10.7216/teksmuh.1587503

Öz

This study analyzes two machine learning models, artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS), to predict thermal insulation of cotton fabric woven with twill. The input parameters include fabric thickness, ends per inch (EPI), and picks per inch (PPI). The ANN model has a 3-8-1 network structure, with output and hidden layers having sigmoid and linear activation functions. The ANFIS model employs sugeno-type fuzzy logic, while the network is trained using the feedforward backpropagation Levenberg-Marquardt technique. The weighted average approach was used in the defuzzification process. MATLAB was used to create both models. The ANN model performs well in predictions, as evidenced by its R2 value of 0.9942, which indicates a significant correlation between the target and prediction values. The ANN model's exceptional performance metrics, such as a mean absolute percentage error (MAPE) of 1.31401 and a root mean squared error (RMSE) of 0.00176, demonstrate its precision and reliability. However, the ANFIS model has considerably lower accuracy metrics, with an R2 value of 0.9570. The ANN offers more accuracy and precision than the ANFIS model, which has an RMSE of 0.00489 and a MAPE of 2.07495. This study will improve the textile engineering prediction model by revealing the intricate connection between fabric characteristics and the thermal insulation of clothing composed of cotton fabric's twill structure.

Anahtar Kelimeler

Ends per Inch (EPI), Picks per Inch (PPI), Thermal Insulation, Artificial Neural Network, Adaptive Neuro-Fuzzy Inference System

Etik Beyan

This paper does not contain any research involving human participants or animals that was conducted by any of the writers themselves.

Kaynakça

1. Das, A. and R. Alagirusamy, Science in clothing comfort. 2010: Woodhead Publishing India Pvt Limited New Delhi.
2. Song, G., Improving comfort in clothing. 2011: Elsevier.
3. Jintu, F. and H.W.K. Tsang, Effect of Clothing Thermal Properties on the Thermal Comfort Sensation During Active Sports. Textile Research Journal, 2008. 78(2): p. 111-118.
4. Ukponmwan, J.O., The Thermal-Insulation Properties of Fabrics. Textile Progress, 1993. 24(4): p. 1-54.
5. Lenhard, R., et al., Verification of the Fanger Model in Real Conditions. MATEC Web of Conferences, 2020. 328.
6. Havenith, G., I. Holmér, and K. Parsons, Personal factors in thermal comfort assessment: clothing properties and metabolic heat production. Energy and Buildings, 2002. 34(6): p. 581-591.
7. Butera, F.M., —Principles of thermal comfort. Renewable and Sustainable Energy Reviews, 1998. 2(1-2): p. 39-66.
8. Oğulata, R.T., The effect of thermal insulation of clothing on human thermal comfort. Fibres & Textiles in Eastern Europe, 2007. 15(2): p. 61.
9. Twill Weaves. 2024 [cited 2024 28 March]; Available from: https://cottonworks.com/en/topics/sourcing-manufacturing/weaving/twill-weaves/.
10. Gokarneshan, N., Fabric structure and design. 1st ed. 2004: New Age International (P) Ltd. 16-21.
11. Belal, S.A., Understanding textiles for a merchandiser. 1st ed. 2009: BMN3 Foundation, Dhaka. 135-144.
12. Damjana Celcar, J.G., Harriet Meinander, Evaluation of Textile Thermal Properties and their Combinations. Tekstilec, 2010. 53: p. 9-32.
13. Shaker, K., et al., Effect of fabric structural design on the thermal properties of woven fabrics. Thermal science, 2019. 23(5 Part B): p. 3059-3066.
14. Ho, C.P., et al., Improving thermal comfort in apparel, in Improving comfort in clothing. 2011, Elsevier. p. 165-181.
15. Halaoua, S., Z. Romdhani, and A. Jemni, Effect of textile woven fabric parameters on its thermal properties. Industria Textila, 2019. 70(1): p. 15-20.
16. Akter, M., et al., Artificial neural network and multiple linear regression modeling for predicting thermal transmittance of plain-woven cotton fabric. Textile Research Journal, 2024. 94(11-12): p. 1279-1296.
17. Mitra, A., et al., Predicting thermal resistance of cotton fabrics by artificial neural network model. Experimental Thermal and Fluid Science, 2013. 50: p. 172-177.
18. Ahmad, S., et al., Effect of weave structure on thermo-physiological properties of cotton fabrics. AUTEX Research Journal, 2015. 15(1): p. 30-34.
19. Majumdar, A., Soft computing in textile engineering. 2010: Elsevier.
20. Subramanian, T.A., K. Ganesh, and S. Bandyopadhyay, 34—a Generalized Equation for Predicting the Lea Strength of Ring-Spun Cotton Yarns. The Journal of The Textile Institute, 2008. 65(6): p. 307-313.
21. Majumdar, A. and A. Ghosh, Yarn Strength Modelling Using Fuzzy Expert System. Journal of Engineered Fibers and Fabrics, 2008. 3(4).
22. BRENT SMITH, B.W., Extending Applicable Ranges of Regression Equations for Yarn Strength Forecasting. Textile Research Journal, 1985.
23. Zurek, W., I. Frydrych, and S. Zakrzewski, A Method of Predicting the Strength and Breaking Strain of Cotton Yarn. 1987. 57(8): p. 439-444.
24. Behera, B. and Y. Goyal, Artificial neural network system for the design of airbag fabrics. Journal of Industrial Textiles, 2009. 39(1): p. 45-55.
25. Behera, B. and B. Karthikeyan, Artificial neural network-embedded expert system for the design of canopy fabrics. Journal of industrial textiles, 2006. 36(2): p. 111-123.
26. Beltran, R., L. Wang, and X. Wang, Predicting worsted spinning performance with an artificial neural network model. Textile research journal, 2004. 74(9): p. 757-763.
27. Chen, Y., T. Zhao, and B. Collier, Prediction of fabric end-use using a neural network technique. Journal of the Textile Institute, 2001. 92(2): p. 157-163.
28. Hu, Z.-H., et al., A hybrid neural network and immune algorithm approach for fit garment design. Textile Research Journal, 2009. 79(14): p. 1319-1330.
29. Hui, P.C., et al., Application of artificial neural networks to the prediction of sewing performance of fabrics. International Journal of Clothing Science and Technology, 2007. 19(5): p. 291-318.
30. Jeong, S.H., J.H. Kim, and C.J. Hong, Selecting optimal interlinings with a neural network. Textile Research Journal, 2000. 70(11): p. 1005-1010.
31. Kang, T.J. and S.C. Kim, Objective evaluation of the trash and color of raw cotton by image processing and neural network. Textile Research Journal, 2002. 72(9): p. 776-782.
32. Khan, Z., et al., An artificial neural network-based hairiness prediction model for worsted wool yarns. Textile Research Journal, 2009. 79(8): p. 714-720.
33. Kuo, C.-F.J., K.-I. Hsiao, and Y.-S. Wu, Using neural network theory to predict the properties of melt spun fibers. Textile Research Journal, 2004. 74(9): p. 840-843.
34. Lin, J.-J., Prediction of yarn shrinkage using neural nets. Textile Research Journal, 2007. 77(5): p. 336-342.
35. Murrells, C.M., et al., An artificial neural network model for the prediction of spirality of fully relaxed single jersey fabrics. Textile Research Journal, 2009. 79(3): p. 227-234.
36. She, F.H., et al., Intelligent animal fiber classification with artificial neural networks. Textile research journal, 2002. 72(7): p. 594-600.
37. Wong, A., et al., Neural network predictions of human psychological perceptions of clothing sensory comfort. Textile research journal, 2003. 73(1): p. 31-37.
38. Yao, G., J. Guo, and Y. Zhou, Predicting the warp breakage rate in weaving by neural network techniques. Textile Research Journal, 2005. 75(3): p. 274-278.
39. Zeng, Y.-C., K. Wang, and C. Yu, Predicting the tensile properties of air-jet spun yarns. Textile Research Journal, 2004. 74(8): p. 689-694.
40. Akankwasa, N.T. and D. Veit, Advances in Modeling and Simulation in Textile Engineering: New Concepts, Methods, and Applications. 2021: Elsevier Science.
41. Das, P.P. and S. Chakraborty, Adaptive neuro-fuzzy inference system-based modelling of cotton yarn properties. Journal of The Institution of Engineers (India): Series E, 2021. 102(2): p. 257-272.
42. Hadizadeh, M., M. Amani Tehran, and A.A. Jeddi, Application of an adaptive neuro-fuzzy system for prediction of initial load—extension behavior of plain-woven fabrics. Textile Research Journal, 2010. 80(10): p. 981-990.
43. Behera, B. and R. Guruprasad, Predicting bending rigidity of woven fabrics using adaptive neuro-fuzzy inference system (ANFIS). Journal of The Textile Institute, 2012. 103(11): p. 1205-1212.
44. Sarkar, J., et al., Comparison Of Anfis And Ann Modeling For Predicting The Water Absorption Behavior Of Polyurethane Treated Polyester Fabric. Heliyon, 2021. 7(9): p. e08000-e08000.
45. Hussain, T., et al., Comparison of artificial neural network and adaptive neuro-fuzzy inference system for predicting the wrinkle recovery of woven fabrics. The Journal of the Textile Institute, 2015. 106(9): p. 934-938.
46. Yu, H. and B.M. Wilamowski, Levenberg–marquardt training, in Intelligent systems. 2018, CRC Press. p. 12-1-12-16.
47. Singh, H. and Y.A. Lone, Deep neuro-fuzzy systems with python. Apress, Berkeley, 2020.
48. Suparta, W. and K.M. Alhasa, Adaptive Neuro-Fuzzy Interference System, in Modeling of Tropospheric Delays Using ANFIS. 2016, Springer International Publishing: Cham. p. 5-18.
49. Lewis, C.D., Industrial and Business Forecasting Methods: A Practical Guide to Exponential Smoothing and Curve Fitting. 1982: Butterworth Scientific.
50. Montgomery, D.C. and G.C. Runger, Applied statistics and probability for engineers. 2010: John wiley & sons.

TWILL DOKUMA PAMUKLU KUMAŞIN ISI YALITIM ÖZELLİKLERİNİN ANN VE ANFIS KULLANILARAK TAHMİN EDİLMESİ

Yıl 2025, Cilt: 32 Sayı: 138, 128 - 145, 30.06.2025

Mahmuda Akter , Elias Khalil , Shah Md. Maruf Hasan Md. Kamrul Hassan Chowdhury

https://doi.org/10.7216/teksmuh.1587503

Öz

Bu çalışma, dimi ile dokunmuş pamuklu kumaşın ısı yalıtımını tahmin etmek için yapay sinir ağı (YSA) ve uyarlamalı ağ tabanlı bulanık mantık çıkarım sistemi (ANFIS) olmak üzere iki makine öğrenme modelini analiz etmektedir. Giriş parametreleri kumaş kalınlığını, inç başına uç sayısını (EPI) ve inç başına atkı sayısını (PPI) içerir. YSA modeli, sigmoid ve doğrusal aktivasyon fonksiyonlarına sahip çıkış ve gizli katmanlardan oluşan 3-8-1 ağ yapısına sahiptir. ANFIS modeli sugeno tipi bulanık mantık kullanırken, ağ ileri beslemeli geri yayılım Levenberg-Marquardt tekniği kullanılarak eğitilmektedir. Durulaştırma işleminde ağırlıklı ortalama yaklaşımı kullanılmıştır. Her iki modeli de oluşturmak için MATLAB kullanıldı. YSA modeli, hedef ve tahmin değerleri arasında anlamlı bir korelasyon olduğunu gösteren 0,9942 R2 değeriyle kanıtlandığı gibi tahminlerde iyi performans gösterir. YSA modelinin 1,31401 ortalama mutlak yüzde hatası (MAPE) ve 0,00176 kök ortalama kare hatası (RMSE) gibi olağanüstü performans ölçümleri, onun hassasiyetini ve güvenilirliğini göstermektedir. Ancak ANFIS modeli, 0,9570 R2 değeriyle önemli ölçüde daha düşük doğruluk ölçümlerine sahiptir. YSA, RMSE'si 0,00489 ve MAPE'si 2,07495 olan ANFIS modelinden daha fazla doğruluk ve hassasiyet sunar. Bu çalışma, kumaş özellikleri ile pamuklu kumaşın dimi yapısından oluşan giysinin ısı yalıtımı arasındaki karmaşık bağlantıyı ortaya çıkararak tekstil mühendisliği tahmin modelini geliştirecektir.

Anahtar Kelimeler

Isı Yalıtımı,, Yapay Sinir Ağı, uyarlamalı ağ tabanlı bulanık mantık çıkarım sistemi

Kaynakça

1. Das, A. and R. Alagirusamy, Science in clothing comfort. 2010: Woodhead Publishing India Pvt Limited New Delhi.
2. Song, G., Improving comfort in clothing. 2011: Elsevier.
3. Jintu, F. and H.W.K. Tsang, Effect of Clothing Thermal Properties on the Thermal Comfort Sensation During Active Sports. Textile Research Journal, 2008. 78(2): p. 111-118.
4. Ukponmwan, J.O., The Thermal-Insulation Properties of Fabrics. Textile Progress, 1993. 24(4): p. 1-54.
5. Lenhard, R., et al., Verification of the Fanger Model in Real Conditions. MATEC Web of Conferences, 2020. 328.
6. Havenith, G., I. Holmér, and K. Parsons, Personal factors in thermal comfort assessment: clothing properties and metabolic heat production. Energy and Buildings, 2002. 34(6): p. 581-591.
7. Butera, F.M., —Principles of thermal comfort. Renewable and Sustainable Energy Reviews, 1998. 2(1-2): p. 39-66.
8. Oğulata, R.T., The effect of thermal insulation of clothing on human thermal comfort. Fibres & Textiles in Eastern Europe, 2007. 15(2): p. 61.
9. Twill Weaves. 2024 [cited 2024 28 March]; Available from: https://cottonworks.com/en/topics/sourcing-manufacturing/weaving/twill-weaves/.
10. Gokarneshan, N., Fabric structure and design. 1st ed. 2004: New Age International (P) Ltd. 16-21.
11. Belal, S.A., Understanding textiles for a merchandiser. 1st ed. 2009: BMN3 Foundation, Dhaka. 135-144.
12. Damjana Celcar, J.G., Harriet Meinander, Evaluation of Textile Thermal Properties and their Combinations. Tekstilec, 2010. 53: p. 9-32.
13. Shaker, K., et al., Effect of fabric structural design on the thermal properties of woven fabrics. Thermal science, 2019. 23(5 Part B): p. 3059-3066.
14. Ho, C.P., et al., Improving thermal comfort in apparel, in Improving comfort in clothing. 2011, Elsevier. p. 165-181.
15. Halaoua, S., Z. Romdhani, and A. Jemni, Effect of textile woven fabric parameters on its thermal properties. Industria Textila, 2019. 70(1): p. 15-20.
16. Akter, M., et al., Artificial neural network and multiple linear regression modeling for predicting thermal transmittance of plain-woven cotton fabric. Textile Research Journal, 2024. 94(11-12): p. 1279-1296.
17. Mitra, A., et al., Predicting thermal resistance of cotton fabrics by artificial neural network model. Experimental Thermal and Fluid Science, 2013. 50: p. 172-177.
18. Ahmad, S., et al., Effect of weave structure on thermo-physiological properties of cotton fabrics. AUTEX Research Journal, 2015. 15(1): p. 30-34.
19. Majumdar, A., Soft computing in textile engineering. 2010: Elsevier.
20. Subramanian, T.A., K. Ganesh, and S. Bandyopadhyay, 34—a Generalized Equation for Predicting the Lea Strength of Ring-Spun Cotton Yarns. The Journal of The Textile Institute, 2008. 65(6): p. 307-313.
21. Majumdar, A. and A. Ghosh, Yarn Strength Modelling Using Fuzzy Expert System. Journal of Engineered Fibers and Fabrics, 2008. 3(4).
22. BRENT SMITH, B.W., Extending Applicable Ranges of Regression Equations for Yarn Strength Forecasting. Textile Research Journal, 1985.
23. Zurek, W., I. Frydrych, and S. Zakrzewski, A Method of Predicting the Strength and Breaking Strain of Cotton Yarn. 1987. 57(8): p. 439-444.
24. Behera, B. and Y. Goyal, Artificial neural network system for the design of airbag fabrics. Journal of Industrial Textiles, 2009. 39(1): p. 45-55.
25. Behera, B. and B. Karthikeyan, Artificial neural network-embedded expert system for the design of canopy fabrics. Journal of industrial textiles, 2006. 36(2): p. 111-123.
26. Beltran, R., L. Wang, and X. Wang, Predicting worsted spinning performance with an artificial neural network model. Textile research journal, 2004. 74(9): p. 757-763.
27. Chen, Y., T. Zhao, and B. Collier, Prediction of fabric end-use using a neural network technique. Journal of the Textile Institute, 2001. 92(2): p. 157-163.
28. Hu, Z.-H., et al., A hybrid neural network and immune algorithm approach for fit garment design. Textile Research Journal, 2009. 79(14): p. 1319-1330.
29. Hui, P.C., et al., Application of artificial neural networks to the prediction of sewing performance of fabrics. International Journal of Clothing Science and Technology, 2007. 19(5): p. 291-318.
30. Jeong, S.H., J.H. Kim, and C.J. Hong, Selecting optimal interlinings with a neural network. Textile Research Journal, 2000. 70(11): p. 1005-1010.
31. Kang, T.J. and S.C. Kim, Objective evaluation of the trash and color of raw cotton by image processing and neural network. Textile Research Journal, 2002. 72(9): p. 776-782.
32. Khan, Z., et al., An artificial neural network-based hairiness prediction model for worsted wool yarns. Textile Research Journal, 2009. 79(8): p. 714-720.
33. Kuo, C.-F.J., K.-I. Hsiao, and Y.-S. Wu, Using neural network theory to predict the properties of melt spun fibers. Textile Research Journal, 2004. 74(9): p. 840-843.
34. Lin, J.-J., Prediction of yarn shrinkage using neural nets. Textile Research Journal, 2007. 77(5): p. 336-342.
35. Murrells, C.M., et al., An artificial neural network model for the prediction of spirality of fully relaxed single jersey fabrics. Textile Research Journal, 2009. 79(3): p. 227-234.
36. She, F.H., et al., Intelligent animal fiber classification with artificial neural networks. Textile research journal, 2002. 72(7): p. 594-600.
37. Wong, A., et al., Neural network predictions of human psychological perceptions of clothing sensory comfort. Textile research journal, 2003. 73(1): p. 31-37.
38. Yao, G., J. Guo, and Y. Zhou, Predicting the warp breakage rate in weaving by neural network techniques. Textile Research Journal, 2005. 75(3): p. 274-278.
39. Zeng, Y.-C., K. Wang, and C. Yu, Predicting the tensile properties of air-jet spun yarns. Textile Research Journal, 2004. 74(8): p. 689-694.
40. Akankwasa, N.T. and D. Veit, Advances in Modeling and Simulation in Textile Engineering: New Concepts, Methods, and Applications. 2021: Elsevier Science.
41. Das, P.P. and S. Chakraborty, Adaptive neuro-fuzzy inference system-based modelling of cotton yarn properties. Journal of The Institution of Engineers (India): Series E, 2021. 102(2): p. 257-272.
42. Hadizadeh, M., M. Amani Tehran, and A.A. Jeddi, Application of an adaptive neuro-fuzzy system for prediction of initial load—extension behavior of plain-woven fabrics. Textile Research Journal, 2010. 80(10): p. 981-990.
43. Behera, B. and R. Guruprasad, Predicting bending rigidity of woven fabrics using adaptive neuro-fuzzy inference system (ANFIS). Journal of The Textile Institute, 2012. 103(11): p. 1205-1212.
44. Sarkar, J., et al., Comparison Of Anfis And Ann Modeling For Predicting The Water Absorption Behavior Of Polyurethane Treated Polyester Fabric. Heliyon, 2021. 7(9): p. e08000-e08000.
45. Hussain, T., et al., Comparison of artificial neural network and adaptive neuro-fuzzy inference system for predicting the wrinkle recovery of woven fabrics. The Journal of the Textile Institute, 2015. 106(9): p. 934-938.
46. Yu, H. and B.M. Wilamowski, Levenberg–marquardt training, in Intelligent systems. 2018, CRC Press. p. 12-1-12-16.
47. Singh, H. and Y.A. Lone, Deep neuro-fuzzy systems with python. Apress, Berkeley, 2020.
48. Suparta, W. and K.M. Alhasa, Adaptive Neuro-Fuzzy Interference System, in Modeling of Tropospheric Delays Using ANFIS. 2016, Springer International Publishing: Cham. p. 5-18.
49. Lewis, C.D., Industrial and Business Forecasting Methods: A Practical Guide to Exponential Smoothing and Curve Fitting. 1982: Butterworth Scientific.
50. Montgomery, D.C. and G.C. Runger, Applied statistics and probability for engineers. 2010: John wiley & sons.

Toplam 50 adet kaynakça vardır.

Ayrıntılar

Birincil Dil	İngilizce
Konular	Tekstil Teknolojisi, Tekstil Bilimleri ve Mühendisliği (Diğer)
Bölüm	Makaleler
Yazarlar	Mahmuda Akter 0000-0001-6730-3960 Elias Khalil 0000-0001-7856-0866 Shah Md. Maruf Hasan 0009-0005-8601-9523 Md. Kamrul Hassan Chowdhury 0009-0001-4596-938X
Yayımlanma Tarihi	30 Haziran 2025
Gönderilme Tarihi	18 Kasım 2024
Kabul Tarihi	4 Haziran 2025
Yayımlandığı Sayı	Yıl 2025 Cilt: 32 Sayı: 138

Kaynak Göster

APA	Akter, M., Khalil, E., Hasan, S. M. M., Chowdhury, M. K. H. (2025). PREDICTING THE THERMAL INSULATION PROPERTIES OF TWILL WOVEN COTTON FABRIC BY USING ANN AND ANFIS. Tekstil Ve Mühendis, 32(138), 128-145. https://doi.org/10.7216/teksmuh.1587503
AMA	Akter M, Khalil E, Hasan SMM, Chowdhury MKH. PREDICTING THE THERMAL INSULATION PROPERTIES OF TWILL WOVEN COTTON FABRIC BY USING ANN AND ANFIS. Tekstil ve Mühendis. Haziran 2025;32(138):128-145. doi:10.7216/teksmuh.1587503
Chicago	Akter, Mahmuda, Elias Khalil, Shah Md. Maruf Hasan, ve Md. Kamrul Hassan Chowdhury. “PREDICTING THE THERMAL INSULATION PROPERTIES OF TWILL WOVEN COTTON FABRIC BY USING ANN AND ANFIS”. Tekstil Ve Mühendis 32, sy. 138 (Haziran 2025): 128-45. https://doi.org/10.7216/teksmuh.1587503.
EndNote	Akter M, Khalil E, Hasan SMM, Chowdhury MKH (01 Haziran 2025) PREDICTING THE THERMAL INSULATION PROPERTIES OF TWILL WOVEN COTTON FABRIC BY USING ANN AND ANFIS. Tekstil ve Mühendis 32 138 128–145.
IEEE	M. Akter, E. Khalil, S. M. M. Hasan, ve M. K. H. Chowdhury, “PREDICTING THE THERMAL INSULATION PROPERTIES OF TWILL WOVEN COTTON FABRIC BY USING ANN AND ANFIS”, Tekstil ve Mühendis, c. 32, sy. 138, ss. 128–145, 2025, doi: 10.7216/teksmuh.1587503.
ISNAD	Akter, Mahmuda vd. “PREDICTING THE THERMAL INSULATION PROPERTIES OF TWILL WOVEN COTTON FABRIC BY USING ANN AND ANFIS”. Tekstil ve Mühendis 32/138 (Haziran 2025), 128-145. https://doi.org/10.7216/teksmuh.1587503.
JAMA	Akter M, Khalil E, Hasan SMM, Chowdhury MKH. PREDICTING THE THERMAL INSULATION PROPERTIES OF TWILL WOVEN COTTON FABRIC BY USING ANN AND ANFIS. Tekstil ve Mühendis. 2025;32:128–145.
MLA	Akter, Mahmuda vd. “PREDICTING THE THERMAL INSULATION PROPERTIES OF TWILL WOVEN COTTON FABRIC BY USING ANN AND ANFIS”. Tekstil Ve Mühendis, c. 32, sy. 138, 2025, ss. 128-45, doi:10.7216/teksmuh.1587503.
Vancouver	Akter M, Khalil E, Hasan SMM, Chowdhury MKH. PREDICTING THE THERMAL INSULATION PROPERTIES OF TWILL WOVEN COTTON FABRIC BY USING ANN AND ANFIS. Tekstil ve Mühendis. 2025;32(138):128-45.

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