Research Article
BibTex RIS Cite

INFRARED THERMOGRAPHY IMAGE BASED CLASSIFICATION OF SOIL DIRT AND FABRIC

Year 2023, Volume: 7 Issue: 3, 441 - 455, 31.12.2023

Mehmet Deniz Mine Seçkin Çetin Gencer Durmuş Koç

https://doi.org/10.46519/ij3dptdi.1339049

Abstract

Soil is the substance most likely to meet nature and dirt people, vehicles, and clothing, especially in outdoor. Both source material and soil samples can be damaged during industrial and criminal investigations. Therefore, there is a need for detection, examination, and identification systems that can minimize contact with forensic evidence and provide accurate results with fewer samples. The study aims to determine the type of soil using a low-cost, easily accessible, and highly sensitive system that can be used easily without interference from the surface properties of the textile or destruction of the structure of the dirt. The working sites and areas of samples to be collected were determined according to the purpose of the study. In this context, samples of the most common soil types were taken from the lands in the Aegean Region of Turkey. Different types of substances were applied and dirtying on the collected samples. The newly formed samples were heated with a heating surface and allowed to cool. During this process, a thermal video was recorded, and feature extraction was performed. 165 samples were obtained from 55 tests. As a result, it is seen that the proposed method can detect samples with 97% accuracy.

Keywords

Thermal Image Fabric Non-Destructive Testing Material Soil Machine Learning

References

  • 1. Tansan, B., Gökbulut, A., Targotay, Ç., Eren, T., "Industry 4.0 in Turkey as an Imperative for Global Competitiveness Report", https://tusiad.org/tr/yayinlar/raporlar/item/download/7848_180faab86b5ec60d04ec929643ce6e45, September 26, 2016.
  • 2. Küsters, D., Praß, N., Gloy, Y.S., "Textile learning factory 4.0–preparing Germany’s textile industry for the digital future", Procedia Manufacturing, Vol. 9, Pages 214–221, 2017.
  • 3. Seçkin, M., Seçkin, A.Ç., Coşkun, A., "Production Fault Simulation and Forecasting from Time Series Data with Machine Learning in Glove Textile Industry", Journal of Engineered Fibers and Fabrics, Vol. 14, Pages 1-12, 2019.
  • 4. Chang, R.I., Lee, C.Y., Hung, Y.H., "Cloud-based analytics module for predictive maintenance of the textile manufacturing process", Applied Sciences, Vol. 11, Issue 21, Pages 1-22, 2021.
  • 5. Shamsuzzaman, M, Mashud, M., Rahman, M. M., Rahman, M.M., Hoq, E., Das, D., "Management and maintenance of textile machinery", Rahman, M. M., Mashud, M., Rahman, M.M. et al editors, Advanced technology in textiles, Pages 31-63, Springer Nature, Singapore, 2023.
  • 6. Bandara, P., Bandara, T., Ranatunga, T., Vimarshana, V., Sooriyaarachchi, S., Silva, C.D., "Automated fabric defect detection", 2018 18th International Conference on Advances in ICT for Emerging Regions (ICTer), Papes 119–125, Sri Lanka, 2018.
  • 7. Li, C., Li, J., Li, Y., He, L., Fu, X., Chen, J., "Fabric defect detection in textile manufacturing: a survey of the state of the art", Security and Communication Networks, Vol. 2021, Pages 1-13, 2021.
  • 8. Seçkin, A.Ç., Seçkin, M., "Detection of Fabric Defects with Intertwined Frame Vector Feature Extraction", Alexandria Engineering Journal, Vol. 61, Issue 4, Pages 2887-2898, 2022.
  • 9. Morison, R., “Improving the forensic value of textiles and fibres through the holistic detection and analysis of acquired characteristics due to environmental factors”, PhD Thesis, University of Technology, Sydney, 2019.
  • 10. Hofmann, M., Adamec, J., Anslinger, K., Bayer, B., Graw, M., Peschel, O., Schulz, M.M., “Detectability of bloodstains after machine washing”, International Journal of Legal Medicine, Vol. 133, Issue 1, Pages 3–16, 2019.
  • 11. Murray, K.R., Fitzpatrick, R.W., Bottrill, R.S., Berry, R., Kobus, H., “Soil transference patterns on bras: image processing and laboratory dragging experiments”, Forensic Science International, Vol. 258, Issue 2016, Pages 88–100, 2016.
  • 12. Murray, K., Fitzpatrick, R., Bottrill, R., Kobus, H., “Soil transference patterns on clothing fabrics and plastic buttons: image processing and laboratory dragging experiments”, Forensic Science and Criminology, Vol. 2, Issue 1, Pages 1–12, 2016.
  • 13. Murray, K.R., Fitzpatrick, R.W., Bottrill, R., Kobus, H., “An investigation of the pattern formed by soil transfer when clothing fabrics are placed on soil using visual examination and image processing analysis”, Forensic Science and Criminology, Vol. 2, Issue 1, Pages, 1–11, 2017.
  • 14. Murray, K.R., Fitzpatrick, R.W., Bottrill, R., Kobus, H., “Patterns produced when soil is transferred to bras by placing and dragging actions: the application of digital photography and image processing to support visible observations”, Forensic Science International, Vol. 276, Issue 2017, Pages 24–40, 2017.
  • 15. Arthur, R.M., Humburg, P.J., Hoogenboom, J., Baiker, M., Taylor, M.C., de Bruin, K.G., “An image-processing methodology for extracting bloodstain pattern features”, Forensic Science International, Vol. 277, Pages 122–132, 2017.
  • 16. Macarthur, S., Hemmings, F. J., “Fibres, yarns and fabrics: an introduction to production, structure and properties”, Robertson, J., Roux, C., Wiggins, K.G. et al editors, “Forensic examination of fibres third edition”, Pages 1-59, CRC Press, London, 2017.
  • 17. Fitzpatrick, R.W., Raven, M.D., “The forensic comparison of trace amounts of soil on a pyjama top with hypersulfidic subaqueous soil from a river as evidence in a homicide cold case”, Fitzpatrick, R.W., Raven, M.D. et al editors, Forensic soil science and geology, Pages 197-218, The geological society, London, 2019.
  • 18. Kern, S.E., Crowe, J.B., Litzau, J.J., Heitkemper, D.T., “Forensic analysis of stains on fabric using direct analysis in real-time ionization with high-resolution accurate mass-mass spectrometry”, Journal of Forensic Sciences, Vol. 63, Issue 2, Pages 592–597, 2018.
  • 19. Pirrie, D., Dawson, L., Graham, G., “Predictive geolocation: forensic soil analysis for provenance determination”, Episodes Journal of International Geoscience, Vol. 40, Issue 2, Pages 141–147, 2017.
  • 20. Chang, W.-T., Chen, T.-H., Yu, C.-C., Kau, J.-Y., “Comparison of embedding methods used in examining cross-sections of automotive paints with micro-fourier transform infrared spectroscopy”, Forensic Science Journal, Vol. 1, Issue 1, Pages 55–60, 2002.
  • 21. Lewis, P.R., Reynolds, K., Gagg, C., “Forensic materials engineering: case studies”, Pages 1-429, CRC Press, Boca Raton, 2003.
  • 22. Edelman, G.J., Hoveling, R.J.M., Roos, M., Leeuwen, T.G. van, Aalders, M.C.G., “Infrared imaging of the crime scene: possibilities and pitfalls”, Journal of Forensic Sciences, Vol. 58, Issue 5, Pages 1156–1162, 2013.
  • 23. Ammer, K., Ring, E.F.J., “Application of thermal imaging in forensic medicine”, The Imaging Science Journal, Vol. 53, Issue 3, Pages 125-131, 2005.
  • 24. Faltaous, S., Liebers, J., Abdelrahman, Y., Alt, F., Schneegass, S., “VPID: towards vein pattern identification using thermal imaging”, i-com, Vol. 18, Issue 3, Pages 259–270, 2019.
  • 25. Brooke, H., Baranowski, M.R., McCutcheon, J.N., Morgan, S.L., Myrick, M.L., “Multimode imaging in the thermal infrared for chemical contrast enhancement. part 3: visualizing blood on fabrics”, Analytical Chemistry, Vol. 82, Issue 20, Pages 8427–8431, 2010.
  • 26. Brooke, H., Baranowski, M.R., McCutcheon, J.N., Morgan, S.L., Myrick, M.L., “Multimode imaging in the thermal infrared for chemical contrast enhancement. part 1: methodolgy”, analytical chemistry, Vol. 82, Issue 20, Pages 8412–8420, 2010.
  • 27. Liu, J., Wang, C., Su, H., Du, B., Tao, D., "Multistage GAN for fabric defect detection", IEEE Transactions on Image Processing, Vol. 29, Pages 3388–3400, 2019.
  • 28. Yildiz, K., Buldu, A., Demetgul, M., Yildiz, Z., "A novel thermal-based fabric defect detection technique", The Journal of The Textile Institute, Vol. 106, Issue 3, Pages 275–283, 2015.
  • 29. Hamdi, A.A., Fouad, M.M., Sayed, M.S., Hadhoud, M.M., "Patterned fabric defect detection system using near infrared imaging", 2017 Eighth International Conference on Intelligent Computing and Information Systems (ICICIS), Pages 111–117, Cairo, 2017.
  • 30. Scott, I.G., Scala, C.M., "A review of non-destructive testing of composite materials", NDT International, Vol. 15, Issue 2, Pages 75–86, 1982.
  • 31. Raj, B., Jayakumar, T., Thavasimuthu, M., "Practical non-destructive testing", Pages 1-184, Woodhead Publishing, Cambridge, 2002.
  • 32. Qu, Z., Jiang, P., Zhang, W., "Development and application of infrared thermography non-destructive testing techniques", Sensors, Vol. 20, Issue 14, Pages 1-26, 2020.
  • 33. Zhao, Z., "Review of non-destructive testing methods for defect detection of ceramics", Ceramics International, Vol. 47, Issue 4, Pages 4389–4397, 2021.
  • 34. Beveridge, A., “Forensic investigation of explosions”, Pages 1-512, CRC Press, London, 1998.
  • 35. Klastersky, J., Schimpff, J., Senn H.-J., “Collection of evidence”, 35. Klastersky, J., Schimpff, J., Senn H.-J. et al editors, Practical homicide investigation: tactics, procedures, and forensic techniques, fourth edition, Pages 571-623, CRC Press, New York, 2006.
  • 36. Casey, E., “Handbook of digital forensics and investigation”, Pages 1-559, Elsevier Academic Press, Burlington, 2009.
  • 37. Kobilinsky, L.F., “Forensic chemistry handbook", Pages 1-501, A Jonh Wiley & Sons, Inc., New Jersey, 2012.
  • 38. Thorp, J., Smith, G.D., “Higher categories of soil classification: order, suborder, and great soil groups”, Soil Sciences, Vol. 67, Issue 2, Pages 117–126, 1949.
  • 39. Dizdar, M.Y., “Türkiye’nin toprak kaynakları”, Pages 1-317, TMMOB Ziraat Odası Mühendisleri Odası Teknik Yayınlar Dizisi, Ankara, 2003.
  • 40. Visioli, A., “Practical PID control”, Pages 1-309, Springer, London, 2006.
  • 41. Reprap, "RAMPS 1.4", https://reprap.org/wiki/RAMPS_1.4, March 18, 2020.
  • 42. Altman, N.S., “An introduction to kernel and nearest-neighbor nonparametric regression”, The American Statistician, Vol. 46, Issue 3, Pages 175–185, 1992.
  • 43. Quinlan, J.R., “Induction of decision trees”, Machine Learning, Vol. 1, Pages 81–106, 1986.
  • 44. Breiman, L., “Random forests”, Machine Learning, Vol. 45, Pages 5–32, 2001. 45. Liaw, A., Wiener, M., “Classification and regression by random forest”, R News, Vol. 2, Issue 3, Pages 18–22, 2002.
  • 46. Bertoni, A., Campadelli, P., Parodi, M. A, “Boosting algorithm for regression”, ICANN 1997: International Conference on Artificial Neural Networks, Pages 343–348, Berlin, 1997.
  • 47. Freund, Y., Schapire, R.E., “Experiments with a new boosting algorithm”, Machine Learning: Proceedings of the Thirteenth International Conference (ICML’96), Pages 148–156, Bari, 1996.
  • 48. Kohavi, R., “A study of cross-validation and bootstrap for accuracy estimation and model selection”, Appears in the International Joint Conference on Artificial Intelligence (IJCAI), Pages 1137–1145, Montreal, 1995.
  • 49. James, G., Witten, D., Hastie, T., Tibshirani, R., “An introduction to statistical learning with applications in r”, Pages 15-419, Springer, New York, 2013.
  • 50. Alpaydin, E., “Introduction to machine learning”, Pages 1-407, The MIT Press, Cambridge, 2004.
  • 51. Korkmaz, A., Büyükgöze, S., “Sahte web sitelerinin sınıflandırma algoritmaları ile tespit edilmesi” [Detection of Fake Websites by Classification Algorithms] [article in Turkish], Avrupa Bilim ve Teknoloji Dergisi, Vol. 16, Papes 826–833, 2019.
  • 52. Luque, A., Carrasco, A., Martín, A., de las Heras, A., “The impact of class imbalance in classification performance metrics based on the binary confusion matrix”, Pattern Recognition, Vol. 91, Issue C, Pages 216–231, 2019.
  • 53. Kira, K., Rendell, L.A., “The feature selection problem: traditional methods and a new algorithm”, AAAI-92, Pages 129–134, California, 1992.
  • 54. Kira, K., Rendell, L.A., “A practical approach to feature selection”, Machine Learning 1992, Pages 249–256, Aberdeen, 1992.
  • 55. Kononenko, I., Šimec, E., Robnik-Šikonja, M., “Overcoming the myopia of inductive learning algorithms with relieff”, Applied Intelligence, Vol. 7, Pages 39–55, 1997.
  • 56. Hock, C.W., Sookne, A.W., Harris, M., “Thermal properties of moist fabrics”, Textile Research, Vol. 14, Issue 5, Pages 144–149, 1944.

Year 2023, Volume: 7 Issue: 3, 441 - 455, 31.12.2023

Mehmet Deniz Mine Seçkin Çetin Gencer Durmuş Koç

https://doi.org/10.46519/ij3dptdi.1339049

Abstract

References

  • 1. Tansan, B., Gökbulut, A., Targotay, Ç., Eren, T., "Industry 4.0 in Turkey as an Imperative for Global Competitiveness Report", https://tusiad.org/tr/yayinlar/raporlar/item/download/7848_180faab86b5ec60d04ec929643ce6e45, September 26, 2016.
  • 2. Küsters, D., Praß, N., Gloy, Y.S., "Textile learning factory 4.0–preparing Germany’s textile industry for the digital future", Procedia Manufacturing, Vol. 9, Pages 214–221, 2017.
  • 3. Seçkin, M., Seçkin, A.Ç., Coşkun, A., "Production Fault Simulation and Forecasting from Time Series Data with Machine Learning in Glove Textile Industry", Journal of Engineered Fibers and Fabrics, Vol. 14, Pages 1-12, 2019.
  • 4. Chang, R.I., Lee, C.Y., Hung, Y.H., "Cloud-based analytics module for predictive maintenance of the textile manufacturing process", Applied Sciences, Vol. 11, Issue 21, Pages 1-22, 2021.
  • 5. Shamsuzzaman, M, Mashud, M., Rahman, M. M., Rahman, M.M., Hoq, E., Das, D., "Management and maintenance of textile machinery", Rahman, M. M., Mashud, M., Rahman, M.M. et al editors, Advanced technology in textiles, Pages 31-63, Springer Nature, Singapore, 2023.
  • 6. Bandara, P., Bandara, T., Ranatunga, T., Vimarshana, V., Sooriyaarachchi, S., Silva, C.D., "Automated fabric defect detection", 2018 18th International Conference on Advances in ICT for Emerging Regions (ICTer), Papes 119–125, Sri Lanka, 2018.
  • 7. Li, C., Li, J., Li, Y., He, L., Fu, X., Chen, J., "Fabric defect detection in textile manufacturing: a survey of the state of the art", Security and Communication Networks, Vol. 2021, Pages 1-13, 2021.
  • 8. Seçkin, A.Ç., Seçkin, M., "Detection of Fabric Defects with Intertwined Frame Vector Feature Extraction", Alexandria Engineering Journal, Vol. 61, Issue 4, Pages 2887-2898, 2022.
  • 9. Morison, R., “Improving the forensic value of textiles and fibres through the holistic detection and analysis of acquired characteristics due to environmental factors”, PhD Thesis, University of Technology, Sydney, 2019.
  • 10. Hofmann, M., Adamec, J., Anslinger, K., Bayer, B., Graw, M., Peschel, O., Schulz, M.M., “Detectability of bloodstains after machine washing”, International Journal of Legal Medicine, Vol. 133, Issue 1, Pages 3–16, 2019.
  • 11. Murray, K.R., Fitzpatrick, R.W., Bottrill, R.S., Berry, R., Kobus, H., “Soil transference patterns on bras: image processing and laboratory dragging experiments”, Forensic Science International, Vol. 258, Issue 2016, Pages 88–100, 2016.
  • 12. Murray, K., Fitzpatrick, R., Bottrill, R., Kobus, H., “Soil transference patterns on clothing fabrics and plastic buttons: image processing and laboratory dragging experiments”, Forensic Science and Criminology, Vol. 2, Issue 1, Pages 1–12, 2016.
  • 13. Murray, K.R., Fitzpatrick, R.W., Bottrill, R., Kobus, H., “An investigation of the pattern formed by soil transfer when clothing fabrics are placed on soil using visual examination and image processing analysis”, Forensic Science and Criminology, Vol. 2, Issue 1, Pages, 1–11, 2017.
  • 14. Murray, K.R., Fitzpatrick, R.W., Bottrill, R., Kobus, H., “Patterns produced when soil is transferred to bras by placing and dragging actions: the application of digital photography and image processing to support visible observations”, Forensic Science International, Vol. 276, Issue 2017, Pages 24–40, 2017.
  • 15. Arthur, R.M., Humburg, P.J., Hoogenboom, J., Baiker, M., Taylor, M.C., de Bruin, K.G., “An image-processing methodology for extracting bloodstain pattern features”, Forensic Science International, Vol. 277, Pages 122–132, 2017.
  • 16. Macarthur, S., Hemmings, F. J., “Fibres, yarns and fabrics: an introduction to production, structure and properties”, Robertson, J., Roux, C., Wiggins, K.G. et al editors, “Forensic examination of fibres third edition”, Pages 1-59, CRC Press, London, 2017.
  • 17. Fitzpatrick, R.W., Raven, M.D., “The forensic comparison of trace amounts of soil on a pyjama top with hypersulfidic subaqueous soil from a river as evidence in a homicide cold case”, Fitzpatrick, R.W., Raven, M.D. et al editors, Forensic soil science and geology, Pages 197-218, The geological society, London, 2019.
  • 18. Kern, S.E., Crowe, J.B., Litzau, J.J., Heitkemper, D.T., “Forensic analysis of stains on fabric using direct analysis in real-time ionization with high-resolution accurate mass-mass spectrometry”, Journal of Forensic Sciences, Vol. 63, Issue 2, Pages 592–597, 2018.
  • 19. Pirrie, D., Dawson, L., Graham, G., “Predictive geolocation: forensic soil analysis for provenance determination”, Episodes Journal of International Geoscience, Vol. 40, Issue 2, Pages 141–147, 2017.
  • 20. Chang, W.-T., Chen, T.-H., Yu, C.-C., Kau, J.-Y., “Comparison of embedding methods used in examining cross-sections of automotive paints with micro-fourier transform infrared spectroscopy”, Forensic Science Journal, Vol. 1, Issue 1, Pages 55–60, 2002.
  • 21. Lewis, P.R., Reynolds, K., Gagg, C., “Forensic materials engineering: case studies”, Pages 1-429, CRC Press, Boca Raton, 2003.
  • 22. Edelman, G.J., Hoveling, R.J.M., Roos, M., Leeuwen, T.G. van, Aalders, M.C.G., “Infrared imaging of the crime scene: possibilities and pitfalls”, Journal of Forensic Sciences, Vol. 58, Issue 5, Pages 1156–1162, 2013.
  • 23. Ammer, K., Ring, E.F.J., “Application of thermal imaging in forensic medicine”, The Imaging Science Journal, Vol. 53, Issue 3, Pages 125-131, 2005.
  • 24. Faltaous, S., Liebers, J., Abdelrahman, Y., Alt, F., Schneegass, S., “VPID: towards vein pattern identification using thermal imaging”, i-com, Vol. 18, Issue 3, Pages 259–270, 2019.
  • 25. Brooke, H., Baranowski, M.R., McCutcheon, J.N., Morgan, S.L., Myrick, M.L., “Multimode imaging in the thermal infrared for chemical contrast enhancement. part 3: visualizing blood on fabrics”, Analytical Chemistry, Vol. 82, Issue 20, Pages 8427–8431, 2010.
  • 26. Brooke, H., Baranowski, M.R., McCutcheon, J.N., Morgan, S.L., Myrick, M.L., “Multimode imaging in the thermal infrared for chemical contrast enhancement. part 1: methodolgy”, analytical chemistry, Vol. 82, Issue 20, Pages 8412–8420, 2010.
  • 27. Liu, J., Wang, C., Su, H., Du, B., Tao, D., "Multistage GAN for fabric defect detection", IEEE Transactions on Image Processing, Vol. 29, Pages 3388–3400, 2019.
  • 28. Yildiz, K., Buldu, A., Demetgul, M., Yildiz, Z., "A novel thermal-based fabric defect detection technique", The Journal of The Textile Institute, Vol. 106, Issue 3, Pages 275–283, 2015.
  • 29. Hamdi, A.A., Fouad, M.M., Sayed, M.S., Hadhoud, M.M., "Patterned fabric defect detection system using near infrared imaging", 2017 Eighth International Conference on Intelligent Computing and Information Systems (ICICIS), Pages 111–117, Cairo, 2017.
  • 30. Scott, I.G., Scala, C.M., "A review of non-destructive testing of composite materials", NDT International, Vol. 15, Issue 2, Pages 75–86, 1982.
  • 31. Raj, B., Jayakumar, T., Thavasimuthu, M., "Practical non-destructive testing", Pages 1-184, Woodhead Publishing, Cambridge, 2002.
  • 32. Qu, Z., Jiang, P., Zhang, W., "Development and application of infrared thermography non-destructive testing techniques", Sensors, Vol. 20, Issue 14, Pages 1-26, 2020.
  • 33. Zhao, Z., "Review of non-destructive testing methods for defect detection of ceramics", Ceramics International, Vol. 47, Issue 4, Pages 4389–4397, 2021.
  • 34. Beveridge, A., “Forensic investigation of explosions”, Pages 1-512, CRC Press, London, 1998.
  • 35. Klastersky, J., Schimpff, J., Senn H.-J., “Collection of evidence”, 35. Klastersky, J., Schimpff, J., Senn H.-J. et al editors, Practical homicide investigation: tactics, procedures, and forensic techniques, fourth edition, Pages 571-623, CRC Press, New York, 2006.
  • 36. Casey, E., “Handbook of digital forensics and investigation”, Pages 1-559, Elsevier Academic Press, Burlington, 2009.
  • 37. Kobilinsky, L.F., “Forensic chemistry handbook", Pages 1-501, A Jonh Wiley & Sons, Inc., New Jersey, 2012.
  • 38. Thorp, J., Smith, G.D., “Higher categories of soil classification: order, suborder, and great soil groups”, Soil Sciences, Vol. 67, Issue 2, Pages 117–126, 1949.
  • 39. Dizdar, M.Y., “Türkiye’nin toprak kaynakları”, Pages 1-317, TMMOB Ziraat Odası Mühendisleri Odası Teknik Yayınlar Dizisi, Ankara, 2003.
  • 40. Visioli, A., “Practical PID control”, Pages 1-309, Springer, London, 2006.
  • 41. Reprap, "RAMPS 1.4", https://reprap.org/wiki/RAMPS_1.4, March 18, 2020.
  • 42. Altman, N.S., “An introduction to kernel and nearest-neighbor nonparametric regression”, The American Statistician, Vol. 46, Issue 3, Pages 175–185, 1992.
  • 43. Quinlan, J.R., “Induction of decision trees”, Machine Learning, Vol. 1, Pages 81–106, 1986.
  • 44. Breiman, L., “Random forests”, Machine Learning, Vol. 45, Pages 5–32, 2001. 45. Liaw, A., Wiener, M., “Classification and regression by random forest”, R News, Vol. 2, Issue 3, Pages 18–22, 2002.
  • 46. Bertoni, A., Campadelli, P., Parodi, M. A, “Boosting algorithm for regression”, ICANN 1997: International Conference on Artificial Neural Networks, Pages 343–348, Berlin, 1997.
  • 47. Freund, Y., Schapire, R.E., “Experiments with a new boosting algorithm”, Machine Learning: Proceedings of the Thirteenth International Conference (ICML’96), Pages 148–156, Bari, 1996.
  • 48. Kohavi, R., “A study of cross-validation and bootstrap for accuracy estimation and model selection”, Appears in the International Joint Conference on Artificial Intelligence (IJCAI), Pages 1137–1145, Montreal, 1995.
  • 49. James, G., Witten, D., Hastie, T., Tibshirani, R., “An introduction to statistical learning with applications in r”, Pages 15-419, Springer, New York, 2013.
  • 50. Alpaydin, E., “Introduction to machine learning”, Pages 1-407, The MIT Press, Cambridge, 2004.
  • 51. Korkmaz, A., Büyükgöze, S., “Sahte web sitelerinin sınıflandırma algoritmaları ile tespit edilmesi” [Detection of Fake Websites by Classification Algorithms] [article in Turkish], Avrupa Bilim ve Teknoloji Dergisi, Vol. 16, Papes 826–833, 2019.
  • 52. Luque, A., Carrasco, A., Martín, A., de las Heras, A., “The impact of class imbalance in classification performance metrics based on the binary confusion matrix”, Pattern Recognition, Vol. 91, Issue C, Pages 216–231, 2019.
  • 53. Kira, K., Rendell, L.A., “The feature selection problem: traditional methods and a new algorithm”, AAAI-92, Pages 129–134, California, 1992.
  • 54. Kira, K., Rendell, L.A., “A practical approach to feature selection”, Machine Learning 1992, Pages 249–256, Aberdeen, 1992.
  • 55. Kononenko, I., Šimec, E., Robnik-Šikonja, M., “Overcoming the myopia of inductive learning algorithms with relieff”, Applied Intelligence, Vol. 7, Pages 39–55, 1997.
  • 56. Hock, C.W., Sookne, A.W., Harris, M., “Thermal properties of moist fabrics”, Textile Research, Vol. 14, Issue 5, Pages 144–149, 1944.
There are 55 citations in total.

Details

Primary Language English
Subjects Software Engineering (Other)
Journal Section Research Article
Authors

Mehmet Deniz 0000-0002-7696-045X

Mine Seçkin 0000-0002-9564-1534

Çetin Gencer 0000-0002-1716-0516

Durmuş Koç 0000-0001-8719-444X

Early Pub Date December 25, 2023
Publication Date December 31, 2023
Submission Date August 7, 2023
Published in Issue Year 2023 Volume: 7 Issue: 3

Cite

APA Deniz, M., Seçkin, M., Gencer, Ç., Koç, D. (2023). INFRARED THERMOGRAPHY IMAGE BASED CLASSIFICATION OF SOIL DIRT AND FABRIC. International Journal of 3D Printing Technologies and Digital Industry, 7(3), 441-455. https://doi.org/10.46519/ij3dptdi.1339049
AMA Deniz M, Seçkin M, Gencer Ç, Koç D. INFRARED THERMOGRAPHY IMAGE BASED CLASSIFICATION OF SOIL DIRT AND FABRIC. IJ3DPTDI. December 2023;7(3):441-455. doi:10.46519/ij3dptdi.1339049
Chicago Deniz, Mehmet, Mine Seçkin, Çetin Gencer, and Durmuş Koç. “INFRARED THERMOGRAPHY IMAGE BASED CLASSIFICATION OF SOIL DIRT AND FABRIC”. International Journal of 3D Printing Technologies and Digital Industry 7, no. 3 (December 2023): 441-55. https://doi.org/10.46519/ij3dptdi.1339049.
EndNote Deniz M, Seçkin M, Gencer Ç, Koç D (December 1, 2023) INFRARED THERMOGRAPHY IMAGE BASED CLASSIFICATION OF SOIL DIRT AND FABRIC. International Journal of 3D Printing Technologies and Digital Industry 7 3 441–455.
IEEE M. Deniz, M. Seçkin, Ç. Gencer, and D. Koç, “INFRARED THERMOGRAPHY IMAGE BASED CLASSIFICATION OF SOIL DIRT AND FABRIC”, IJ3DPTDI, vol. 7, no. 3, pp. 441–455, 2023, doi: 10.46519/ij3dptdi.1339049.
ISNAD Deniz, Mehmet et al. “INFRARED THERMOGRAPHY IMAGE BASED CLASSIFICATION OF SOIL DIRT AND FABRIC”. International Journal of 3D Printing Technologies and Digital Industry 7/3 (December 2023), 441-455. https://doi.org/10.46519/ij3dptdi.1339049.
JAMA Deniz M, Seçkin M, Gencer Ç, Koç D. INFRARED THERMOGRAPHY IMAGE BASED CLASSIFICATION OF SOIL DIRT AND FABRIC. IJ3DPTDI. 2023;7:441–455.
MLA Deniz, Mehmet et al. “INFRARED THERMOGRAPHY IMAGE BASED CLASSIFICATION OF SOIL DIRT AND FABRIC”. International Journal of 3D Printing Technologies and Digital Industry, vol. 7, no. 3, 2023, pp. 441-55, doi:10.46519/ij3dptdi.1339049.
Vancouver Deniz M, Seçkin M, Gencer Ç, Koç D. INFRARED THERMOGRAPHY IMAGE BASED CLASSIFICATION OF SOIL DIRT AND FABRIC. IJ3DPTDI. 2023;7(3):441-55.
 Download Cover Image

download

International Journal of 3D Printing Technologies and Digital Industry is lisenced under Creative Commons Atıf-GayriTicari 4.0 Uluslararası Lisansı