Research Article
Gupta, R. R., Evidential proof as questioned document examination report for criminal cases, IP International Journal of Forensic Medicine and Toxicological Sciences 5:90-94 (2020).
10.18231/j.ijfmts.2020.021Grant, J., The role of paper in questioned document work, Journal of the Forensic Science Society 13(2):91-95 (1973).
10.1016/S0015-7368(73)70774-X4760120Schlesinger, H. L. and Settle, D. M., A large-scale study of paper by neutron activation analysis, Journal of Forensic Sciences 16(3):309-330 (1971).
Ellen, D., Day, S., and Davies, C., Scientific examination of documents: methods and techniques, CRC Press (2018).
10.4324/9780429491917Bisesi, M. S., Kelly, J. S., and Lindblom, B. S., Section XI: ASTM Guidelines for Forensic Document Examination, In Scientific Examination of Questioned Documents, CRC Press, pp. 383-396 (2006).
10.1201/9781420003765Foner, H. A. and Adan, N., The characterization of papers by X-ray diffraction (XRD): measurement of cellulose crystallinity and determination of mineral composition, Journal of the Forensic Science Society 23(4):313-321 (1983).
10.1016/S0015-7368(83)72269-3Spence, L. D., Baker, A. T., and Byrne, J. P., Characterization of document paper using elemental compositions determined by inductively coupled plasma mass spectrometry, Journal of Analytical Atomic Spectrometry 15(7):813-819 (2000).
10.1039/b001411gSpence, L. D., Francis, R. B., and Tinggi, U., Comparison of the elemental composition of office document paper: evidence in a homicide case, Journal of Forensic Sciences 47(3):648-651 (2002).
Andrasko, J., Microreflectance FTIR techniques applied to materials encountered in forensic examination of documents, Journal of Forensic Sciences 41(5):812-823 (1996).
10.1520/JFS14003JKher, A., Mulholland, M., Reedy, B., and Maynard, P., Classification of document papers by infrared spectroscopy and multivariate statistical techniques, Applied Spectroscopy 55(9):1192-1198 (2001).
10.1366/0003702011953199Kher, A., Stewart, S., and Mulholland, M., Forensic classification of paper with infrared spectroscopy and principal components analysis, Journal of Near Infrared Spectroscopy 13(4):225-229 (2005).
10.1255/jnirs.540Kuptsov, A. H., Applications of Fourier transform Raman spectroscopy in forensic science, Journal of Forensic Sciences 39(2):305-318 (1994).
10.1520/JFS13604JMiyata, H., Shinozaki, M., Nakayama, T., and Enomae, T., A discrimination method for paper by Fourier transform and cross correlation, Journal of Forensic Sciences 47(5):1125-1132 (2002).
10.1520/JFS15491JEbara, H., Kondo, A., and Nishida, S., Analysis of coated and non-coated papers by pyrolysis gas-chromatography, Rep Natl Res Inst Police Sci 2(35):88-98 (1982).
Hwang, S. W., Park, G., Kim, J., Kang, K. H., and Lee, W. H., One-Dimensional Convolutional Neural Networks with Infrared Spectroscopy for Classifying the Origin of Printing Paper, BioResources 19(1):1633-1351 (2024).
10.15376/biores.19.1.1633-1651Lee, Y. J., Lee, T. J., and Kim, H. J., Classification Analysis of Copy Papers Using Infrared Spectroscopy and Machine Learning Modeling, BioResources 19(1):160-182 (2024).
10.15376/biores.19.1.160-182Zięba-Palus, J., Wesełucha-Birczyńska, A., Trzcińska, B., Kowalski, R., and Moskal, P., Analysis of degraded papers by infrared and Raman spectroscopy for forensic purposes, Journal of Molecular Structure 1140:154-162 (2017).
10.1016/j.molstruc.2016.12.012Yan, C., Cheng, Z., Luo, S., Huang, C., Han, S., Han, X., Du, Y., and Ying, C., Analysis of handmade paper by Raman spectroscopy combined with machine learning, Journal of Raman Spectroscopy 53(2):260-271 (2022).
10.1002/jrs.6280Kumar, R., Kumar, V., and Sharma, V., Discrimination of various paper types using diffuse reflectance ultraviolet-visible near-infrared (UV-Vis-NIR) spectroscopy: forensic application to questioned documents, Applied Spectroscopy 69(6):714-720 (2015).
10.1366/14-0766325955217Lee, J., Kim, H., Yook, S., and Kang, T. Y., Identification of document paper using hybrid feature extraction, Journal of Forensic Sciences 68(5):1808-1815 (2023).
10.1111/1556-4029.1533037420317Hastie, T., Rosset, S., Zhu, J., and Zou, H., Multi-class adaboost, Statistics and its Interface 2(3):349-360 (2009).
10.4310/SII.2009.v2.n3.a8Friedman, J. H., Greedy function approximation: a gradient boosting machine. Annals of statistics, 1189-1232 (2001).
10.1214/aos/1013203451Chen, T. and Guestrin, C., Xgboost: A scalable tree boosting system, In Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining, pp. 785-794 (2016).
10.1145/2939672.2939785Louppe, G., Wehenkel, L., Sutera, A., and Geurts, P., Understanding variable importances in forests of randomized trees, Advances in neural information processing systems, 26 (2013).
Nielsen, P. P., Automatic registration of grazing behaviour in dairy cows using 3D activity loggers, Applied Animal Behaviour Science 148(3-4):179-184 (2013).
10.1016/j.applanim.2013.09.001DeVries, T. J., Von Keyserlingk, M. A. G., Weary, D. M., and Beauchemin, K. A., Validation of a system for monitoring feeding behavior of dairy cows, Journal of Dairy Science 86(11):3571-3574 (2003).
10.3168/jds.S0022-0302(03)73962-914672187- Publisher :Korea Technical Association of The Pulp and Paper Industry
- Publisher(Ko) :한국펄프종이공학회
- Journal Title :Journal of Korea TAPPI
- Journal Title(Ko) :펄프종이기술
- Volume : 56
- No :4
- Pages :65-73
- Received Date : 2024-08-07
- Revised Date : 2024-08-16
- Accepted Date : 2024-08-17
- DOI :https://doi.org/10.7584/JKTAPPI.2024.8.56.4.65