PREDICTION OF DEMOGRAPHICAL CHARACTERISTICS USING K-MEANS ALGORITHMS

It is crucially important to predict demographic characteristics of criminals from the footprint area at the crime scene. Demographic characteristics include age, weight, height and gender. This article has thus investigated the effect of the tibial rotations on predictions of the demographical characteristics using the K-Means (KM) clustering algorithms. Satisfactorily important predictions have been carried out through the dataset consisting of 484 healthy subjects in the designed study here. The produced results revealed that it is of great potentiality to do also for criminals. The results are therefore believed to be vitally important for most fields of forensic science. Specifically, it can provide important clues when diagnosing criminals. Note that the KM algorithms have been found to be very encouraging processing system for modelling in the assessment of the demographic characteristics.

Keywords:

Forensics science tibial rotation, biomechanics, KM algorithm,

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[1] J.C. Kupper, B. Loitz-Ramage, D.T. Corr, D.A. Hart, J. L. Ronsky, Measuring knee joint laxity: a review of applicable models and the need for new approaches to minimize variability, Clinical Biomechanics, 22(1), 1-13, 2007.
[2] J. O. S. E. P. H. Hamill, B. T. Bates, and K. G. Holt, Timing of lower extremity joint actions during treadmill running, Medicine and Science in Sports and Exercise, 24(7), 807-813, 1992.
[3] S.F. Dye, An evolutionary perspective of the knee, J Bone Joint Surg Am, 69(7), 976-983, 1987.
[4] M. Sari, B.G. Cetiner, Predicting effect of physical factors on tibial motion using artificial neural networks, Expert Systems with Applications, 36(6), 9743-9746, 2009.
[5] L. R. Osternig, B. T. Bates, and S. L. James, “Patterns of tibial rotary torque in knees of healthy subjects,” Medicine and Science in Sports and Exercise, vol. 12, no. 3, pp. 195-199, 1980.
[6] O. S. Mills and M. L. Hull, Rotational flexibility of the human knee due to varus/valgus and axial moments in vivo, Journal of Biomechanics, 24(8), 673-690, 1991.
[7] K.E. Moglo, A. Shirazi-Adl, Biomechanics of passive knee joint in drawer: load transmission in intact and ACL-deficient joints, The Knee, vol. 10, no. 3, pp. 265-276, 2003.
[8] T. Armour, L. Forwell, R. Litchfield, A. Kirkley, N. Amendola, P. J. Fowler, Isokinetic evaluation of internal/external tibial rotation strength after the use of hamstring tendons for anterior cruciate ligament reconstruction, The American Journal of Sports Medicine, 32(7), 1639-1643, 2004.
[9] P. Johal, A. Williams, P. Wragg, D. Hunt, W. Gedroyc, Tibio-femoral movement in the living knee. A study of weight bearing and non-weight bearing knee kinematics using ‘interventional’ MRI, Journal of Biomechanics, 38(2), 269-276, 2005.
[10] B.G. Cetiner, M. Sari, Tibial rotation assessment using artificial neural networks, Mathematical and Computational Applications, 15(1), 34-44, 2010.
[11] M. Sari, Relationship between physical factors and tibial motion in healthy subjects: 2D and 3D analysis, Advances in Therapy, 24(4), 772-783, 2007.
[12] A. Cimbiz, U. Cavlak, M. Sari, H. Hallaceli, F. Beydemir, A new clinical design measuring the vertical axial rotation through tibial shaft resulting from passive knee and subtalar joints rotation in healthy subjects: a reliability study, Journal of Medical Sciences, 6(5), 751-757, 2006.
[13] D. Tiberio, The effect of excessive subtalar joint pronation on patellofemoral mechanics: a theoretical model. Journal of Orthopaedic & Sports Physical Therapy. 1987;9(4):160-165.
[14] L. Lang, R. Volpe, Measurement of tibial torsion. Journal of the American Podiatric Medical Association. 1998; 88(4):160-165.
[15] M.A. Freeman, V. Pinskerova, The movement of the knee studied by magnetic resonance imaging. Clinical Orthopaedics and Related Research, (2003): 410, 35–43.
[16] F. Lin, G. Wang, J.L. Koh, R.W. Hendrix, L.Q. Zhang, In vivo and noninvasive three-dimensional patellar tracking induced by individual heads of quadriceps. Medicine and Science in Sports and Exercise, 2004, 36(1), 93–101.
[17] H. Yu, Z. Liu, Wang, G. An automatic method to determine the number of clusters using decision-theoretic rough set. International Journal of Approximate Reasoning 2014, 55, 101–115.
[18] J. MacQueen et al. Some methods for classification and analysis of multivariate observations. In: Proceedings of the fifth Berkeley symposium on mathematical statistics and probability. vol. 1. Oakland, CA, USA. 1967. p. 281-297.
[19] A.K. Jain, Data clustering: 50 years beyond K-means. Pattern recognition letters. 2010; 31(8):651-666.
[20] A. Alguwaizani, Degeneracy on K-means clustering. Electronic Notes in Discrete Mathematics. 2012; 39:13-20.
[21] S. Das, A. Abraham, A. Konar, Automatic clustering using an improved differential evolution algorithm. IEEE Trans. Syst. Man Cybern.-Part A Syst. Hum. 2008; 38:218–237.

Sigma Mühendislik ve Fen Bilimleri Dergisi-Cover

ISSN: 1304-7191
Yayın Aralığı: Yılda 4 Sayı
Yayıncı: Yıldız Teknik Üniversitesi

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