Prediction of Demographical Characteristics using K-Means Algorithms

Sarı M., Tuna C., Demir İ.

SIGMA JOURNAL OF ENGINEERING AND NATURAL SCIENCES-SIGMA MUHENDISLIK VE FEN BILIMLERI DERGISI, vol.38, no.2, pp.1051-1059, 2020 (ESCI) identifier

  • Publication Type: Article / Article
  • Volume: 38 Issue: 2
  • Publication Date: 2020
  • Journal Name: SIGMA JOURNAL OF ENGINEERING AND NATURAL SCIENCES-SIGMA MUHENDISLIK VE FEN BILIMLERI DERGISI
  • Journal Indexes: Emerging Sources Citation Index (ESCI), Academic Search Premier, Directory of Open Access Journals
  • Page Numbers: pp.1051-1059
  • Keywords: Forensics science, tibial rotation, biomechanics, KM algorithm, PHYSICAL FACTORS, TIBIAL MOTION, KNEE-JOINT, MOVEMENT
  • Yıldız Technical University Affiliated: Yes

Abstract

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.