Biomechanical investigation of a hip simulator during physical activity
Efstratios
Ntantis
American University of the Middle East, Dubai, UAE
author
Dimitra
Pithara
American University of the Middle East, Dubai, UAE
author
text
article
2021
eng
Biomechanics is a combination of engineering with anatomy and physiology. Studies of biomechanics and ergonomics established to examine the effects of forces on human and animal bodies. Research on biomechanical behavior of the human body during physical activity help to design special equipment for particular sports and exercises in order to avoid injuries. Spine is considered as one of the most complex structure of the human musculoskeletal system providing support on human movement. This paper aims to explore the ability of a hip simulator to withstand similar forces acting on vertebral body and thus the possibility to replace the spine simulator. The simulation analysis of the hip simulator performed with CREO design software was created to show the ability of the design set-up to withstand the forces acting on the hip and the loading acting on the vertebral body. For this research the data has been downloaded from ORTHOLOAD website and for inserting the data in the hip simulator the angles needed to be calculated.
Journal of Bioengineering Research
Tissues and Biomaterial Research Group-(TBRG)
2645-5633
3
v.
2
no.
2021
1
11
https://www.journalbe.com/article_129825_b568eea1dd21eb174907508d4d5be1c5.pdf
dx.doi.org/10.22034/jbr.2021.283277.1038
Benefiting Machine Learning Methods to Detect Fraud in the Validation of Bank Customers' Cards
Parisa
Ahmadi
Department of Computer, Faculty of Engineering, Aghigh University, Shahin shahr, Isfahan, Iran.
author
Ahmad
Yousefi
Department of Computer, Naein Branch, Islamic Azad University, Naein, Isfahan, Iran
author
Farid
Rezazadeh
Department of Computer, Faculty of Engineering, Aghigh University, Shahin shahr, Isfahan, Iran.
author
text
article
2021
eng
The existence of money laundering and banking fraud is one of the major challenges of the banking system in any country. Crediting customers based on their track record and performance is a method to address the banking challenges. Classification methods can be used to validate customers, but these methods own high error. In this paper, machine learning methods are applied on banking data set to classify them and to reduce the error in customer validation. To this end, first the machine learning methods are trained and then tested using the banking data set. Experiments on the banking data set show that the accuracy of the proposed method for validating customers is less than 81.6%. So, the accuracy index of the random forest, decision tree, support vector machine, and multilayer artificial neural network are 80.50\%, 80.05\%, 80.93\%, and 81.58\%, respectively. The best performance is related to multilayer artificial neural network and, accordingly, the multilayer artificial neural network method can be used in detection of the validation of bank customers' cards.
Journal of Bioengineering Research
Tissues and Biomaterial Research Group-(TBRG)
2645-5633
3
v.
2
no.
2021
12
19
https://www.journalbe.com/article_130339_27bc1c5a6c139e0c5871af7338f665a6.pdf
dx.doi.org/10.22034/jbr.2021.284617.1039