Volume 5, Issue 6, December 2017, Page: 90-96
Post Mortem Interval: Necrobiome Analysis Using Artificial Neural Networks
Khenchouche Abdelhalim, Depatment of Microbiology, Faculty of Natural Science and Life, UFAS Setif1 University, Setif, Algeria; Laboratory of Health and Environment, Faculty of Medicine, UFAS Setif1 University, Setif, Algeria
Bouharati Khaoula, Laboratory of Health and Environment, Faculty of Medicine, UFAS Setif1 University, Setif, Algeria
Bouharati Saddek, Depatment of Microbiology, Faculty of Natural Science and Life, UFAS Setif1 University, Setif, Algeria; Department of Electronics, Laboratory of Intelligent Systems, UFAS Setif1 University, Setif, Algeria
Mahnane Abbas, Laboratory of Health and Environment, Faculty of Medicine, UFAS Setif1 University, Setif, Algeria
Hamdi-Cherif Mokhtar, Laboratory of Health and Environment, Faculty of Medicine, UFAS Setif1 University, Setif, Algeria
Received: Oct. 5, 2017;       Accepted: Oct. 18, 2017;       Published: Dec. 8, 2017
DOI: 10.11648/j.cbb.20170506.13      View  1578      Downloads  87
Abstract
Aims: In criminal investigations, it is necessary to determine the date and time of the death of a person. Different techniques are used. In this study, we try to analyze the necrobioma that characterizes all the bacteria that populate a corpse. It would be necessary to determine which bacteria first inhabit a dead organism? Which bodies are the first organs to be affected? Which microorganisms will tend to multiply post-mortem? How to establish a dynamics of bacterial diffusion and an occupation gradient according to the moment of death? Several factors are involved in this dynamic. Mathematical modeling becomes very complex. In this study, we propose an intelligent system to predict the exact date of death of the number and species found at time (t). Materials and Methods: The purpose is to determine and enumerate the bacterial colonies in the study organ. Establish the bacterial dynamics as a function of time. In this study, an artificial neural network is established. The input variables are bacterial species, their growth rates, growth conditions (temperature, humidity, soil type, and bacterial species). The rate of bacterial species in specified organ is considered as output variable. The time taken for a bacterial species to reach this rate under defined conditions determines the date of death of the person. Results: Since input variables are considered complex, uncertain, an artificial neural network demonstrates its ability to solve such complexity. After the learning phase of the network from the real data, this creates a function of correspondence between the space of inputs and output. The established system makes it possible to instantly read the time elapsed after death from the introduction of the random values at the input with the maximum precision. The proposed system remains extensible to enter variables that may have an effect on the output.
Keywords
Necrobiome, Microbial Dynamics, Post Mortem, Intelligent Systems, ANN
To cite this article
Khenchouche Abdelhalim, Bouharati Khaoula, Bouharati Saddek, Mahnane Abbas, Hamdi-Cherif Mokhtar, Post Mortem Interval: Necrobiome Analysis Using Artificial Neural Networks, Computational Biology and Bioinformatics. Vol. 5, No. 6, 2017, pp. 90-96. doi: 10.11648/j.cbb.20170506.13
Copyright
Copyright © 2017 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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