@article{10.11648/j.cbb.20170506.13,
  author = {Khenchouche Abdelhalim and Bouharati Khaoula and Bouharati Saddek and Mahnane Abbas and Hamdi-Cherif Mokhtar},
  title = {Post Mortem Interval: Necrobiome Analysis Using Artificial Neural Networks},
  journal = {Computational Biology and Bioinformatics},
  volume = {5},
  number = {6},
  pages = {90-96},
  doi = {10.11648/j.cbb.20170506.13},
  url = {https://doi.org/10.11648/j.cbb.20170506.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.cbb.20170506.13},
  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.},
 year = {2017}
}

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TY  - JOUR
T1  - Post Mortem Interval: Necrobiome Analysis Using Artificial Neural Networks
AU  - Khenchouche Abdelhalim
AU  - Bouharati Khaoula
AU  - Bouharati Saddek
AU  - Mahnane Abbas
AU  - Hamdi-Cherif Mokhtar
Y1  - 2017/12/08
PY  - 2017
N1  - https://doi.org/10.11648/j.cbb.20170506.13
DO  - 10.11648/j.cbb.20170506.13
T2  - Computational Biology and Bioinformatics
JF  - Computational Biology and Bioinformatics
JO  - Computational Biology and Bioinformatics
SP  - 90
EP  - 96
PB  - Science Publishing Group
SN  - 2330-8281
UR  - https://doi.org/10.11648/j.cbb.20170506.13
AB  - 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.
VL  - 5
IS  - 6
ER  - 

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