The Framework of Risk-based Decision Support System (DSS+R) for Forensic Investigation in Detecting Human Cadaver of Clandestine Graves

International Conference on Design, Engineering and Computer Sciences 2018

工业技术;计算机科学

Mohamad Noor, Noor Maizura^1 ; Ahmad Nubli, Amirul Harfirie^1 ; Mohemad, Rosmayati^1 ; Bakar, Zuriana Abu^1

School of Informatics and Applied Mathematics, Universiti Malaysia Terengganu, Kuala Nerus, Terengganu

21030, Malaysia^1

关键词: Biomolecular analysis;    Decision making process;    Environmental factors;    Forensic investigation;    Fundamental component;    Ideal solutions;    Multi-criteria decision making;    Risk-based decision-support system;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/453/1/012017/pdf
DOI  :  10.1088/1757-899X/453/1/012017

来源: IOP

【 摘 要 】

Decision-making process in detecting buried bodies in clandestine grave is crucial to solve criminal cases. Generally, cadaveric derived lipid is used as 'biomarkers' for biomolecular analysis to detect a clandestine grave. However, there are a few environmental factors around the grave that could help in increasing the chance of detecting the clandestine grave. The existing framework has many factors which are inefficient in locating human cadaver. For example, risk is not implemented in the framework which may affect the percentage of locating the graves. Moreover, most of the framework is not computerized which mean it requires a lot of time to locate graves. Therefore, the proposed modified framework will contain guidelines for risk management in decision-making processes. The framework is expected to illustrate guidelines that involve computerized risk and decision-making process. The chosen method is a multi-criteria decision-making (MCDM) method which is known as the Technique for Order of Preference by Similarity to the Ideal Solution (TOPSIS) where the risks that have been identified can be analysed according to their weight of priorities. Hence, it makes the forensic investigation work more efficient and helps to reduce human errors. This research demonstrates that the inclusion of risk as a fundamental component of DSS is able to improve accuracy and efficiency in the decision-making process.