IDENTIFICATION OF CRIMINAL CASE DIAGNOSTIC ISSUES: A MODULAR ANN APPROACH

Sotarat Thammaboosadee () and Bunthit Watanapa ()
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Sotarat Thammaboosadee: School of Information Technology, King Mongkut's University of Technology Thonburi, 126 Prachautit Rd., Bangmod, Thungkru, Bangkok, 10140, Thailand
Bunthit Watanapa: School of Information Technology, King Mongkut's University of Technology Thonburi, 126 Prachautit Rd., Bangmod, Thungkru, Bangkok, 10140, Thailand

International Journal of Information Technology & Decision Making (IJITDM), 2013, vol. 12, issue 03, 523-546

Abstract: A knowledge discovery model has been developed to manage the facts discovered in criminal cases in the court of law and to identify the relevant diagnostic issues. This study focuses on the offence against life and body section of the criminal law codes of Thailand. To identify the criminal case diagnostic issues, a set of artificial neural networks (ANN) classifiers is heuristically configured and modularly organized to operate upon the discovered facts. This modular network of ANNs forms an effective system in terms of determining power and ability to trace or infer the relevant reasoning of such a determination. Experiments have been conducted to demonstrate the applicability of ANN for various case studies and to generate comparative results for providing insights into both technical and legal aspects of these cases. In this study, a modular ANN with the support of Principal Component Analysis (PCA) as an automatic input selection mechanism provided the best results with accuracy up to 99%, using 10-fold cross-validation. A sample case is included to illustrate the effectiveness of the proposed system.

Keywords: Criminal law; data mining; diagnostic issues; knowledge discovery; modular neural network; 22E46; 53C35; 57S20 (search for similar items in EconPapers)
Date: 2013
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Citations: View citations in EconPapers (1)

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DOI: 10.1142/S021962201350020X

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