Applying Computational Intelligence to Community Policing and Forensic Investigations

Community Policing operates in an interconnected world and as such could benefit from the latest technological advances such as computational or artificial intelligence. In this chapter, we describe a new method for applying computational intelligence to Community Policing and forensic investigation based on heterogeneous data. To illustrate the proposed method, we apply it to the well-known VAST Challenge 2014. Experimental results show how the proposed method can reduce the process of investigation in finding anomalies in heterogeneous data. The integration of data with the proposed method can simplify the tedious and time consuming job of processing huge amount of data and assist the human expert in making decision and analysis.

[1]  Yongbum Kim,et al.  Development of an Anomaly Detection Model for a Bank's Transitory Account System , 2014, J. Inf. Syst..

[2]  Faye Rona Mitchell,et al.  An Overview of Artificial Intelligence Based Pattern Matching in a Security and Digital Forensic Context , 2014, Cyberpatterns.

[3]  Tal Hassner,et al.  Age and Gender Estimation of Unfiltered Faces , 2014, IEEE Transactions on Information Forensics and Security.

[4]  Alessandro Guarino,et al.  Digital Forensics as a Big Data Challenge , 2013, ISSE.

[5]  Plamen P. Angelov,et al.  Empirical data analysis: A new tool for data analytics , 2016, 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC).

[6]  Taghi M. Khoshgoftaar,et al.  Intrusion detection and Big Heterogeneous Data: a Survey , 2015, Journal of Big Data.

[7]  Plamen Angelov.  Typicality distribution function — A new density-based data analytics tool , 2015, 2015 International Joint Conference on Neural Networks (IJCNN).

[8]  Jignesh M. Patel,et al.  Big data and its technical challenges , 2014, CACM.

[9]  Fabian Nater.  Abnormal behavior detection in surveillance videos , 2012 .

[10]  Won Hyung Park,et al.  A study on the live forensic techniques for anomaly detection in user terminals , 2013 .

[11]  Soumen Bhowmik,et al.  A Literature Survey on Human Identification by Gait , 2016 .

[12]  Plamen Angelov,et al.  Anomaly detection based on eccentricity analysis , 2014, 2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS).

[13]  Angelo J. Canty,et al.  Bootstrap diagnostics and remedies , 2006 .

[14]  Plamen Angelov,et al.  Outside the box: an alternative data analytics framework , 2014, J. Autom. Mob. Robotics Intell. Syst..

[15]  Ajith Abraham,et al.  Computational Intelligence in Digital Forensics , 2014, Computational Intelligence in Digital Forensics.

[16]  Junyong Liu,et al.  Big Data Management in Digital Forensics , 2014, 2014 IEEE 17th International Conference on Computational Science and Engineering.

[17]  Mita Nasipuri,et al.  Hand Biometrics in Digital Forensics , 2014, Computational Intelligence in Digital Forensics.

[18]  Jean-Luc Dugelay,et al.  Bag of soft biometrics for person identification , 2010, Multimedia Tools and Applications.

[19]  Plamen Angelov,et al.  Real-Time Fault Detection Using Recursive Density Estimation , 2014, Journal of Control, Automation and Electrical Systems.

[20]  Enrico Grosso,et al.  Biometrics in Forensic Science: Challenges, Lessons and New Technologies , 2014, BIOMET.

[21]  R. Segurado,et al.  Prenatal detection of structural cardiac defects and presence of associated anomalies: a retrospective observational study of 1262 fetal echocardiograms , 2015, Prenatal diagnosis.