Fuzzy based binary feature profiling for modus operandi analysis

Abstract

It is a well-known fact that some criminals follow perpetual methods of operations known as modi operandi. Modus operandi is a commonly used term to describe the habits in committing crimes. These modi operandi are used in relating criminals to crimes for which the suspects have not yet been recognized. This paper presents the design, implementation and evaluation of a new method to find connections between crimes and criminals using modi operandi. The method involves generating a feature matrix for a particular criminal based on the flow of events of his/her previous convictions. Then, based on the feature matrix, two representative modi operandi are generated: complete modus operandi and dynamic modus operandi. These two representative modi operandi are compared with the flow of events of the crime at hand, in order to generate two other outputs: completeness probability (CP) and deviation probability (DP). CP and DP are used as inputs to a fuzzy inference system to generate a score which is used in providing a measurement for the similarity between the suspect and the crime at hand. The method was evaluated using actual crime data and ten other open data sets. In addition, comparison with nine other classification algorithms showed that the proposed method performs competitively with other related methods proving that the performance of the new method is at an acceptable level.

Robust proportional overlapping analysis for feature selection in binary classification within functional genomic experiments

“…A feature selection method for binary classification problems was introduced by Dashtban, Balafar & Suravajhala (2018) , in which the traditional bat algorithm is extended with more refined formulations, improved and multi-objective operators and a novel local search strategy. Other examples of feature selection methods could be found in MotieGhader et al (2020) , Dashtban & Balafar (2017) , Nematzadeh et al (2019) , Maghsoudloo et al (2020) , Rostami et al (2020) , Shamsara & Shamsara (2020) , Ao et al (2020) , Statnikov et al (2005) , Rana et al (2019) , Chamikara et al (2016) , Nardone, Ciaramella & Staiano (2019) and the references cited therein.…”

Forensic intelligence and the analytical process

“…Chi, Lin, Jin, Xu, and Qi () developed a decision support system for detecting serial crimes—their underlying technique is pairwise classification based on similarity, which they state is interpretable and easy to tune. Chamikara et al () use fuzzy logic for matching sets of behavioral features.…”

“…Sri Lanka Police developed the Crime Investigation Decision Support System (CIDSS, Chamikara et al, ), a web based intelligent crime analysis system. Various processes were incorporated into the system, for instance a crime clock and periodic pattern visualizer (temporal aspects of crimes), crime map and hotspot visualizer (based on geographic information system), and crime comparator and modus operandi analysis (fuzzy matching of links, Chamikara et al, ). The data mining tools are used for information extraction and analysis of the historical and current crime data.…”

Privacy preserving distributed machine learning with federated learning

“…This growing availability of different sources of data has been able to revolutionize leading fields such as healthcare technologies to achieve excellent achievements in many areas such as drug discovery, early outbreak detection, epidemic control analysis, which were once considered to be complicated [2,3]. However, data related to the fields such as healthcare, banking and policing are massively convoluted with sensitive private data [6,7,8]. It is essential to go for extreme measures to protect sensitive data while analyzing them to generate meaningful insights [9,10].…”