Criminal behavioral data analysis for recidivation estimation in convicted offenders.

The act of continuing to commit crimes after being imprisoned for a first-time offence and freed is known as recidivism. The level of delinquent behavior in an individual character, that is closely associated to repeated recidivation, can be determined by assessing offenders behavioral features. The dataset includes 220 offenders, with a total of 204 participants whose data was used to create the desired dataset. The raw information was acquired using a questionnaire form that included personality traits, parental and family characteristics, socio-demographic characteristics, crime details, cumulative jail behavior elements, and the HCR-20 risk assessment technique. Behavior sample was gathered from several jails and correction facility in the Indian state of Jharkhand for the objective for initial relapse estimation from first convicts in the current study. The dataset can be used by criminologists, sociologists, psychologists, and academicians to determine an offender's pattern and psychological qualities. Specialists in detention centre undertook the felony evaluation.

Juvenile's Delinquent Behavior, Risk Factors, and Quantitative Assessment Approach: A Systematic Review.

Background: Not only in India but also worldwide, criminal activity has dramatically increasing day by day among youth, and it must be addressed properly to maintain a healthy society. This review is focused on risk factors and quantitative approach to determine delinquent behaviors of juveniles. Materials and Methods: A total of 15 research articles were identified through Google search as per inclusion and exclusion criteria, which were based on machine learning (ML) and statistical models to assess the delinquent behavior and risk factors of juveniles. Results: The result found ML is a new route for detecting delinquent behavioral patterns. However, statistical methods have used commonly as the quantitative approach for assessing delinquent behaviors and risk factors among juveniles. Conclusions: In the current scenario, ML is a new approach of computer-assisted techniques have potentiality to predict values of behavioral, psychological/mental, and associated risk factors for early diagnosis in teenagers in short of times, to prevent unwanted, maladaptive behaviors, and to provide appropriate intervention and build a safe peaceful society.

Fast leukocyte image segmentation using shadowed sets.

Leukocyte image segmentation acts as the foundation for all automated image based hematological disease recognition systems. Perfection in image segmentation is a necessary condition for improving the diagnostic accuracy in automated cytology. Even though much effort has been put in developing suitable segmentation routines, the problem still remains open in areas like pathological imaging. Clustering is an essential image segmentation procedure which segments an image into desired regions. This paper introduces a novel Shadowed C-means (SCM) clustering approach towards leukocyte segmentation in blood microscopic images. The segmented nucleus and cytoplasm of a leukocyte can be used for feature extraction which can lead to acute leukemia detection. Absence of parameter tuning in SCM with acceptable segmentation performance gives the proposed scheme an edge over standard cluster based segmentation techniques. Comparative analysis reveals that the proposed algorithm is fast and robust in segmenting stained blood microscopic images in the presence of outliers.