A Smart Rehabilitation System (SRS) for Criminals in Smart Cities

Abstract

     This article suggests designing an intelligent system to rehabilitate criminals in smart cities, which consists of two categories: the first category suggests a “smart social system," which suggests managing the behaviors (good or bad) of individuals as the root of crime committing. To manage any criminal behavior, we proposed electronic recording of the criminal behavior as the first step, then submitting the criminal with its behavior under proposed rehabilitation theories as the second step to examine the behavior enhancement. This rehabilitation system depends on the prize-and-penalty principle. The penalty in this system is a suspended sentence with community services and fines instead of prison punishment. The second proposed category is constructing the smart system techniques by automating the proposed “smart social system” as a part of the smart police organization under the smart city. The methodology suggests working as a training system to submit the criminals that should be going to prison in standard cases under the proposed rehabilitation system process within a specific period. The proposed system suggested three categories of behaviors into which a prisoner may fall; he might fall into the category of "very bad people," where he needs to go to prison due to his worst actions. Second, he might fall into the "very good person" category, so this system suggests his prison punishment is now over and he is now free and can be released because he has enhanced his behavior. whereas the third category is the gradual process of a person whose actions lie in between these two characteristics; for this scenario, our proposed system suggests that this person needs rehabilitation and training to improve his behavior. A uniform crossover for the genetic algorithm has been implemented to check the performance of the proposed system. Thus, this could be very useful in improving the crime-preventing systems of the population in smart cities.