The growing availability of information technologies has enabled law enforcement agencies to collect detailed data about various crimes. Classification is the procedure of finding a model (or function) that depicts and distinguishes data classes or notions, with the end goal of having the ability to utilize the model to predict the crime labels. In this research classification is applied to crime dataset to predict the "crime category" for diverse states of the United States of America (USA). The crime data set utilized within this research is real in nature, it was gathered from socio-economic data from 1990 US census. Law enforcement data from 1990 US LEMAS survey, and from the 1995 FBI UCR. This paper compares two different classification algorithms namely -Naïve Bayesian and Back Propagation (BP) for predicting "Crime Category" for distinctive states in USA. The result from the analysis demonstrated that Naïve Bayesian calculation out performed BP calculation and attained the accuracy of 90.2207% for group 1 and 94.0822% for group 2. This clearly indicates that Naïve Bayesian calculation is supportive for prediction in diverse states in USA.

Abstract

The entity of intelligent building is integrated with diversified service function of control, automation and communication of devices in its environment, and to perform them in joined manner via intelligent tasks. Rapid improvement in sensor technologies and advancement in electronics have given rise to heterogeneous systems growth in intelligent building. Most of these subsystems are dissimilar and not intended to perform interoperation task. Consequently, it is rather difficult to perform decision making with the combination of these systems considering the variety of data that are not efficient in adapting to the changing environment. One of the recent decision support solutions provided was Left–right Hidden Markov Model (LR-HMM) which uses left-right algorithm to improve accuracy of prediction based on single timely decision. However, it leads to low accuracy when multiple timely decisions are performed. Therefore, to ensure timely decision, the accuracy of prediction should be improved when performing multiple decisions. We propose a new decision model to improve performance in such situations. The goal is to improve the accuracy of prediction when multiple decisions are performed. Experiments are conducted to evaluate the performance of the proposed Re-estimated Ergodic Hidden Markov Model (RE-HMM), and show that it improves the average accuracy compared with LR-HMM. It is examined when tested on the Local Area Network (LAN) settings.

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