Improved method of classification algorithms for crime prediction

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

HMM-based decision model for smart home environment

The smart home environment typically includes various systems with high level of heterogeneity characteristics. Smart home environment are configured in such a way that it comfort driven as well as achieving optimized security and task-oriented without human intervention inside the home. Smart home environment contain diversified systems ranging from entertainment to automation like devices that is heterogeneous in nature. For the reason that of systems heterogeneity, it is frequently challenging to execute interoperation around them and realize desired services preferred by the home occupants. The interoperation complexity stands at the bottleneck in ensuring various tasks executed jointly among diversified systems in smart home environment. In this paper, we present a Hidden-Markov Model (HMM) based decision model for smart home environment by providing decision support ability. The implementation has been carried out in such a way that quality information is acquired among the systems to demonstrate the effectiveness of interoperability among them. This proposed decision model is tested and proven that there is an elevated amount of reliability on this decision model in the smart home setting

Hidden markov model for decision making among heterogeneous systems in intelligent building

The idea of intelligent building promises the ability to automate the environment by installing the needed devices for controlling context aware, personalized, adaptive and anticipatory services. Intelligent building can in this way be referred to a term normally used to characterize a building that incorporates technology and services through networking to improve power efficiency and enhance the nature of living. The inability of systems, devices and sensors to interoperate is the main drawback in intelligent building. They operate at different platform, different configuration and different languages. Hence it is difficult to perform intelligent building operations due to high heterogeneity. The idea behind this study is to design an effective model to resolve the difficulty of decision making among subsystems in a building environment. Existing work done by Perumal et al. (2013) had tackled the problem of interoperation using the Event Condition Action (ECA) mechanism to perform decision making among subsystems. The ECA mechanism uses the rule based to trigger actions and yet the model resulted in poor response time. In order to improve the response time a machine learning algorithm like Hidden Markov Model (HMM) instead of the rule-based is used. HMM is chosen due to the characteristics it possesses such as probabilistic, statistical, machine learning as well as its robustness and scalability has been seen as an efficient and effective model to tackle the problem of interoperation in the intelligent building. We hypothesized that the response time can be improved without sacrificing the system accuracy through machine learning. From our experimentation results showed that HMM managed to reach 95% accuracy on all the data set generated from the pre-defined rule-based and reduced the response time significantly. The model is compared with other selected machine learning such as Naïve Bayes and Fuzzy Logic to show the correctness of the system. The framework of Perumal et al. (2013) was improved by replacing the ECA with the HMM and implementing the framework in the intelligent building

Comparison of hidden Markov Model and Naïve Bayes algorithms among events in smart home environment

The smart home environment consists of numerous subsystems which are heterogeneous in nature. Smart home environment are configured in such a way that it comfort driven as well as achieving optimized security and task-oriented without human intervention inside the home. The subsystems, due to their diversified nature develop difficulties as the events communicate making the smart home uncomfortable. The complexity of decision making in handling events stands at the bottleneck in ensuring various tasks executed jointly among diversified systems in smart home environment. In this paper, we propose Hidden Markov Model (HMM) and Naïve Bayes (NB) to test the accuracy and response time of the home data and to compare between the two algorithms. The result experimented shows that the HMM algorithm stands at higher accuracy and better response time than the NB. The implementation has been carried out in such a way that quality information is acquired among the systems to demonstrate the effectiveness of decision making among events in the smart home environment