The increasing trend of problem representation and high-dimensional data collection calls for the utilization of feature selection in many machines learning tasks and big data representations. However, identifying meaningful features from thousands of related features in the smart home data which are dissimilar in nature remains a nontrivial task. This has prompted for the deployment of a feature selection algorithm (FSA) that provides two possible solutions. First, to provide an efficient scheme that best optimizes the features for subsystem decisions and second, tackles feature subset selection bias problem. In this paper, a MFES framework for feature selection is proposed that uses a hybrid mechanism to tackle the problem of feature subset selection bias in intelligent building data. The mechanism uses the effectiveness of filters and accuracy of wrappers to obtain significant features for prediction. The proposed MFES framework resulted in 92.17

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.

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.

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 Naive 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.

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.