Ehsan Nazerfard

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One of the most common functions of smart environments is to monitor and assist older adults with their activities of daily living. Activity recognition is a key component in this application. It is essentially a temporal classification problem which has been modeled in the past by naïve Bayes classifiers and hidden Markov models (HMMs). In this paper,(More)
Recent advances in the areas of pervasive computing, data mining, and machine learning offer unique opportunities to provide health monitoring and assistance for individuals facing difficulties to live independently in their homes. Several components have to work together to provide health monitoring for smart home residents including, but not limited to,(More)
The increasing aging population has inspired many machine learning researchers to find innovative solutions for assisted living. A problem often encountered in assisted living settings is activity recognition. Although activity recognition has been vastly studied by many researchers, the temporal features that constitute an activity usually have been(More)
An important problem that arises during the data mining process in many new emerging application domains is mining data with temporal dependencies. One such application domain is activity discovery and recognition. Activity discovery and recognition is used in many real world systems, such as assisted living and security systems, and it has been vastly(More)
In spite of the significant work that has been done to discover and recognize activities in the smart home research, less attention has been paid to predict the future activities that the resident is likely to perform. An activity prediction module can play a major role in design of a smart home. For instance, by taking advantage of an activity prediction(More)
This paper presents a sequence-based activity prediction approach which uses Bayesian networks in a novel two-step process to predict both activities and their corresponding features. In addition to the proposed model, we also present the results of several search and score (S&S) and constraint-based (CB) Bayesian structure learning algorithms. The(More)
This paper proposes an approach to the problem of building block extraction in the context of evolutionary algorithms (with binary strings). The method is based upon the construction of a GMDH neural network model of a population of promising solutions with the aim of extracting building blocks from the resultant network. The operation of the proposed(More)
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