Learn More
Within the confines of a Healthcare Enterprise Memory (HEM), most traditional medical systems do not sufficiently provide the necessary assistance to healthcare practitioners in the handling of critical situations. Furthermore, localized knowledge repositories are often lacking the required knowledge for problem solving. Therefore, in this paper, we present(More)
Service placement algorithms have been introduced to automatically manage services in distributed environments. However, most of these algorithms aim to improve service performance or to reduce operation cost and do not consider the reliability and availability of their resources. Most of them do not consider events when machines in their resource pool(More)
Tacit knowledge of health-care experts is an important source of experiential know-how, yet due to various operational and technical reasons, such health-care knowledge is not entirely harnessed and put into professional practice. Emerging knowledge-management (KM) solutions suggest strategies to acquire the seemingly intractable and nonarticulated tacit(More)
In this paper, we propose a new multi-objective evolutionary algorithm-based ensemble optimizer coupled with neural network models for undertaking feature selection and classification problems. Specifically, the Modified micro Genetic Algorithm (MmGA) is used to form the ensemble optimizer. The aim of the MmGA-based ensemble optimizer is two-fold, i.e. to(More)
Sentiment analysis (SA) has become one of the most active and progressively popular areas in information retrieval and text mining due to the expansion of the World Wide Web (WWW). SA deals with the computational treatment or the classification of user’s sentiments, opinions and emotions hidden within the text. Aspect extraction is the most vital and(More)
E-commerce business is becoming more and more popular as the number of customers shopping online is increasing every day. Companies ask their customers to review products and services offered by them over their websites. For the big companies, the number of reviews could be in the thousands. So it is almost impossible for any company to read these reviews(More)
This study presents a sentence-to-sentence semantic relatedness measures for paraphrase detection. The proposed measures adopt the shortest path between synsets in WordNet as the core to measure the relatedness between two sentences. The interlinked synsets in WordNet are based on the conceptual-semantic relation between two synsets. Thus the distance(More)
It is widely recognized that knowledge discovery and data mining in the health domain are two techniques than scientists and researchers are always looking into areas for improvements and accurateness in prediction. In this paper, we present a multi-tier knowledge acquisition, amalgamation and learning info-structure for the learning of rules that have been(More)