Nowadays, the emergence of new technologies gives rise to a huge amount of data in different fields such as public transportation, community services, scientific research, etc. Due to the aging population, healthcare is becoming more important in our daily life to reduce public burdens. For example, manually archiving massive electronic medical files, such as X-ray images, is impossible. However, precise classification is essential for further work, such as diagnosis. In this report, we applied a spectral clustering algorithm to classify chest disease X-ray images. We also employed the "pure" K-means algorithm for comparison. Three types of indexes are used to quantify the performances of both algorithms. Our analysis result shows that spectral clustering can successfully classify chest X-ray images based on the presence of disease spots on the lungs and the performance is superior to “pure" K-means clustering.

Nowadays, Electronic Health Records (EHR) include critical information in the text format. In order to make medical decisions more efficient, the text should be processed and code deliberated. In this report, we applied Artificial Intelligence (AI) techniques to improve stroke risk prediction based on the EHR text. The system based on Natural Language Processing (NLP) generates structured text from EHR, followed by applying Machine Learning (ML) techniques to classify the text as a "good" or "bad" indicator, which is used for prediction. The ML models here we used include logistic regression and Support Vector Machine (SVM). Our results show that both models can classify the text precisely and make predictions accurately.

Search Engines are always making efforts to better understand their user's need and improve user satisfaction. This research examines the important issue of user dependency (effectively a combination of loyalty and satisfaction) on web search engines, first studying existing dependency and then modeling that dependency. An algorithm developed to find a quantitative value of "user dependency" on Search Engine is presented. Here, the term 'user dependency' implies the psychological satisfaction of a user with the search results presented for a search session. It's an indicative measure of user's trust on Search Engine and impacts the user's choice to use the same Search Engine in future. This paper investigates factors that influence a search session and uses a fuzzy based approach to determine the dependency and overall trust the user places on the Search Engine. The proposed algorithm accepts 'user rating for the search session' as input and based on the 'user satisfaction with search' generates a value for user dependency. The findings have implications for search engines in improving their ranking algorithms based on explicit user feedback on the search experience. The algorithm has been implemented and tested using Visual Basic environment developed for this study. The validity of algorithm and correctness of its result is evaluated using a survey conducted with a sample of users. Results have been validated for accuracy and their conformance to sampled user's satisfaction.

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