Literature survey Yaliang Li, Chaochun Liu did early work on medical crowdsourced question answering (QA) websites are booming in recent years and an ever-increasing number of patients and doctors are involved. The valuable information from these crowdsourced QA websites can benefit patients, doctors, and society. One key to unleashing the power of these quality control websites is to extract medical knowledge from noisy question-answer pairs and filter out unrelated or even incorrect information. Xiaoxin Yin, Jiawei Han. Say no to plagiarism. Get a tailor-made essay on "Why Violent Video Games Shouldn't Be Banned"? Get an Original Essay The Web has become the most important source of information for most of us. Unfortunately, there is no guarantee for the correctness of the information on the web. Additionally, different websites often provide conflicting information on a topic, such as different specifications for the same product. In this article we propose a new problem called Veracity, which studies how to find true facts from a large amount of conflicting information on many topics provided by various websites. We design a general framework for the truthfulness problem and invent an algorithm called Truth Finder, which uses the relationships between websites and their information, that is, a website is trustworthy if it provides a lot of true information and one piece of information is likely to be true if it is provided by many trustworthy websites. Our experiments show that Truth Finder successfully finds true facts among conflicting information and identifies trustworthy websites better than popular search engines. Paresh Karande. Health is one of the topics increasingly used to evaluate the health conditions of patients suffering from specific disorders or diseases. It was hypothesized that the identification of the variables is able to reflect the general health conditions of the individual. Our goal is to model the relationship between health variables using an integrated linear regression and inference system model. Linguistic data were collected via a guided interview and fed into the sparse and deep inference system to produce health indices. We therefore propose a new deep learning plan to hypothesize possible diseases given the questions of wellness seekers. The proposed plan consists of two key parts. The main part extracts the discriminative therapeutic signs from raw elements. The second evaluates the raw components and their signs as information hubs in one layer and hidden hubs in the next layer, individually. Meanwhile, the relationships between these two layers are detected by pre-preparation with pseudo-labeled information. Subsequently, the protected hubs serve as raw components for extracting more exclusive brands. With the incremental and optional reworking of these two segments, our plan produces a loosely bonded deep construction modeling with three protected Liqiang Nie layers. A national survey conducted by the Pew Research Center1 in January 2013 reported that one in three American adults went online to understand their medical condition in the past 12 months from the date of the report. To better cater to health seekers, a growing number of community-based health services have emerged, including HealthTap2, HaoDF3, and WebMD4. Please note: this is just an example. Get a custom paper from our expert writers now. Get a custom essay They are spreading personalized healthcare knowledge and connecting the.