Nowadays, communication between patient and doctor during an appointment has changed significantly owning to the opportunity that medical portals provide. Whether or not necessarily appreciated by the doctors, the patients became more aware of the first symptoms' suggesting a particular disease and the medical procedures that apply as a standard. Estimating amount of reliable factual medical information in a document is carried out by parametrizing space of digital documents and dividing it into subsequent layers that represent distribution of the system responses computed as random variables to a query about medical information. Analyzed are the following attributes: dynamism of decrease of query words numbers in the documents, precision, recall in the metric space layers, their mutual correlation and specifically the amount of reliable medical information in the documents. Sensitivity of estimators is explored in order to determine the final decision about further browsing digital documents of the metric space for more medical information that satisfies the user's need. For identification of the true positive information in the space layer and then, in each document of this layer, matching of medical terminology with the document contents, is processed following binary Boolean search space model.
DevOps has become an emerging force within the Information Technology field in today's development/operations climate. Information security within a DevOps environment has become a focal point for most organizations that have implemented the DevOps methodology and its principles. In most cases, the ability to secure a DevOps environment and the organization's ability to adhere to, and comply with, industry specific standards, frameworks and best practice is an integral part of information security within a DevOps environment. This investigation aims to address those issues that may arise when an organization seeks to adhere to and comply with industry standards, frameworks and best practice in a manner that does not limit the velocity of the organization's automated delivery/deployment pipeline. This study investigates this by collecting and analyzing industry and academic literature; and through a prototype demonstration, understanding technical compliance and its requirements within a DevOps environment, using existing industry tools and solutions.
A huge amount of user generated content related to movies is created with the popularization of web 2.0. With these continues exponential growth of data, there is an inevitable need for recommender systems as people find it difficult to make informed and timely decisions. Movie recommendation systems assist users to find the next interest or the best recommendation. In this proposed approach the authors apply the relationship of user feature-scores derived from user-item interaction via ratings to optimize the prediction algorithm's input parameters used in the recommender system to improve the accuracy of predictions with less past user records. This addresses a major drawback in collaborative filtering, the cold start problem by showing an improvement of 8.4% compared to the base collaborative filtering algorithm. The user-feature generation and evaluation of the system is carried out using the `MovieLens 100k dataset'. The proposed system can be generalized to other domains as well.
The prevalence of social media has made information sharing possible across the globe. The downside, unfortunately, is the wide spread of misinformation. Methods applied in most previous rumor classifiers give an equal weight, or attention, to words in the microblog, and do not take the context beyond microblog contents into account; therefore, the accuracy becomes plateaued. In this research, we propose an ensemble neural architecture to detect rumor on Twitter. The architecture incorporates word attention and context from the author to enhance the classification performance. In particular, the word-level attention mechanism enables the architecture to put more emphasis on important words when constructing the text representation. To derive further context, microblog posts composed by individual authors are exploited since they can reflect style and characteristics in spreading information, which are significant cues to help classify whether the shared content is rumor or legitimate news. The experiment on the real-world Twitter dataset collected from two well-known rumor tracking websites demonstrates promising results.
In information retrieval, documents are usually retrieved using lexical matching which matches where words in a user's query with words found in a set of documents. A significant model used in information retrieval is the vector space model where these words are represented as a vector in space and are assigned weights using a favorite weighting technique called TFIDF (Term Frequency Inverse Document Frequency). In this thesis, we have devised three new weighting techniques to improve the TFIDF weighting technique. The first technique is Dispersed Words Weight Augmentation (DWWA) which gives more weight to the words distributed in most of the document's paragraphs; we consider that those words are more significant than words found in few paragraphs. The second technique is called Title Weight Augmentation (TWA) which gives more weight to the words found in the document's title and first paragraphs. The third technique is called First Ranked Words Weight Augmentation (FRWWA) which increments further the weight of the most frequent words in a document. We tested the three techniques, and we found more relevant documents were retrieved in our system.
