Using Online Data in Predicting Stock Price Movements

A lot of research has been focusing on incorporating online data into models of various phenomena. The chapter focuses on one specific problem coming from the domain of capital markets where the information contained in online environments is quite topical. The presented experiments were designed to reveal the association between online texts (from Yahoo! Finance, Facebook, and Twitter) and changes in stock prices of the corresponding companies. As the method for quantifying the association, machine learning-based classification was chosen. The experiments showed that the data preparation procedure had a substantial impact on the results. Thus, different stock price smoothing, the lags between the release of documents and related stock price changes, levels of a minimal stock price change, different weighting schemes for structured document representation, and classifiers were studied. The chapter also shows how to use currently available open source technologies to implement a system for accomplishing the task.

Open Science and Its Impact on Libraries, Publishers, and Authors

The open science movement enables the accessibility and reusability of research output across the globe. Researchers and other stakeholders in the research process can now easily collaborate to add to the body of knowledge. This chapter documents how open science is impacting the role of libraries, publishers, and authors in the digital era. A structured document analysis and web analysis were done to find out how authors, publishers, and librarians are affected by open science. It was found that librarians are taking advantage of open science to provide various information sources to patrons, the publishers are now charging article processing fees to make the journal articles open access upon publishing, and authors are now able to access many information sources during the research process and enjoy greater visibility of their research output. The author recommends the adoption of open science especially in the developing countries and the enactment of policies that support open science at national, regional, and international levels.

Query based Text Summarization

Now a day’s, The advancement of technology implies the large amount of online textual Information, this leads to a need for Text Summarizers which can provide important textual data from large data source like www, into a well-structured document formats. It becomes an alive analysis activity to specify the related data that is retrieved from huge set of documents which are established from various sectors like social media. Text summary is the action of precise the documents by protecting the key concepts of the text. This process can be accomplished via extractive summary and abstractive summary. In this paper, we suggested summarization technique that is to be applied on online information depending upon the User required Document

Perception of patients after bariatric surgery on quality of life

Objective: This study aimed at understanding the perception of bariatric patients about their Quality of Life (QoL) after bariatric surgery.Methods: Descriptive and qualitative study performed at a reference hospital for bariatric surgery in the State of Ceara/Brazil. Subjects were 27 patients who were experiencing postoperative and data collection was made from interviews with semi-structured document. Data was subjected to qualitative content analysis, which allowed the emergence of categories: “In searching for a healthier life”; “The weight cycling villain and failure in other treatments”; “The meaning of quality of life and a reflection of obesity in daily life”; “The effects on quality of life of individuals after bariatric surgery”. The project was approved by the Ethics Committee (CEP 538/2011).Results: For most patients, QoL is defined as having a healthy life with the possibility of exercising without tiring easily, being able to eat properly, performing better at work and having a more active social life.Conclusions: Thus, it is seen that bariatric surgery was effective on QoL improvements on self-esteem, self-care, work performance, leisure and social relationships.

Using Online Data in Predicting Stock Price Movements

A lot of research has been focusing on incorporating online data into models of various phenomena. The chapter focuses on one specific problem coming from the domain of capital markets where the information contained in online environments is quite topical. The presented experiments were designed to reveal the association between online texts (from Yahoo! Finance, Facebook, and Twitter) and changes in stock prices of the corresponding companies. As the method for quantifying the association, machine learning-based classification was chosen. The experiments showed that the data preparation procedure had a substantial impact on the results. Thus, different stock price smoothing, the lags between the release of documents and related stock price changes, levels of a minimal stock price change, different weighting schemes for structured document representation, and classifiers were studied. The chapter also shows how to use currently available open source technologies to implement a system for accomplishing the task.

SYHDA – System of Hydrological Data Acquisition and Analysis

ABSTRACT Acquisition and analysis of hydrological series are essential activities for hydrological studies in watersheds. However, they require time and are usually complex and susceptible to human errors. In order to minimize these problems, computational tools are often used for hydrological analysis, although there are few of them in Brazil. This technical note aims to provide a structured document with information on the motivation, development, conception, main functionalities, and applications of the System of Hydrological Data Acquisition and Analysis (SYHDA). SYHDA is a software intended for acquisition and analysis of hydrological data (rainfall and streamflow) and was fully idealized to enable the user to deal with the country’s leading hydrology databases. It has several modules that include analysis by descriptive statistics and graphical tools, seasonality analysis of streamflows, non-parametric tests, and at-site and regional probabilistic modeling. SYHDA has been used in numerous scientific studies, which give grounds to affirm that it demonstrates a great potential to be used in both everyday and complex activities demanded by the scientific and technical community of hydrology and related areas.