Machine Learning in Automated Text Categorization

The automated categorization (or classification) of texts into predefined categories has witnessed a booming interest in the last ten years, due to the increased availability of documents in digital form and the ensuing need to organize them. In the research community the dominant approach to this problem is based on machine learning techniques: a general inductive process automatically builds a classifier by learning, from a set of preclassified documents, the characteristics of the categories. The advantages of this approach over the knowledge engineering approach (consisting in the manual definition of a classifier by domain experts) are a very good effectiveness, considerable savings in terms of expert manpower, and straightforward portability to different domains. This survey discusses the main approaches to text categorization that fall within the machine learning paradigm. We will discuss in detail issues pertaining to three different problems, namely document representation, classifier construction, and classifier evaluation.Comment: Accepted for publication on ACM Computing Survey

Improving Medication Adherence In Hypertensive Patients: A Scoping Review

Nos últimos anos, o interesse na área da adesão terapêutica tem aumentado de forma significativa. O panorama da adesão tem sido estudado particularmente na área do tratamento da hipertensão arterial; de facto, já foram desenvolvidas numerosas intervenções na tentativa de melhorar a adesão terapêutica em doentes hipertensos. No entanto, este tem sido um esforço frequentemente frustrante e desorganizado. O objetivo do presente estudo foi a realização de uma scoping review de intervenções destinadas a melhorar a adesão terapêutica em doentes hipertensos, de forma a fornecer uma visão mais clara e estruturada desta área. Além disso, desenvolveu-se um novo sistema de categorização de intervenções, baseado em evidência. A presente revisão foi realizada de acordo com o PRISMA-ScR statement. As bases de dados utilizadas foram a MEDLINE e a Web of Science, sendo que se incluíram estudos desde a criação das bases de dados até o dia 17 de agosto de 2020. De um número inicial de 2994 estudos não duplicados, 45 artigos foram incluídos após a realização das fases de screening e elegibility. Estes artigos foram analisados de acordo com o seu desenho, características dos participantes e estratégias de gestão de adesão aplicadas. De igual forma, avaliaram-se os seus outcomes relativos a indicadores de adesão terapêutica e pressão arterial, bem como os métodos utilizados para medir adesão. Posteriormente, cada intervenção descrita foi categorizada de acordo com um novo sistema de categorização, baseado em evidência e desenhado de acordo com o framework de conceptual clustering, amplamente utilizado em machine learning. Ao apresentar uma visão geral e organizada desta área de investigação, criando ainda uma nova ferramenta de categorização de intervenções, este trabalho revela-se um marco importante no desenvolvimento informado e eficiente de futuras intervenções em adesão terapêutica. Adicionalmente, para profissionais de saúde esta é uma fonte de informação valiosa sobre adesão terapêutica em doentes hipertensos.In recent years, interest in medication adherence has greatly increased. Adherence has been particularly well studied in the context of arterial hypertension treatment. Numerous interventions have addressed this issue, however, the effort to improve adherence has been often frustrating and frequently disorganized. The aim of present study was to perform a scoping review of medication adherence interventions in hypertensive patients, so that a clear overview was achieved. Moreover, an evidence-based categorization of interventions was developed. The review was performed according to the PRISMA-ScR statement. MEDLINE and Web of Science were searched, and studies published from database inception until August 17, 2020 were included. A total of 2994 non-duplicate studies were retrieved. After screening and eligibility phases, a total of 45 articles were included. Studies were analyzed regarding their design, participant characteristics and management of adherence strategies employed. Furthermore, medication adherence and blood pressure outcomes, as well as adherence measuring tools were evaluated. Each study's intervention was then categorized using a novel evidence-based system of categorization, derived from the conceptual clustering framework used in machine learning. This work is an important step in pushing for better informed and more efficient future research efforts, both by providing an overview of the research field and by creating a new, evidence-based intervention categorization tool. It also provides valuable information to clinicians about medication adherence to antihypertensive therapy

IA-Regional-Radio - social network for radio recommendation

International audienceThis chapter describes the functions of a system proposed for the music hit recommendation from social network data base. This system carries out the automatic collection, evaluation and rating of music reviewers, the possibility for listeners to rate musical hits and recommendations deduced from auditor's pro les in the form of regional internet radio. First, the system searches and retrieves probable music reviews from the Internet. Subsequently, the system carries out an evaluation and rating of those reviews. From this list of music hits the system directly allows notation from our application. Finally the system automatically create the record list di used each day depended form the region, the year season, day hours and age of listeners. Our system uses linguistics and statistic methods for classifying music opinions and data mining techniques for recommendation part needed for recorded list creation. The principal task is the creation of popular intelligent radio adaptive on auditor's age and region - IA-Regional-Radio

SCS: 60 years and counting! A time to reflect on the Society's scholarly contribution to M&S from the turn of the millennium.

The Society for Modeling and Simulation International (SCS) is celebrating its 60th anniversary this year. Since its inception, the Society has widely disseminated the advancements in the field of modeling and simulation (M&S) through its peer-reviewed journals. In this paper we profile research that has been published in the journal SIMULATION: Transactions of the Society for Modeling and Simulation International from the turn of the millennium to 2010; the objective is to acknowledge the contribution of the authors and their seminal research papers, their respective universities/departments and the geographical diversity of the authors' affiliations. Yet another objective is to contribute towards the understanding of the overall evolution of the discipline of M&S; this is achieved through the classification of M&S techniques and its frequency of use, analysis of the sectors that have seen the predomination application of M&S and the context of its application. It is expected that this paper will lead to further appreciation of the contribution of the Society in influencing the growth of M&S as a discipline and, indeed, in steering its future direction

Ten years of science news: A longitudinal analysis of scientific culture in the Spanish digital press

[EN]This article presents our study of science coverage in the digital Spanish press over the last decade. We employed automated information retrieval procedures to create a corpus of 50,763 text units dealing with science and technology, and used automated text-analysis procedures in order to provide a general picture of the structure, characteristics and evolution of science news in Spain. We found between 6% and 7% of science coverage, a clear high proportion of biomedicine and predominance of science over technology, although we also detected an increase in technological content during the second half of the decade. Analysing the extrinsic and intrinsic features of science culture, we found a predominance of intrinsic features that still need further analysis. Our attempt to use specialised software to examine big data was effective, and allowed us to reach these preliminary conclusions

A Review of Diagnostic Techniques for ISHM Applications

System diagnosis is an integral part of any Integrated System Health Management application. Diagnostic applications make use of system information from the design phase, such as safety and mission assurance analysis, failure modes and effects analysis, hazards analysis, functional models, fault propagation models, and testability analysis. In modern process control and equipment monitoring systems, topological and analytic , models of the nominal system, derived from design documents, are also employed for fault isolation and identification. Depending on the complexity of the monitored signals from the physical system, diagnostic applications may involve straightforward trending and feature extraction techniques to retrieve the parameters of importance from the sensor streams. They also may involve very complex analysis routines, such as signal processing, learning or classification methods to derive the parameters of importance to diagnosis. The process that is used to diagnose anomalous conditions from monitored system signals varies widely across the different approaches to system diagnosis. Rule-based expert systems, case-based reasoning systems, model-based reasoning systems, learning systems, and probabilistic reasoning systems are examples of the many diverse approaches ta diagnostic reasoning. Many engineering disciplines have specific approaches to modeling, monitoring and diagnosing anomalous conditions. Therefore, there is no "one-size-fits-all" approach to building diagnostic and health monitoring capabilities for a system. For instance, the conventional approaches to diagnosing failures in rotorcraft applications are very different from those used in communications systems. Further, online and offline automated diagnostic applications are integrated into an operations framework with flight crews, flight controllers and maintenance teams. While the emphasis of this paper is automation of health management functions, striking the correct balance between automated and human-performed tasks is a vital concern

Business impacts of web accessibility in the Austrian hotel sector

Diese Magisterarbeit behandelt die wirtschaftlichen Auswirkungen von barrierefreiem Web im Tourismus Bereich und fokussiert dabei insbesondere auf die österreichische Hotelbranche. Ein Literatur Review gibt vorerst einen Überblick über die gegenwärtige Situation von barrierefreiem Web und barrierefreiem Tourismus. Ferner werden mit Hilfe von statistikbasierten Schätzungen wirtschaftliche Auswirkungen von barrierefreiem Tourismus in der österreichischen Hotelbranche ermittelt. Um die Komplexität der Barrierefreiheit in der Tourismusbranche adäquat darstellen zu können, wird ein drei-dimensionales Hotelkategorisierungsmodell entwickelt, mit Hilfe dessen die Evaluierung des Status quo eines Hotels im Hinblick auf Barrierefreiheit vorgenommen werden kann. Darüber hinaus ist dieses Kategorisierungsmodell für weiterführende Benchmarking Aktivitäten einsetzbar. Eine Fallstudien-Analyse vereint quantitative und qualitative Forschungsmethoden und untersucht die betriebswirtschaftlichen Effekte von barrierefreiem Web in der Hotel Branche. Ergebnisse zeigen, dass derzeit nur ein Bruchteil der österreichischen Hotels über eine barrierefreie Webseite verfügt, obwohl durch die Implementierung von Barrierefreiheit zusätzliche Investitionen entfallen würden sowie Vorteile für alle Kunden generiert werden könnten. Die im Zuge dieser Magisterarbeit durchgeführten Studien bekräftigen bisherige Forschungsergebnisse, welche das fehlende Bewusstsein von Tourismus Akteuren in Bezug auf barrierefreies Web als Hauptgrund für die mangelnde Implementierung identifizierten. Ferner kann festgestellt werden, dass Reisende mit Beeinträchtigungen eine wirtschaftlich signifikante Touristengruppe darstellen und in ihren Eigenschaften sehr loyal und zunehmend mobil sind.This contribution aims to investigate the business impacts of web accessibility in the tourism industry with the focus on the Austrian hotel sector. Case study research methods are used for the research. The results of the conducted studies verify previous research, showing that tourism stakeholders considerably lack awareness of web accessibility. The literature review on web accessibility and accessible tourism gives an overview on the current status. Estimations based on statistical data are used to assess the economical impact of accessible tourism in the Austrian hotel sector. A three dimensional hotel categorization model on accessibility is introduced to encompass the complexity of accessibility in the hotel sector. It can be used to evaluate the status quo of hotel accessibility and may therefore provide a valuable tool for further benchmarking activities. Quantitative and qualitative studies are used to assess the implications of web accessibility in the hotel sector in praxis. The study on the accessibility of the Austrian hotel web pages shows that currently only a fraction has implemented web accessibility. The conducted in-depth interviews reveal that implementing web accessibility doesn’t require additional investment and has advantages for all costumers. The interviews also confirm the results of previous studies: travelers with disabilities can represent a significant guest group, are very loyal to the place their like and they are increasingly mobile, this groups of travelers is ever more important. Additionally, this study confirmed that accessibility has to be promoted, so people with disabilities can find these hotels