1,720,973 research outputs found
Resource Optimization in Heterogeneous Distributed Data Stream Mining with Performance Assessment on Asthma Hospitalization Predictive Modeling
No full textThe Big Data Era has presented many opportunities for data mining techniques to discover knowledge patterns across an exponentially growing diverse collection of data. In many application domains, data exists in a distributed fashion across geographical locations; as such, the nature of the data collected by each location may differ from its peer nodes in its network, thus causing heterogeneity in the data. Such scenarios of distributed databases require mining methods that are distinct from traditional homogeneous distributed databases where the structure is identical across locations. The current demand is to analyze real-time data; consequently, stream computing is becoming a popular choice. Data generated continuously at a high pace at distributed sites or locations are termed as distributed data streams. Recent approaches toward Distributed Data Mining (DDM) have focused on addressing the heterogeneous nature of data sources. However, such approaches do not prioritize the reduction of data communication costs which could be prohibitive in large-scale sensor networks where bandwidth is a limited resource. In fact, higher communication and computational costs are the two most prominent problems encountered in heterogeneous distributed environments. An effort to decrease communication in the distributed environment adversely influences classification accuracy; therefore, a research challenge lies in maintaining a balance between transmission cost, computational cost, and accuracy.
This research covers the heterogeneous distributed data mining problem, extendable to the case where data arrives continuously in streaming mode. We propose a suite of algorithms to address specific issues in mining data from heterogeneous distributed streaming settings. Our experimental testing reveals that performance efficiency can be achieved across a wide range of datasets. The first algorithm, Performance Optimizer in Distributed Stream Mining (PODSM), having its roots in Bayesian Inference, is targeted towards reducing the communication volume and resource time in a heterogeneous DDM environment while retaining prediction accuracy. A reduction of 34.66% in communication was obtained for one of the datasets with nearly 27% savings in resource time. The second algorithm, Minimized Tree for Distributed Mining (MTDM), presents an efficient and robust method for learning the relationship between various distributed sites using a tree. In this regard, a saving of 37.65% in resource time has been reported for one dataset while improving the accuracy by 1.33%. To assess the algorithms’ competency, we validated them on a case study built using real datasets from real-world sources to predict demands for asthma-related emergency hospitalizations into Low or High classes. Considerable savings in terms of communication and resource time were attained upon execution of PODSM and MTDM while preserving accuracy levels, thus portraying their potential to achieve a good trade-off between accuracy and resource utilization. The study concludes that PODSM and MTDM are proficient in conjoint servicing heterogeneous distributed data sources in any resource-constrained scenario. Moreover, the capability of the algorithms to maintain a balance between accuracy, communication, and resource time makes them flexible enough for a diverse range of applications
The Use of Mobile Technology in Addressing Medication Adherence: A Mixed Methods Study
No full textIt has been estimated that every year in New Zealand, hundreds of thousands of medicines are dispensed to patients but never used. Ensuring patients’ adherence to prescribed medication is a significant challenge, and lack of Medication Adherence (MA) creates a burden on the overloaded healthcare system. Studies have reported that MA is estimated to cause at least 10% of unplanned hospitalisations and approximately 125,000 deaths annually in the US alone. Recent studies have highlighted the role of technology in providing interventions that contribute to MA. The most common technological interventions to assist MA fall under the umbrella of digital technology like text messaging, voice calls, mobile applications and digital pill boxes. However, the efficacy of these solutions varied from one study to another for several reasons, including variations in users’ acceptance, performance expectancy, and facilitating conditions that lead to low usage or abandonment and, therefore, a lack of sustainability. What remains unclear is the role of end-users and the factors that contribute to the usage of these types of technologies. Our research aims to investigate the use of digital technology in addressing medication adherence. This thesis has four primary objectives: (1) to investigate the perceptions of multidisciplinary experts (e.g. health providers, health system designers and health informatics researchers) in New Zealand on mHealth interventions in addressing MA; (2) to co-design and develop a mobile app Minimum Viable Product (MVP) based on the theoretical investigation; (3) to evaluate the MVP with end-users through focus-groups; and (4) to assess the feasibility, acceptability and efficacy of the MVP by end-users through a pilot study. To achieve our aims, we followed a Design Science Research (DSR) methodology, and a complex mixed-methods design was implemented to include the integration of multiple forms of data collection at different stages of the study (i.e. questionnaires, interviews, focus groups and a pilot study) as well as multiple forms of data analysis (grounded theory, descriptive statistics, statistical analysis) to answer the research questions. The results from the quantitative study (questionnaire) informed the qualitative study (interviews and focus groups), which in turn validated the qualitative results. Purposeful sampling was utilised throughout the phases of the study. Descriptive and exploratory data analyses were performed for the quantitative data and inductive thematic analysis for the qualitative data. The findings of the quantitative and qualitative parts of the study were mixed and integrated at different points of the DSR methodology. The results show that four main features contributed to improving MA: (1) medication reminders using multi-channel notifications; (2) medication intake acknowledgement and history reporting; (3) auto-loading of medication; and (4) caregiver involvement. These novel features remained significant when built into the MVP and benefited the end-users in improving their medication intake during the pilot. This study confirmed that mHealth could have a significant impact on improving MA. Our findings from the rigorous iterative design process with end-users produced novel results validated through the trial by end-users. This work indicated that medication management applications could gain and sustain high usage, when integrated with an extensive digital health system, and can successfully keep the patient at the centre of care, connected and informed
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
Author-wise bibliometric analysis based on entropy.
No full textAuthor-wise bibliometric analysis based on entropy.</p
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