1,720,959 research outputs found
Cloud-based zero-trust and data quality management context aware system
Full text linkThe complex nature of modern healthcare systems and the widespread distribution of healthcare infrastructure made the interoperability within healthcare information system challenging. This could poses security risks, missing data, miscommunication , in addition to the human and technical-based errors. This dissertation focuses on utilizing an advanced AI system to overcome the challenges of clinical analysis, data confidentiality, availability, and integrity.
There are three main contributions of this research. First, implement TongueTransUNet, which is a well-managed architecture that utilizes a vision transformer, UNet encoder-decoder convolutional neural network, contrastive loss and quality control process supported with human-reinforcement feedback to extract tongue fingerprint. Second, design ZTCloudGuard for access control within the telehealth cloud-based eco-system between. The architecture manage users, devices, and output attributes by deriving a score to assess the mutual relationship considering semantic and syntactic analysis. Third, utilize hybrid qualitative and quantitative evaluation metrics and conduct comparative analysis to other related research. The main applications to this research are minimizing medical errors, protecting healthcare practitioners, detecting unrelated input and undesired output.
An ablation study using synthetic healthcare information attributes and word2vec model was conducted to judge the model results. The outcomes showed robustness and enhancement by focusing on high-quality input and rejecting unacceptable data. If the automatic process fails or goes below a predefined threshold, an extra reinforcement verification layer is introduced to the algorithm to add manual and human feedback.Graduat
Optimized hardware accelerators for data mining applications
Full text linkData mining plays an important role in a variety of fields including bioinformatics, multimedia, business intelligence, marketing, and medical diagnosis. Analysis of today’s
huge and complex data involves several data mining algorithms including clustering and
classification. The computational complexity of machine learning and data mining algorithms, that are frequently used in today’s applications such as embedded systems, makes the design of efficient hardware architectures for these algorithms a challenging issue for the development of such systems. The aim of this work is to optimize the performance of hardware acceleration for data mining applications in terms of speed and area. Most of the previous accelerator architectures proposed in the literature have been obtained using ad hoc techniques that do not allow for design space exploration, some did not consider the size (number of samples) and dimensionality (number of features in each sample) of the datasets. To obtain practical architectures that are amenable for hardware implementation, size and dimensionality of input datasets are taken into consideration in this work. For one-dimensional data, algorithm-level optimizations are investigated to design a fast and area-efficient hardware accelerator for clustering one-dimensional datasets using the well-known K-Means clustering algorithm. Experimental results show that the optimizations adopted in the proposed architecture result in faster convergence of the algorithm using less hardware resources while maintaining the quality of clustering results. The computation of similarity distance matrices is one of the computational kernels that are generally required by several machine learning and data mining algorithms to measure the degree of similarity between data samples. For these algorithms, distance calculation is considered a computationally intensive task that accounts for a significant portion of the processing time. A systematic methodology is presented to explore the design space of 2-D and 1-D processor array architectures for similarity distance computation involved in processing datasets of different sizes and dimensions. Six 2-D and six 1-D processor array architectures are developed systematically using linear scheduling and projection operations. The obtained
architectures are classified based on the size and dimensionality of input datasets, analyzed in terms of speed and area, and compared with previous architectures in the literature. Motivated by the necessity to accommodate large-scale and high-dimensional data, nonlinear scheduling and projection operations are finally introduced to design a scalable processor array architecture for the computation of similarity distance matrices. Implementation results of the proposed architecture show improved compromise between area and speed. Moreover, it scales better for large and high-dimensional datasets since the architecture is fully parameterized and only has to deal with one data dimension in each time step.Graduate2019-12-3
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe 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
Full text link“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
Full text linkWe 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
Full text linkWe 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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