1,721,316 research outputs found
Data Extraction for Improved Prediction Outcomes in Organ Transplantation - A Hybrid Approach
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Multimodal Fuzzy Fusion for Biometric Identity Management
No full textBiometric identity management based only on the single biometric modality is not accurate or robust enough to be used in uncontrolled environments. This paper describes a fusion of face and voice biometric traits, based on fuzzy logic approach for speaker identity verification. In this approach, a scheme based on membership function and fuzzy integral is proposed to fuse information from the two modalities. Equal Error rate is used to evaluate the fusion scheme. Experimental results show the fusion scheme improves identity verification performance substantially and makes the system robust to environmental degradations such as acoustic noise and visual compression artefact
Deep learning in gene expression modeling
No full textDeveloping computational intelligence algorithms for learning insights from data has been a growing intellectual challenge. Much advances have already been made through data mining but there is an increasing research focus on deep learning to exploit the massive improvement in computational power. This chapter presents recent advancements in deep learning research and identifies some remaining challenges as drawn from using deep learning in the application area of gene expression modelling. It highlights deep learning (DL) as a branch of Machine Learning (ML), the various models and theoretical foundations, its motivations as to why we need deep learning in the context of evolving Big Data, particularly in the area of gene expression level classification. We present a review, and strengths and weaknesses of various DL models and their computational power to specific to gene expression modeling. Deep learning models are efficient feature selectors and therefore work best in high dimension datasets. We present major research challenges in feature extraction and selection using different deep models. Our case studies are drawn from gene expression datasets. Hence we report some of the key formats of gene expression datasets used for deep learning. As ongoing research we will discuss the future prospects of deep learning for gene expression modelling.</p
Use of Artificial Neural Networks in the Prediction of Kidney Transplant Outcomes
No full textTraditionally researchers have used statistical methods to predict medical outcomes. However, statistical techniques do not provide sufficient in-formation for solving problems of high complexity. Recently more attention has turned to a variety of artificial intelligence modeling techniques such as Artificial Neural Networks (ANNs), Case Based Reasoning (CBR) and Rule Induction (RI). In this study we sought to use ANN to predict renal transplantation outcomes. Our results showed that although this was possible, the positive predictive power of the trained ANN was low, indicating a need for improvement if this approach is to be useful clinically. We also highlight potential problems that may arise when using incomplete clinical datasets for ANN train-ing including the danger of pre-processing data in such a way that misleading high predictive value is obtaine
Advances in smart, multimedia and computer gaming technologies
No full textThe chapter summarizes the contents of this book highlighting recent advances in smart systems, multimedia, and serious gaming technologies through a fusion of these approaches. Such fusion is a nascent area that potentially can hybridize the features and advantages of the relevant areas, and, as a result, provide users with advanced and enhanced functionality and features, which currently does not exist.</p
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
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