18,644 research outputs found
Assessing performance of artificial neural networks and re-sampling techniques for healthcare datasets.
Re-sampling methods to solve class imbalance problems have shown to improve classification accuracy by mitigating the bias introduced by differences in class size. However, it is possible that a model which uses a specific re-sampling technique prior to Artificial neural networks (ANN) training may not be suitable for aid in classifying varied datasets from the healthcare industry. Five healthcare-related datasets were used across three re-sampling conditions: under-sampling, over-sampling and combi-sampling. Within each condition, different algorithmic approaches were applied to the dataset and the results were statistically analysed for a significant difference in ANN performance. The combi-sampling condition showed that four out of the five datasets did not show significant consistency for the optimal re-sampling technique between the f1-score and Area Under the Receiver Operating Characteristic Curve performance evaluation methods. Contrarily, the over-sampling and under-sampling condition showed all five datasets put forward the same optimal algorithmic approach across performance evaluation methods. Furthermore, the optimal combi-sampling technique (under-, over-sampling and convergence point), were found to be consistent across evaluation measures in only two of the five datasets. This study exemplifies how discrete ANN performances on datasets from the same industry can occur in two ways: how the same re-sampling technique can generate varying ANN performance on different datasets, and how different re-sampling techniques can generate varying ANN performance on the same dataset
Identifying and responding to people with mild learning disabilities in the probation service
It has long been recognised that, like many other individuals, people with learningdisabilities find their way into the criminal justice system. This fact is not disputed. Whathas been disputed, however, is the extent to which those with learning disabilities arerepresented within the various agencies of the criminal justice system and the ways inwhich the criminal justice system (and society) should address this. Recently, social andlegislative confusion over the best way to deal with offenders with learning disabilities andmental health problems has meant that the waters have become even more muddied.Despite current government uncertainty concerning the best way to support offenders withlearning disabilities, the probation service is likely to continue to play a key role in thesupervision of such offenders. The three studies contained herein aim to clarify the extentto which those with learning disabilities are represented in the probation service, toexamine the effectiveness of probation for them and to explore some of the ways in whichprobation could be adapted to fit their needs.Study 1 and study 2 showed that around 10% of offenders on probation in Kent appearedto have an IQ below 75, putting them in the bottom 5% of the general population. Study 3was designed to assess some of the support needs of those with learning disabilities in theprobation service, finding that many of the materials used by the probation service arelikely to be too complex for those with learning disabilities to use effectively. To addressthis, a model for service provision is tentatively suggested. This is based on the findings ofthe three studies and a pragmatic assessment of what the probation service is likely to becapable of achieving in the near future
Recommended from our members
The Epidemiology and Genetic Architecture of Vitamin D Deficiency in African Children
Vitamin D deficiency is a common public health problem worldwide. However, little is known about the epidemiology of vitamin D deficiency in Africa. In this thesis, I aimed to determine: 1) the prevalence of and risk factors associated with vitamin D deficiency in studies conducted in Africa; 2) the prevalence and predictors of vitamin D deficiency in African children; 3) the association between vitamin D and iron deficiency in African children; and 4) genetic variants that influence vitamin D status in Africans.
In a systematic review and meta-analyses of previous vitamin D studies in Africa, the average prevalence of low vitamin D status was 18.5%, 34.2% and 59.5% using cut-offs of 25-hydroxyvitamin D (25(OH)D) levels of <30 nmol/L, <50 nmol/L and <75 nmol/L, respectively. Populations at risk of vitamin D deficiency included newborns, women, and people living in high latitudes or urban areas.
In an epidemiological study of young children living in Africa, the prevalence of low vitamin D status was 0.6%, 7.8% and 44.5% using cut-offs of 25(OH)D levels of GC2 variant of the group-specific component (GC) gene, which encodes vitamin D binding protein.
Vitamin D deficiency was also associated with 80% higher odds of iron deficiency in these children. Adjusted regression models revealed that vitamin D deficiency was associated with higher ferritin and hepcidin levels suggesting lower iron status, and reduced sTfR and transferrin levels and increased TSAT and serum iron levels suggesting improved iron status.
Genome-wide association study (GWAS) in Africans revealed genetic variants that influence vitamin D status in vitamin D metabolism genes: DHCR7/NADSYN1, CYP2R1 and GC. However, the majority of SNPs from previous European GWASs did not replicate in the current GWAS.
Findings from this thesis indicate that vitamin D deficiency is prevalent in many African populations and should be considered in public health strategies in Africa
Gender diversity and earnings management: the case of female directors with financial background
Past evidence generally suggests that the presence of female directors on corporate boards tends to improve earnings quality due to these directors’ superior monitoring abilities. However, it is not clear which characteristics and skills of female directors drive such abilities. In this paper, we focus on the financial background of female directors, an area which remains largely unexplored in existing literature. The results show that the participation of female directors with relevant financial background improves earnings quality more than the participation of female directors without such background. In addition, our findings suggest that only female directors possessing relevant financial background and having fewer outside directorships are able to mitigate earnings management and therefore overcommitting expert female directors with more outside directorships would diminish their monitoring ability. We did not find any evidence suggesting that female directors without relevant financial background are able to mitigate earnings management, irrespective of their outside directorships or tenure. We interpret our findings within a theoretical framework that draws on a number of economic and social theories. The results are generally robust after controlling for potential endogeneity problems
Sponsorship image and value creation in E-sports
.E-sports games can drive the sports industry forward and sponsorship is the best way to engage consumers of this new sport. The purpose of this study is to examine the effect of sponsorship image and consumer participation in co-creation consumption activities on fans’ sponsorship response (represented by the variables interest, purchase intention and word of mouth) in e-sports. Four antecedent variables build sponsorship image (i.e., ubiquity of sport, sincerity of sponsor, attitude to sponsor and team identification). A quantitative approach is used for the purposes of this study. Some 445 questionnaires were filled in by fans who watch e-sports in Spain; these are analyzed using partial least squares structural equation modeling (PLS-SEM). The outcomes show that sponsor antecedents are crucial factors if a sponsor wants to change their sponsorship image and influence sponsorship response, and that it is also possible to use participation to improve responsesS
Building body identities - exploring the world of female bodybuilders
This thesis explores how female bodybuilders seek to develop and maintain a viable sense of self despite being stigmatized by the gendered foundations of what Erving Goffman (1983) refers to as the 'interaction order'; the unavoidable presentational context in which identities are forged during the course of social life. Placed in the context of an overview of the historical treatment of women's bodies, and a concern with the development of bodybuilding as a specific form of body modification, the research draws upon a unique two year ethnographic study based in the South of England, complemented by interviews with twenty-six female bodybuilders, all of whom live in the U.K. By mapping these extraordinary women's lives, the research illuminates the pivotal spaces and essential lived experiences that make up the female bodybuilder. Whilst the women appear to be embarking on an 'empowering' radical body project for themselves, the consequences of their activity remains culturally ambivalent. This research exposes the 'Janus-faced' nature of female bodybuilding, exploring the ways in which the women negotiate, accommodate and resist pressures to engage in more orthodox and feminine activities and appearances
Recommended from our members
Co-design As Healing: Exploring The Experiences Of Participants Facing Mental Health Problems
This thesis is an exploration of the healing role of co-design in mental health. Although co-design projects conducted within mental health settings are rising, existing literature tends to focus on the object of design and its outcomes while the experiences of participants per se remain largely unexplored. The guiding research question of this study is not how we design things that improve mental health, but how co-designing, as an act, might do so.
The thesis presents two projects that were organized in collaboration with the mental health charity Islington Mind and the Psychosis Therapy Project (PTP) in London.
The project at Islington Mind used a structured design process inviting participants to design for wellbeing. A case study analysis provides insights on how participants were impacted, summarizing key challenges and opportunities.
The design at PTP worked towards creating a collective brief in an emergent fashion, finally culminating in a board game. The experiences of participants were explored through Interpretative Phenomenological Analysis (IPA), using semi-structured interview data. The analysis served to identify key themes characterising the experience of co-design such as contributing, connecting, thinking and intentioning. In addition, a mixed-methods analysis of questionnaires and interview data exploring participants' wellbeing, showed that all participants who engaged fairly consistently in the project improved after the project ended, although some participants' scores returned to baseline six months later.
Reflecting on both projects, an approach to facilitation within mental health is outlined, detailing how the dimensions of weaving and layered participation, nurturing mattering and facilitating attitudes interlace. This contribution raises awareness of tacit dimensions in the practice of facilitation, articulating the nuances of how to encourage and sustain meaningful and ethical engagement and offering insights into a range of tools. It highlights the importance of remaining reflexive in relation to attitudes and emotions and discusses practical methodological and ethical challenges and ways to resolve them which can be of benefit to researchers embarking on a similar journey.
The thesis also offers detailed insights on how methodologies from different fields were integrated into a whole, arguing for transparency and reflexivity about epistemological assumptions, and how underlying paradigms shift in an interdisciplinary context.
Based on the overall findings, the thesis makes a case for considering design as healing (or a designerly way of healing), highlighting implications at a systems, social and individual level. It makes an original contribution to our understanding of design, highlighting its healing character, and proposes a new way to support mental health. The participants in this study not only had increased their own wellbeing through co-designing, but were also empowered and contributed towards healing the world. Hence, the thesis argues for a unique, holistic perspective of design and mental health, recognizing the interconnectedness of the individual, social and systemic dimensions of the healing processes that are ignited
Data-to-text generation with neural planning
In this thesis, we consider the task of data-to-text generation, which takes non-linguistic
structures as input and produces textual output. The inputs can take the form of
database tables, spreadsheets, charts, and so on. The main application of data-to-text
generation is to present information in a textual format which makes it accessible to
a layperson who may otherwise find it problematic to understand numerical figures.
The task can also automate routine document generation jobs, thus improving human
efficiency. We focus on generating long-form text, i.e., documents with multiple paragraphs. Recent approaches to data-to-text generation have adopted the very successful
encoder-decoder architecture or its variants. These models generate fluent (but often
imprecise) text and perform quite poorly at selecting appropriate content and ordering
it coherently. This thesis focuses on overcoming these issues by integrating content
planning with neural models. We hypothesize data-to-text generation will benefit from
explicit planning, which manifests itself in (a) micro planning, (b) latent entity planning, and (c) macro planning. Throughout this thesis, we assume the input to our
generator are tables (with records) in the sports domain. And the output are summaries
describing what happened in the game (e.g., who won/lost, ..., scored, etc.).
We first describe our work on integrating fine-grained or micro plans with data-to-text generation. As part of this, we generate a micro plan highlighting which records
should be mentioned and in which order, and then generate the document while taking
the micro plan into account.
We then show how data-to-text generation can benefit from higher level latent entity planning. Here, we make use of entity-specific representations which are dynam ically updated. The text is generated conditioned on entity representations and the
records corresponding to the entities by using hierarchical attention at each time step.
We then combine planning with the high level organization of entities, events, and
their interactions. Such coarse-grained macro plans are learnt from data and given
as input to the generator. Finally, we present work on making macro plans latent
while incrementally generating a document paragraph by paragraph. We infer latent
plans sequentially with a structured variational model while interleaving the steps of
planning and generation. Text is generated by conditioning on previous variational
decisions and previously generated text.
Overall our results show that planning makes data-to-text generation more interpretable, improves the factuality and coherence of the generated documents and re duces redundancy in the output document
Detection of Hyperpartisan news articles using natural language processing techniques
Yellow journalism has increased the spread of hyperpartisan news on the internet. It is very difficult for online news article readers to distinguish hyperpartisan news articles from mainstream news articles. There is a need for an automated model that can detect hyperpartisan news on the internet and tag them as hyperpartisan so that it is very easy for readers to avoid that news. A hyperpartisan news detection article was developed by using three different natural language processing techniques named BERT, ELMo, and Word2vec. This research used the bi-article dataset published at SEMEVAL-2019. The ELMo word embeddings which are trained on a Random forest classifier has got an accuracy of 0.88, which is much better than other state of art models. The BERT and Word2vec models have got the same accuracy of 0.83. This research tried different sentence input lengths to BERT and proved that BERT can extract context from local words. Evidenced from the described ML models, this study will assist the governments, news’ readers, and other political stakeholders to detect any hyperpartisan news, and also helps policy to track, and regulate, misinformation about the political parties and their leaders
- …