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Browsing by Author "Obembe, Funmi"

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    Covid-19 and the tourism industry: an early-stage sentiment analysis of the impact of social media and stakeholder communication
    (Elsevier, 2021-09-28) Obembe, Demola; Kolade, Oluwaseun; Obembe, Funmi; Owoseni, Adebowale; Mafimisebi, Oluwasoye
    This paper examines tourist public responses to crisis communications during the early stages of Covid-19. Using the social-mediated crisis communication model, the paper explores the key factors that influence public sentiments during nascent periods of the crisis. The choice of data collection dates was determined by key milestones events with significant implications in relation to UK tourism. Sentiment analysis of data sets of public tweets and news articles were done in order to interrogate how the trends and performance of the airlines and the tourism sector have been shaped by the sentiments of the tourism publics, the crisis communication interventions from key institutional actors, and the news sentiments about tourism organizations, particularly airlines. Sentiment analysis, also known as opinion mining, falls under natural language processing (NLP) and is used to identify different sentiments and polarities in texts. Our findings indicate that institutional actors have a significant impact on the sentiments of tourism publics. Our study contributes to existing research on crisis communication by illuminating how public narrative about, and stakeholder responses to, crisis are shaped not just by organizational communication strategies but also institutional actors, on the one hand, and the interested publics too.
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    Deep Learning and Tacit Knowledge Transfer – An Exploratory Study
    (2020-12) Obembe, Funmi; Obembe, Demola
    In 1966, Michael Polanyi wrote his seminal piece on the ‘tacitness’ of knowledge, essentially bringing to the fore, the non-codifiability of knowledge and the possibility for individuals to know more than they are able to express. Nearly thirty years later Nonaka and Takeuchi (1995) popularised the possibility for knowledge conversion between the tacit and explicit dimensions of knowledge. They proposed that organisations are able to create knowledge through a spiral of interactions between socialisation, externalisation, combination and internalisation of knowledge. Since then, various attempts have been made to develop mechanisms for codifying tacit knowledge including; storytelling, modelling, and more recently, various artificial intelligence/machine learning algorithms. In this study we examine the use of deep learning for representing, codifying and eventually transferring tacit knowledge. We draw on existing research on the role of artificial intelligence in Knowledge Management as well as current works on Deep learning to explore the potential role that deep learning can play in the learning, representation and transfer of tacit knowledge. Deep learning, as a subset of machine learning in artificial intelligence which provides algorithms that mimic the way the brain works and offers significant prospects for knowledge externalisation. Specifically, it can provide a means for representing knowledge in a different manner to human representation. This alternative machine representation is premised on the notion that if tacit knowledge can be learned and represented in a way that can then be codified, the knowledge modelled in such a way is then transferable. Arguably, where deep learning is able to capture and represent tacit knowledge, the ability for knowledge to be codified and externalised will increase exponentially and invariably constitute a significant breakthrough in the ability for both individuals and organisations to access and combine existing knowledge as well as to create new knowledge.
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    Developing a Probabilistic Graphical Structure from a Model of Mental-Health Clinical Risk Expertise
    (Springer, 2010-09) Obembe, Funmi; Buckingham, Christopher D
    This paper explores the process of developing a principled approach for translating a model of mental-health risk expertise into a probabilistic graphical structure. The Galatean Risk Screening Tool [1] is a psychological model for mental health risk assessment based on fuzzy sets. This paper details how the knowledge encapsulated in the psychological model was used to develop the structure of the probability graph by exploiting the semantics of the clinical expertise. These semantics are formalised by a detailed specification for an XML structure used to represent the expertise. The component parts were then mapped to equivalent probabilistic graphical structures such as Bayesian Belief Nets and Markov Random Fields to produce a composite chain graph that provides a probabilistic classification of risk expertise to complement the expert clinical judgements.
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    Graphical Modelling in Mental Health Risk Assessments
    (2010-11) Obembe, Funmi; Buckingham, Christopher D
    Probabilistic models can be a combination of graph and probability theory that provide numerous advantages when it comes to the representation of domains involving uncertainty. In this paper, we present the development of a chain graph for assessing the risks associated with mental health problems, which is a domain that has high amounts of inherent uncertainty. The Galatean mental health Risk and Social care Tool, GRiST, has been developed to support mental-health risk assessments by using a psychological model to represent the expertise of mental-health practitioners. It is a hierarchical knowledge structure based on fuzzy sets for reasoning with uncertainty. This paper describes how a chain graph can be developed from the psychological model to provide a probabilistic evaluation of risk that complements the one generated by GRiST’s clinical expertise.
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    The Impact of Digital Transformation on Knowledge Management During COVID-19
    (European Conference on Knowledge Management (ECKM), 2021-09) Obembe, Funmi; Obembe, Demola
    Technology and digital transformations are increasingly important in today’s world. The COVID-19 pandemic that the entire world has been grappling with for the last year has made this even more so. The speed at which different organisations across various sectors have had to embrace digital transformations has been unprecedented. In some sectors this has been driven by the need to simply survive during this pandemic. However, beyond just responding to crisis, digital transformation, and the use of data to drive it has over the years brought about disruptions which have led to great innovations and progress. In many instances these innovations have not only been driven by digital transformations but by a merging of digital transformations and intelligent/adaptive knowledge management systems that have arisen from it. Even before the emergence of the pandemic, digital transformations, AI, machine learning techniques and various innovative technologies had started to be used to design intelligent and adaptive knowledge management systems. COVID-19 has greatly accelerated the uptake of these technologies across a wide range of sectors. Organisations that would successfully navigate these times and be ready for the future need their knowledge management systems to be intelligent and highly adaptive. Digital transformations and innovative technologies are increasingly making this possible. In this work in progress paper, we start to explore the impact of digital transformation and innovative technologies on organisations’ knowledge management systems and the changes in the factors that contribute to whether organisations adopt these innovative technologies/digital transformations in times of crisis such as during the COVID-19 pandemic. Knowledge management systems that can respond to inevitable changes that arise in crisis situations such as COVID-19 are invaluable. These systems are positioned to naturally produce actionable intelligence resulting in competitive advantage.
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    An Open Data Academic Portal: A Preliminary Study
    (2020-03) Obembe, Funmi
    The use of appropriate data in education is crucial and the current deluge of Open data portals provide numerous opportunities to marry the right datasets with relevant research and teaching. However, for academics finding the right datasets can be challenging, this is the gap that the Open Data Academic Portal (ODAP) looks to bridge. A data portal that points to numerous datasets available in various Open data portals whilst also classifying and grouping them into datasets based on discipline areas and research type categories. For instance, categories such as machine learning, data analytics and further subcategories under these (such as collaborative filtering under machine learning) would be provided. The grouping and matching of datasets from various portals will be done using algorithms developed to harvest meta data from data portals and categorise and classify them using information available in these files. For instance, for systems running CKAN (the standard open source software for Open Data Portals) the algorithm will make use of the tags and group fields in the meta data for the various datasets. The expected outcomes are the provision of an application that provides a means of easy access to relevant data for use in academia for effective teaching and research using real world data. This would also facilitate results that are of benefit not just in the academic sphere but also in society as a whole.
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    The Use of Gamification as an Innovative Practice Pedagogy to Enhance Student Engagement during COVID-19 Pandemic
    (De Montfort University Press, 2022-05-11) Obembe, Funmi
    In this paper I share my experience of using gamification on a Big Data Analytics module to enhance student engagement with online teaching and learning during the COVID-19 p.andemic
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