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National University of Ireland, Galway, Ireland

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73 Papers

Driving the Bus: A Radiology Case Study Utilizing Rich Picture Diagramming and CATWOE to Capture Staff Perceptions and Inform Service Improvement Scenarios

Topics: Communication, Collaboration and Information Sharing; Knowledge Management Projects; Healthcare Information Systems; Tools and Technologies for Knowledge Management

Authors: Mary Conlon ^{1

;

2} , Owen Molloy ^{1

;

2} and Nora ZolzerBryce ^{1

;

2}

Affiliations: ¹ College of Engineering and Informatics, National University of Ireland, Galway, Ireland , ² Irish Wheelchair Association, Ireland

Digital Object Identifier: 10.5220/0010145600880099

Proceedings of KMIS 2020, ISBN: 978-989-758-474-9

Quick Abstract

Introduction: Demand for computed tomography (CT) services is increasing. This empirical work in a CT setting, examines how qualitative methods were used to identify staff perceptions and opportunities for service improvement. The use of soft systems methodologies (SSM) as an action research tool in radiology is considered. Methods: Hospital Staff were interviewed to create a root definition of the CT service. In a diagramming session, a rich picture (RP) was created and augmented with staff input. Utilizing the RP, a session was facilitated with radiology decision makers to identify a list of culturally desirable and feasible process improvement scenarios. Results: Root definitions were created of the CT service from the perspective of the staff. The RP graphically illustrated the key features of the CT service and represented a shared understanding of the service. A targeted set of culturally feasible and desirable service improvement recommendations were identified. Four directly attributable implemented workflow changes were identified. Conclusion: RP diagramming provided an opportunity to involve staff in research while capturing their perceptions and resulted in a shared understanding as well as targeted opportunities for CT service improvement. The implemented workflow changes resulting from the SSM approach demonstrated its use as an action research tool.

Knowledge Management in Healthcare: Information Requirements When Creating a Decision Support Tool in Radiology

Topics: Business Intelligence & CRM; Tools and Technology for Knowledge Management; Business Process Management; Healthcare Information Systems

Authors: Mary Conlon and Owen Molloy

Affiliation: College of Engineering and Informatics, National University of Ireland and Galway

Digital Object Identifier: 10.5220/0008352703170324

Proceedings of KMIS 2019, ISBN: 978-989-758-382-7

Quick Abstract

Introduction: This empirical work examines the information requirements when undertaking a process modelling project in a Healthcare setting such as a CT (Computed Tomography) department. Using qualitative and quantitative methods we map the process, incorporating patient, staff and process related components so as to quantify resource utilisation and the service experienced by the patient. Method: In this study, semi structured interviews are used to identify patient complexity factors/characteristics. Process mapping and involvement of stakeholders are discussed as is the identification and analysis of data. A discrete event simulation (DES) model of the process is designed and performance metrics identified. Results: Yearly demand for Radiology services are increasing significantly. Factors determining patient complexity and variation include patient type, infectiousness, mobility, exam type and patient care needs. A strong correlation between age and infectiousness was observed. Conclusion: DES modelling, though data intensive, provides decision makers with insights into resource utilisation, process capacity, delays and disruptions and in doing so supports operations, management and the adoption of good practices in Healthcare.

Related Ontology Subjects/Areas/Topics (12)

Applications	e-Business	Knowledge-Based Systems
Artificial Intelligence	Enterprise Engineering	Software Engineering
Business Intelligence	Enterprise Information Systems	Symbolic Systems
Business Process Management	Knowledge Management and Information Sharing	Tools and Technology for Knowledge Management

Measuring Individuals’ Knowledge, Attitude and Behaviour on Specific Ocean Related Topics

Topic: Studies, Metrics & Benchmarks

Authors: Conor McCrossan and Owen Molloy

Affiliation: National University of Ireland, University Road, Galway and Ireland

Digital Object Identifier: 10.5220/0008353003250332

Proceedings of KMIS 2019, ISBN: 978-989-758-382-7

Quick Abstract

In order to measure the effectiveness of Ocean Literacy (OL) tools we can measure people’s knowledge of, and attitude and behaviour towards, specific ocean-related topics, both before and after their use of the tool. The research described in this paper aims at development of more accurate, focused survey tools. In particular we are interested in ensuring that we can accurately assess knowledge on specific topics, rather than assessing broad ocean literacy levels. Surveys were created to measure the levels of knowledge, attitude, and behaviour of university students. The topics which the surveys focused on were micro-plastics, coastal tourism, and sustainable fisheries. The knowledge, attitude, and behaviour questions in the surveys are based on work carried out as part of the H2020 ResponSEAble project on Ocean Literacy. The results show that while the students have a high level of pro-ocean-environmental attitude, their existing behaviour is low to medium, and their future intended behaviour is at a higher level than their existing behaviour. The findings provide useful pointers on how to improve both the ocean literacy tools (no statistically significant correlation between knowledge and either attitude or behaviour) as well as the design of the survey and questions themselves.

Related Ontology Subjects/Areas/Topics (5)

Artificial Intelligence		Knowledge-Based Systems		Symbolic Systems
Knowledge Management and Information Sharing		Studies, Metrics & Benchmarks

The Impact of Environmental and Evolutionary Factors on the Emergence of Cooperation among Evolved Mobile Agents

Topics: Evolutionary Computing; Cooperation and Coordination; Mobile Agents

Authors: Maud Gibbons , Josephine Griffith and Colm O’Riordan

Affiliation: Discipline of Information Technology, National University of Ireland, Galway and Ireland

Digital Object Identifier: 10.5220/0007344500690079

Proceedings of ICAART 2019, ISBN: 978-989-758-350-6

Quick Abstract

This paper presents work investigating the influence of various environmental and evolutionary factors on the evolution of cooperation in a spatial game theoretical setting. These include agent mobility, population density, agent lifespan, and the placement mechanism. In the model considered, a population of agents inhabit a toroidal lattice grid, in which they participate in the Prisoner’s Dilemma game. The agents have the ability to respond to, and learn from, environmental stimuli. In particular, agents learn movement strategies to compete with other agents in the game, which may result in improved payoffs by increasing the number of beneficial interactions. We compare the levels of cooperation and the corresponding movement strategies evolved under the various environmental and evolutionary settings. We present results indicating that, given suitable densities and evolutionary settings, cooperators in well-mixed populations develop a suitable movement strategy to promote the evolution of cooperation. Additionally, we show that cooperation may emerge without significant aid from mobile strategies given a placement mechanism conducive to the formation of cooperator clusters.

Related Ontology Subjects/Areas/Topics (7)

Agents	Cooperation and Coordination	Mobile Agents
Artificial Intelligence	Evolutionary Computing	Soft Computing
Computational Intelligence

Comparing Small Population Genetic Algorithms over Changing Landscapes

Topic: Genetic Algorithms

Authors: Michael Curley and Seamus Hill

Affiliation: National University of Ireland Galway, Ireland

Digital Object Identifier: 10.5220/0006497802390246

Proceedings of IJCCI 2017, ISBN: 978-989-758-274-5

Quick Abstract

This paper examines the performance and adaptability of a number of small population Genetic Algorithms (GAs) over a selection of dynamic landscapes. Much of the research in this area tends to focus on GA with relatively large populations for problem optimisation. However there is research, which suggests that GAs with smaller populations can also be effective over changing landscapes. This research compares the performance and adaptability of a number of these small population GA over changing landscapes. With small population GAs, convergence can occur quickly, which in turn affects the adaptability of a GA over dynamic landscapes. In this paper five GA variants using small population sizes are run over well-known unimodal and multimodal problems, which were tailored to produce dynamic landscapes. Adaptability within the population is considered a desirable feature for a GA to optimise a changing landscape and different methods are used to maintain a level of diversity within a population to avoid the problem of premature convergence, thereby allowing the GA population adapt to the dynamic nature of the search space. Initial results indicate that small population GAs can perform well in searching changing landscapes, with GAs which possess the ability to maintain diversity within the population, outperforming those that do not.

Related Ontology Subjects/Areas/Topics (7)

Artificial Intelligence	Genetic Algorithms	Intelligent Control Systems and Optimization
Computational Intelligence	Informatics in Control, Automation and Robotics	Soft Computing
Evolutionary Computing

Improving Document Clustering Performance: The Use of an Automatically Generated Ontology to Augment Document Representations

Topics: Clustering and Classification Methods; Context Discovery

Authors: Stephen Bradshaw ¹ , Colm O'Riordan ¹ and Daragh Bradshaw ²

Affiliations: ¹ National University Ireland Galway, Ireland , ² National University Limerick, Ireland

Digital Object Identifier: 10.5220/0006500202150223

Proceedings of KDIR 2017, ISBN: 978-989-758-271-4

Quick Abstract

Clustering documents is a common task in a range of information retrieval systems and applications. Many approaches for improving the clustering process have been proposed. One approach is the use of an ontology to better inform the classifier of word context, by expanding the items to be clustered. Wordnet is commonly cited as an appropriate source from which to draw the additional terms; however, it may not be sufficient to achieve strong performance. We have two aims in this paper: first, we show that the use of Wordnet may lead to suboptimal performance. This problem may be accentuated when a document set has been drawn from comments made in social forums; due to the unstructured nature of online conversations compared to standard document sets. Second, we propose a novel method which involves constructing a bespoke ontology that facilitates better clustering. We present a study of clustering applied to a sample of threads from a social forum and investigate the effectiveness of the application of these methods.

Related Ontology Subjects/Areas/Topics (6)

Artificial Intelligence		Context Discovery		Knowledge-Based Systems
Clustering and Classification Methods		Knowledge Discovery and Information Retrieval		Symbolic Systems

Passage Level Evidence for Effective Document Level Retrieval

Topics: Mining Text and Semi-Structured Data; Information Extraction

Authors: Ghulam Sarwar , Colm O'Riordan and John Newell

Affiliation: National University of Ireland and Galway, Ireland

Digital Object Identifier: 10.5220/0006502800830090

Proceedings of KDIR 2017, ISBN: 978-989-758-271-4

Quick Abstract

Several researchers have considered the use of passages within documents as useful units of representation as individual passages may capture accurately the topic of discourse in a document. In this work, each document is indexed as a series of unique passages. We explore and analyse a number of similarity measures which take into account the similarity at passage level with the aim of improving the quality of the answer set. We define a number of such passage level approaches and compare their performance. Mean average precision (MAP) and precision at k documents (P@k) are used as measures of the quality of the approaches. The results show that for the different test collections, the rank of a passage is a useful measure, and when used separately or in conjunction with the document score can give better results as compared to other passage or document level similarity approaches.

Related Ontology Subjects/Areas/Topics (6)

Artificial Intelligence		Knowledge Discovery and Information Retrieval		Mining Text and Semi-Structured Data
Information Extraction		Knowledge-Based Systems		Symbolic Systems

An Analysis of Online Twitter Sentiment Surrounding the European Refugee Crisis

Topics: Mining Multimedia Data; Bioinformatics & Pattern Discovery; Visual Data Mining and Data Visualization; Software Development; Information Extraction

Authors: David Pope and Josephine Griffith

Affiliation: National University of Ireland Galway, Ireland

Digital Object Identifier: 10.5220/0006051902990306

Proceedings of KDIR 2016/IC3K, ISBN: 978-989-758-203-5

Quick Abstract

Using existing natural language and sentiment analysis techniques, this study explores different dimensions of mood states of tweet content relating to the refugee crisis in Europe. The study has two main goals. The first goal is to compare the mood states of negative emotion, positive emotion, anger and anxiety across two populations (English and German speaking). The second goal is to discover if a link exists between significant real-world events relating to the refugee crisis and online sentiment on Twitter. Gaining an insight into this comparison and relationship can help us firstly, to better understand how these events shape public attitudes towards refugees and secondly, how online expressions of emotion are affected by significant events.

Related Ontology Subjects/Areas/Topics (9)

Artificial Intelligence	Knowledge Discovery and Information Retrieval	Software Development
BioInformatics & Pattern Discovery	Knowledge-Based Systems	Symbolic Systems
Information Extraction	Mining Multimedia Data	Visual Data Mining and Data Visualization

An Evolutionary Approach to Formation Control with Mobile Robots

Topics: Genetic Algorithms; Artificial Life; Evolutionary Robotics and Intelligent Agents; Co-Evolution and Collective Behavior; Swarm/Collective Intelligence

Authors: Jane Holland , Josephine Griffith and Colm O'Riordan

Affiliation: National University of Ireland, Ireland

Digital Object Identifier: 10.5220/0006068602250230

Proceedings of ECTA 2016/IJCCI, ISBN: 978-989-758-201-1

Quick Abstract

The field of swarm robotics studies multi-robot systems, emphasising decentralised and self-organising behaviours that deal with limited individual abilities, local sensing and local communication. A robotic system needs to be flexible to environmental changes, robust to failure and scalable to large groups. These desired features can be achieved through collective behaviours such as aggregation, synchronisation, coordination and exploration. We aim to analyse these emerging behaviours by applying an evolutionary approach to a specific robotic system, called the Kilobot, in order to learn behaviours. If successful, not only would the cost and computation time for evolutionary computation in mobile robotics decrease, but the reality-gap could also narrow.

Related Ontology Subjects/Areas/Topics (11)

Artificial Intelligence	Evolutionary Computing	Intelligent Control Systems and Optimization
Artificial Life	Evolutionary Robotics and Intelligent Agents	Soft Computing
Co-Evolution and Collective Behavior	Genetic Algorithms	Swarm/Collective Intelligence
Computational Intelligence	Informatics in Control, Automation and Robotics

Diversifying Techniques & Neutrality in Genetic Algorithms

Topics: Genetic Algorithms; Representation Techniques

Authors: Seamus Hill and Colm O'Riordan

Affiliation: National University of Ireland, Ireland

Digital Object Identifier: 10.5220/0006036201400147

Proceedings of ECTA 2016/IJCCI, ISBN: 978-989-758-201-1

Quick Abstract

This paper examines the implicit maintenance of diversity within a population through the inclusion of a layered genotype-phenotype map (GP-map) in a Genetic Algorithm (GA), based on the principal of Neutral theory. The paper compares a simple GA (SGA), incorporating a variety of diversifying techniques, to the multi-layered GA (MGA) as proposed by the authors. The MGA creates a neutral representation by including a layered GP-map based on the biological concepts of Transcription and Translation. In standard GAs, each phenotype is represented by a distinct genotype. However by allowing a higher number of alleles to encode phenotypic information on the genotype, one can create a situation where a number of genotypes may represent the same phenotype. Through this process one can introduce the idea of redundancy or neutrality into the representation. This representation allows for adaptive mutation (hot spots) and silent mutation (cold spots). This combination enables the level of diversity to dynamically adjust during the search, and directs the search towards closely related neutral sets. Previous work has shown that introducing this type of representation can be beneficial; in this paper we show how this representation is useful at introducing and maintaining diversity. Here we compare the performance of the MGA against traditional diversifying techniques used in conjunction with a SGA over a fully deceptive changing landscape.

Related Ontology Subjects/Areas/Topics (8)

Artificial Intelligence	Genetic Algorithms	Representation Techniques
Computational Intelligence	Informatics in Control, Automation and Robotics	Soft Computing
Evolutionary Computing	Intelligent Control Systems and Optimization

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