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Faculty of Accounting and Informatics

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Author: search.filters.author.Agbehadji, Israel Edem

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    Health information system and health care applications performance in the healthcare arena : a bibliometric analysis
    (MDPI AG, 2022-11-12) Epizitone, Ayogeboh; Moyane, Smangele Pretty; Agbehadji, Israel Edem
    There have been several studies centred on health information systems with many insights provided to enhance health care applications globally. These studies have provided theoretical schemes for fortifying the enactment and utilisation of the Health Information System (HIS). In addition, these research studies contribute greatly to the development of HIS in alignment with major stakeholders such as health practitioners and recipients of health care. Conversely, there has been trepidation about HIS' sustainability and resilience for healthcare applications in the era of digitalization and globalization. Hence, this paper investigates research on HIS with a primary focus on health care applications to ascertain its sustainability and resilience amidst the transformation of the global healthcare space. Therefore, using a bibliometric approach, this paper measures the performance of health information systems and healthcare for health care applications using bibliometric data from the web of science database. The findings reveal solid evidence of the constructive transformation of health information systems and health care applications in the healthcare arena, providing ample evidence of the adaptation of HIS and health care applications within the healthcare arena to the fourth industrial revolution and, additionally, revealing the resilient alignment of health care applications and health information systems.
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    Meta-heuristic search methods for big data analytics and visualization of frequently changed patterns
    (2019-03-20) Agbehadji, Israel Edem; Millham, Richard Charles; Yang, Hongji
    Throughout the world, data plays a prominent role in making decisions relevant to the socio-economic growth of organizations. As organizations grow, they tend to use diverse technologies or platforms to collect data and make data readily available for quick decision-making. These technologies have resulted in exponential growth of data whereby the problem of managing this data in a limited time interval increases in complexity, starting from the preprocessing stage to the visualization stage. Apart from the issue of managing the huge growth of data, finding a suitable method to manage certain aspects of this frequently changed data has been overlooked. These frequent changes in data form the topic of interest of this thesis. Consequently, there is a need to develop a framework both to manage big data at different stages of processing, from preprocessing to visualization, and to handle frequently changed data. The need to develop such a framework arises because traditional methods/algorithms are limited to finding frequent patterns of frequently occurring items while overlooking frequently changed data, which has a numeric and time dimension that can provide interesting business insights. Additionally, traditional visualization methods are challenged with performance scalability and response time. This thesis looked at resolving this limitation by using a meta-heuristic/bio-inspired algorithm that is modelled based on observation of the behavior and characteristics of two different animals, namely the kestrel and the dung beetle. The motivation behind the use of these animals is their ability to explore, exploit and adapt to different situations in their natural environment. The development of the computational model and testing with actual data were formulated as a six-step procedure. Based on the six steps, the proposed computational model was evaluated against selected comparative algorithms, namely BAT, WSA-MP, PSO, Firefly and ACO. The main findings on optimal value/results suggest that, in handling frequently changed data during the data preprocessing, pattern discovery and visualization stages, the proposed computational models performed optimally against the comparative meta-heuristic algorithms on test datasets. Further statistical tests, using the Wilcoxon signed rank test, were conducted on optimal results from the comparative meta-heuristic algorithms. The basis for using the statistical procedure was to select the best choice of algorithm without making any underlying assumption on accuracy of results from the comparative meta-heuristic algorithms. Theoretically, the study contributes to enhancing frequency of item frameworks by including time and numeric dimensions of item occurrence. Practically, the contribution of the study lies in its finding frequently changed patterns in big data analytics. Additionally, the concept of half-life of substances/trails was applied as part of the computational model, and this also forms part of the unique contribution of this thesis. The half-life constitutes the lifetime of interestingness of recent patterns that were discovered. In summary, this thesis is about the mathematical formulation of animal behavior and characteristics into an implementable big data management algorithm and its application to frequently changed patterns.
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    Kestrel-based search algorithm for association rule mining and classification of frequently changed items
    (IEEE, 2016) Agbehadji, Israel Edem; Fong, Simon; Millham, Richard
    Nature inspired approaches have been used in the design of computer solutions for real life problems. These computer solutions take the form of algorithms which characterize specific behaviour of animals or birds in their natural habitat. The two bio-inspired computational concepts in modern times includes evolutionary and swarm intelligence. A novel introduction to the bio-inspired computational concepts of swarm behaviour is the study of characteristics of kestrel birds. The study presents, as a concept paper, a meta-heuristic algorithm called kestrel-based search algorithm (KSA) for association rule mining and classification of frequently changed items on big data environment. This algorithm aims to find best possible rules and patterns in dataset using minimum support and minimum confidence.