Automatic Breast Cancer Classification Using Novel Feature Extraction for Magnetic Resonance Imaging and Image Processing Technique

  • Hanan Saad J Alshanbari

    Student thesis: Doctoral ThesisDoctor of Philosophy

    Abstract

    The main objective of the current research is to establish a clearer understanding of Emotional Intelligence (EI) and its theoretical underpinnings. The main argument put forward within this thesis is that by considering various EI-related constructs as extensions of well-established affective individual differences, the field of EI can benefit from existing frameworks and from these build a stable theoretical structure that can support appropriate applications of EI. This thesis begins by exploring the understanding of EI by Academics, Students and HR Professionals. Here, a lack of consensuses and a number of common inconsistencies and contradictions provide a convincing rationale to revisit EI theory. A literature review examining existing EI theory is then presented, leading to the development of three unique approaches to EI, structured as extensions to well-established individual differences. Here EI is considered an umbrella label for affect-related individual differences, encapsulating Ability EI as a second-stratum factor of intelligence, Affect related Personality as a collection of affective traits, and Emotion Regulation as a theoretically-robust alternative to EI competencies/mixed EI. Adopting these three positions, the current thesis presents an Integrated Model of Affective Individual Differences, which places Emotion Regulation as the mechanism by which Ability EI and Affect-related Personality influence affective outcomes. The current thesis then provides two empirical studies to test the conceptualisations of EI, and subsequent Integrated Model of Affect-related Individual Differences. Data provides strong support for contextualising the three approaches to EI within existing models of individual differences, and mixed support for the pathways proposed by the Integrated Model of Affect-related Individual Differences. The current thesis thus provides four key contributions. First, contradictions and theoretical inconsistencies are identified and presented as key barriers towards a robust understanding of EI. Second, a novel approach to organising and facilitating understanding of EI content domain is provided by contextualising the various approaches to EI within existing individual-difference frameworks. Third, the Integrated Model of Affect-related Individual Differences proposed represents a novel structure to support understanding and application of EI. Fourth, empirical evidence is presented to demonstrate the efficacy of such conceptualisations and models.
    Date of Award1 Apr 2014
    Original languageEnglish
    Awarding Institution
    • Coventry University
    SupervisorSaad Amin (Supervisor)

    Cite this

    Automatic Breast Cancer Classification Using Novel Feature Extraction for Magnetic Resonance Imaging and Image Processing Technique
    Alshanbari, H. S. J. (Author). 1 Apr 2014

    Student thesis: Doctoral ThesisDoctor of Philosophy