Combined supervised and unsupervised learning to identify subclasses of disease for better prediction

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonDisease subtyping, which aids in the development of personalised treatments, remains a challenge in data analysis because of the many different ways to group patients based upon their data. However, if I can identify subclasses of disease, this will help to develop better models that are more specific to individuals and should therefore improve prediction and understanding of the underlying characteristics of the disease in question. In addition, patients might suffer from multiple disease complications. Models that are tailored to individuals could improve both prediction of multiple complications and understanding of underlying disease characteristics. However, AI models can become outdated over time due to either sudden changes in the underlying data, such as those caused by new measurement methods, or incremental changes, such as the ageing of the study population. This thesis proposes a new algorithm that integrates consensus clustering methods with classification in order to overcome issues with sample bias. The method was tested on a freely available dataset of real-world breast cancer cases and data from a London hospital on systemic sclerosis, a rare and potentially fatal condition. The results show that nearest consensus clustering classification improves accuracy and prediction significantly when this algorithm is compared with competitive similar methods. In addition, this thesis proposes a new algorithm that integrates latent class models with classification. The new algorithm uses latent class models to cluster patients within groups; this results in improved classification and aids in the understanding of the underlying differences of the discovered groups. The method was tested on data from patients with systemic sclerosis (SSc), a rare and potentially fatal condition, and coronary heart disease. Results show that the latent class multi-label classification (MLC) model improves accuracy when compared with competitive similar methods. Finally, this thesis implemented the updated concept drift method (DDM) to monitor AI models over time and detect drifts when they occur. The method was tested on data from patients with SSc and patients with coronavirus disease (COVID)

A study of cohesion in international postgraduate Business students’ multimodal written texts: an SF-MDA of a key topic in finance

Empirical research studies of finance students’ language use have investigated students’ performance in finance courses and the effect of class attendance on students’ performance.Similarly, research on accounting students’ texts has been directed at readability of accounting narratives and lexical choices. Systemic Functional Linguistics (SFL) based research in multimodal communication and representation has been confined to school and workplace contexts. Whereas multimodal communication investigations in tertiary contexts has been conducted across the fields of mathematics, science and computing, and nursing, business courses have not been explored. The purpose of this paper is to report on a case study designed to investigate the key multimodal academic literacy and numeracy practices of ten international Master of Commerce Accounting students enrolled at an Australian university. Specifically, it aims to provide an account of the salient textual and the logical patterns through the analysis of cohesive devices in a key topic in the Principles of Finance course, namely capital budgeting techniques and management reports. This study is pertinent as most international ESL/EFL students’ enrolments in Australia and elsewhere is in business programs. This study is underpinned by Halliday’s (1985) Systemic Functional Linguistics (SFL) approach to language and Halliday and Hasan’s (1976) cohesion analysis scheme. The study employs a Systemic Functional Multimodal Discourse Analysis (SF-MDA) for the analysis of cohesive devices in the participants’ multimodal texts. Lexical cohesion formed the largest percentage of use, and in particular repetition of the same lexical items, followed by reference.The findings contribute to the description of the meaning-making processes in these multimodal artefacts. They provide a potential research tool for similar investigations across a broad range of educational settings. Implications of the findings for finance students and educators are finally presented

English-Arabic Translator Education Through Systemic Functional Linguistics: Analysis of Cohesive Devices in Investopedia Business Texts

In translation courses, students are asked to practice translation skills by translating a source text (ST) in a specific field. While teachers usually select texts based on topic and language accuracy, some such texts do not provide rich textual features that help students practice and improve their translation skills. This study aimed to analyze the cohesive features in business texts collected from “Investopedia” to investigate their suitability for use as STs to practice translation skills in the field of finance and administration. It was framed by Halliday’s (1978) systemic functional linguistics (SFL) approach to language and Halliday and Hassan’s (1976) cohesion analysis scheme. The findings demonstrated that the most prominent type used was lexical cohesion, followed by reference and conjunctions. Ellipses and substitution were rarely used. The findings indicated that the intensive use of lexical cohesion and the various subcategories used in these texts can help enrich the background knowledge of financial terminology and provide a communicative understanding of the ST while practicing various elements of textual features. The study provided a demonstration of the significance of SFL in providing coherent and cohesive STs that facilitate the needs of translation instructors and students in the field of finance and administration. Other SFL tools can be employed to provide a better understanding of these texts

The Challenges and Barriers Facing Successful Lean Implementation in the Qatari Manufacturing Organizations

The success of Toyota, the inventor of Lean Systems has drawn the attention of academics, researchers, and executives in the last decades. However, existing lean research is largely focused on developed economies in Europe, the Americas, and in Southeast Asia with little focus on the Arab world in general and Qatar specifically. There has been an emergence of manufacturing organizations and Small and Medium- sized Enterprises (SME) in Qatar recently which was the focus of this research. Despite the many attempts to implement lean in organizations, there have been a large number of failed attempts. This research aims to study the Qatari Manufacturing Organizations attempts in implementing lean. The main goal of the thesis is to shed a light on the current state of awareness of lean in Qatari Manufacturing Industry and SME and the potential barriers and challenges that prevent the successful adoption of lean. In order to do so, an online survey was designed through the SurveyMonkey tool and organizations were asked to participate. Numerical and statistical results from the survey are discussed along with Pareto charts and two hypothesis tests. The finding indicates that there is no difference between ISO certified and Non-ISO certified organizations in lean implementation. Further, it is understood that most of the manufacturing organizations in Qatar have some level of awareness of lean concepts/tools and practices. The results also show that organizations are not implementing lean concepts and are not using quality management practices effectively. The main obstacle that manufacturing organizations in Qatar face when implementing lean is Lack of Employee Engagement and Unsupportive Organization Culture . However, it is noted that the research has some limitations such as having most participating organizations in the survey from specific sectors

Is Comprehensive Income Superior To Net Income In Equity Valuation? Evidence From The Capital Market Of Kuwait

The study examines whether comprehensive income numbers reported under International Financial Reporting Standards (IFRS) have value relevance over net income in equity valuation. We use a sample of firms that are listed in Kuwait Stock Exchange from banking, investment, real estate, industrial, basic materials, telecommunications, consumer services, oil & gas and health care sectors during the years 2012-2015.The study applies a methodology used by Collins, Maydew and Weiss (1997) that is based on Ohlson (1995) equity valuation model and Theil (1971) technique to measure and compare the relative and the incremental explanatory power of comprehensive income and net income. The study provides evidence that comprehensive income is not superior to net income in equity valuation. Reporting other comprehensive income gains and losses as elements of the income statement produces a measure of earnings that decreases the explanatory power of the valuation model; decreases the incremental information content of earnings. Other comprehensive income gains and losses when added as an explanatory variable to the valuation model did not enhance significantly its explanatory power.The results we obtained supports the current requirement by the IFRS and US GAAP of deferring other comprehensive gains and losses and contributes to the literature on the value relevance of other comprehensive income gains and losses in emerging capital markets

IN VITRO ANTIFUNGAL ACTIVITY OF THYME OIL AGAINST CANDIDA ALBICANS ISOLATED FROM NAIL FUNGUS (ONYCHOMYCOSIS)

Objectives: The aim of this study is to investigate the antifungal activity of thyme oil against Candida albicans of nail fungus (onychomycosis) from patients in Syria. Methods: An in vitro study was carried out using the following C. albicans strains involved in onychomycosis using well diffusion (WD) testing: C. albicans (ATCC 90028) and 15 strains were compiled from Aleppo Hospital. The antifungal activity of thyme oil was determined in the form of inhibition zone using antifungal assay agar WD testing. In all experiments, the obtained results indicated that thyme oil has inhibitory effects on C. albicans (ATCC 90028) and some 15 strains. Results: This study showed that thyme oil was active against the tested C. albicans strains. Thyme oil was more effective against C. albicans compared to terbinafine. thyme oil may have potential for use in the development of clinically useful antifungal preparations. Conclusion: Thyme oil might be highly effective in the natural prevention treatment of onychomycosis