An exploration of improvements to semi-supervised fuzzy c-means clustering for real-world biomedical data

This thesis explores various detailed improvements to semi-supervised learning (using labelled data to guide clustering or classification of unlabelled data) with fuzzy c-means clustering (a ‘soft’ clustering technique which allows data patterns to be assigned to multiple clusters using membership values), with the primary aim of creating a semi-supervised fuzzy clustering algorithm that shows good performance on real-world data. Hence, there are two main objectives in this work. The first objective is to explore novel technical improvements to semi-supervised Fuzzy c-means (ssFCM) that can address the problem of initialisation sensitivity and can improve results. The second objective is to apply the developed algorithm on real biomedical data, such as the Nottingham Tenovus Breast Cancer (NTBC) dataset, to create an automatic methodology for identifying stable subgroups which have been previously elicited semi-manually. Investigations were conducted into detailed improvements to the ss-FCM algorithm framework, including a range of distance metrics, initialisation and feature selection techniques and scaling parameter values. These methodologies were tested on different data sources to demonstrate their generalisation properties. Evaluation results between methodologies were compared to determine suitable techniques on various University of California, Irvine (UCI) benchmark datasets. Results were promising, suggesting that initialisation techniques, feature selection and scaling parameter adjustment can increase ssFCM performance. Based on these investigations, a novel ssFCM framework was developed, applied to the NTBC dataset, and various statistical and biological evaluations were conducted. This demonstrated highly significant improvement in agreement with previous classifications, with solutions that are biologically useful and clinically relevant in comparison with Sorias study [141]. On comparison with the latest NTBC study by Green et al. [63], similar clinical results have been observed, confirming stability of the subgroups. Two main contributions to knowledge have been made in this work. Firstly, the ssFCM framework has been improved through various technical refinements, which may be used together or separately. Secondly, the NTBC dataset has been successfully automatically clustered (in a single algorithm) into clinical sub-groups which had previously been elucidated semi-manually. While results are very promising, it is important to note that fully, detailed validation of the framework has only been carried out on the NTBC dataset, and so there is limit on the general conclusions that may be drawn. Future studies include applying the framework on other biomedical datasets and applying distance metric learning into ssFCM. In conclusion, an enhanced ssFCM framework has been proposed, and has been demonstrated to have highly significant improved accuracy on the NTBC dataset

An exploration of improvements to semi-supervised fuzzy c-means clustering for real-world biomedical data

This thesis explores various detailed improvements to semi-supervised learning (using labelled data to guide clustering or classification of unlabelled data) with fuzzy c-means clustering (a ‘soft’ clustering technique which allows data patterns to be assigned to multiple clusters using membership values), with the primary aim of creating a semi-supervised fuzzy clustering algorithm that shows good performance on real-world data. Hence, there are two main objectives in this work. The first objective is to explore novel technical improvements to semi-supervised Fuzzy c-means (ssFCM) that can address the problem of initialisation sensitivity and can improve results. The second objective is to apply the developed algorithm on real biomedical data, such as the Nottingham Tenovus Breast Cancer (NTBC) dataset, to create an automatic methodology for identifying stable subgroups which have been previously elicited semi-manually. Investigations were conducted into detailed improvements to the ss-FCM algorithm framework, including a range of distance metrics, initialisation and feature selection techniques and scaling parameter values. These methodologies were tested on different data sources to demonstrate their generalisation properties. Evaluation results between methodologies were compared to determine suitable techniques on various University of California, Irvine (UCI) benchmark datasets. Results were promising, suggesting that initialisation techniques, feature selection and scaling parameter adjustment can increase ssFCM performance. Based on these investigations, a novel ssFCM framework was developed, applied to the NTBC dataset, and various statistical and biological evaluations were conducted. This demonstrated highly significant improvement in agreement with previous classifications, with solutions that are biologically useful and clinically relevant in comparison with Sorias study [141]. On comparison with the latest NTBC study by Green et al. [63], similar clinical results have been observed, confirming stability of the subgroups. Two main contributions to knowledge have been made in this work. Firstly, the ssFCM framework has been improved through various technical refinements, which may be used together or separately. Secondly, the NTBC dataset has been successfully automatically clustered (in a single algorithm) into clinical sub-groups which had previously been elucidated semi-manually. While results are very promising, it is important to note that fully, detailed validation of the framework has only been carried out on the NTBC dataset, and so there is limit on the general conclusions that may be drawn. Future studies include applying the framework on other biomedical datasets and applying distance metric learning into ssFCM. In conclusion, an enhanced ssFCM framework has been proposed, and has been demonstrated to have highly significant improved accuracy on the NTBC dataset

Profiling Obese Subgroups in National Health and Nutritional Status Survey Data using Machine Learning Techniques: A Case Study from Brunei Darussalam

National Health and Nutritional Status Survey (NHANSS) is conducted annually by the Ministry of Health in Negara Brunei Darussalam to assess the population health and nutritional patterns and characteristics. The main aim of this study was to discover meaningful patterns (groups) from the obese sample of NHANSS data by applying data reduction and interpretation techniques. The mixed nature of the variables (qualitative and quantitative) in the data set added novelty to the study. Accordingly, the Categorical Principal Component (CATPCA) technique was chosen to interpret the meaningful results. The relationships between obesity and the lifestyle factors like demography, socioeconomic status, physical activity, dietary behavior, history of blood pressure, diabetes, etc., were determined based on the principal components generated by CATPCA. The results were validated with the help of the split method technique to counter verify the authenticity of the generated groups. Based on the analysis and results, two subgroups were found in the data set, and the salient features of these subgroups have been reported. These results can be proposed for the betterment of the healthcare industry.Comment: A Case study of Obese Subgroups from Brunei Darussalam: 15 Pages, 4 figures, journa

Reflections on the Arts, Environment, and Culture After Ten Years of The Goose

To mark the tenth anniversary of The Goose, we asked prominent ecologically-minded scholars, writers, artists, and educators from across Canada to reflect on the relationship between the arts, culture, and the environment. Their comments illuminate a wide range of triumphs and tensions, from the politics and practices of environmentalist writing and art, to the connections between the environment and matters of diversity and justice, to the past and future of ALECC (Association for Literature, Environment, and Culture in Canada), to the world of a single poem

Uso de las redes sociales en los estudiantes del 1° grado, Sección “A” del Nivel Secundaria del Centro de Educación Básica Alternativa “Lord Kelvin” del distrito y provincia de Moyobamba, año 2018

La presente investigación denominada: “Uso de las redes sociales en los estudiantes del 1° grado, Sección “A” del Nivel Secundaria del Centro de Educación Básica Alternativa “Lord Kelvin” del distrito y provincia de Moyobamba, año 2018”, es de tipo de No experimental, con un enfoque cuantitativo. El objetivo de la investigación fue de describir que redes sociales utilizan más los estudiantes del 1° grado, Sección “A” del Nivel Secundaria, teniendo en cuenta que en la actualidad y tras los avances tecnológicos existe mayor acceso a las redes sociales. La metodología utilizada corresponde a una investigación de tipo descriptivo simple, en donde se utilizó la encuesta y el cuestionario para la recolección de datos aplicado a una muestra de 28 alumnos en total, de los cuales 20 estudiantes son hombres y 08 estudiantes son mujeres. Para el análisis de datos se ha utilizado las tablas de frecuencia simples y de doble entrada para relacionar la variable de estudio y para visualizar los resultados se utilizó los gráficos. En la investigación se obtuvo a los siguientes resultados y conclusiones que el 43% de estudiantes utilizan de manera excesiva las redes sociales, mientras que 25% lo utiliza de manera moderada; sin embargo, el 32% utiliza las redes sociales de manera eventual. Siendo el Facebook la red social más utilizada con un 93% de estudiantes

The Use of Modified Mindfulness-Based Stress Reduction and Mindfulness-Based Cognitive Therapy Program for Family Caregivers of People Living with Dementia: A Feasibility Study

Purpose The aim of this study was to investigate the feasibility and preliminary efficacy of a modified mindfulness-based stress reduction (MBSR) program and mindfulness-based cognitive therapy (MBCT) program for reducing the stress, depressive symptoms, and subjective burden of family caregivers of people with dementia (PWD). Methods A prospective, parallel-group, randomized controlled trial design was adopted. Fifty-seven participants were recruited from the community and randomized into either the modified MBSR group (n = 27) or modified MBCT group (n = 26), receiving seven face-to-face intervention sessions for more than 16 weeks. Various psychological outcomes were measured at baseline (T0), immediately after intervention (T1), and at the 3-month follow-up (T2). Results Both interventions were found to be feasible in view of the high attendance (more than 70.0%) and low attrition (3.8%) rates. The mixed analysis of variance (ANOVA) results showed positive within-group effects on perceived stress (p = .030, Cohen's d = 0.54), depressive symptoms (p = .002, Cohen's d = 0.77), and subjective caregiver burden (p < .001, Cohen's d = 1.12) in both interventions across the time points, whereas the modified MBCT had a larger effect on stress reduction, compared with the modified MBSR (p = .019). Conclusion Both the modified MBSR and MBCT are acceptable to family caregivers of PWD. Their preliminary effects were improvements in stress, depressive symptoms, and subjective burden. The modified MBCT may be more suitable for caregivers of PWD than the MBSR. A future clinical trial is needed to confirm their effectiveness in improving the psychological well-being of caregivers of PWD

They Dont See Us: Asian Students Perceptions of Sexual Violence and Sexual Harassment on Three California Public University Campuses.

Sexual violence and sexual harassment (SVSH) are prevalent among college and university students; however, the experiences of ethnic minority students, especially Asians, are understudied. This study aimed to reduce this gap by exploring Asian students perceptions of SVSH on three public university campuses in Southern California. We examined their perceptions about the campus environment related to SVSH, attitudes, and behaviors toward help seeking, and utilization of on-campus resources. A total of 23 in-depth interviews were conducted with Asian students enrolled at the three University of California campuses. Thematic coding was conducted to generate main themes and subthemes. Five main themes emerged: (a) SVSH is considered a taboo topic in Asian culture and family systems, and Asian student survivors are often reluctant to disclose incidents or seek support services. (b) Students did not feel their campus environments were tailored to understand or meet the sociocultural realities and needs of Asian student survivors. (c) Campus SVSH services and reporting processes were seen as non-transparent. (d) Peers were the major source of support and SVSH information, as opposed to official campus-based resources and training. (e) Survivors often conduct an internal cost-benefit analysis evaluating their decision about whether to report. This study highlights the lack of conversation surrounding SVSH in Asian families, and how the cultural stigma of sex and sexual violence prevented Asian students from receiving knowledge and resources about these topics in their families. Instead of relying on formal campus resources (e.g., Title IX and confidential advocacy services, mental health services), many students turn to their peers for support. Thus, facilitating peer support groups, training university students to support each other through SVSH incidents, and tailoring campus services to the diverse cultural backgrounds of students are key considerations to foster a safe campus environment and prevent SVSH

The use of functional performance tests and simple anthropomorphic measures to screen for comorbidity in primary care

This is an accepted manuscript of an article published by Wiley in International Journal of Older People Nursing on 07/07/2020, available online: https://onlinelibrary.wiley.com/doi/abs/10.1111/opn.12333 The accepted version of the publication may differ from the final published version.Background Many older adults are unaware that they have comorbid diseases. Increased adiposity and reduced muscle mass are identified as key contributors to many chronic diseases in older adults. Understanding the role they play in the development of comorbidities in older populations is of prime importance. Objectives To identify the optimal body shape associated with three common functional performance tests and to determine which anthropometric and functional performance test best explains comorbidity in a sample of older adults in Hong Kong. Methods A total of 432 older adults participated in this cross‐sectional study. Researchers assessed their body height, body mass index, waist circumference, waist‐to‐hip ratio, handgrip strength (kg), functional reach (cm) and results in the timed‐up‐and‐go (TUG) test (seconds). The Charlson Comorbidity Index was used to assess comorbidity. Results Allometric modelling indicated that the optimal body shape associated with all functional performance tests would have required the participants to be taller and leaner. The only variable that predicted comorbidity was the TUG test. The inclusion of body size/shape variables did not improve the prediction model. Conclusion Performance in the TUG test alone was found to be capable of identifying participants at risk of developing comorbidities. The TUG test has potential as a screening tool for the early detection of chronic diseases in older adults. Implications for Practice Many older people are unaware of their own co‐existing illnesses when they consult physicians for a medical condition. TUG can be a quick and useful screening measure to alert nurses in primary care to the need to proceed with more detailed assessments. It is an especially useful screening measure in settings with high patient volumes and fiscal constraints. TUG is low cost and easy to learn and is therefore also relevant for nurses and health workers in low‐resource, low‐income countries.School of Nursing, The Hong Kong Polytechnic UniversityPublished onlin

Digital Mapping Of Invasive Acacia Mangium Willd. Trees Along Telisai-Lumut Highway Along The Andulau Forest Reserve

Invasive alien Acacia trees have become a serious environmental problem in Brunei Darussalam, spreading into the vulnerable heath and mixed dipterocarp forest ecosystem where it has started replacing the native flora and contributing to forest fire. In this work, we study the spread of Acacia trees by analyzing images taken by drones along a newly developed highway within the vicinity of Andulau Forest Reserve in Brunei Darussalam. Based on the analysis, we aim to understand the Acacia spread and its habitat preference, which will be a critical factor in planning the future roadmap to maintain a sustainable and healthy forest ecosystem, and safety from potential forest fires. The Unmanned Aerial Vehicles (UAVs) were utilized to capture high-resolution images along the Telisai-Lumut highway and were subsequently analyzed images using ArcGIS software, to map and study the Acacia’s distribution and habitat preferences, which will aid in understanding of Acacia’s rapid dispersion. Our preliminary results show highest Acacia density and numbers closer to the highway. The barren loose sandy soil combined with the open terrain limits local forest tree growth but seems to provide good habitat for Acacia trees. Our results suggest that the highway provides an important dispersal opportunity for Acacia trees, bringing them in direct proximity of an undisturbed forest reserve. This may increase the risk of spread of this species into the forest, and importantly, given the fire proneness of Acacia, may lead to wildfires that threaten the neighbouring forest reserve. Keeping vegetation short and removing Acacia’s close to the highway may mitigate these risks. Efforts such as spreading awareness on Acacia’s invasiveness, identification and removal of Acacia trees, habitat restoration projects and meticulous evaluation for any introduced species should be done