Web Based Disease Data Visualization and Interpretation System

Malaysians suffer from both communicable and non-communicable diseases. However, an easy to use tool is not available for the doctors, officers from Ministry of Health and also the public to analyze the disease. The data is not centralized and even if the user can collect sufficient data to analyze the data, many people do not have a clue about the overwhelming data. This project will develop a website that will visualize the disease data on a map stating the location that the diseases happened. As everyone might interpret the result differently, a paragraph of interpretation will be generated along with the visualization to give an impartial description about the data. This research consists of all the dengue and Tuberculosis cases in Daerah Kinta. Tuberculosis disease is chosen because it is more common in rural places like some parts of Daerah Kinta. On the other hand, Dengue is chosen because it is a very common vector borne disease in Malaysia. The methodology used is prototyping as it allows more users' feedback in the early stage of the system. Mock ups will be generated to allow users to interact with it. The map is shown by using Google maps API. The author will use Google Geocoding API to convert the addresses where diseases happened into longitude and langtitude to mark it on the map. The interpretation is generated by comparing the number of disease case in one period of time in an area with the medical standard provided by Jabatan Kesihatan Daerah Kinta

Web Based Disease Data Visualization and Interpretation System

Malaysians suffer from both communicable and non-communicable diseases. However, an easy to use tool is not available for the doctors, officers from Ministry of Health and also the public to analyze the disease. The data is not centralized and even if the user can collect sufficient data to analyze the data, many people do not have a clue about the overwhelming data. This project will develop a website that will visualize the disease data on a map stating the location that the diseases happened. As everyone might interpret the result differently, a paragraph of interpretation will be generated along with the visualization to give an impartial description about the data. This research consists of all the dengue and Tuberculosis cases in Daerah Kinta. Tuberculosis disease is chosen because it is more common in rural places like some parts of Daerah Kinta. On the other hand, Dengue is chosen because it is a very common vector borne disease in Malaysia. The methodology used is prototyping as it allows more users' feedback in the early stage of the system. Mock ups will be generated to allow users to interact with it. The map is shown by using Google maps API. The author will use Google Geocoding API to convert the addresses where diseases happened into longitude and langtitude to mark it on the map. The interpretation is generated by comparing the number of disease case in one period of time in an area with the medical standard provided by Jabatan Kesihatan Daerah Kinta

The Boy from Boort

Hank Nelson was an academic, film-maker, teacher, graduate supervisor and university administrator. His career at The Australian National University (ANU) spanned almost 40 years of notable accomplishment in expanding and deepening our understanding of the history and politics of Papua New Guinea, the experience of Australian soldiers at war, bush schools and much else. This book is a highly readable tribute to him, written by those who knew him well, including his students, and also contains wide-ranging works by Hank himself. –Professor Stewart Firth, ANU

Designing a social space for co-creation of multimedia contents

Thesis (S.M.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, February 2013."February 2013." Cataloged from PDF version of thesis.Includes bibliographical references (p. 85-87).People can have more insights and social experiences when they collaborate on collecting, revisiting, and utilizing their contents, such as images and videos; however, designing a social space that offers rich co-creation and exploration of multimedia contents remains a challenge. I propose a new system, SparkInfo, which enables users to create, exchange and augment their multimedia elements in ways that are personally unique and sociable. SparkInfo is designed for a group of people, who have created multimedia elements for the same purpose or at the same event, to collect their elements in one place and have a meaningful experience of their co-created media resources. SparkInfo provides a social space for the co-creation of multimedia resources. In the process of exploring and embellishing their materials, SparkInfo users can create new ideas, stories, and information. By utilizing this process, the users are able to experience how SparkInfo can embody the cycle of knowledge building, re-mixing, and sharing.by Jee Yeon Hwang.S.M

Novel medical imaging technologies for processing epithelium and endothelium layers in corneal confocal images. Developing automated segmentation and quantification algorithms for processing sub-basal epithelium nerves and endothelial cells for early diagnosis of diabetic neuropathy in corneal confocal microscope images

Diabetic Peripheral Neuropathy (DPN) is one of the most common types of diabetes that can affect the cornea. An accurate analysis of the corneal epithelium nerve structures and the corneal endothelial cell can assist early diagnosis of this disease and other corneal diseases, which can lead to visual impairment and then to blindness. In this thesis, fully-automated segmentation and quantification algorithms for processing and analysing sub-basal epithelium nerves and endothelial cells are proposed for early diagnosis of diabetic neuropathy in Corneal Confocal Microscopy (CCM) images. Firstly, a fully automatic nerve segmentation system for corneal confocal microscope images is proposed. The performance of the proposed system is evaluated against manually traced images with an execution time of the prototype is 13 seconds. Secondly, an automatic corneal nerve registration system is proposed. The main aim of this system is to produce a new informative corneal image that contains structural and functional information. Thirdly, an automated real-time system, termed the Corneal Endothelium Analysis System (CEAS) is developed and applied for the segmentation of endothelial cells in images of human cornea obtained by In Vivo CCM. The performance of the proposed CEAS system was tested against manually traced images with an execution time of only 6 seconds per image. Finally, the results obtained from all the proposed approaches have been evaluated and validated by an expert advisory board from two institutes, they are the Division of Medicine, Weill Cornell Medicine-Qatar, Doha, Qatar and the Manchester Royal Eye Hospital, Centre for Endocrinology and Diabetes, UK

Métodos de classificação de imagens de satélite para delineamento de banhados

As Áreas Úmidas (AUs) são ecossistemas de importância global, que apresentam altos níveis de diversidade ecológica e produtividade primária e secundária. Os Banhados são um tipo de AU, característicos nos estados do Sul do Brasil, no Uruguai e na Argentina. O delineamento e classificação desses ecossistemas é uma tarefa árdua, dada as características estruturais hidrológicas, de solos, de cobertura vegetal e espectrais. No estado Rio Grande do Sul os Banhados são considerados Áreas de Preservação Permanente, porém, não há um inventário e tampouco um delineamento desses ambientes. Deste modo, o objetivo destatese é comparar diferentes métodos baseados em sensoriamento remoto ativo e passivo e aprendizado de máquina(AP)para o delineamento de Banhados. Para isto, utilizamos três abordagens: i) aplicação de índices espectrais de sensoriamento remoto e árvore de decisão; ii) integração de imagens SAR de dupla e quádrupla polarização em bandas C e L e árvore de decisão; e, iii) análise multisensor (ativo e passivo), Geobia e diferentes classificadores. Nossos resultados mostram que os índices espectrais de sensoriamento remoto apresentaram acurácias entre 77,9% e 95,9%; a aplicação de imagens SAR resultou em acurácias entre 56,1% e 72,9%, ambos pelo algoritmo Árvore de Decisão. Para a abordagem multisensor utilizando Geobia e diferentes classificadores, as acurácias variaram entre 95,5% e 98,5%, sendo que, o k-NN foi o algoritmo que apresentou maior acurácia entre os modelos avaliados, demonstrando o potencial da análise multisensor (ativo e passivo) e doaprendizado de máquinapara o delineamento e classificação de Banhados. Adotamos como estudo de caso um Banhado localizado no Sul do Brasil, porém recomendamos que devido as semelhanças hidrológicas, estruturais e espectrais desses ambientes, essas metodologias possam ser aplicadas em outras áreas de Banhados (marshes).Wetlands are ecosystems of global importance, with high levels of ecological diversity and primary and secondary productivity.Marshes are a type of wetland characteristic of the southernBrazil, Uruguay and Argentina.The delineationand classification of these ecosystems is an arduous task, given the hydrological structure, soil, vegetation and spectral characteristics.In the Rio Grande do Sul state, marshesare considered Permanent Preservation Areas, however, there is no inventory and no delineationof these environments.Thus, the aim of this thesis is to compare different active and passive remote sensing based methodsand machine learningfor the delineationof marshes. For this, we use three approaches: i) application of spectral indices of remote sensing and decision tree; ii) integration of dual and quad-poll SAR images in C and L-bands and decision tree, and iii) multisensor analysis (active and passive), Geobia and different classification methods. Our results show that the spectral indexes of remote sensing presented accuracy between 77.9% and 95.9%; the application of SAR images resulted in accuracy between 56.1% and 72.9%, both using the Decision Tree algorithm. For the multisensor approach using Geobia and different classifiers, the accuracy varied between 95.5% to 98.5%, k-NN was the algorithm that showed greater accuracy among the models evaluated, demonstrating the potential of the multisensor analysis (activeand passive) and machine learningfor marshesdelineation and classification. Our study was carried out in a marsh located in the southernBrazil, however due to the hydrological, structural and spectral similarities of these environments, the methodologies can be applied in other marshes area

Name, Shame and Blame: Criminalising Consensual Sex in Papua New Guinea

This book is an exceptional contribution to our knowledge of the nexus between the criminal law and negative attitudes of society, and what effects criminalization has on the social lives of prostitutes and males who have sex with males, and whether these effects might provide evidence to support the argument for law reform