25,063 research outputs found

    Assessing performance of artificial neural networks and re-sampling techniques for healthcare datasets.

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    Re-sampling methods to solve class imbalance problems have shown to improve classification accuracy by mitigating the bias introduced by differences in class size. However, it is possible that a model which uses a specific re-sampling technique prior to Artificial neural networks (ANN) training may not be suitable for aid in classifying varied datasets from the healthcare industry. Five healthcare-related datasets were used across three re-sampling conditions: under-sampling, over-sampling and combi-sampling. Within each condition, different algorithmic approaches were applied to the dataset and the results were statistically analysed for a significant difference in ANN performance. The combi-sampling condition showed that four out of the five datasets did not show significant consistency for the optimal re-sampling technique between the f1-score and Area Under the Receiver Operating Characteristic Curve performance evaluation methods. Contrarily, the over-sampling and under-sampling condition showed all five datasets put forward the same optimal algorithmic approach across performance evaluation methods. Furthermore, the optimal combi-sampling technique (under-, over-sampling and convergence point), were found to be consistent across evaluation measures in only two of the five datasets. This study exemplifies how discrete ANN performances on datasets from the same industry can occur in two ways: how the same re-sampling technique can generate varying ANN performance on different datasets, and how different re-sampling techniques can generate varying ANN performance on the same dataset

    Metaphors of London fog, smoke and mist in Victorian and Edwardian Art and Literature

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    Julian Wolfreys has argued that after 1850 writers employed stock images of the city without allowing them to transform their texts. This thesis argues, on the contrary, that metaphorical uses of London fog were complex and subtle during the Victorian and Edwardian periods, at least until 1914. Fog represented, in particular, formlessness and the dissolution of boundaries. Examining the idea of fog in literature, verse, newspaper accounts and journal articles, as well as in the visual arts, as part of a common discourse about London and the state of its inhabitants, this thesis charts how the metaphorical appropriation of this idea changed over time. Four of Dickens's novels are used to track his use of fog as part of a discourse of the natural and unnatural in individual and society, identifying it with London in progressively more negative terms. Visual representations of fog by Constable, Turner, Whistler, Monet, Markino, O'Connor, Roberts and Wyllie and Coburn showed an increasing readiness to engage with this discourse. Social tensions in the city in the 1880s were articulated in art as well as in fiction. Authors like Hay and Barr showed the destruction of London by its fog because of its inhabitants' supposed degeneracy. As the social threat receded, apocalyptic scenarios gave way to a more optimistic view in the work of Owen and others. Henry James used fog as a metaphorical representation of the boundaries of gendered behaviour in public, and the problems faced by women who crossed them. The dissertation also examines fog and individual transgression, in novels and short stories by Lowndes, Stevenson, Conan Doyle and Joseph Conrad. After 1914, fog was no more than a crude signifier of Victorian London in literature, film and, later, television, deployed as a cliche instead of the subtle metaphorical idea discussed in this thesis

    Building body identities - exploring the world of female bodybuilders

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    This thesis explores how female bodybuilders seek to develop and maintain a viable sense of self despite being stigmatized by the gendered foundations of what Erving Goffman (1983) refers to as the 'interaction order'; the unavoidable presentational context in which identities are forged during the course of social life. Placed in the context of an overview of the historical treatment of women's bodies, and a concern with the development of bodybuilding as a specific form of body modification, the research draws upon a unique two year ethnographic study based in the South of England, complemented by interviews with twenty-six female bodybuilders, all of whom live in the U.K. By mapping these extraordinary women's lives, the research illuminates the pivotal spaces and essential lived experiences that make up the female bodybuilder. Whilst the women appear to be embarking on an 'empowering' radical body project for themselves, the consequences of their activity remains culturally ambivalent. This research exposes the 'Janus-faced' nature of female bodybuilding, exploring the ways in which the women negotiate, accommodate and resist pressures to engage in more orthodox and feminine activities and appearances

    Analysis of reliable deployment of TDOA local positioning architectures

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    .Local Positioning Systems (LPS) are supposing an attractive research topic over the last few years. LPS are ad-hoc deployments of wireless sensor networks for particularly adapt to the environment characteristics in harsh environments. Among LPS, those based on temporal measurements stand out for their trade-off among accuracy, robustness and costs. But, regardless the LPS architecture considered, an optimization of the sensor distribution is required for achieving competitive results. Recent studies have shown that under optimized node distributions, time-based LPS cumulate the bigger error bounds due to synchronization errors. Consequently, asynchronous architectures such as Asynchronous Time Difference of Arrival (A-TDOA) have been recently proposed. However, the A-TDOA architecture supposes the concentration of the time measurement in a single clock of a coordinator sensor making this architecture less versatile. In this paper, we present an optimization methodology for overcoming the drawbacks of the A-TDOA architecture in nominal and failure conditions with regards to the synchronous TDOA. Results show that this optimization strategy allows the reduction of the uncertainties in the target location by 79% and 89.5% and the enhancement of the convergence properties by 86% and 33% of the A-TDOA architecture with regards to the TDOA synchronous architecture in two different application scenarios. In addition, maximum convergence points are more easily found in the A-TDOA in both configurations concluding the benefits of this architecture in LPS high-demanded applicationS

    Sharing the Shore: Hybridity and Developing Environmentalisms in the Indiana Dunes

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    This thesis centers on the Indiana Dunes National Park, located in Northwestern Indiana, and the implications of this hybrid landscape on modern American environmentalism. Through secondary source research, historical analysis, and interviews with Miller Beach residents and a park ranger, this research concludes that the Indiana Dunes demonstrate an environmentalism that exists outside of the nature-culture binary. By incorporating the park into existing cities and industrial developments, the Indiana Dunes can be seen as a model for an environmental justice-driven space that diverges from the historic elitism of the National Park service. This research concludes that, while hybrid landscapes come with their own challenges, the hybridity of the Indiana Dunes ultimately points to a bright future for the National Park Service, one that makes public green space accessible and that radically rethinks what it means to be a National Park

    Network Slicing for Industrial IoT and Industrial Wireless Sensor Network: Deep Federated Learning Approach and Its Implementation Challenges

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    5G networks are envisioned to support heterogeneous Industrial IoT (IIoT) and Industrial Wireless Sensor Network (IWSN) applications with a multitude Quality of Service (QoS) requirements. Network slicing is being recognized as a beacon technology that enables multi-service IIoT networks. Motivated by the growing computational capacity of the IIoT and the challenges of meeting QoS, federated reinforcement learning (RL) has become a propitious technique that gives out data collection and computation tasks to distributed network agents. This chapter discuss the new federated learning paradigm and then proposes a Deep Federated RL (DFRL) scheme to provide a federated network resource management for future IIoT networks. Toward this goal, the DFRL learns from Multi-Agent local models and provides them the ability to find optimal action decisions on LoRa parameters that satisfy QoS to IIoT virtual slice. Simulation results prove the effectiveness of the proposed framework compared to the early tools

    Non-Print Information Resources and The Preservation Approaches Recommendation in Tanzanian Academic Libraries

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     Background: Non-print information resources are increasingly becoming more important as vital learning materials in higher learning institutions. Academic libraries therefore, have to acquire, process, organize and preserve them for current and future use. Purpose: This paper aims to assess the factors affecting the non-print information resources and their recommended preservation approaches in academic libraries.  Method: The study adopted a convergent parallel mixed approach which collects and analyses data to produce integrated findings by using both qualitative and quantitative techniques in a single study. Data was collected by means of questionnaire and in-depth interview. Result: The study revealed that dust, loss of data on disc and hard disc, loss of data due to server failure, high heat, and excessive light, fading of disc surface, high humidity, fungus on disc surface, atmospheric pollutants and virus attack were factors affecting non-print information resources. It was also revealed that highly recommended preservation approaches were good cleanliness of library where information resources are kept, educating library users on how to handle and use information resources, migrating information resources from obsolete storage media to modern storage media, technology preservation and refreshing. Conclusion: The study concludes that library staff need to adopt recommended preservation approaches to safeguard the important information in academic libraries but also system librarians in academic libraries need to be employed to assist in trouble shooting issues.  Keywords: Non-Print Information Resources; Information Resources; Information Resources Preservation; Preservation Approaches; Academic Library   Abstrak  Latar Belakang: Sumber informasi non-cetak sekarang ini menjadi semakin penting sebagai bahan pembelajaran vital di perguruan tinggi. Oleh karena itu, perpustakaan akademik harus memperoleh, memproses, mengatur, dan melestarikannya untuk penggunaan saat ini dan masa depan. Tujuan: Makalah ini bertujuan untuk menilai faktor-faktor yang mempengaruhi sumber informasi non-cetak dan pendekatan pelestarian yang direkomendasikan di perpustakaan akademik. Metode: Studi ini mengadopsi pendekatan campuran paralel konvergen yang mengumpulkan dan menganalisis data untuk menghasilkan temuan yang terintegrasi dengan menggunakan teknik kualitatif dan kuantitatif dalam satu studi. Pengumpulan data dilakukan dengan kuesioner dan wawancara mendalam. Temuan: Hasil penelitian menunjukkan bahwa debu, hilangnya data pada disk/hard disk, hilangnya data karena kegagalan server, panas yang tinggi, dan cahaya yang berlebihan, memudarnya permukaan disk, kelembaban tinggi, jamur pada permukaan disk, polutan atmosfer dan serangan virus adalah faktor yang mempengaruhi sumber informasi non-cetak. Diungkapkan juga bahwa pendekatan pelestarian yang sangat direkomendasikan adalah kebersihan perpustakaan tempat sumber informasi disimpan, mendidik pengguna perpustakaan tentang cara menangani dan menggunakan sumber informasi, migrasi sumber informasi dari media penyimpanan usang ke media penyimpanan modern, pelestarian teknologi dan penyegaran koleksi. Kesimpulan: Studi ini menyimpulkan bahwa staf perpustakaan perlu mengadopsi pendekatan pelestarian yang direkomendasikan untuk melindungi informasi penting di perpustakaan akademik, tetapi juga pustakawan di perpustakaan akademik perlu dioptimalkan untuk membantu memecahkan masalah yang ada.  Kata kunci: Sumber Informasi Non-Cetak; Sumber Daya Informasi; Pelestarian Sumber Daya Informasi; Pendekatan Pelestarian; Perpustakaan Akademik&nbsp

    Enhancing Parkinson’s Disease Prediction Using Machine Learning and Feature Selection Methods

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    Several millions of people suffer from Parkinson’s disease globally. Parkinson’s affects about 1% of people over 60 and its symptoms increase with age. The voice may be affected and patients experience abnormalities in speech that might not be noticed by listeners, but which could be analyzed using recorded speech signals. With the huge advancements of technology, the medical data has increased dramatically, and therefore, there is a need to apply data mining and machine learning methods to extract new knowledge from this data. Several classification methods were used to analyze medical data sets and diagnostic problems, such as Parkinson’s Disease (PD). In addition, to improve the performance of classification, feature selection methods have been extensively used in many fields. This paper aims to propose a comprehensive approach to enhance the prediction of PD using several machine learning methods with different feature selection methods such as filter-based and wrapper-based. The dataset includes 240 recodes with 46 acoustic features extracted from 3 voice recording replications for 80 patients. The experimental results showed improvements when wrapper-based features selection method was used with KNN classifier with accuracy of 88.33%. The best obtained results were compared with other studies and it was found that this study provides comparable and superior results

    Structure and adsorption properties of gas-ionic liquid interfaces

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    Supported ionic liquids are a diverse class of materials that have been considered as a promising approach to design new surface properties within solids for gas adsorption and separation applications. In these materials, the surface morphology and composition of a porous solid are modified by depositing ionic liquid. The resulting materials exhibit a unique combination of structural and gas adsorption properties arising from both components, the support, and the liquid. Naturally, theoretical and experimental studies devoted to understanding the underlying principles of exhibited interfacial properties have been an intense area of research. However, a complete understanding of the interplay between interfacial gas-liquid and liquid-solid interactions as well as molecular details of these processes remains elusive. The proposed problem is challenging and in this thesis, it is approached from two different perspectives applying computational and experimental techniques. In particular, molecular dynamics simulations are used to model gas adsorption in films of ionic liquids on a molecular level. A detailed description of the modeled systems is possible if the interfacial and bulk properties of ionic liquid films are separated. In this study, we use a unique method that recognizes the interfacial and bulk structures of ionic liquids and distinguishes gas adsorption from gas solubility. By combining classical nitrogen sorption experiments with a mean-field theory, we study how liquid-solid interactions influence the adsorption of ionic liquids on the surface of the porous support. The developed approach was applied to a range of ionic liquids that feature different interaction behavior with gas and porous support. Using molecular simulations with interfacial analysis, it was discovered that gas adsorption capacity can be directly related to gas solubility data, allowing the development of a predictive model for the gas adsorption performance of ionic liquid films. Furthermore, it was found that this CO2 adsorption on the surface of ionic liquid films is determined by the specific arrangement of cations and anions on the surface. A particularly important result is that, for the first time, a quantitative relation between these structural and adsorption properties of different ionic liquid films has been established. This link between two types of properties determines design principles for supported ionic liquids. However, the proposed predictive model and design principles rely on the assumption that the ionic liquid is uniformly distributed on the surface of the porous support. To test how ionic liquids behave under confinement, nitrogen physisorption experiments were conducted for micro‐ and mesopore analysis of supported ionic liquid materials. In conjunction with mean-field density functional theory applied to the lattice gas and pore models, we revealed different scenarios for the pore-filling mechanism depending on the strength of the liquid-solid interactions. In this thesis, a combination of computational and experimental studies provides a framework for the characterization of complex interfacial gas-liquid and liquid-solid processes. It is shown that interfacial analysis is a powerful tool for studying molecular-level interactions between different phases. Finally, nitrogen sorption experiments were effectively used to obtain information on the structure of supported ionic liquids
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