12,959 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

    Consent and the Construction of the Volunteer: Institutional Settings of Experimental Research on Human Beings in Britain during the Cold War

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    This study challenges the primacy of consent in the history of human experimentation and argues that privileging the cultural frameworks adds nuance to our understanding of the construction of the volunteer in the period 1945 to 1970. Historians and bio-ethicists have argued that medical ethics codes have marked out the parameters of using people as subjects in medical scientific research and that the consent of the subjects was fundamental to their status as volunteers. However, the temporality of the creation of medical ethics codes means that they need to be understood within their historical context. That medical ethics codes arose from a specific historical context rather than a concerted and conscious determination to safeguard the well-being of subjects needs to be acknowledged. The British context of human experimentation is under-researched and there has been even less focus on the cultural frameworks within which experiments took place. This study demonstrates, through a close analysis of the Medical Research Council's Common Cold Research Unit (CCRU) and the government's military research facility, the Chemical Defence Experimental Establishment, Porton Down (Porton), that the `volunteer' in human experiments was a subjective entity whose identity was specific to the institution which recruited and made use of the subject. By examining representations of volunteers in the British press, the rhetoric of the government's collectivist agenda becomes evident and this fed into the institutional construction of the volunteer at the CCRU. In contrast, discussions between Porton scientists, staff members, and government officials demonstrate that the use of military personnel in secret chemical warfare experiments was far more complex. Conflicting interests of the military, the government and the scientific imperative affected how the military volunteer was perceived

    The place where curses are manufactured : four poets of the Vietnam War

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    The Vietnam War was unique among American wars. To pinpoint its uniqueness, it was necessary to look for a non-American voice that would enable me to articulate its distinctiveness and explore the American character as observed by an Asian. Takeshi Kaiko proved to be most helpful. From his novel, Into a Black Sun, I was able to establish a working pair of 'bookends' from which to approach the poetry of Walter McDonald, Bruce Weigl, Basil T. Paquet and Steve Mason. Chapter One is devoted to those seemingly mismatched 'bookends,' Walt Whitman and General William C. Westmoreland, and their respective anthropocentric and technocentric visions of progress and the peculiarly American concept of the "open road" as they manifest themselves in Vietnam. In Chapter, Two, I analyze the war poems of Walter McDonald. As a pilot, writing primarily about flying, his poetry manifests General Westmoreland's technocentric vision of the 'road' as determined by and manifest through technology. Chapter Three focuses on the poems of Bruce Weigl. The poems analyzed portray the literal and metaphorical descent from the technocentric, 'numbed' distance of aerial warfare to the world of ground warfare, and the initiation of a 'fucking new guy,' who discovers the contours of the self's interior through a set of experiences that lead from from aerial insertion into the jungle to the degradation of burning human feces. Chapter Four, devoted to the thirteen poems of Basil T. Paquet, focuses on the continuation of the descent begun in Chapter Two. In his capacity as a medic, Paquet's entire body of poems details his quotidian tasks which entail tending the maimed, the mortally wounded and the dead. The final chapter deals with Steve Mason's JohnnY's Song, and his depiction of the plight of Vietnam veterans back in "The World" who are still trapped inside the interior landscape of their individual "ghettoes" of the soul created by their war-time experiences

    The temporality of rhetoric: the spatialization of time in modern criticism

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    Every conception of criticism conceals a notion of time which informs the manner in which the critic conceives of history, representation and criticism itself. This thesis reveals the philosophies of time inherent in certain key modern critical concepts: allegory, irony and the sublime. Each concept opens a breach in time, a disruption of chronology. In each case this gap or aporia is emphatically closed, elided or denied. Taking the philosophy of time elaborated by Giorgio Agamben as an introductory proposition, my argument turns in Chapter One to the allegorical temporality which Walter Benjamin sees as the time of photography. The second chapter examines the aesthetics of the sublime as melancholic or mournful untimeliness. In Chapter Three, Paul de Man's conception of irony provides an exemplary instance of the denial of this troubling temporal predicament. In opposition to the foreclosure of the disturbing temporalities of criticism, history and representation, the thesis proposes a fundamental rethinking of the philosophy of time as it relates to these categories of reflection. In a reading of an inaugural meditation on the nature of time, and in examining certain key contemporary philosophical and critical texts, I argue for a critical attendance to that which eludes those modes of thought that attempt to map time as a recognizable and essentially spatial field. The Confessions of Augustine provide, in the fourth chapter, a model for thinking through the problems set up earlier: Augustine affords us, precisely, a means of conceiving of the gap or the interim. In the final chapter, this concept is developed with reference to the criticism of Arnold and Eliot, the fiction of Virginia Woolf and the philosophy of cinema derived from Deleuze and Lyotard. In conclusion, the philosophical implications of the thesis are placed in relation to a conception of the untimeliness of death

    Exploring the effects of spinal cord stimulation for freezing of gait in parkinsonian patients

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    Dopaminergic replacement therapies (e.g. levodopa) provide limited to no response for axial motor symptoms including gait dysfunction and freezing of gait (FOG) in Parkinson’s disease (PD) and Richardson’s syndrome progressive supranuclear palsy (PSP-RS) patients. Dopaminergic-resistant FOG may be a sensorimotor processing issue that does not involve basal ganglia (nigrostriatal) impairment. Recent studies suggest that spinal cord stimulation (SCS) has positive yet variable effects for dopaminergic-resistant gait and FOG in parkinsonian patients. Further studies investigating the mechanism of SCS, optimal stimulation parameters, and longevity of effects for alleviating FOG are warranted. The hypothesis of the research described in this thesis is that mid-thoracic, dorsal SCS effectively reduces FOG by modulating the sensory processing system in gait and may have a dopaminergic effect in individuals with FOG. The primary objective was to understand the relationship between FOG reduction, improvements in upper limb visual-motor performance, modulation of cortical activity and striatal dopaminergic innervation in 7 PD participants. FOG reduction was associated with changes in upper limb reaction time, speed and accuracy measured using robotic target reaching choice tasks. Modulation of resting-state, sensorimotor cortical activity, recorded using electroencephalography, was significantly associated with FOG reduction while participants were OFF-levodopa. Thus, SCS may alleviate FOG by modulating cortical activity associated with motor planning and sensory perception. Changes to striatal dopaminergic innervation, measured using a dopamine transporter marker, were associated with visual-motor performance improvements. Axial and appendicular motor features may be mediated by non-dopaminergic and dopaminergic pathways, respectively. The secondary objective was to demonstrate the short- and long-term effects of SCS for alleviating dopaminergic-resistant FOG and gait dysfunction in 5 PD and 3 PSP-RS participants without back/leg pain. SCS programming was individualized based on which setting best improved gait and/or FOG responses per participant using objective gait analysis. Significant improvements in stride velocity, step length and reduced FOG frequency were observed in all PD participants with up to 3-years of SCS. Similar gait and FOG improvements were observed in all PSP-RS participants up to 6-months. SCS is a promising therapeutic option for parkinsonian patients with FOG by possibly influencing cortical and subcortical structures involved in locomotion physiology

    Comparative study of classification algorithms for quality assessment of resistance spot welding joints from preand post-welding inputs

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    Resistance spot welding (RSW) is a widespread manufacturing process in the automotive industry. There are different approaches for assessing the quality level of RSW joints. Multi-input-single-output methods, which take as inputs either the intrinsic parameters of the welding process or ultrasonic nondestructive testing variables, are commonly used. This work demonstrates that the combined use of both types of inputs can significantly improve the already competitive approach based exclusively on ultrasonic analyses. The use of stacking of tree ensemble models as classifiers dominates the classification results in terms of accuracy, F-measure and area under the receiver operating characteristic curve metrics. Through variable importance analyses, the results show that although the welding process parameters are less relevant than the ultrasonic testing variables, some of the former provide marginal information not fully captured by the latter

    Machine learning based adaptive soft sensor for flash point inference in a refinery realtime process

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    In industrial control processes, certain characteristics are sometimes difficult to measure by a physical sensor due to technical and/or economic limitations. This fact is especially true in the petrochemical industry. Some of those quantities are especially crucial for operators and process safety. This is the case for the automotive diesel Flash Point Temperature (FT). Traditional methods for FT estimation are based on the study of the empirical inference between flammability properties and the denoted target magnitude. The necessary measures are taken indirectly by samples from the process and analyzing them in the laboratory, this process implies time (can take hours from collection to flash temperature measurement) and thus make it very difficult for real-time monitorization, which in fact results in security and economical losses. This study defines a procedure based on Machine Learning modules that demonstrate the power of real-time monitorization over real data from an important international refinery. As input, easily measured values provided in real-time, such as temperature, pressure, and hydraulic flow are used and a benchmark of different regressive algorithms for FT estimation is presented. The study highlights the importance of sequencing preprocessing techniques for the correct inference of values. The implementation of adaptive learning strategies achieves considerable economic benefits in the productization of this soft sensor. The validity of the method is tested in the reality of a refinery. In addition, real-world industrial data sets tend to be unstable and volatile, and the data is often affected by noise, outliers, irrelevant or unnecessary features, and missing data. This contribution demonstrates with the inclusion of a new concept, called an adaptive soft sensor, the importance of the dynamic adaptation of the conformed schemes based on Machine Learning through their combination with feature selection, dimensional reduction, and signal processing techniques. The economic benefits of applying this soft sensor in the refinery's production plant and presented as potential semi-annual savings.This work has received funding support from the SPRI-Basque Gov- ernment through the ELKARTEK program (OILTWIN project, ref. KK- 2020/00052)

    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

    How to Be a God

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    When it comes to questions concerning the nature of Reality, Philosophers and Theologians have the answers. Philosophers have the answers that can’t be proven right. Theologians have the answers that can’t be proven wrong. Today’s designers of Massively-Multiplayer Online Role-Playing Games create realities for a living. They can’t spend centuries mulling over the issues: they have to face them head-on. Their practical experiences can indicate which theoretical proposals actually work in practice. That’s today’s designers. Tomorrow’s will have a whole new set of questions to answer. The designers of virtual worlds are the literal gods of those realities. Suppose Artificial Intelligence comes through and allows us to create non-player characters as smart as us. What are our responsibilities as gods? How should we, as gods, conduct ourselves? How should we be gods
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