873 resultados para Support Vector Machines and Naive Bayes Classifier
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Objective: To examine patients' experiences of information and support provision for age-related macular degeneration (AMD) in the UK. Study design: Exploratory qualitative study investigating patient experiences of healthcare consultations and living with AMD over 18 months. Setting: Specialist eye clinics at a Birmingham hospital. Participants: 13 patients diagnosed with AMD. Main outcome measures: Analysis of patients' narratives to identify key themes and issues relating to information and support needs. Results: Information was accessed from a variety of sources. There was evidence of clear information deficits prior to diagnosis, following diagnosis and ongoing across the course of the condition. Patients were often ill informed and therefore unable to self-advocate and recognise when support was needed, what support was available and how to access support. Conclusions: AMD patients have a variety of information needs that are variable across the course of the condition. Further research is needed to determine whether these experiences are typical and identify ways of translating the guidelines into practice. Methods of providing information need to be investigated and improved for this patient group.
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MOTIVATION: G protein-coupled receptors (GPCRs) play an important role in many physiological systems by transducing an extracellular signal into an intracellular response. Over 50% of all marketed drugs are targeted towards a GPCR. There is considerable interest in developing an algorithm that could effectively predict the function of a GPCR from its primary sequence. Such an algorithm is useful not only in identifying novel GPCR sequences but in characterizing the interrelationships between known GPCRs. RESULTS: An alignment-free approach to GPCR classification has been developed using techniques drawn from data mining and proteochemometrics. A dataset of over 8000 sequences was constructed to train the algorithm. This represents one of the largest GPCR datasets currently available. A predictive algorithm was developed based upon the simplest reasonable numerical representation of the protein's physicochemical properties. A selective top-down approach was developed, which used a hierarchical classifier to assign sequences to subdivisions within the GPCR hierarchy. The predictive performance of the algorithm was assessed against several standard data mining classifiers and further validated against Support Vector Machine-based GPCR prediction servers. The selective top-down approach achieves significantly higher accuracy than standard data mining methods in almost all cases.
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Electrocardiography (ECG) has been recently proposed as biometric trait for identification purposes. Intra-individual variations of ECG might affect identification performance. These variations are mainly due to Heart Rate Variability (HRV). In particular, HRV causes changes in the QT intervals along the ECG waveforms. This work is aimed at analysing the influence of seven QT interval correction methods (based on population models) on the performance of ECG-fiducial-based identification systems. In addition, we have also considered the influence of training set size, classifier, classifier ensemble as well as the number of consecutive heartbeats in a majority voting scheme. The ECG signals used in this study were collected from thirty-nine subjects within the Physionet open access database. Public domain software was used for fiducial points detection. Results suggested that QT correction is indeed required to improve the performance. However, there is no clear choice among the seven explored approaches for QT correction (identification rate between 0.97 and 0.99). MultiLayer Perceptron and Support Vector Machine seemed to have better generalization capabilities, in terms of classification performance, with respect to Decision Tree-based classifiers. No such strong influence of the training-set size and the number of consecutive heartbeats has been observed on the majority voting scheme.
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Identification of humans via ECG is being increasingly studied because it can have several advantages over the traditional biometric identification techniques. However, difficulties arise because of the heartrate variability. In this study we analysed the influence of QT interval correction on the performance of an identification system based on temporal and amplitude features of ECG. In particular we tested MLP, Naive Bayes and 3-NN classifiers on the Fantasia database. Results indicate that QT correction can significantly improve the overall system performance. © 2013 IEEE.
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Resource discovery is one of the key services in digitised cultural heritage collections. It requires intelligent mining in heterogeneous digital content as well as capabilities in large scale performance; this explains the recent advances in classification methods. Associative classifiers are convenient data mining tools used in the field of cultural heritage, by applying their possibilities to taking into account the specific combinations of the attribute values. Usually, the associative classifiers prioritize the support over the confidence. The proposed classifier PGN questions this common approach and focuses on confidence first by retaining only 100% confidence rules. The classification tasks in the field of cultural heritage usually deal with data sets with many class labels. This variety is caused by the richness of accumulated culture during the centuries. Comparisons of classifier PGN with other classifiers, such as OneR, JRip and J48, show the competitiveness of PGN in recognizing multi-class datasets on collections of masterpieces from different West and East European Fine Art authors and movements.
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This dissertation identifies, examines, and assesses the relative influence of identified empirically and conceptually relevant variables on incarcerated substance abusers' expectations of postrelease adjustment. A purposive sampling procedure was used to recruit 101 male and female substance-abusing offenders participating in prison- and jail-based drug treatment programs in south Florida. A 92-item survey questionnaire was used to collect basic demographic data; measure inmate preincarceration characteristics, social support, and rehabilitation program participation; and record archival data. Regression equations were developed utilizing ten different measures of the participants' expectations of their postrelease adjustment. Two equations yielded statistically significant F ratios; maintaining a stable living and maintaining abstinence. Twenty-two percent of the variance in respondents' expectations of maintaining a stable living was explained by preincarceration characteristics, social support, and rehabilitation program participation (F = 1.89; df = 13,87; p $<$.05). The only significant predictor variable was perception of social support (b = $-$.05; t = $-$3.6; p $<$.001). Twenty-three percent of the variance in respondents' expectations of maintaining abstinence from substances was explained by preincarceration characteristics, social support, and rehabilitation program participation (F = 2; df = 13,87; p $<$.05). Once again, the only significant predictor variable was perception of social support. The results of the analyses indicate that social support was the only important variable for understanding these respondents' efficacy expectations of postrelease abstinence and stable living. The results of this investigation demonstrate the complexity of the social support variable for prisoners, and identify social support as a potential rehabilitative resource for substance-abusing inmates. The results of this investigation underscore the importance of continued, detailed empirical study in order to understand and clarify how social support, efficacy expectations, and actual postrelease performance interrelate for this population of offenders.
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The current study was designed to explore the salience of social support, immigrant status, and risk in middle childhood and early adolescence across two time periods as indicated by measures of school adjustment and well-being. Participants included 691 children of public elementary schools in grades 4 and 6 who were interviewed in 1997 (Time 1) and reinterviewed two years later (Time 2); 539 were U.S.-born, and 152 were foreign-born. ^ Repeated measures multivariate analyses of variance (MANOVA's) were conducted to assess the effects of immigrant status and risk on total support, well-being, and school adjustment from Time 1 to Time 2. Follow-up analyses, including Student-Newman-Keuls post hoc tests, were used to test the significance of the differences among the means of support categories (low and high), immigrant status (U.S. born and non-U.S. born), risk (low and high) and time (time 1 and time 2). ^ Results showed that immigrant participants in the high risk group reported significantly lower levels of support than their peers. Further, children of low risk at Time 2 indicated the highest levels of support. Second, immigrant preadolescents, preadolescents who reported low levels of social support, and preadolescents of the high risk reported lower levels of emotional well-being. There was also an interaction of support by risk by time, indicating that children who are at risk and had low levels of social support reported more emotional problems at Time 1. Finally, preadolescents who are at risk and preadolescents who reported lower levels of support were more likely to show school adaptation problems. Findings from this study highlight the importance of a multivariable approach to the study of support, emotional adjustment, and academic adjustment of immigrant preadolescents. ^
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The need to provide computers with the ability to distinguish the affective state of their users is a major requirement for the practical implementation of affective computing concepts. This dissertation proposes the application of signal processing methods on physiological signals to extract from them features that can be processed by learning pattern recognition systems to provide cues about a person's affective state. In particular, combining physiological information sensed from a user's left hand in a non-invasive way with the pupil diameter information from an eye-tracking system may provide a computer with an awareness of its user's affective responses in the course of human-computer interactions. In this study an integrated hardware-software setup was developed to achieve automatic assessment of the affective status of a computer user. A computer-based "Paced Stroop Test" was designed as a stimulus to elicit emotional stress in the subject during the experiment. Four signals: the Galvanic Skin Response (GSR), the Blood Volume Pulse (BVP), the Skin Temperature (ST) and the Pupil Diameter (PD), were monitored and analyzed to differentiate affective states in the user. Several signal processing techniques were applied on the collected signals to extract their most relevant features. These features were analyzed with learning classification systems, to accomplish the affective state identification. Three learning algorithms: Naïve Bayes, Decision Tree and Support Vector Machine were applied to this identification process and their levels of classification accuracy were compared. The results achieved indicate that the physiological signals monitored do, in fact, have a strong correlation with the changes in the emotional states of the experimental subjects. These results also revealed that the inclusion of pupil diameter information significantly improved the performance of the emotion recognition system. ^
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The purpose of this study was to determine if there was a difference in the self-determined evaluations of work performance and support needs by adults with mental retardation in supported employment and in sheltered workshop environments. The instrument, Job Observation and Behavior Scale: Opportunity for Self-Determination (JOBS: OSD; Brady, Rosenberg, & Frain, 2006), was administered to 38 adults with mental retardation from sheltered workshops and 32 adults with mental retardation from supported employment environments. Cross-tabulations with Chi-square tests and independent samples t-tests were conducted to evaluate differences between the two groups, sheltered workshop and supported work. Two Multivariate Analyses of Variance (MANOVAs) were conducted to determine the effect of work environment on Quality of Performance (QP) and Types of Support (TS) test scores and their subscales. ^ This study found that there were significant differences between the groups on the QP Behavior and Job Duties subscales. The sheltered workshop group perceived themselves as performing significantly better on job duties than the supported work group. Conversely, the supported work group perceived themselves to have better behavior than the sheltered workshop group. However, there were no significant differences between groups in their perception of support needs for the three subscales. ^ The findings imply that work environment affects the self-determined evaluations of work performance by adults with mental retardation. Recommendations for further study include (a) detailing the characteristics of supported work and sheltered workshops that support and/or discourage self-determined behaviors, (b) exploring the behavior of adults with mental retardation in sheltered workshops and supported work environments, and (c) analysis of the support needs for and understanding of them by adults with mental retardation in sheltered workshops and in supported work environments. ^
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The purpose of this study is to investigate supervisory support as a moderator of the effects of role conflict and role ambiguity on emotional exhaustion and job satisfaction. This study also examines the moderating role of supervisory support on the relationship between emotional exhaustion and job satisfaction. Data were collected from a sample of frontline hotel employees in Northern Cyprus. The aforementioned relationships were tested based on hierarchical multiple regression analysis. The results demonstrate that supervisory support mitigates the impact of role conflict on emotional exhaustion and further reveal that supervisory support reduces the effect of emotional exhaustion on job satisfaction. There is no empirical support for the rest of the hypothesized relationships. Implications of the empirical results are discussed, and future research directions are offered.
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The proliferation of legalized gaming has significantly changed the nature of the hospitality industry. While several aspects of gaming have flourished, none has become more popular, profitable, or technologically advanced as the slot machine. While more than half of all casino gambling, and earnings, is generated by slot machines, little has been written about the technology integral to these devices. The author describes the workings of computer-controlled slot machines and exposes some of the popular operating myths.
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The rapid growth of virtualized data centers and cloud hosting services is making the management of physical resources such as CPU, memory, and I/O bandwidth in data center servers increasingly important. Server management now involves dealing with multiple dissimilar applications with varying Service-Level-Agreements (SLAs) and multiple resource dimensions. The multiplicity and diversity of resources and applications are rendering administrative tasks more complex and challenging. This thesis aimed to develop a framework and techniques that would help substantially reduce data center management complexity.^ We specifically addressed two crucial data center operations. First, we precisely estimated capacity requirements of client virtual machines (VMs) while renting server space in cloud environment. Second, we proposed a systematic process to efficiently allocate physical resources to hosted VMs in a data center. To realize these dual objectives, accurately capturing the effects of resource allocations on application performance is vital. The benefits of accurate application performance modeling are multifold. Cloud users can size their VMs appropriately and pay only for the resources that they need; service providers can also offer a new charging model based on the VMs performance instead of their configured sizes. As a result, clients will pay exactly for the performance they are actually experiencing; on the other hand, administrators will be able to maximize their total revenue by utilizing application performance models and SLAs. ^ This thesis made the following contributions. First, we identified resource control parameters crucial for distributing physical resources and characterizing contention for virtualized applications in a shared hosting environment. Second, we explored several modeling techniques and confirmed the suitability of two machine learning tools, Artificial Neural Network and Support Vector Machine, to accurately model the performance of virtualized applications. Moreover, we suggested and evaluated modeling optimizations necessary to improve prediction accuracy when using these modeling tools. Third, we presented an approach to optimal VM sizing by employing the performance models we created. Finally, we proposed a revenue-driven resource allocation algorithm which maximizes the SLA-generated revenue for a data center.^
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In this study we have identified key genes that are critical in development of astrocytic tumors. Meta-analysis of microarray studies which compared normal tissue to astrocytoma revealed a set of 646 differentially expressed genes in the majority of astrocytoma. Reverse engineering of these 646 genes using Bayesian network analysis produced a gene network for each grade of astrocytoma (Grade I–IV), and ‘key genes’ within each grade were identified. Genes found to be most influential to development of the highest grade of astrocytoma, Glioblastoma multiforme were: COL4A1, EGFR, BTF3, MPP2, RAB31, CDK4, CD99, ANXA2, TOP2A, and SERBP1. All of these genes were up-regulated, except MPP2 (down regulated). These 10 genes were able to predict tumor status with 96–100% confidence when using logistic regression, cross validation, and the support vector machine analysis. Markov genes interact with NFkβ, ERK, MAPK, VEGF, growth hormone and collagen to produce a network whose top biological functions are cancer, neurological disease, and cellular movement. Three of the 10 genes - EGFR, COL4A1, and CDK4, in particular, seemed to be potential ‘hubs of activity’. Modified expression of these 10 Markov Blanket genes increases lifetime risk of developing glioblastoma compared to the normal population. The glioblastoma risk estimates were dramatically increased with joint effects of 4 or more than 4 Markov Blanket genes. Joint interaction effects of 4, 5, 6, 7, 8, 9 or 10 Markov Blanket genes produced 9, 13, 20.9, 26.7, 52.8, 53.2, 78.1 or 85.9%, respectively, increase in lifetime risk of developing glioblastoma compared to normal population. In summary, it appears that modified expression of several ‘key genes’ may be required for the development of glioblastoma. Further studies are needed to validate these ‘key genes’ as useful tools for early detection and novel therapeutic options for these tumors.
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Parent involvement (PI) in schooling has consistently been correlated with improved academic achievement in children. However, despite the apparent benefits of parent involvement, many schools serving low-income communities report consistent difficulty in facilitating the involvement of parents in their children's schooling. ^ The purpose of this exploratory pilot study was to examine key variables associated with a PI program at a school that served a low-income community. The program was selected because it sustained the involvement of parents for a prolonged period of time. It was also selected because the program was facilitated by social workers. ^ Derived from the literature, four lines of inquiry were examined: (a) the relationship between PI and parent strengths and development; (b) the relationship between PI and children's academic achievement; (c) facilitators for PI; and (d) barriers to PI. These lines of inquiry yielded the study's four primary research questions. The study employed a cross-sectional research design to address them. ^ Thirty-three parents, representing 16 school-involved (SI) parents and 17 non-school involved (NSI) parents, served as study participants. All 33 parents resided in a high poverty community. ^ Quantitative methods were selected to examine differences between study participants and PI. Measures of parental empowerment, social support, self-esteem, and direct and indirect measures of their children's academic achievement were utilized. Qualitative methods were developed to identify and describe SI and NSI parents' perceptions of facilitators for and barriers to PI. ^ This study's findings suggest that PI may yield important benefits for SI parents. These benefits include parents' perceptions of their empowerment, social support, and self-esteem. This study's findings also suggest a relationship between PI and reduced rates of children's school suspensions. This study did not, however, support relationships between PI and children's standardized test scores. This study concludes that despite the apparent benefits of PI for SI parents, PI may nonetheless be a proxy for several unspecified interventions that effect parents, children, schools and communities alike. More precise specifications and robust measures of PI are needed. ^
Resumo:
Parent involvement (PI) in schooling has consistently been correlated with improved academic achievement in children. However, despite the apparent benefits of parent involvement, many schools serving low-income communities report consistent difficulty in facilitating the involvement of parents in their children's schooling. The purpose of this exploratory pilot study was to examine key variables associated with a PI program at a school that served a low-income community. The program was selected because it sustained the involvement of parents for a prolonged period of time. It was also selected because the program was facilitated by social workers. Derived from the literature, four lines of inquiry were examined: (a) the relationship between PI and parent strengths and development; (b) the relationship between PI and children's academic achievement; (c) facilitators for PI; and (d) barriers to PI. These lines of inquiry yielded the study's four primary research questions. The study employed a cross-sectional research design to address them. Thirty-three parents, representing 16 school-involved (SI) parents and 17 nonschool involved (NSI) parents, served as study participants. All 33 parents resided in a high poverty community. Quantitative methods were selected to examine differences between study participants and PI. Measures of parental empowerment, social support, self-esteem, and direct and indirect measures of their children's academic achievement were utilized. Qualitative methods were developed to identify and describe SI and NSI parents' perceptions of facilitators for and barriers to PI. This study's findings suggest that PI may yield important benefits for SI parents. These benefits include parents' perceptions of their empowerment, social support, and self-esteem. This study's findings also suggest a relationship between PI and reduced rates of children's school suspensions. This study did not, however, support relationships between PI and children's standardized test scores. This study concludes that despite the apparent benefits of PI for SI parents, PI may nonetheless be a proxy for several unspecified interventions that effect parents, children, schools and communities alike. More precise specifications and robust measures of PI are needed.