431 research outputs found

    Residual Control Chart for Binary Response with Multicollinearity Covariates by Neural Network Model

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    Quality control studies have dealt with symmetrical data having the same shape with respect to left and right. In this research, we propose the residual (r) control chart for binary asymmetrical (non-symmetric) data with multicollinearity between input variables via combining principal component analysis (PCA), functional PCA (FPCA) and the generalized linear model with probit and logit link functions, and neural network regression model. The motivation in this research is that the proposed control chart method can deal with both high-dimensional correlated multivariate data and high frequency functional multivariate data by neural network model and FPCA. We show that the neural network r control chart is relatively efficient to monitor the simulated and real binary response data with the narrow length of control limits

    WEATHER IMPACT ON ROAD ACCIDENT SEVERITY IN MARYLAND

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    This study was conducted to analyze and quantify the impact of weather factors on road accident severity, based on Maryland accident data during 2007-2010. In order to find a better model fitted related variables, three candidate models multinomial logit (MNL), ordered probit logit (OP), and neural networks were chosen to examine in SAS. The results showed that the Multilayer Perceptron Model in neural networks performed the best and is the accident severity model of choice. During the model construction, eight factors related to weather condition were considered. They were: air temperature, average wind speed, total precipitation in the past 24 hours, visibility, slight, moderate, heavy precipitation and relative humidity. Based on the comparison criteria, we concluded that MNL regression is more interpretive than OP and Neural Networks models. All factors except visibility and heavy precipitation had significant impact on accident severity when considering the data from the entire Maryland highway system. Using MNL, a data subset with accident records only in a section of US route 50 was examined. After excluding the impact factors other than weather, a narrow significant variable set was obtained

    Анализ Π²Ρ€Π΅ΠΌΠ΅Π½Π½Ρ‹Ρ… рядов ΠΈ ΠΏΡ€ΠΎΠ³Π½ΠΎΠ·ΠΈΡ€ΠΎΠ²Π°Π½ΠΈΠ΅ Ρ†Π΅Π½ Π½Π° Π·ΠΎΠ»ΠΎΡ‚ΠΎ (XAUUSD) с использованиСм машинного обучСния

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    ΠšΠ°ΠΆΠ΄Ρ‹ΠΉ дСнь происходит мноТСство Ρ€ΠΎΠ·Π½ΠΈΡ‡Π½Ρ‹Ρ… ΠΈ коммСрчСских банковских сдСлок с ΠΎΠΊΠΎΠ»ΠΎ 11 ΠΌΠΈΠ»Π»ΠΈΠ°Ρ€Π΄Π°ΠΌΠΈ Π·ΠΎΠ»ΠΎΡ‚Π°. Π§Ρ‚ΠΎΠ±Ρ‹ ΠΏΠΎΠ»ΡƒΡ‡ΠΈΡ‚ΡŒ ΠΏΡ€ΠΈΠ±Ρ‹Π»ΡŒ Π½Π° этом Π²ΠΎΠ»Π°Ρ‚ΠΈΠ»ΡŒΠ½ΠΎΠΌ Ρ€Ρ‹Π½ΠΊΠ΅, Π½Π°ΠΌ Π½Π΅ΠΎΠ±Ρ…ΠΎΠ΄ΠΈΠΌΠΎ Ρ€Π°Π·Ρ€Π°Π±ΠΎΡ‚Π°Ρ‚ΡŒ Ρ€Π°Π·Π»ΠΈΡ‡Π½Ρ‹Π΅ инструмСнты для прогнозирования ΠΈ Π°Π½Π°Π»ΠΈΠ·Π° Π±ΡƒΠ΄ΡƒΡ‰ΠΈΡ… Ρ†Π΅Π½, Ρ‡Ρ‚ΠΎΠ±Ρ‹ ΠΏΡ€ΠΈΠ½ΠΈΠΌΠ°Ρ‚ΡŒ ΡΠΎΠΎΡ‚Π²Π΅Ρ‚ΡΡ‚Π²ΡƒΡŽΡ‰ΠΈΠ΅ Ρ€Π΅ΡˆΠ΅Π½ΠΈΡ. Π’ своСм исслСдовании я использовал историчСскиС Π΄Π°Π½Π½Ρ‹Π΅ ΠΎ Π·ΠΎΠ»ΠΎΡ‚Π΅, ΠΏΠΎΠ»ΡƒΡ‡Π΅Π½Π½Ρ‹Π΅ ΠΎΡ‚ банковской Π³Ρ€ΡƒΠΏΠΏΡ‹ Dukascopy Swiss ΠΈ использовал инструмСнты искусствСнного ΠΈΠ½Ρ‚Π΅Π»Π»Π΅ΠΊΡ‚Π°, Ρ‚Π°ΠΊΠΈΠ΅ ΠΊΠ°ΠΊ LSTM ΠΈ ARIMA, для прогнозирования Π±ΡƒΠ΄ΡƒΡ‰ΠΈΡ… Ρ†Π΅Π½.Every day there are many retail and commercial banking trades around 11B of gold. To make a profit in this violent market we need to develop different tools to predict or analyze future prices to make suitable decisions. In my research, I used the historical data of gold and I obtained this data from Dukascopy Swiss banking group and used AI tools like LSTM and Arima to predict future prices

    False memory and delusions in Alzheimer's disease

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    Aims: This thesis aimed to investigate the relationship between memory errors and delusions in Alzheimer’s disease (AD), in order to further elucidate the mechanisms underlying delusion formation. This was achieved by undertaking narrative and systematic review of relevant literature, by exploring the relationship between performance on memory and metamemory tasks and delusions in AD patient populations and by investigating the neuroanatomical correlates of memory errors and delusions in AD patient populations. // Methods: I recruited 27 participants with and without delusions in AD and compared performance on measures of context memory, false memory and metamemory. I explored statistically significant behavioural findings further in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort of participants with AD (n = 733). I then conducted hypothesis-driven region of interest and exploratory voxel-based morphometric analyses to determine the relationship between false memory and delusions and regional brain volume in the ADNI cohort. This informed similar analyses of neuroimaging data in my own participants (n = 8). // Results: In both samples, individuals with delusions in AD had higher false recognition rates on recognition memory tasks than those without delusions. False recognition was inversely correlated with volume of medial temporal lobe, ventral visual stream and prefrontal cortex in both samples. In the ADNI sample, false recognition was also inversely correlated with anterior cingulate cortex (ACC) volume bilaterally. Participants with delusions had reduced volume of right ACC and increased volume of right parahippocampal gyrus compared to the control group. // Conclusions: These two complementary studies provide evidence of specific memory impairments associated with both delusions and a distinct pattern of brain atrophy in AD. Simple cognitive interventions can reduce false recognition rates in AD. Given the significant risks associated with antipsychotic drug treatment of delusions, exploring how these non-pharmacological interventions potentially affect psychosis symptoms in AD is an important next step

    Functional Imaging Connectome of the Human Brain and its Associations with Biological and Behavioral Characteristics

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    Functional connectome of the human brain explores the temporal associations of different brain regions. Functional connectivity (FC) measures derived from resting state functional magnetic resonance imaging (rfMRI) characterize the brain network at rest and studies have shown that rfMRI FC is closely related to individual subject\u27s biological and behavioral measures. In this thesis we investigate a large rfMRI dataset from the Human Connectome Project (HCP) and utilize statistical methods to facilitate the understanding of fundamental FC-behavior associations of the human brain. Our studies include reliability analysis of FC statistics, demonstration of FC spatial patterns, and predictive analysis of individual biological and behavioral measures using FC features. Covering both static and dynamic FC (sFC and dFC) characterizations, the baseline FC patterns in healthy young adults are illustrated. Predictive analyses demonstrate that individual biological and behavioral measures, such as gender, age, fluid intelligence and language scores, can be predicted using FC. While dFC by itself performs worse than sFC in prediction accuracy, if appropriate parameters and models are utilized, adding dFC features to sFC can significantly increase the predictive power. Results of this thesis contribute to the understanding of the neural underpinnings of individual biological and behavioral differences in the human brain

    Data Mining-based Survival Analysis and Simulation Modeling for Lung Transplant

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    The objective of this research is to develop a decision support methodology for the lung transplant procedure by investigating the UNOS nation-wide dataset via data mining-based survival analysis and simulation-based optimization. Traditional statistical techniques have various limitations which hinder the exploration of the information hidden under the voluminous data. The deployment of the structural equation modeling integrated with decision trees provides a more effective matching between the donor organ and the recipient. Such an integration preceded by powerful data mining models to determine which variables to include for survival analysis is validated via the simulation-based optimization.The suggested data mining-based survival analysis was superior to the conventional statistical methods in predicting the lung graft survivability and in determining the critical variables to include in organ matching and allocation. The proposed matching index derived via structural equation model-based decision trees was validated to be a more effective priority-ranking mechanism than the current lung allocation scoring system. This validation was established by a simulation-based optimization model. It was demonstrated that with this novel matching index, a substantial improvement was achieved in the survival rate while only a short delay was caused in the average waiting time of candidate patients on the list. Furthermore, via the response surface methodology-based simulation optimization the optimal weighting scheme for the components of the novel matching index was determined by jointly optimizing the lung transplant performance measures, namely, the justice principle in terms of the waiting time and the utility principle in terms of the survival rate. The study presents uniqueness in that it provides a means to integrate the data mining modeling as well as simulation optimization with the survival analysis so that more useful information hidden in the large amount of data can be discovered. The developed methodology improves the modeling of matching and allocation system in terms of both interpretability and predictability. This will be beneficial to medical professionals at a great deal.Industrial Engineering & Managemen

    Quantitative analysis for modeling uncertainty in construction costs of transportation projects with external factors

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    Highway construction costs are subject to significant upward and downward variations from project to project and over time. Variations in construction cost disturb transportation agencies in making right investment decisions and estimating accurate construction costs for projects. Transportation agencies face considerable uncertainty in estimating project costs that often leads to significant over- and under-estimation of highway construction costs. The underestimation of project costs can lead to cost overrun, financial problem, and project delay or cancellation. The overestimation of project costs results in an inefficient budget allocation of public funds that could be used on other needed projects. Transportation agencies can also face credibility issues with the public if cost estimation problems remain unresolved. A wide range of variables has been identified in different studies to explain variations in construction cost. There is a value in conducting a research study that attempts to consider a comprehensive list of variables with potentials to explain the variations. The study needs to simultaneously take into account all possible explanatory variables to examine their relations with construction costs. The overarching objective of this research is to assess the effects of several potential variables on explaining variations in submitted unit price bids for major asphalt line items in highway projects. First, stepwise regression analysis will be utilized to develop an explanatory model for describing variations in the submitted unit price bid. The identified variables used to build the explanatory model are classified into two major tiers. Tier 1 represents project specific factors, such as variables related to project characteristics, project location and its distance to major supply sources and price adjustment clauses. Tier 2 represents global and external factors, such as variables related to level of activities in local highway construction market, macroeconomic indicators and energy market conditions. Secondly, it is shown that there is a significant spatial correlation between construction project cost and geographical location of the project that a generalized linear modeling approach may overlook. Geographically weighted regression analysis will be conducted to develop explanatory models for describing variations in the submitted unit price bids considering the spatial correlation. Lastly, the effect of natural disasters on highway construction costs will be examined. Cumulative sum (CUSUM) control chart will be utilized to monitor and detect the change in submitted unit price bids for hurricane-impacted and not hurricane-impacted areas. The primary contributions of this research to the existing body of knowledge are: (1) creation of a multiple regression model to explain variations in submitted unit price bids; (2) creation of local regression models to describe variations in the submitted unit price bids considering the spatial correlation; and (3) empirical assessment of the impact of natural disasters on the variation in the submitted unit price bids. The primary contributions of this research to the state of practice are: (1) enhancing the capability of cost engineers in preparing more-accurate budgets and bids; (2) aiding a bottom-up estimating approach that requires more knowledge about the projects and market; and (3) helping capital project planners set and adjust the timing of the project lettings in the light of market conditions.Ph.D

    The Effects of Inaccurate and Missing Highway-Rail Grade Crossing Inventory Data on Crash and Severity Model Estimation and Prediction

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    Highway-Rail Grade Crossings (HRGCs) present a significant safety risk to motorists, pedestrians, and train passengers as they are intersections where roads and railways intersect. Every year, HRGCs in the US experience a high number of crashes leading to injuries and fatalities. Estimations of crash and severity models for HRGCs provide insights into safety and mitigation of the risk posed by such incidents. The accuracy of these models plays a vital role in predicting future crashes at these crossings, enabling necessary safety measures to be taken proactively. In the United States, most of these models rely on the Federal Railroad Administration\u27s (FRA) HRGCs inventory database, which serves as the primary source of information for these models. However, errors or incomplete information in this database can significantly impact the accuracy of the estimated crash model parameters and subsequent crash predictions. This study examined the potential differences in expected number of crashes and severity obtained from the Federal Railroad Administration\u27s (FRA) 2020 Accident Prediction and Severity (APS) model when using two different input datasets for 560 HRGCs in Nebraska. The first dataset was the unaltered, original FRA HRGCs inventory dataset, while the second was a field-validated inventory dataset, specifically for those 560 HRGCs. The results showed statistically significant differences in the expected number of crashes and severity predictions using the two different input datasets. Furthermore, to understand how data inaccuracy impacts model estimation for crash frequency and severity prediction, two new zero-inflated negative binomial models for crash prediction and two ordered probit models for crash severity, were estimated based on the two datasets. The analysis revealed significant differences in estimated parameters’ coefficients values of the base and comparison models, and different crash-risk rankings were obtained based on the two datasets. The results emphasize the importance of obtaining accurate and complete inventory data when developing HRGCs crash and severity models to improve their precision and enhance their ability to predict and prevent crashes. Advisor: Aemal J. Khatta
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