Ischemic stroke has been known to convert to Hemorrhagic stroke with ease. It is crucial to identify high risk patients with relevant diseases. This study uses the Taiwan National Health Insurance database to collate ...
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ISBN:
(纸本)9781728126074
Ischemic stroke has been known to convert to Hemorrhagic stroke with ease. It is crucial to identify high risk patients with relevant diseases. This study uses the Taiwan National Health Insurance database to collate and analyze the relevant diseases leading to Hemorrhagic from Ischemic stroke. We propose several novel machine learning based algorithms and indexing methods for disease transformation prediction and accuracy enhancements. They are a modified swarm algorithm, partialleastsquare(PLS) algorithm and genetic algorithms. From the petition application files accumulated from 2006 to 2013 within the National Health Insurance Database, 8,483 patients with ischemic stroke is collected. Among them, the 1,145 patients with both ischemic stroke and hemorrhagic stroke were screened according to the ICD-9-CM diagnostic code. The disease history of each patient is vectorized and stacked into a matrix for analysis. The PLS/GA process is then applied on the disease history matrix, trying to filter out the candidate diseases leading to such transformation. A total of 750 diseases were found to be associated with ischemic stroke and hemorrhagic stroke through the PLS/GA process. A modified PSO algorithm is developed to further weight these selected diseases. A quartile rule is then applied to filter these weighted diseases to ten most influential diseases which are dizziness, constipation, chronic renal failure, hypertension, diabetes, hyperlipidemia, anxiety, muscle pain, prostatic hypertrophy, etc. In addition to normally known diseases to such conversion in the literature such as hypertension and diabetes, we also discovered more potential diseases. The initial analysis accuracy of our proposed methods reaches 86% on average, while that of the traditional Neural Network algorithm utilizing the same training data was only 49.5%.
Indoor positioning accuracy affects the usability of many IOT applications such as personnel and merchandise tracking. Among the many indoor positioning methods, the signal pattern matching method is also known as the...
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ISBN:
(纸本)9781538620922
Indoor positioning accuracy affects the usability of many IOT applications such as personnel and merchandise tracking. Among the many indoor positioning methods, the signal pattern matching method is also known as the feature fingerprint method. The deployment is simple and low cost. Its working principle is through the collection of wireless signal samples to establish a fingerprint database. When receiving the localization request, the input data is put into the comparison with fingerprint database to determine the exact location of the target emitting the same wireless signal patterns. Many studies are based on this principle to develop different algorithms to improve the positioning accuracy. The shortcomings of this approach is that the positioning precision is easily perturbed by the noisy environment conditions. These noisy signal samples recorded in the fingerprint database will lose the reference value, when low efficiency indexing algorithm or wrong models are used. This paper proposes several novel machine learning based algorithms and indexing methods for indoor positioning accuracy enhancements. They are a modified swarm algorithm, partialleastsquare(PLS) algorithm and genetic algorithms. Different from regular fingerprint localization methods which make use of statistics based models to characterize the signal patterns, our methods make use of several A. I. based algorithms to classify the signal patterns for localization. Compared to other statistics based indoor fingerprint localization methods, our method can reach a precision of 95 % with low development cost and a resolution of 16 cm in a complex computer laboratory environment.
Evaluation of historical buildings' value is one of the most important steps in the process of urban construction, and the coefficient of index system is the key of the evaluation process. This paper analyzes the ...
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A simple and low cost spectrophotometric assay is proposed in this work for simultaneous determination of copper and iron levels in insulating oils. The procedure requires minimum sample volume (250 mu L), it is carri...
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A simple and low cost spectrophotometric assay is proposed in this work for simultaneous determination of copper and iron levels in insulating oils. The procedure requires minimum sample volume (250 mu L), it is carried out in disposable micro tubes (1.5 mL) and aqueous standards are used for calibration. In brief, after dilution with n-propanol, the wear metals are extracted with hydrochloric acid and, after neutralization, the analytes are reduced to the oxidation states Cu(I) and Fe(II) respectively. Finally, the mixture of bathocuproine-disulfonic acid and bathophenantroline-disulfonic acid is added for selective formation of copper and iron complexes. Then, chloroform and methanol are added, the sample is centrifuged and absorption spectra are registered in the upper-aqueous layer (470- 550 nm). Multivariate calibration, covering concentration range 0- 4 mu g/mL of each analyte in oil is performed using partialleastsquares regression. The test solutions are prepared by spiking the aqueous standards of copper and iron to metal-free oil diluted with n-propanol (1:1). The prediction results were in agreement with those obtained using univariate spectrophotometric assay (one ligand) and those obtained by electrothermal atomic absorption spectrometry (after dissolution of wear metals and dilution with n-propanol).
Reaction mixtures of enzyme catalyzed hydrolysis of penicillin and sugar concentrations in plant cell culture were monitored with attenuated total reflection Fourier transform infrared spectroscopy (ATR/FT-IR). Partia...
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Reaction mixtures of enzyme catalyzed hydrolysis of penicillin and sugar concentrations in plant cell culture were monitored with attenuated total reflection Fourier transform infrared spectroscopy (ATR/FT-IR). partial least square algorithm was used for estimation and measurement errors for penicillin, 6-aminopenicillanic Acid (6-APA), phenylacetic acid (PAA), glucose, fructose, and sucrose were 0.45, 0.53, 0.59, 0.35, 0.22, and 0.11 g/L respectively in the solutions for calibration. Concentration profiles for these compounds were obtained by using ATR/FT-IR in real reaction and cultivation processes.
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