With the continuous improvement of smart language systems, a large amount of language text data has emerged. How to efficiently and accurately process these text data has become an important challenge. Therefore, a sp...
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With the continuous improvement of smart language systems, a large amount of language text data has emerged. How to efficiently and accurately process these text data has become an important challenge. Therefore, a speech classification recommendation system based on improved Naive bayesian algorithm is proposed. The system first adopts the traditional bayesian algorithm to classify language texts. The Term Frequency-Inverse Document Frequency and rank factor are combined to increase the weight of feature languages. Then, the classified language texts are combined with the improved algorithm for language classification recommendation. Finally, performance testing and simulation applications are conducted on the system. From the results, in the Gutenberg corpus, the research algorithm had the highest accuracy and completeness, with 98.5 % and 91.6 %, respectively, and the lowest values were 92.6 % and 89.4 %. The average values were 95.5 % and 91.1 %, with an F1 value of about 92.6 %. In the Brown corpus, the average accuracy, completeness, and F1 value of the designed algorithm were 96.2 %, 91.2 %, and 93.2 %, respectively. When the number of online customers reached 1000, the response time of the designed Chinese system was 1.15 s, the classification recommendation accuracy was 95 %, and the system stability was about 83 % on average. The response time of the English system was 0.64 s, the classification recommendation accuracy was 96 %, and the system stability was about 90 % on average. It shows that the designed method can significantly enhance the operation accuracy of the classification recommendation system.
Background and PurposeIschemic core estimation by CT perfusion (CTp) is a diagnostic challenge, mainly because of the intrinsic noise associated with perfusion data. However, an accurate and reliable quantification of...
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Background and PurposeIschemic core estimation by CT perfusion (CTp) is a diagnostic challenge, mainly because of the intrinsic noise associated with perfusion data. However, an accurate and reliable quantification of the ischemic core is critical in the selection of patients for reperfusion therapies. Our study aimed at assessing the diagnostic accuracy of two different CTp postprocessing algorithms, that is, the bayesian Method and the oscillation index singular value decomposition (oSVD). MethodsAll the consecutive stroke patients studied in the extended time window (>4.5 hours from stroke onset) by CTp and diffusion-weighted imaging (DWI), between October 2019 and December 2021, were enrolled. The agreement between both algorithms and DWI was assessed by the Bland-Altman plot, Wilcoxon signed-rank test, Spearman's rank correlation coefficient, and the intraclass correlation coefficient (ICC). ResultsTwenty-four patients were enrolled (average age: 72 +/- 15 years). The average National Institutes of Health Stroke Scale was 14.42 +/- 6.75, the median Alberta Stroke Program Early CT score was 8.50 (interquartile range [IQR] = 7.75-9), and median time from stroke onset to neuroimaging was 7.5 hours (IQR = 6.5-8). There was an excellent correlation between DWI and oSVD (rho = .87, p-value < .001) and DWI and bayesian algorithm (rho = .94, p-value < .001). There was a stronger ICC between DWI and bayesian algorithm (.97, 95% confidence interval [CI]: .92-.99, p-value < .001) than between DWI and oSVD (.59, 95% CI: .26-.8, p-value < .001). DiscussionThe agreement between bayesian algorithm and DWI was greater than between oSVD and DWI in the extended window. The more accurate estimation of the ischemic core offered by the bayesian algorithm may well play a critical role in the accurate selection of patients for reperfusion therapies.
In recent years, cementitious composites with a compressive strength of more than 100 MPa have become popular for tall earthquake-resistant buildings. However, in these composites, attention is paid to sustainability ...
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In recent years, cementitious composites with a compressive strength of more than 100 MPa have become popular for tall earthquake-resistant buildings. However, in these composites, attention is paid to sustainability criteria along with strength. In this study, physical, mechanical, and microstructural properties of cementitious composites with high early-age strength were investigated by using industrial wastes such as RHA (rice husk ash) and GBFS (ground blast furnace slag). In cementitious composites, the cement dosage was chosen as 1000 kg and the w/c (water/cement) or w/b (water/binder) ratio was 0.22. RHA was used instead of cement at the rate of 5 and 10%, and GBFS at the rate of 5, 10, and 15%. Cementitious composites were subjected to three different cures: normal water (WC), hot water (HWC), and steam (SC). As the RHA content increased, the flow diameter of the mixtures decreased. GBFS relatively improved the workability of cementitious composites. While the porosity of WC applied mixtures varies between 2.7 and 4.3%, the porosity of HWC or SC applied mixtures decreases up to 1.3%. The water absorption of all cementitious composites is less than 2%. The 3-day compressive strengths of the mixtures are between 53.4 and 90.6 MPa, and the 90-day compressive strengths are between 100.2 and 123.3 MPa. In addition, the 3-day flexural strength of the mixtures exceeds 7 MPa. On the 90th day, cementitious composites with a flexural strength of approximately 18 MPa were produced. As the RHA content increased, it decreased the 3-day flexural and compressive strengths but improved the mechanical properties on the 90th day. Dense needle-like CSH gels were observed in SEM examinations. With the developed ANN model, it has been determined that the material quantities (RHA and GBFS content) and curing conditions will be predicted with high accuracy for optimum compressive strength. As a result, it has been determined that a compressive strength higher than 120 MPa and a fl
Recently, research on intrusion detection in computer systems has received much attention to the computational intelligence society. Many intelligence learning algorithms applied to the huge volume of complex and dyna...
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Recently, research on intrusion detection in computer systems has received much attention to the computational intelligence society. Many intelligence learning algorithms applied to the huge volume of complex and dynamic dataset for the construction of efficient intrusion detection systems (IDSs). Despite of many advances that have been achieved in existing IDSs, there are still some difficulties, such as correct classification of large intrusion detection dataset, unbalanced detection accuracy in the high speed network traffic, and reduce false positives. This paper presents a new approach to the alert classification to reduce false positives in intrusion detection using improved self adaptive bayesian algorithm (ISABA). The proposed approach applied to the security domain of anomaly based network intrusion detection, which correctly classifies different types of attacks of KDD99 benchmark dataset with high classification rates in short response time and reduce false positives using limited computational resources.
Carboplatin is associated with significantly less nephrotoxicity and neurotoxicity than is cisplatin. The dose-limiting toxicity of carboplatin is myelotoxicity. A number of dosing methods have been described that all...
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Carboplatin is associated with significantly less nephrotoxicity and neurotoxicity than is cisplatin. The dose-limiting toxicity of carboplatin is myelotoxicity. A number of dosing methods have been described that allow a value for the area under the concentration-time curve to be targeted on the basis of the patient's renal function. Recently a formalised analysis of the pharmacodynamic response to carboplatin revealed a therapeutic window in which the response rate was maximal and toxicity, tolerable. Optimal therapy would result from targeting this window in the individual patient. The aim of this study was to develop a bayesian dose-individualisation method for carboplatin. The method involved (1) development of a highperformance liquid chromatography (HPLC) method to measure serum concentrations of carboplatin;(2) a pharmacokinetic study in 12 women receiving carboplatin for ovarian cancer to estimate the population pharmacokinetic values for this group of patients;(3) development of population models to describe the concentration-time course of carboplatin in serum along with associated errors;and (4) development of an algorithm that uses a sequential bayesian design, which enables estimation of future doses of carboplatin on the basis of feedback from serum concentrations. The results of each of the stages were (1) the coefficient of variation of the assay was 6.3% within day and 8.4% between days (r(2) = 0.9993), and the limit of detection was 0.25 mg/l;(2) Patients' ages ranged from 49 to 68 years, their weights varied from 46 to 85 kg, and their glomerular filtration rate ranged from 3.2 to 7.4 l/h. A geometric mean clearance (Cl) of 6.8 L/h and a steady-state volume of distribution (Vss) of 221 were estimated, which are similar to previously published data;(3) and a two-compartment model best described the data. Two error models were developed, the first describing the error associated with the assay and the second, the error of the two-compartment model,
every year, there is significant growth in the Number of graduates molded by higher education institutions. the Number of graduates is increasing compared to the job openings in the market. Datamining is one of the nu...
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ISBN:
(纸本)9781665492546
every year, there is significant growth in the Number of graduates molded by higher education institutions. the Number of graduates is increasing compared to the job openings in the market. Datamining is one of the numerous types of research from the dataset obtained from the Caraga state university cabadbaran campus. This study aimed to determine the correlation of BSIT grades to the employment alignment in it-related jobs using the bayesian algorithm of Caraga state university cabadbaran campus from 2015-to 2018. the study produces that the bayesian algorithm gathered 177 graduates from 2015-2018. Only 88.5 or 50% answered that they are working related to their field specialization in information technology and also already permanent employees. 72 or 40.688% of student graduates responded that they are working but not aligned in their field specialization but are permanent employees. There are, 10.17% resigned and still finding a new job, and they are also graduate students taking care of their babies. The study would help the students and university to increase the various aspect of education the student and see to it that they would yield quality education in the succeeding years.
The discipline of journalism needs to gradually transform perspective. It combines with the characteristics of academic discourse of news communication to build a strong Chinese characteristically academic discourse s...
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ISBN:
(纸本)9783038351153
The discipline of journalism needs to gradually transform perspective. It combines with the characteristics of academic discourse of news communication to build a strong Chinese characteristically academic discourse system of news, so as to make a positive contribution to the development of Chinese journalism discipline. First, we analyze the general compensation model on the basis of compensation algorithm. Based on more compensation formulas, we analyze the principle of compensation. According to the basic requirements of the news communication and the unique function of Chinese characteristically academic discourse, with the basic requirements of the unique characteristics of academic discourse for news communication and Chinese characteristics, and combined with the relative reliability features extraction method of information builds academic discourse system of Chinese characteristics news communication based on bayesian algorithm. We apply the computer to make simulation analysis on the effect of different classes' weighting factors on the system structure. The results show that the system structure has a high efficiency, good stability, and strong practicability, which has provided theoretical and technical support for the research in this field to a certain extent.
The aim of this study is to realize radar emitter Identification with high-efficiency. In this article, an approach based on naive bayesian algorithm is introduced. For recognizing radar radiations, this paper utilize...
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ISBN:
(纸本)9781728143231;9781728143224
The aim of this study is to realize radar emitter Identification with high-efficiency. In this article, an approach based on naive bayesian algorithm is introduced. For recognizing radar radiations, this paper utilizes Naive Bayes classifier for radar signal sorting, and selects the pulse parameters (direction of arrival, pulse width, pulse repetition frequency and radar frequency) as features for training the classifier. The paper also compares the method based on naive bayesian algorithm with the methods based on artificial neural network, K-means algorithm and support vector machine. The performance evaluation shows that naive Bayes classifier can achieve a high recognition accuracy, and the approach presented in this article is proved to be more efficient.
In order to improve the accuracy and maintenance efficiency of fault diagnosis for secondary loop in intelligent substation, this paper proposes a fault diagnosis method based on improved bayesian algorithm. Firstly, ...
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ISBN:
(纸本)9781728156224
In order to improve the accuracy and maintenance efficiency of fault diagnosis for secondary loop in intelligent substation, this paper proposes a fault diagnosis method based on improved bayesian algorithm. Firstly, the physical loop and virtual loop models of the secondary loop are established according to the SCD file, and their mapping relationship is established. Secondly, the set of suspicious fault components in the secondary loop is obtained according to the abnormal degree table of physical components. Then, the improved bayesian algorithm is used to evaluate the components in the set of suspicious fault components. Finally, the output of the bayesian algorithm is the bayesian anomaly degree of the suspicious fault components. The component with the highest bayesian anomaly degree is considered as the fault component. In order to detect the effectiveness and the feasibility of the proposed method, this paper conducts a fault diagnosis case and the diagnosis results demonstrate that the proposed diagnosis method can obtain good discriminant result.
This paper presents a new bayesian strategy for the estimation of smooth signals corrupted by Gaussian noise. The method assumes a smooth evolution of a succession of continuous signals that can have a numerical or an...
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ISBN:
(纸本)9780992862657
This paper presents a new bayesian strategy for the estimation of smooth signals corrupted by Gaussian noise. The method assumes a smooth evolution of a succession of continuous signals that can have a numerical or an analytical expression with respect to some parameters. The bayesian model proposed takes into account the Gaussian properties of the noise and the smooth evolution of the successive signals. In addition, a gamma Markov random field prior is assigned to the signal energies and to the noise variances to account for their known properties. The resulting posterior distribution is maximized using a fast coordinate descent algorithm whose parameters are updated by analytical expressions. The proposed algorithm is tested on satellite altimetric data demonstrating good denoising results on both synthetic and real signals. The proposed algorithm is also shown to improve the quality of the altimetric parameters when combined with a parameter estimation strategy.
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