scholarly journals Epidemiological characteristics of bacillary dysentery from 2009 to 2016 and its incidence prediction model based on meteorological factors

2019 ◽  
Vol 24 (1) ◽  
Author(s):  
Qiuyu Meng ◽  
Xun Liu ◽  
Jiajia Xie ◽  
Dayong Xiao ◽  
Yi Wang ◽  
...  
Keyword(s):  
Urban Areas ◽  
Prediction Models ◽  
Mean Squared Error ◽  
Bacillary Dysentery ◽  
Meteorological Factors ◽  
Health Planning ◽  
Scientific Basis ◽  
Air Pressure ◽  
Support Vector ◽  
Epidemiological Characteristics

Abstract Background This study aimed to analyse the epidemiological characteristics of bacillary dysentery (BD) caused by Shigella in Chongqing, China, and to establish incidence prediction models based on the correlation between meteorological factors and BD, thus providing a scientific basis for the prevention and control of BD. Methods In this study, descriptive methods were employed to investigate the epidemiological distribution of BD. The Boruta algorithm was used to estimate the correlation between meteorological factors and BD incidence. The genetic algorithm (GA) combined with support vector regression (SVR) was used to establish the prediction models for BD incidence. Results In total, 68,855 cases of BD were included. The incidence declined from 36.312/100,000 to 23.613/100,000, with an obvious seasonal peak from May to October. Males were more predisposed to the infection than females (the ratio was 1.118:1). Children < 5 years old comprised the highest incidence (295.892/100,000) among all age categories, and pre-education children comprised the highest proportion (34,658 cases, 50.335%) among all occupational categories. Eight important meteorological factors, including the highest temperature, average temperature, average air pressure, precipitation and sunshine, were correlated with the monthly incidence of BD. The obtained mean absolute percent error (MAPE), mean squared error (MSE) and squared correlation coefficient (R2) of GA_SVR_MONTH values were 0.087, 0.101 and 0.922, respectively. Conclusion From 2009 to 2016, BD incidence in Chongqing was still high, especially in the main urban areas and among the male and pre-education children populations. Eight meteorological factors, including temperature, air pressure, precipitation and sunshine, were the most important correlative feature sets of BD incidence. Moreover, BD incidence prediction models based on meteorological factors had better prediction accuracies. The findings in this study could provide a panorama of BD in Chongqing and offer a useful approach for predicting the incidence of infectious disease. Furthermore, this information could be used to improve current interventions and public health planning.

scholarly journals Application of support vector machine for the prediction of strip crown in hot strip rolling

2020 ◽  
Author(s):  
Ya-feng Ji ◽  
Le-Bao Song ◽  
Hao Yuan ◽  
Wen Peng ◽  
Hua-Ying Li ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Prediction Accuracy ◽  
Prediction Models ◽  
Mean Squared Error ◽  
Prediction Performance ◽  
Support Vector ◽  
Hot Strip Rolling ◽  
Strip Rolling ◽  
Hot Strip ◽  
Strip Crown

Abstract In order to enhance the prediction accuracy of the strip crown and improve the quality of final product in the hot strip rolling, an optimized model based upon support vector machine (SVM) is proposed firstly. Meanwhile, for purposes of enriching data information and ensuring data quality, the actual data from a hot-rolled plant are collected to establish prediction model, as well as the prediction performance of models was evaluated by using multiple indicators. Besides, the traditional SVM model and the combined prediction models with the particle swarm optimization (PSO) and the cuckoo search (CS) optimization algorithm are also proposed. Furthermore, the prediction performance comparisons of the three different methods are discussed and validated. The results show that the CS-SVM has the highest prediction accuracy compared to the other two methods, and the root mean squared error (RMSE) of the proposed CS-SVM is 2.05µm, and 98.11% of prediction data have an absolute error below 4.5μm. In addition, the results also demonstrated that the CS-SVM not only with faster convergence speed and higher prediction accuracy but can be well applied to the actual hot strip rolling production.

Effects of Meteorological Factors on PM10 Pollution in Yantai Urban Areas

2012 ◽  
Vol 178-181 ◽  
pp. 328-331
Author(s):  
Jian Guo Song ◽  
Ming Chang ◽  
Xin Zhi Wang ◽  
Wei Liu
Keyword(s):  
Relative Humidity ◽  
Frequency Distribution ◽  
Urban Areas ◽  
Meteorological Factors ◽  
Air Pressure ◽  
The Other ◽  
Average Temperature ◽  
The North

This paper makes analysis and statistics about the frequency distribution of average temperature, pressure, humidity and wind conditions between moderate pollution days of PM10(API>200) and conventional days from 2008 to 2010 in Yantai. The result shows that the frequency of PM10 pollution which occurred in winter is close to the sum of the other seasons. PM10 pollution days appears easily under such conditions: the average temperature below 10°C, average air pressure is higher than 101.0kPa, relative humidity is less than 70%, or average weed speed of 3-7m/s with the north-south wind.

scholarly journals Machine Learning Prediction Models of Electrical Efficiency of Photovoltaic-Thermal Collectors

2019 ◽  
Author(s):  
Mohammad Hossein Ahmadi ◽  
Alireza Baghban ◽  
Ely Salwana ◽  
Milad Sadeghzadeh ◽  
Mohammad Zamen ◽  
...  
Keyword(s):  
Solar Collector ◽  
Fossil Fuels ◽  
Prediction Models ◽  
Mean Squared Error ◽  
Support Vector ◽  
Inlet Temperature ◽  
Collector System ◽  
Squared Error ◽  
Neuro Fuzzy ◽  
Great Performance

Solar energy is a renewable resources of energy which is broadly utilized and have the least pollution impact between the available alternatives of fossil fuels. In this investigation, machine leaening approaches of neural networks (NN), neuro-fuzzy and least squares support vector machine (LSSVM) are used to build the models for prediction of the thermal performance of a photovoltaic-thermal solar collector (PV/T) by estimating its efficiency as an output of the model while inlet temperature, flow rate, heat, solar radiation, and heat of sun are input of the designed model. Experimental measurements was prepared by designing a solar collector system and 100 data extracted. Different analyses are also performed to examine the credibility of the introduced approaches revealing great performance. The suggested LSSVM model represented the best performance regarding the mean squared error (MSE) of 0.003 and correlation coefficient (R2) value of 0.99, respectively.

scholarly journals A Comparative Study of Energy Big Data Analysis for Product Management in a Smart Factory

2022 ◽  
Vol 34 (2) ◽  
pp. 1-17
Author(s):  
Rahman A. B. M. Salman ◽  
Lee Myeongbae ◽  
Lim Jonghyun ◽  
Yongyun Cho ◽  
Shin Changsun
Keyword(s):  
Economic Growth ◽  
Support Vector Machine ◽  
Coefficient Of Variation ◽  
Prediction Models ◽  
Mean Squared Error ◽  
Training Data ◽  
Smart Factory ◽  
Support Vector ◽  
Product Management ◽  
Data Set

Energy has been obtained as one of the key inputs for a country's economic growth and social development. Analysis and modeling of industrial energy are currently a time-insertion process because more and more energy is consumed for economic growth in a smart factory. This study aims to present and analyse the predictive models of the data-driven system to be used by appliances and find out the most significant product item. With repeated cross-validation, three statistical models were trained and tested in a test set: 1) General Linear Regression Model (GLM), 2) Support Vector Machine (SVM), and 3) boosting Tree (BT). The performance of prediction models measured by R2 error, Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Coefficient of Variation (CV). The best model from the study is the Support Vector Machine (SVM) that has been able to provide R2 of 0.86 for the training data set and 0.85 for the testing data set with a low coefficient of variation, and the most significant product of this smart factory is Skelp.

An Efficient Hybrid Grey Wolf Optimization Based KELM Approach for Prediction of the Discharge Coefficient of Submerged Radial Gates

2021 ◽  
Author(s):  
Kiyoumars Roushangar ◽  
Saman Shahnazi ◽  
Arman Alirezazadeh Sadaghiani
Keyword(s):  
Discharge Coefficient ◽  
Prediction Models ◽  
Mean Squared Error ◽  
Gaussian Process Regression ◽  
Support Vector ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
Experimental Database ◽  
Learning Machine ◽  
Downstream Flow

Abstract Radial gates are widely used hydraulic structures for flow control in irrigation canals. Accurately prediction of discharge coefficient through radial gates is considered as a challenging hydraulic subject, particularly under highly submerged flow conditions. Incurring the advantages of Kernel-depend Extreme Learning Machine (KELM), this study offers a Grey Wolf Optimization-based KELM (GWO-KELM) for effective prediction of discharge coefficient through submerged radial gates. Additionally, Support Vector Machine (SVM), and Gaussian Process Regression (GPR) methods are also presented for comparative purposes. To build prediction models using GWO-KELM, GPR, and SVM an extensive experimental database was established, consisting of 2125 data samples gathered by the US Bureau of Reclamation. From simulation results, it is observed that the proposed GWO-KELM approach with input parameters of the ratio of the downstream flow depth to the gate opening (y3/w) and submergence ratio (y1-y3/w) provides the best performance with the correlation coefficient (R) of 0.983, the Determination Coefficient (DC) of 0.966 and the Root Mean Squared Error (RMSE) of 0.027. Furthermore, the obtained results showed that the employed kernel-depend methods are capable of a statistically predicting the discharge coefficient under varied submergence conditions with satisfactory level of accuracy.

Workload Prediction on Google Cluster Trace

2014 ◽  
Vol 6 (3) ◽  
pp. 34-52 ◽  
Author(s):  
Md. Rasheduzzaman ◽  
Md. Amirul Islam ◽  
Rashedur M. Rahman
Keyword(s):  
Resource Utilization ◽  
Prediction Models ◽  
Mean Squared Error ◽  
Experimental Result ◽  
Percentage Error ◽  
Support Vector ◽  
Workload Prediction ◽  
Inference System ◽  
Squared Error ◽  
Efficient Resource

Workload prediction in cloud systems is an important task to ensure maximum resource utilization. So, a cloud system requires efficient resource allocation to minimize the resource cost while maximizing the profit. One optimal strategy for efficient resource utilization is to timely allocate resources according to the need of applications. The important precondition of this strategy is obtaining future workload information in advance. The main focus of this analysis is to design and compare different forecasting models to predict future workload. This paper develops model through Adaptive Neuro Fuzzy Inference System (ANFIS), Non-linear Autoregressive Network with Exogenous inputs (NARX), Autoregressive Integrated Moving Average (ARIMA), and Support Vector Regression (SVR). Public trace data (workload trace version II) which is made available by Google were used to verify the accuracy, stability and adaptability of different models. Finally, this paper compares these prediction models to find out the model which ensures better prediction. Performance of forecasting techniques is measured by some popular statistical metric, i.e., Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Sum of Squared Error (SSE), Normalized Mean Squared Error (NMSE). The experimental result indicates that NARX model outperforms other models, e.g., ANFIS, ARIMA, and SVR.

scholarly journals Epidemiological Characteristics of Hand, Foot, and Mouth Disease Outbreaks in Qingdao, 2009-2018

2021 ◽  
Author(s):  
Jing Jia ◽  
Fei Kong ◽  
Xueling Xin ◽  
Jiwei Liang ◽  
Hualei Xin ◽  
...  
Keyword(s):  
Urban Areas ◽  
Prevention And Control ◽  
Response Times ◽  
Descriptive Analysis ◽  
Foot And Mouth Disease ◽  
Scientific Basis ◽  
Mouth Disease ◽  
Foot And Mouth ◽  
And Control ◽  
Epidemiological Characteristics

Background: In China, hand, foot, and mouth disease (HFMD) outbreaks have become an important issue recent years. We analyzed the epidemiological characteristics of HFMD outbreaks in Qingdao during 2009- 2018, and provided evidences for prevention and control of the disease. Methods: Data were analyzed by descriptive analysis and correlation analysis, and throat swabs were detected for enterovirus RNA using RT-PCR. Results: Overall, 116 HFMD outbreaks were reported in Qingdao during 2009-2018, with the epidemic of the outbreaks exhibiting a decreasing tendency. The characteristics of outbreaks presented two patterns, including two-peak pattern and rural area to urban-rural fringe area to urban areas pattern. Male patients were predominant in these outbreaks. The location of the outbreaks changed from nursery to community. Non-EV71/CA16 enteroviruses were gradually becoming predominant enteroviruses serotypes. The durations of outbreaks were positively correlated with response times and the number of cases. Conclusion: The epidemiological characteristics analysis of HFMD outbreaks could provide a scientific basis for the prevention and control the disease. Reporting and handling promptly are the keys to control epidemic outbreaks of HFMD.

In silico Prediction of Inhibitory Constant of Thrombin Inhibitors Using Machine Learning

2019 ◽  
Vol 21 (9) ◽  
pp. 662-669 ◽  
Author(s):  
Junnan Zhao ◽  
Lu Zhu ◽  
Weineng Zhou ◽  
Lingfeng Yin ◽  
Yuchen Wang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Regression Tree ◽  
Large Data ◽  
Thrombin Inhibitors ◽  
Coagulation Cascade ◽  
Gradient Boosting ◽  
Support Vector ◽  
Data Set ◽  
Descriptor Selection

Background: Thrombin is the central protease of the vertebrate blood coagulation cascade, which is closely related to cardiovascular diseases. The inhibitory constant Ki is the most significant property of thrombin inhibitors. Method: This study was carried out to predict Ki values of thrombin inhibitors based on a large data set by using machine learning methods. Taking advantage of finding non-intuitive regularities on high-dimensional datasets, machine learning can be used to build effective predictive models. A total of 6554 descriptors for each compound were collected and an efficient descriptor selection method was chosen to find the appropriate descriptors. Four different methods including multiple linear regression (MLR), K Nearest Neighbors (KNN), Gradient Boosting Regression Tree (GBRT) and Support Vector Machine (SVM) were implemented to build prediction models with these selected descriptors. Results: The SVM model was the best one among these methods with R2=0.84, MSE=0.55 for the training set and R2=0.83, MSE=0.56 for the test set. Several validation methods such as yrandomization test and applicability domain evaluation, were adopted to assess the robustness and generalization ability of the model. The final model shows excellent stability and predictive ability and can be employed for rapid estimation of the inhibitory constant, which is full of help for designing novel thrombin inhibitors.

Rapid Determining Contents of the Rhubarb Anthraquinones Compounds by Support Vector Machines Modeling based on Near Infrared Spectra

2020 ◽  
Vol 16 ◽  
Author(s):  
Linqi Liu ◽  
JInhua Luo ◽  
Chenxi Zhao ◽  
Bingxue Zhang ◽  
Wei Fan ◽  
...  
Keyword(s):  
Infrared Spectra ◽  
Near Infrared ◽  
Mean Squared Error ◽  
Rapid Determination ◽  
Partial Least Square ◽  
Least Square ◽  
Support Vector ◽  
Near Infrared Spectra ◽  
Aloe Emodin ◽  
Relative Differences

BACKGROUND: Measuring medicinal compounds to evaluate their quality and efficacy has been recognized as a useful approach in treatment. Rhubarb anthraquinones compounds (mainly including aloe-emodin, rhein, emodin, chrysophanol and physcion) are its main effective components as purgating drug. In the current Chinese Pharmacopoeia, the total anthraquinones content is designated as its quantitative quality and control index while the content of each compound has not been specified. METHODS: On the basis of forty rhubarb samples, the correlation models between the near infrared spectra and UPLC analysis data were constructed using support vector machine (SVM) and partial least square (PLS) methods according to Kennard and Stone algorithm for dividing the calibration/prediction datasets. Good models mean they have high correlation coefficients (R2) and low root mean squared error of prediction (RMSEP) values. RESULTS: The models constructed by SVM have much better performance than those by PLS methods. The SVM models have high R2 of 0.8951, 0.9738, 0.9849, 0.9779, 0.9411 and 0.9862 that correspond to aloe-emodin, rhein, emodin, chrysophanol, physcion and total anthraquinones contents, respectively. The corresponding RMSEPs are 0.3592, 0.4182, 0.4508, 0.7121, 0.8365 and 1.7910, respectively. 75% of the predicted results have relative differences being lower than 10%. As for rhein and total anthraquinones, all of the predicted results have relative differences being lower than 10%. CONCLUSION: The nonlinear models constructed by SVM showed good performances with predicted values close to the experimental values. This can perform the rapid determination of the main medicinal ingredients in rhubarb medicinal materials.

scholarly journals Epidemiological Characteristics of Carrying Viral Hepatitis B Antigen in the Population of the Nišava District

2014 ◽  
Vol 31 (2) ◽  
pp. 121-128
Author(s):  
Marina Kostić ◽  
Biljana Kocić ◽  
Nataša Rančić
Keyword(s):  
Hepatitis B ◽  
Health Care Providers ◽  
Urban Areas ◽  
Drug Users ◽  
Risk Groups ◽  
Epidemiological Surveillance ◽  
Annual Reports ◽  
Care Providers ◽  
Upward Trend ◽  
Epidemiological Characteristics

Summary The aim of this paper was to determine the trend of diseases and epidemiological characteristics of viral antigen carrying of hepatitis B for better implementation of prevention and control of the disease activity. The annual reports, reports of diseases - deaths from infectious diseases, epidemiological survey of the Public Health Institute (IPH) Niš were used as the material. The period from 2002 to 2011 in the Nišava District was considered. A descriptive method was used. HBsAg carrying shows an upward trend (y=15+3.27 x). Most carriers are males (57.27%), live in urban areas (98.16/ 100.000 population), average age 41.92 years old ±SD 18.59, pensioners (22.42%). 54.05% are nephrology patients (almost all retirees under the age of 60 years old). Only 15.76% were hospitalized. The data on the vaccination status are insufficient. In 5.45%, co-infection with hepatitis C virus was found. 63.33% belong to the group of patients for whom there were no data on the mode of transmission. Hemodialysis patients make 16.67%, blood donors 9.39%, 6.36% pregnant women and injecting drug users 1.21%. The upward trend of carrying, the presence of all known risk groups in the population of carrying in the Nišava District points to the need for improved epidemiological surveillance, strict application of protective measures, conducting of statutory vaccination of all categories of people exposed to particular risk of infection as well as continuing education on preventive measures of both population and health care providers.

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