scholarly journals PM2.5 Prediction using Machine Learning Hybrid Model for Smart Health

2019 ◽  
Vol 9 (1) ◽  
pp. 6500-6504
Keyword(s):  
Hybrid Model ◽  
Air Pollutants ◽  
Regression Models ◽  
Experimental Result ◽  
Respiratory Problems ◽  
The Public ◽  
Forecasting System ◽  
Smart Health ◽  
Iot Devices ◽  
Pm2.5 Concentration

Air Pollution is one of the current serious issue attributable to people's health causing cardiopulmonary deaths, lung cancer and several respiratory problems. Air is polluted by numerous air pollutants, among which Particulate Matter (PM2.5) is considered harmful consists of suspended particles with a diameter less than 2.5 micrometers.This paper aims to acquire PM2.5 data through IoT devices,store it in Cloud and propose an improved hybrid model that predicts the PM2.5 concentration in the air. Finally through forecasting system we alert the public in case of an undesired condition. The experimental result shows that our proposed hybrid model achieve better performance than other regression models.

scholarly journals An Outlier-Robust Point and Interval Forecasting System for Daily PM2.5 Concentration

2021 ◽  
Vol 9 ◽  
Author(s):  
Ziqi Yin ◽  
Xin Fang
Keyword(s):  
Air Pollution ◽  
Effective Technique ◽  
Capital Cities ◽  
The Public ◽  
Ensemble Strategy ◽  
Forecasting System ◽  
Provincial Capital ◽  
Original Time ◽  
Air Pollution Forecasting ◽  
Pm2.5 Concentration

Air pollution forecasting, particularly of PM2.5 levels, can be used not only to deliver effective warning information to the public but also to provide support for decisions regarding the control and treatment of air pollution problems. However, there are still some challenging issues in air pollution forecasting that urgently need to be solved, such as how to handle and model outliers, improve forecasting stability, and correct forecasting results. In this context, this study proposes an outlier-robust forecasting system to attempt to tackle the abovementioned issues and bridge the gap in the current research. Specifically, the system developed consists of two parts that deal with point and interval forecasting, respectively. For point forecasting, a data preprocessing module is proposed based on outlier handling and data decomposition to mitigate the negative influences of outliers and noise, which can also help the model capture the main characteristics of the original time series. Meanwhile, an outlier-robust forecasting module is designed for better modeling of the preprocessed data. For the model to further improve its accuracy, a nonlinear correction module based on an error ensemble strategy is developed that can provide more accurate forecasting results. Finally, the interval forecasting part of the system is based on a newly proposed artificial intelligence–based distribution evaluation and the results of the point forecasting part to present the range of future changes. Experimental results and analysis utilizing daily PM2.5 concentration from two provincial capital cities in China are discussed to verify the superiority and effectiveness of the system developed, which can be considered an effective technique for point and interval forecasting of daily PM2.5 concentration.

scholarly journals Behavioral and Attitudinal Correlates of Trusted Sources of COVID-19 Vaccine Information in the US

2021 ◽  
Vol 11 (4) ◽  
pp. 56
Author(s):  
Carl A. Latkin ◽  
Lauren Dayton ◽  
Jacob R. Miller ◽  
Grace Yi ◽  
Afareen Jaleel ◽  
...  
Keyword(s):  
Regression Models ◽  
Logistic Regression Models ◽  
The Public ◽  
Public Health Systems ◽  
Online Study ◽  
News Sources ◽  
The Us ◽  
Vulnerable People ◽  
Two Factors ◽  
Vaccine Information

There is a critical need for the public to have trusted sources of vaccine information. A longitudinal online study assessed trust in COVID-19 vaccine information from 10 sources. A factor analysis for data reduction revealed two factors. The first factor contained politically conservative sources (PCS) of information. The second factor included eight news sources representing mainstream sources (MS). Multivariable logistic regression models were used. Trust in Dr. Fauci was also examined. High trust in MS was associated with intention to encourage family members to get COVID-19 vaccines, altruistic beliefs that more vulnerable people should have vaccine priority, and belief that racial minorities with higher rates of COVID-19 deaths should have priority. High trust in PCS was associated with intention to discourage friends from getting vaccinated. Higher trust in PCS was also associated with participants more likely to disagree that minorities with higher rates of COVID-19 deaths should have priority for a vaccine. High trust in Dr. Fauci as a source of COVID-19 vaccine information was associated with factors similar to high trust in MS. Fair, equitable, and transparent access and distribution are essential to ensure trust in public health systems’ abilities to serve the population.

scholarly journals A novel causality-centrality-based method for the analysis of the impacts of air pollutants on PM2.5 concentrations in China

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Bocheng Wang
Keyword(s):  
Air Pollutants ◽  
Regression Models ◽  
Large Population ◽  
Vital Role ◽  
Superior Performance ◽  
Causal Direction ◽  
Northern Cities ◽  
Main Components ◽  
Surrounding Areas ◽  
Centrality Analysis

AbstractIn this paper, we analyzed the spatial and temporal causality and graph-based centrality relationship between air pollutants and PM2.5 concentrations in China from 2013 to 2017. NO2, SO2, CO and O3 were considered the main components of pollution that affected the health of people; thus, various joint regression models were built to reveal the causal direction from these individual pollutants to PM2.5 concentrations. In this causal centrality analysis, Beijing was the most important area in the Jing-Jin-Ji region because of its developed economy and large population. Pollutants in Beijing and peripheral cities were studied. The results showed that NO2 pollutants play a vital role in the PM2.5 concentrations in Beijing and its surrounding areas. An obvious causality direction and betweenness centrality were observed in the northern cities compared with others, demonstrating the fact that the more developed cities were most seriously polluted. Superior performance with causal centrality characteristics in the recognition of PM2.5 concentrations has been achieved.

scholarly journals Sociodemographic associations of geographic variation in paediatric tonsillectomy and adenoidectomy

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Aimy H. L. Tran ◽  
Danny Liew ◽  
Rosemary S. C. Horne ◽  
Joanne Rimmer ◽  
Gillian M. Nixon
Keyword(s):  
Geographic Variation ◽  
Language Proficiency ◽  
Regression Models ◽  
English Language ◽  
Population Analysis ◽  
English Language Proficiency ◽  
The State ◽  
The Public ◽  
Factors Associated ◽  
Administrative Datasets

AbstractGeographic variation of paediatric tonsillectomy, with or without adenoidectomy, (A/T) has been described since the 1930s until today but no studies have investigated the factors associated with this variation. This study described the geographical distribution of paediatric A/T across the state of Victoria, Australia, and investigated area-level factors associated with this variation. We used linked administrative datasets capturing all paediatric A/T performed between 2010 and 2015 in Victoria. Surgery data were collapsed by patient residence to the level of Local Government Area. Regression models were used to investigate the association between likelihood of surgery and area-level factors. We found a 10.2-fold difference in A/T rates across the state, with areas of higher rates more in regional than metropolitan areas. Area-level factors associated with geographic variation of A/T were percentage of children aged 5–9 years (IRR 1.07, 95%CI 1.01–1.14, P = 0.03) and low English language proficiency (IRR 0.95, 95% CI 0.90–0.99, P = 0.03). In a sub-population analysis of surgeries in the public sector, these factors were low maternal educational attainment (IRR 1.09, 95% CI 1.02–1.16, P < 0.001) and surgical waiting time (IRR 0.99635 95% CI 0.99273–0.99997, P = 0.048). Identifying areas of focus for improvement and factors associated with geographic variation will assist in improving equitable provision of paediatric A/T and decrease variability within regions.

scholarly journals Estimation of Outdoor PM2.5 Infiltration into Multifamily Homes Depending on Building Characteristics Using Regression Models

2021 ◽  
Vol 13 (10) ◽  
pp. 5708
Author(s):  
Bo-Ram Park ◽  
Ye-Seul Eom ◽  
Dong-Hee Choi ◽  
Dong-Hwa Kang
Keyword(s):  
Regression Analysis ◽  
Correlation Analysis ◽  
Field Test ◽  
Outer Surface ◽  
Regression Models ◽  
Field Tests ◽  
Test Results ◽  
Correlation And Regression Analysis ◽  
Building Characteristics ◽  
Pm2.5 Concentration

The purpose of this study was to evaluate outdoor PM2.5 infiltration into multifamily homes according to the building characteristics using regression models. Field test results from 23 multifamily homes were analyzed to investigate the infiltration factor and building characteristics including floor area, volume, outer surface area, building age, and airtightness. Correlation and regression analysis were then conducted to identify the building factor that is most strongly associated with the infiltration of outdoor PM2.5. The field tests revealed that the average PM2.5 infiltration factor was 0.71 (±0.19). The correlation analysis of the building characteristics and PM2.5 infiltration factor revealed that building airtightness metrics (ACH50, ELA/FA, and NL) had a statistically significant (p < 0.05) positive correlation (r = 0.70, 0.69, and 0.68, respectively) with the infiltration factor. Following the correlation analysis, a regression model for predicting PM2.5 infiltration based on the ACH50 airtightness index was proposed. The study confirmed that the outdoor-origin PM2.5 concentration in highly leaky units could be up to 1.59 times higher than that in airtight units.

Tracking of Wearable IoT Devices Through WAP Using Intelligent Rule-Based Location Aware Approach

2021 ◽  
Vol 20 (Supp01) ◽  
pp. 2140005
Author(s):  
L. Sai Ramesh ◽  
S. Shyam Sundar ◽  
K. Selvakumar ◽  
S. Sabena
Keyword(s):  
Mobile Devices ◽  
Task Completion ◽  
End User ◽  
Access Points ◽  
The Public ◽  
Location Aware ◽  
Unique Identity ◽  
Iot Devices ◽  
Public Environment ◽  
Specific Scenario

Usage of the internet is increasing in the daily life of humans due to the need for speedy task completion for their daily services. Most of the living time is spent in some indoor environment which provides WiFi which is the basic need of internet connectivity using Wireless Access Points (WAP). Nowadays, most of the devices are IoT-based ones, which connect with the outer world through the access points in the existing environment. The wearable IoT devices may be misplaced somewhere and we need a specific scenario which helps to identify the misplaced mobile devices based on access points where they are connected by their unique identity such as MAC address. Most of the time, unrestricted WiFi access provided in the public environment is used by the end-user. In that scenario, the tracking of misplaced mobile devices is creating an issue when the WiFi is in switch-off mode. This paper proposes a technique for tracking a mobile device by using a location-aware approach with KNN and intelligent rules by tracking the channel accessed by the user to find the misplaced path by examining the device connected WAP positions.

scholarly journals PENGETAHUAN IBU HAMIL TENTANG BAHAYA MEROKOK TERHADAP KEHAMILAN DAN JANIN

2020 ◽  
Vol 3 (1) ◽  
pp. 91
Author(s):  
Melva Manurung
Keyword(s):  
Health Education ◽  
Pregnant Women ◽  
Fetal Development ◽  
Neonatal Death ◽  
Fetal Death ◽  
Additional Material ◽  
Smoking During Pregnancy ◽  
Respiratory Problems ◽  
The Public ◽  
Almost All

Smoking during pregnancy can endanger pregnancy and the fetus, especially the health of pregnant women and fetal development in the womb. One of the complications of pregnancy that causes fetal death is due to oxygenation disorders. In Indonesia, more than half of households have at least one smoker, and almost all smokers’ smokes at home. The cause of neonatal death is fetal death in utero, asphyxia or respiratory problems due to smoking and premature. This study aims to determine the knowledge of pregnant women about the dangers of smoking to pregnancy and the fetus in Gasaribu Village, Laguboti, Toba Samosir Regency. This research was conducted in September-October 2019 using adescriptive research design. The number of samples was in this study were 40 pregnant women. Sampling is done by using saturated sampling. The results showed that good knowledge of 16 people (40%) was enough 22 people (55%) and less 2 people (5%).  The results of this study are expected to be used as additional material in adding knowledge and information to increase real health education (real) to the public about the dangers of smoking to pregnancy and the fetus.

scholarly journals Interrelationship of Indoor particulate matter and Respiratory dust deposition of women in the residence of Dhanbad City, India

2021 ◽  
Author(s):  
SHRAVAN KUMAR ◽  
Manish Kumar Jain
Keyword(s):  
Particulate Matter ◽  
Health Risks ◽  
Indoor Air ◽  
Air Pollutants ◽  
Fine Particles ◽  
Dust Deposition ◽  
Respiratory Problems ◽  
Indoor Particulate Matter ◽  
Indoor Air Pollutants ◽  
Indoor Conditions

Abstract Women spend relatively more time in indoor conditions in developing countries. Exposure to various indoor air pollutants leads them to higher health risks according to Household air quality in which they reside. Particulate matter (PM) exposure with their exposure duration inside the household plays a significant role in women's Respiratory problems. We measured size segregated particulate matter concentrations in 63 residences at different locations. Respiratory dust depositions (RDDs) for 118 women in their different respiratory regions like head-airway (HD), tracheobronchial (TB), and alveolar (AL) region for the three PM size fractions (PM10, PM2.5 & PM1) were investigated. For different positions like Light exercise and the Sitting condition, RDD values found for AL region was 0.091 µgmin− 1 (SD: 0.067, 0.012–0.408) and 0.028 µgmin− 1 (SD: 0.021, 0.003–0.126) for PM10, 0.325 µgmin− 1 (SD: 0.254, 0.053–1.521) and 0.183 µgmin− 1 (SD: 0.143, 0.031–0.857) for PM2.5, 0.257 µgmin− 1 (SD: 0.197, 0.043–1.04) and 0.057 µgmin− 1 (SD: 0.044, 0.009–0.233) respectively for PM1 to females. RDDs values in the AL region significantly increases as PM10 (11%), PM2.5 (68%), and PM1 (21%), confirm that for women, the AL region is the most prominent affected zone by fine particles (PM2.5).

Development of a Diabetes Melitus Detection and Prediction Model Using Light Gradient Boosting Machine and K-Nearest Neighbour

2021 ◽  
Vol 3 (1) ◽  
Author(s):  
B. A Omodunbi
Keyword(s):  
Diabetes Mellitus ◽  
Machine Learning ◽  
Hybrid Model ◽  
Learning Model ◽  
Experimental Result ◽  
Gradient Boosting ◽  
Light Gradient ◽  
Machine Learning Model ◽  
Gradient Boosting Machine ◽  
Receiver Operating

Diabetes mellitus is a health disorder that occurs when the blood sugar level becomes extremely high due to body resistance in producing the required amount of insulin. The aliment happens to be among the major causes of death in Nigeria and the world at large. This study was carried out to detect diabetes mellitus by developing a hybrid model that comprises of two machine learning model namely Light Gradient Boosting Machine (LGBM) and K-Nearest Neighbor (KNN). This research is aimed at developing a machine learning model for detecting the occurrence of diabetes in patients. The performance metrics employed in evaluating the finding for this study are Receiver Operating Characteristics (ROC) Curve, Five-fold Cross-validation, precision, and accuracy score. The proposed system had an accuracy of 91% and the area under the Receiver Operating Characteristic Curve was 93%. The experimental result shows that the prediction accuracy of the hybrid model is better than traditional machine learning

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