An IoT based system for magnify air pollution monitoring and prognosis using hybrid artificial intelligence technique.

Air pollution is the existence of atmospheric chemicals damaging the health of human beings and other living organisms or damaging the environment or resources. Rarely any common contaminants are smog, nicotine, mold, yeast, biogas, or carbon dioxide. The paper will primarily observe, visualize and anticipate pollution levels. In particular, three algorithms of Artificial Intelligence were used to create good forecasting models and a predictive AQI model for 4 distinct gases: carbon dioxide, sulphur dioxide, nitrogen dioxide, and atmospheric particulate matter. Thus, in this paper, the Air Qualification Index is developed utilizing Linear Regression, Support Vector Regression, and the Gradient Boosted Decision Tree GBDT Ensembles model over the next 5 h and analyzes air qualities using various sensors. The hypothesized artificial intelligence models are evaluated to the Root Mean Squares Error, Mean Squared Error and Mean absolute error, depending upon the performance measurements and a lower error value model is chosen. Based on the algorithm of the Artificial Intelligent System, the level of 5 air pollutants like CO2, SO2, NO2, PM 2.5 and PM10 can be predicted immediately by integrating the observations with errors. It may be used to detect air quality from distance in large cities and can assist lower the degree of environmental pollution.

Analysis on novel coronavirus (COVID-19) using machine learning methods.

In this paper, we are working on a pandemic of novel coronavirus (COVID-19). COVID-19 is an infectious disease, it creates severe damage in the lungs. COVID-19 causes illness in humans and has killed many people in the entire world. However, this virus is reported as a pandemic by the World Health Organization (WHO) and all countries are trying to control and lockdown all places. The main objective of this work is to solve the five different tasks such as I) Predicting the spread of coronavirus across regions. II) Analyzing the growth rates and the types of mitigation across countries. III) Predicting how the epidemic will end. IV) Analyzing the transmission rate of the virus. V) Correlating the coronavirus and weather conditions. The advantage of doing these tasks to minimize the virus spread by various mitigation, how well the mitigations are working, how many cases have been prevented by this mitigations, an idea about the number of patients that will recover from the infection with old medication, understand how much time will it take to for this pandemic to end, we will be able to understand and analyze how fast or slow the virus is spreading among regions and the infected patient to reduce the spread based clear understanding of the correlation between the spread and weather conditions. In this paper, we propose a novel Support Vector Regression method to analysis five different tasks related to novel coronavirus. In this work, instead of simple regression line we use the supported vectors also to get better classification accuracy. Our approach is evaluated and compared with other well-known regression models on standard available datasets. The promising results demonstrate its superiority in both efficiency and accuracy.

The consumer price index prediction using machine learning approaches: Evidence from the United States.

The consumer price index (CPI) is one of the most important macroeconomic indicators for determining inflation, and accurate predictions of CPI changes are important for a country's economic development. This study uses multivariate linear regression (MLR), support vector regression (SVR), autoregressive distributed lag (ARDL), and multivariate adaptive regression splines (MARS) to predict the CPI of the United States. Data from January 2017 to February 2022 were randomly selected and divided into two stages: 80 % for training and 20% for testing. The US CPI was modeled for the observed period and relied on a mix of elements, including crude oil price, world gold price, and federal fund effective rate. Evaluation metrics-mean absolute percentage value, mean absolute error, root mean square error, R-squared, and correlation of determination-were employed to estimate forecasted values. The MLR, SVR, ARDL, and MARS models attained high accuracy parameters, while the MARS algorithm generated higher accuracy in US CPI forecasts than the others in the testing phase. These outputs could support the US government in overseeing economic policies, sectors, and social security, thereby boosting national economic development.

Forecasting riverine total nitrogen loads using wavelet analysis and support vector regression combination model in an agricultural watershed.

In the context of non-point source pollution management and algal blooms control, the reliable nutrient forecasting is of critical importance. Considering the highly stochastic, non-linear, and non-stationary natures involved in riverine total nitrogen (TN) load time series data, some traditional statistical and artificial intelligence models are inherently unable to give accurate nutrient forecasts due to their mechanism and structure characteristics. In this study, based on the wavelet analysis (WA) and support vector regression (SVR), a promising combined WA-SVR model was proposed for forecasting riverine TN loads. The data pro-processing tool WA was employed to decompose the time series data of riverine TN load for revealing its dominator. Subsequently, all wavelet components were used as inputs to SVR for WA-SVR model. The continuous riverine TN loads during 2004-2012 in the ChangLe River watershed of eastern China were estimated by using a calibrated Load Estimator model. Performance criteria, namely, determination coefficient (R2), Nash-Sutcliffe model efficiency (NS), and mean square error (MSE) were applied to assess the performance of the developed models. The effects of different mother wavelets on the efficiency of the conjunction model were investigated. The results demonstrated that the mother wavelet played a crucial role for the successful implementation of the WA-SVR model. Among the 23 selected mother wavelet functions, dmey wavelet performed best in forecasting the daily and monthly TN loads. Furthermore, the performance of the optimal WA-SVR model was compared with that of single SVR model without wavelet decomposition. The comparison indicated that the hybrid model provided better accuracy than that of single SVR model. For daily riverine TN loads, the R2, NS, and MSE values of WA-SVR model during the test stage were 0.9699, 0.9658, and 0.4885 × 107 kg/day, respectively. For monthly riverine TN loads, the R2, NS, and MSE values of the model during the test stage were 0.9163, 0.9159, and 0.3237 × 1010 kg/month, respectively. The overall results strongly suggested that the combined WA-SVR method can successfully forecast riverine TN loads in agricultural watersheds.

Associations of mRNA:microRNA for the Shared Downstream Molecules of EGFR and Alternative Tyrosine Kinase Receptors in Non-small Cell Lung Cancer.

Lung cancer is the top cancer killer worldwide with high mortality rate. Majority belong to non-small cell lung cancers (NSCLCs). The epidermal growth factor receptor (EGFR) has been broadly explored as a drug target for therapy. However, the drug responses are not durable due to the acquired resistance. MicroRNAs (miRNAs) are small non-coding and endogenous molecules that can inhibit mRNA translation initiation and degrade mRNAs. We wonder if some downstream molecules shared by EGFR and the other tyrosine kinase receptors (TKRs) further transduce the signals alternatively, and some miRNAs play the key roles in affecting the expression of these downstream molecules. In this study, we investigated the mRNA:miRNA associations for the direct EGFR downstream molecules in the EGFR signaling pathway shared with the other TKRs, including c-MET (hepatocyte growth factor receptor), Ron (a protein tyrosine kinase related to c-MET), PDGFR (platelet-derived growth factor receptor), and IGF-1R (insulin-like growth factor receptor-1). The multiple linear regression and support vector regression (SVR) models were used to discover the statistically significant and the best weighted miRNAs regulating the mRNAs of these downstream molecules. These two models revealed the similar mRNA:miRNA associations. It was found that the miRNAs significantly affecting the mRNA expressions in the multiple regression model were also those with the largest weights in the SVR model. To conclude, we effectively identified a list of meaningful mRNA:miRNA associations: phospholipase C, gamma 1 (PLCG1) with miR-34a, phosphoinositide-3-kinase, regulatory subunit 2 (PIK3R2) with miR-30a-5p, growth factor receptor-bound protein 2 (GRB2) with miR-27a, and Janus kinase 1 (JAK1) with miR-302b and miR-520e. These associations could make great contributions to explore new mechanism in NSCLCs. These candidate miRNAs may be regarded as the potential drug targets for treating NSCLCs with acquired drug resistance.

Support vector regression model of wastewater bioreactor performance using microbial community diversity indices: effect of stress and bioaugmentation.

The relationship between microbial community structure and function has been examined in detail in natural and engineered environments, but little work has been done on using microbial community information to predict function. We processed microbial community and operational data from controlled experiments with bench-scale bioreactor systems to predict reactor process performance. Four membrane-operated sequencing batch reactors treating synthetic wastewater were operated in two experiments to test the effects of (i) the toxic compound 3-chloroaniline (3-CA) and (ii) bioaugmentation targeting 3-CA degradation, on the sludge microbial community in the reactors. In the first experiment, two reactors were treated with 3-CA and two reactors were operated as controls without 3-CA input. In the second experiment, all four reactors were additionally bioaugmented with a Pseudomonas putida strain carrying a plasmid with a portion of the pathway for 3-CA degradation. Molecular data were generated from terminal restriction fragment length polymorphism (T-RFLP) analysis targeting the 16S rRNA and amoA genes from the sludge community. The electropherograms resulting from these T-RFs were used to calculate diversity indices - community richness, dynamics and evenness - for the domain Bacteria as well as for ammonia-oxidizing bacteria in each reactor over time. These diversity indices were then used to train and test a support vector regression (SVR) model to predict reactor performance based on input microbial community indices and operational data. Considering the diversity indices over time and across replicate reactors as discrete values, it was found that, although bioaugmentation with a bacterial strain harboring a subset of genes involved in the degradation of 3-CA did not bring about 3-CA degradation, it significantly affected the community as measured through all three diversity indices in both the general bacterial community and the ammonia-oxidizer community (α = 0.5). The impact of bioaugmentation was also seen qualitatively in the variation of community richness and evenness over time in each reactor, with overall community richness falling in the case of bioaugmented reactors subjected to 3-CA and community evenness remaining lower and more stable in the bioaugmented reactors as opposed to the unbioaugmented reactors. Using diversity indices, 3-CA input, bioaugmentation and time as input variables, the SVR model successfully predicted reactor performance in terms of the removal of broad-range contaminants like COD, ammonia and nitrate as well as specific contaminants like 3-CA. This work was the first to demonstrate that (i) bioaugmentation, even when unsuccessful, can produce a change in community structure and (ii) microbial community information can be used to reliably predict process performance. However, T-RFLP may not result in the most accurate representation of the microbial community itself, and a much more powerful prediction tool can potentially be developed using more sophisticated molecular methods.