Air is one of the most fundamental constituents for sustenance life on earth. The meteorological, traffic factors, consumption non-renewable energy sources, and industrial parameters are steadily increasing air pollution. These factors affect welfare prosperity earth; therefore, nature quality in our environment needs to be monitored continuously. Quality Index (AQI), which indicates quality, i...

For forecasting the maximum 5-day accumulated precipitation over the winter season at lead times of 3, 6, 9 and 12 months over Canada from 1950 to 2007, two nonlinear and two linear regression models were used, where the models were support vector regression (SVR) (nonlinear and linear versions), nonlinear Bayesian neural network (BNN) and multiple linear regression (MLR). The 118 stations were...

Although crop-growth monitoring is important for agricultural managers, it has always been a difficult research topic. However, unmanned aerial vehicles (UAVs) equipped with RGB and hyperspectral cameras can now acquire high-resolution remote-sensing images, which facilitates accelerates such monitoring. To explore the effect of single indicator multiple indicators, this study combines six grow...

Multiclass logistic regression (MLR) is a fundamental machine learning model to do multiclass classification. However, it is very challenging to perform MLR on large scale data where the feature dimension is high, the number of classes is large and the number of data samples is numerous. In this paper, we build a distributed framework to support large scale multiclass logistic regression. Using...

Quantitative structure-activity relationship (QSAR) models were employed for prediction the activity of P2X7 receptor antagonists. A data set consisted of 50 purine derivatives was utilized in the model construction where 40 and 10 of these compounds were in the training and test sets respectively. A suitable group of calculated molecular descriptors was selected by employing stepwise multiple ...

India is an agricultural country and most of economy of India depends upon the agriculture. Rainfall plays an important role in agriculture so early prediction of rainfall plays an important role in the economy of India. Rainfall prediction has been the one of the most challenging issue around the world in last year. Widely used techniques for prediction are Regression analysis, clustering, and...

The measurement and prediction of dye concentration is important in the design, planning and management of wastewater treatment. Soft computing techniques can be used as a support tool for analyzing data and making prediction. In this study, Central Composite Design (CCD) and adaptive neuro-fuzzy inference system (ANFIS) are employed to identify and predict the output intensity ratio of light t...

Aleixandre-Tudó J.L., Alvarez I., García M.J., Lizama V., Aleixandre J.L. (2015): Application of multivariate regression methods to predict sensory quality of red wines. Czech J. Food Sci., 33: 217–227. Several multivariate methods including partial least squares (PLS) regression, principal component regression (PCR) or multiple linear regression (MLR) have been applied to predict wine quality,...

A new variable selection wrapper method named the Monte Carlo variable selection (MCVS) method was developed utilizing the framework of the Monte Carlo cross-validation (MCCV) approach. The MCVS method reports the variable selection results in the most conventional and common measure of statistical hypothesis testing, the P-values, thus allowing for a clear and simple statistical interpretation...

In the present work, the support vector machine (SVM) and Adaboost-SVM have been used to develop a classification model as a potential screening mechanism for a novel series of 5-HT(1A) selective ligands. Each compound is represented by calculated structural descriptors that encode topological features. The particle swarm optimization (PSO) and the stepwise multiple linear regression (Stepwise-...