Wastewater treatment plant monitoring is an essential part of effective wastewater management. The analysis of eight physico-chemical parameters of untreated wastewater was carried out at Vidyaranyapuram sewage treatment plant, Mysore, India. Factor analysis (FA) was applied to the untreated wastewater data matrix, and pollution was found to be the most contributing factor, explaining 22.31% of the total variance (chloride, biochemical oxygen demand, chemical oxygen demand and total dissolved solids). The second most contributing factor was found to be nitrification which explained 21.11% of the total variance (pH and nitrate), whereas the salinization factor contributed 16.98% of the total variance (total solids and total suspended solids). FA regression scores could not satisfactorily classify the data matrix with respect to the seasonal variations. Discriminant analysis (DA) was used to find the seasonal variations in the data matrix, and the standard mode DA explained 66.6% of total variance by grouping the cases with respect to seasons.
Fuzzy principal component regression (FPCR) is proposed to model the non-linear process of sewage treatment plant (STP) data matrix. The dimension reduction of voluminous data was done by principal component analysis (PCA). The PCA score values were partitioned by fuzzy-c-means (FCM) clustering, and a Takagi-Sugeno-Kang (TSK) fuzzy model was built based on the FCM functions. The FPCR approach was used to predict the reduction in chemical oxygen demand (COD) and biological oxygen demand (BOD) of treated wastewater of Vidyaranyapuram STP with respect to the relations modeled between fuzzy partitioned PCA scores and target output. The designed FPCR model showed the ability to capture the behavior of non-linear processes of STP. The predicted values of reduction in COD and BOD were analyzed by performing the linear regression analysis. The predicted values for COD and BOD reduction showed positive correlation with the observed data.
Mammography and X-ray imaging of the breast are considered as the mainstay of breast cancer screening. In the past several years, there has been tremendous interest in image processing and analysis techniques in mammography. The fractal is an irregular geometric object with an infinite nesting of structure of different sizes. Fractals can be used to make models of any objects. The most important properties of fractals are self-similarity, chaos, and non-integer fractal dimension. The fractal dimension analysis has been applied to study the wide range of objects in biology and medicine and has been used to detect small tumors, microcalcification in mammograms, tumors in brain, and to diagnose blood cells and human cerebellum. Fractal theory also provides an appropriate platform to build oncological-related software program because the ducts within human breast tissue have fractal properties. Fractal analysis of mammogram was used for the breast parenchymal density assessment. The fractal dimension of the surface is determined by utilizing the Boxcounting method. The Mammograms were collected from HCG Hospital, Bangalore. In this study, a method was developed in the Visual Basic for extracting the suspicious
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