Multi-dataset analyses

Multi-dataset analyses such as qualitative and quantitative comparisons. 

Methods Results The flow diagram (Fig. 2) outlines the methods used for the review and separation of the rocks present in the area. Image enhancement is done to increase the variance in the dataset. Contrast manipulation, spatial feature manipulation, and multi-image manipulation are used as digital enhancement techniques (Lillesand et al. 2007). In this study multi-image manipulation is used, which includes Band Ratio and Principal Component Analysis. 

In this section, a safety dataset, resulting from over 20 years of practical experience with risk analysis of chemical processes, is presented. These data build the base of risk analysis in the fine chemicals and pharmaceutical industries, essentially in multi-purpose plants. Therefore, the dataset introduces plant considerations only at its end. This allows exchanging them without any need for recollecting the whole dataset, in cases where the process is transferred from one plant unit to another. Moreover, this dataset may be used in the frame of different risk analysis methods. 

The stable carbon isotope ratios of dissolved inorganic carbon (DIC) and benthic foraminiferal calcite generally are determined with isotope ratio gas mass spectrometers calibrated via NBS 19 international standard to the VPDB (Vienna Pee Dee Belemnite) scale. All values are given in 8-notation versus VPDB with an overall precision of measurements including sample preparation usually better than +0.06 and +0.1%o for calcite and DIC carbon isotopes, respectively. Except one single-specimen based dataset (Hill et al. 2004), all stable isotope data from papers referred to in this overview are from species-specific multi-specimens analyses. The number of specimens used for a single analysis depended on size and weight of species but usually varied between 2 and 25. 

This chapter provides a tutorial on the fundamental concept of Parallel factor (PARAFAC) analysis and a practical example of its application. PARAFAC, which attains clarity and simplicity in sorting out convoluted information of highly complex chemical systems, is a powerful and versatile tool for the detailed analysis of multi-way data, which is a dataset represented as a multidimensional array. Its intriguing idea to condense the essence of the information present in the multi-way data into a very compact matrix representation referred to as scores and loadings has gained considerable popularity among scientists in many different areas of research activities. 

The combination of fusion rules (e.g., maximum and mean-fusion) gave rise to the multi-fusion similarity (MFS) maps that were developed for the visual characterization and comparison of compound databases [139]. This approach has been employed to explore SARs of compound datasets [140] and to compare combinatorial libraries [141]. Consensus approaches are also applied in diversity analysis of compound collections complementary 2D and 3D representations are used to obtain a comprehensive characterization of the diversity of large compound databases [60, 142]. 

The principal component analysis (PCA) or Karhunen-Loeve analysis is useful for analysis of highly correlated multi-spectral remotely sensed data [13, 14]. The transformation of raw remote sensor data using PCA can result in new principal component images that may be more interpretable than the original data [14, 15]. For PCA the transformation is applied to a correlated set of multi-spectral data, application of the transformation to the correlated remote sensor data will result in another uncorrelated multi-spectral dataset that has certain ordered variance properties. This transformation is conceptualized by considering the two-dimensional distribution of pixel values obtained in two bands that can be labeled as and X. The spread or variance of the distribution of points is an indication of the correlation and quality of information associated with both bands, if aU the points are clustered in an extremely tight zone in two-dimensional space, these data will provide very little information. 

The landslide inventory is naturally event-based. It is not possible to use a multi-temporal landshde inventory as the traditional landslide susceptibiUty analysis. Therefore, the inventory of landsUdes triggered by the earthquake was randomly partitioned into two subsets, training dataset and testing dataset. From the application of ANN model, the landslide susceptibility map is constructed. The results of verification show 81.274% of success rate and 79.915% of prediction rate. The verification results are of high values. It shows that the ANN model can be used as a precise tool in the earthquake triggered landslide susceptibility mapping when a sufficient number of data are available. 

Typically, in this report, probit analysis was applied (when appropriate) to the total database of an individual study, with species, duration and/or gender being treated as factors (if multi-level). However, if sufficient response data were available, the total study database was split into subsets, with probit analyses applied to each subset. This produced a PS estimate and LCt5o(s) for each data subset. In some instances involving multiple species, a probit analysis was not possible for every species subset that resulted from the split of a multispecies dataset. In such cases, the LCtjo estimates were based on the probit analysis of the main dataset, while probit analyses were performed on the viable subsets to obtain additional PS estimates. 

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