Specifying the visualizing type of multi-dimensional data

Abstract

Knowing the type of data is widely required to make better and faster decisions. However, the process requires the user to provide information about the configuration of the data. This paper presents the first attempt to analyze data to extract it is type automatically from multi-dimensional data sets. This is useful not only for experts but also to users, also reduces manual search effort. Layers of multi-dimensional data are formed and evaluated, and the focus is on the most efficient ones. Experiments on experimental and real data demonstrate the efficiency and effectiveness of the proposed method.

CON_PCA method to solve the human monochromacy color blindness

“…Images with close colors or shadows are difficult to recognize. Easy tasks like buying fruit, choosing clothes, and analyzing traffic signs become challenging work [2], [3], [5], [8], [12]. Recognizing some objects depends on color variance and luminance.…”

“…Information visualization provides an effective and high-quality way to display information visually, mainly to knowledge and interpretation, without difficulty [4]. Information visualization is the study of interactive visual representation of information to enhance human understanding [5], [6]. This approach considerably influences because it helps the visible needs of humans.…”

“…Color is the reaction to light is the consequence of the brain's interpretation of color through ocular neurological processes. Colors seen by the human eye correspond to the visible region of the spectrum wavelength of 400-700 nanometers [5]. Information can be expressed using colors, as some colors provide guidelines to users, for example, street maps are designed in a way that can be without difficulty understood.…”

“…When observing Figure 4, the results of the proposed technique for color analysis are better than the results of the original data sets. Although the results are grey because the individual with this type of color blindness does no longer see colors, the percentages of the facts are close to 1 and the reason is that the percentage was calculated based on (5). We divided the number of colors by 255.…”

Software engineering's role in visualizing large data as medical datasets

“…r(xi, xj) ≈ d(yi, yj), 1≤ i ≤ n to project X's data point accurately in Y space in Equation 2 [29]. The goal of applying dimension reduction (DR) methods is to project large dimensional datasets to a low dimensional space while preserving all of the original datasets information [30].…”