Brief Explanation of the Basic Framework of the Principal Componant Analysis and Fuzzy Logic

In a narrow sense, fuzzy logic can be defined as a logical system, which is an expansion of multi-valued logic. Whereas in a wider sense it is almost similar with fuzzy sets theory. It is a method for computing based on "degrees of truth or fact" rather than the "true or false" (1 or 0). The idea of fuzzy logic was first proposed by Dr. Lotfi Zadeh of the University of California at Berkeley in the 1965 [65].

The primary advantage of PCA is that principal component analysis creates the orthogonal components that displays the 100% of variance present in the original dataset.

First I broke down the criteria in this report and assigned weights. The weights determine which level of importance each criterion had. The higher weight figure was, the more important that aspect was. After the weights were determined,

Principal component analysis (PCA) is one of the most widely used multivariate statistical techniques. PCA could be used to extract the important information from the data table that contains the observations described by dependent variables. Then, PCA used a set of new orthogonal variables, which called principal components (PCs), to express the important information. Besides, PCA could also represent the pattern of similarity of observations and of the variables by drawing them as points in maps [5].

The goal of the PCa biomarker field as well as the cancer biomarker field in general, is to develop simple, non-invasive tests that can allow early cancer detection, classify the tumor so the patient can receive the most appropriate therapy and monitor

1) A state of nature is an actual event that may occur in the future.

Using Singular Value Decomposition (SVD) a tensor \boldsymbol{D} can be decomposed into a diagonal matrix with three non-negative elements \lambda_{1}, \lambda_{2} and \lambda_{3}, known as the eigenvalues, and a matrix composed of three orthogonal eigenvectors \boldsymbol{e}_{1}, \boldsymbol{e}_{2}, and \boldsymbol{e}_{3}, known as the eigenvectors. Tensor \boldsymbol{D} is usually represented by an ellipsoid such that the lengths of the major axes of the ellipsoid are proportional to the square roots of the eigenvalues, and the directions of the three major axes of the ellipsoid correspond to the three eigenvectors.

The purpose of this paper is to review a business case presented by a laboratory services company and make recommendations on how best to incorporate required changes and the impact it will have on current and future operations. Prior to making any recommendation, a review of the company’s existing business operations is required. This assessment will afford the decision maker various tools in which informed decisions can be made. More times than not, multiple options are available and can prove successful, even dealing with situations that require complex decisions. There are several decision based modeling schemes out there, but for this paper three models were compared against the business case to determine which afforded the greatest supportability.

In change detection, there are two ways to relate PCA. The first method is counting two image dates to a single file, and the second methods is subtracting the second image date from the corresponding image of the first date after performing PCA individually. The disadvantages of PCA can

Rule based systems are the simplest form of artificial intelligence.These were invented in the early 1970’s and are still in use today.

This is a small scenario that is described which is based on sales amount. If the company wants to impose any other condition,they can easily do that by using ths fuzzy xquery technique.so it is easy to understand that by using fuzzy membership function we are getting more efficient output than the output which was genetred by the simple query method.Fuzzy crisp input is taking the values on the basis of given membership function and produce the optimized output by calculating them within the aspiration level.This aspiration level is playing a very vital role because all the three fuzzy funcaion are balanced by these aspiration

Artificial intelligence (AI) is thought of as an abstract concept confined within the borders of sci-fi movies, when in reality, it is a complex and thriving field that has already found its way into humans’ daily lives. Many people scoff and murmur at the sight of a new, time-saving app or a self-driving car, while others are excited to keep pushing technology to its limits, or even past humans’ limits. However, the question that computer scientists find themselves asking is, “How smart is too smart?”

The problem that John Thompson identifies is whether to expand his product line by manufacturing and marketing a new product,

Another problem that is seen within the role of PCA is also due to the

To go back to a previous example, my washing machine determines which type of cycle to use based on the weight of the clothing. A “small” load or a “medium” load is not rigidly defined, but the fuzzy logic allows the washing machine to approximate the type of cycle it should use. An amount of clothes on the cusp of both “small” and “medium” could be placed in either category, depending on other variables such as fabric mix and the amount of detergent (“Fuzzy logic”). In this specific example fuzzy logic and soft computing can lead to errors, such as a wash cycle erroneously stopping after the clothes become heavier due to