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This research is focused on a methodology created to analyse imprecise information, that is full of attributes defined as "rough set". The methodology will be then applied to the real estate appraisal question, representing a further possible method of evaluation. Up to now the main approaches to the real estate appraisal have been income, market and cost. My intention is to analyse this theory showing a practical application on a group of real estate transactions made by a real estate agent. This application will show how it is possible to get values to classify a real estate. A comparison between this method and the most common statistics instruments will be highlighted.

In recent years, text mining wasan important topic because of the growth of digital text data from many sources such as government document, email, social media, website, etc. The English poemsare one of the text data to categorization English Poems will use text categorization, text categorization is a method in which classify documents into one or more categories that were predefined the category based on the text content in a document. In this paper we will solve the problem of how to categorize the English poem into one of the English Poems categorizations by using text mining technique and machine learning algorithm, Our data set consist of seven categorizations for poems the data set is divided into two-part training (learning) and testing data. In the proposed model we apply the text preprocessing for the documents file to reduce the number of feature and reduce dimensionality the preprocessing process converts the text poem to features and remove the irrelevant feature by using text mining process (tokenize, remove stop word and stemming), to reduce the feature vector of the remaining feature we usetwo methods for feature selection and use rough set theory as machine learning algorithm to perform the categorization, and we get 88% success classification of the proposed model.

A novel weighted rough set-based classification approach is introduced for the evaluation of fetal nature acquired from a CardioTocoGram (CTG) signal. The classification is essential to anticipate newborn's well-being, particularly for the life-threatening cases. CTG monitoring comprises of electronic fetal heart rate (FHR), fetal activities and the uterine contraction (UC) signals. These signals are extensively used as a part of the pregnancy and give extremely significant data on fetal health. The obtained data from these recordings can be utilized to anticipate the condition of the newborn baby, which gives an open door for early medication before perpetual deficiency to the fetus. The dimension of the obtained features from CTG is high and decreases the accuracy of classification algorithms. In this article, supervised particle swarm optimization (PSO) with a rough set-based dimensionality reduction method is used to find a minimal set of significant features from CTG extracted features. The proposed weighted rough set classifier (WRSC) method is utilized for predicting the fetal condition as normal and pathological states. The performance of the proposed WRSC algorithm is compared with various classification algorithms such as bijective soft set neural network classifier (BISONN), rough set-based classifier (RST), multi-layered perceptron (MLP), decision table (DT), Java repeated incremental pruning (JRIP) classifier, J48 and Naïve Bayes (NB) classifiers. The experimental results demonstrated that the proposed algorithm is capable of forecasting the fetal state with 98.5% classification accuracy, and the results show that the proposed classification algorithm performed considerably superior than other classification techniques.

Comments on Paul Pierson's article(2000), "Not Just What, but When: Timing and Sequence in Political Processes," arguing that the adoption of path dependence from economics to political science does not realistically explain political events. Path dependence explains economic outcomes because of the central mechanism in economics, the market. Technologically inferior products have sometimes succeeded in the free market because of a brief market advantage that made the product more attractive & allowed it to become the popular choice. The market does not, however, have counterparts in the political sciences, & successful outcomes are not the result of individual choices or combined tastes, which would be similar to a market. Path dependence is used to explain countertheoretical outcomes in economics, but using it to describe permanence, stasis, or tenacity in politics may not capture the central dynamics of political change. Bridges considers timing, sequence, & temporal processes, pointing out that taken together, they are not really history. The author concludes that the most powerful theory for the social scientist would be a theory that provokes & informs research & aids in forming questions & organizing the results of the research. L. A. Hoffman