Increased advancement in a variety of study subjects and information technologies, has increased the number of published research articles. However, researchers are facing difficulties and devote a significant time amount in locating scientific research publications relevant to their domain of expertise. In this article, an approach of document classification is presented to cluster the text documents of research articles into expressive groups that encompass a similar scientific field. The main focus and scopes of target groups were adopted in designing the proposed method, each group include several topics. The word tokens were separately extracted from topics related to a single group. The repeated appearance of word tokens in a document has an impact on the document's weight, which is computed using the term frequency-inverse document frequency (TF-IDF) numerical statistic. To perform the categorization process, the proposed approach employs the paper's title, abstract, and keywords, as well as the categories' topics. We exploited the K-means clustering algorithm for classifying and clustering the documents into primary categories. The K-means algorithm uses category weights to initialize the cluster centers (or centroids). Experimental results have shown that the suggested technique outperforms the k-nearest neighbors algorithm in terms of accuracy in retrieving information.
Genetic Algorithm is established on the process of natural biological evolution that is used to simulate and mimic natural in looking for optimal solution of certain problem. Genetic Algorithms is employed in the electric power system (EPS) to optimize its' parameters using natural operators. This paper depicts the concept of Genetic Algorithm as an optimization tool to synthesize the optimal parameters of a specific case study, Perdwad Power Station in Kurdistan, which are Power System Stabilizer (PSS) and Excitation system. It's trend to automate the real case as a smart state. A population set of parameters is randomly generated, which is, proper solutions for the search space. Whereas two parent chromosomes from a population is selected to produce offspring which is depending on the fitness. The parent's selection has a bigger chance when the fitness is better.The obtained results have proved that Genetic Algorithm are a powerful tools for optimizing the PSS parameters, and more robustness for the studied PSS. The simulations were executed by SIMULINK\MATLAB environment. The simulation results show the parameters of genetic controllers, smart control, are better than from conventional controllers.
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