Zero dispersion shifted fibers have been used in the Wavelength Division Multiplexing networks. These systems generally have tendency of experiencing the non-linear effects such as Four Wave Mixing. Dispersion is intentionally added to the fibers for mitigating the effects of FWM. But this purposely added dispersion is required to be removed in order to avoid the ill effects of dispersion. Such dispersion is removed with the use of dispersion compensating components. The performance of the FWM mitigating components like the DCF, OPC and their hybrid configuration DCF+ OPC is analyzed and compared in terms of FWM reduction.

This paper investigates opinion detection and sentiment analysis (SA) to automatic classifying Arabic comments and placing text documents into proper category positive or negative based on their contents. In this paper K-Nearest Neighbor (KNN) classifier is used to determine the polarity of the document and is studied the performance of K-NN classification when applying the different distance measures. The K-NN algorithm is based on learning by comparing a given test documents with training documents.  Measuring the similarity between documents is presented also in the paper; the three distance metrics (Euclidean distance, Cosine similarity and jaccard distance) are presented. The effect of these three measures is performed on three available resources (dataset) composed of Arabic language. The Evaluation parameters such as accuracy, recall and precision are measured the experimental results give that the performance of the jaccard measure with k =15 is better than other measures for Dataset.">

Breast cancer is a curse to complete human race. It grows inside breast cells silently, and soon it spreads to the whole body; as a result, the patient dies very soon. According to recently published reports, about 12.5% women are suspected of developing invasive breast cancer during their lifetime in the United States. Mammography, an imaging technique, is used in most breast cancer detection methods. A typical mammogram has poor contrast; as a result, doctors often overlook microcalcifications while using mammograms. In this paper, we introduce a computer based analysis to examine mammograms and extract features from them, then we apply data mining techniques to help in diagnosing the tumor as normal or abnormal.

In general the research studies which are associated with the data mining process is crucial in reference of data pre-processing and decision on the pre-processed data. The present study is based on the data mining of log of telecom company user and their behaviour with the services of the company. The study tries to find the bunch of customer who is uncertain for the company that’s the reason they are under churning condition. There are two steps process for finding the churn condition by the help of data mining. The study uses k-mean clustering and decision tree in association of decision table as well as prediction by the help of visualizations. Finally the experimental for the study gives a decision in the form of cluster of churning user.