A: Convolutional neural networks (CNNs) have found applications in many image processing tasks, such as feature extraction, image classification, and object recognition. It has also been shown that the inverse of CNNs, so-called deconvolutional neural networks, can be used for inverse problems such as plasma tomography. In essence, plasma tomography consists in reconstructing the 2D plasma profile on a poloidal cross-section of a fusion device, based on line-integrated measurements from multiple radiation detectors. Since the reconstruction process is computationally intensive, a deconvolutional neural network trained to produce the same results will yield a significant computational speedup, at the expense of a small error which can be assessed using different metrics. In this work, we discuss the design principles behind such networks, including the use of multiple layers, how they can be stacked, and how their dimensions can be tuned according to the number of detectors and the desired tomographic resolution for a given fusion device. We describe the application of such networks at JET and COMPASS, where at JET we use the bolometer system, and at COMPASS we use the soft X-ray diagnostic based on photodiode arrays. K : Computerized Tomography (CT) and Computed Radiography (CR); Plasma diagnostics -interferometry, spectroscopy and imaging 1Corresponding author. 2See the author list of Overview of the JET preparation for Deuterium-Tritium Operation by E. Joffrin et al. in Nucl.

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A real world challenging task of the web master of an organization is to match the needs of user and keep their attention in their web site. So, only option is to capture the intuition of the user and provide them with the recommendation list. Web usage mining is a kind of data mining method that provide intelligent personalized online services such as web recommendations, it is usually necessary to model users" web access behavior. Web usage mining includes three process, namely, preprocessing, pattern discovery and pattern analysis. After the completion of these three phases the user can find the required usage patterns and use this information for the specific needs. The data abstraction is achieved through data preprocessing. The aim of discovering frequent sequential access patterns in Web log data is to obtain information about the navigational behavior of the users. In the proposed system, an efficient sequential pattern mining algorithm is used to identify frequent sequential web access patterns. The access patterns are retrieved from a Graph, which is then used for matching and generating web links for recommendations.

Web log file shows the behavior of user when they access the website. It is in text format and automatically generated by web server whenever user accesses the website. Entry of particular web page with date, time, cs-method, cs-uri-stem and other information is generated automatically in text file for every access of web page. Web Usage Mining is different mining technique apply on weblog file to discover the different pattern which is useful for efficient design of website, performance enhancement of server etc. Mining of web log file consist of three steps Data Preprocessing, Pattern Discovery and Pattern Analysis. Data preprocessing task convert the web log file in database by applying data extraction, data storage and data cleaning technique. In existing technique data extraction from the log file is perform line by line and store in multidimensional array and then extracted data field will be store in database and then data cleaning is performed. In this research paper, a new technique is being proposed for Data Preprocessing of web log using Microsoft Excel. The experimental results show that the proposed new technique using Microsoft excel file is an effective compare to existing technique.