Visual analytics becomes an important approach for discovering patterns in big data. As visualization struggles from high dimensionality of data, issues like concept hierarchy on each dimension add more difficulty and make visualization a prohibitive task. Data cube offers multi-perspective aggregated views of large data sets and has important applications in business and many other areas. It has high dimensionality, concept hierarchy, vast number of cells, and comes with special exploration operations such as roll-up, drill-down, slicing and dicing. All these issues make data cubes very difficult to visually explore. Most existing approaches visualize a data cube in 2D space and require preprocessing steps. In this paper, we propose a visu

This research aims to analyze and simulate biochemical real test data for uncovering the relationships among the tests, and how each of them impacts others. The data were acquired from Iraqi private biochemical laboratory. However, these data have many dimensions with a high rate of null values, and big patient numbers. Then, several experiments have been applied on these data beginning with unsupervised techniques such as hierarchical clustering, and k-means, but the results were not clear. Then the preprocessing step performed, to make the dataset analyzable by supervised techniques such as Linear Discriminant Analysis (LDA), Classification And Regression Tree (CART), Logistic Regression (LR), K-Nearest Neighbor (K-NN), Naïve Bays (NB

Copper Telluride Thin films of thickness 700nm and 900nm, prepared thin films using thermal evaporation on cleaned Si substrates kept at 300K under the vacuum about (4x10-5 ) mbar. The XRD analysis and (AFM) measurements use to study structure properties. The sensitivity (S) of the fabricated sensors to NO2 and H2 was measured at room temperature. The experimental relationship between S and thickness of the sensitive film was investigated, and higher S values were recorded for thicker sensors. Results showed that the best sensitivity was attributed to the Cu2Te film of 900 nm thickness at the H2 gas.

In this work the structural, optical and sensitive properties of Cerium - Copper oxide thin film prepared on silicon and glass substrate by the spray pyrolysis technique at a temperature of (200, 250, 300 °C). The results of (XRD) showed that all the prepared films were of a polycrystalline installation and monoclinic crystal structure with a preferable directions was (111) of CuO. Optical characteristics observed that the absorption coefficient has values for all the prepared CuO: Ce% (104 cm-1) in the visible spectrum, indicating that all the thin films prepared have a direct energy gap. Been fabrication of gas sensors of (CuO: Ce %) within optimum preparation conditions and study sensitivity properties were examined her exposed to ni

 In this paper, we propose a method using continuous wavelets to study the multivariate fractional Brownian motion through the deviations of the transformed random process to find an efficient estimate of Hurst exponent using eigenvalue regression of the covariance matrix. The results of simulations experiments shown that the performance of the proposed estimator was efficient in bias but the variance get increase as signal change from short to long memory the MASE increase relatively. The estimation process was made by calculating the eigenvalues for the variance-covariance matrix of Meyer’s continuous wavelet details coefficients.

      The Costing Accounting is one the analytic tools which plays important role by support the management in planning&  control and decisions-making ,as it became attendant necessity to establish any project whether industrial ,commercial ,service or agriculture ..etc.

     The consolidated accounting system has committed the companies to have their active costing system in which the management can obtain their own data, but we found most of the economic units face problems of applying the costing system because of reasons related to the system design itself or might be related to the requirements of the application success.   

In many video and image processing applications, the frames are partitioned into blocks, which are extracted and processed sequentially. In this paper, we propose a fast algorithm for calculation of features of overlapping image blocks. We assume the features are projections of the block on separable 2D basis functions (usually orthogonal polynomials) where we benefit from the symmetry with respect to spatial variables. The main idea is based on a construction of auxiliary matrices that virtually extends the original image and makes it possible to avoid a time-consuming computation in loops. These matrices can be pre-calculated, stored and used repeatedly since they are independent of the image itself. We validated experimentally th