محمد رضا عبد الزهرة محمد الكعبي

Cloud computing has demonstrated to be a modern model that offers IT assets depend on the payper- use basis as a service over the internet. Scientific workflow applications are benefiting from running on the cloud computing services. However, the challenge is to optimize workflow scheduling algorithms are still required an additional work. This study proposes max-min+ which is an extension for the max-min algorithm that is able to minimize the total execution time and cost of workflow execution. We tested the proposed algorithm via WorkflowSim with using 5 realistic scientific workflow applications. The results of max-min+ outperforms the original max-min algorithm by minimizing the overall workflow execution time.

The proposed method presents two efficient measures for face recognition. The approach is based on a proper weighting of (correlation coefficients) between corresponding face regions. The regions are a right eye, left eye, nose, and mouth as defined by Viola-Jones Algorithm. In addition, the measures have incorporated the holistic face region with a maximal weight. The recognition performance measure used is the distance between the reference image and the next confusing image in the database, where a better measure gives a bigger distance, hence better confidence in the recognition process. The proposed measures have been compared with structural similarity measure (SSIM), where superior performance has been obtained. Illumination adjustment of face images has been considered via differential thresholding that may enhance recognition. The recognition process assumed one face image as a reference image to be recognized in a database that includes the same reference person but with a different face pose (image).

Data science has become important as the new way in the new business area, and the knowledge nowadays represents as a critical function in the whole of all sciences of different organizations, so these two factors (data science and knowledge) are becoming a higher priority of the most job wanted and social services in the world. So nowadays different enterprise jobs are related to knowledge extracting. In this paper will present how to study and analysis of data that related to social services which are criminal data. The study consists of different mining tools by using algorithms like classifications and clustering techniques. This paper presents different crimes with related information distributed as day, months, years and locations. Different crimes are classified into sets according to (day, month, and year) to get information when the crime happens (knowledge) in which type of crime with which time it occurred, and finally different clusters of the logged data using k-means algorithms. Different tools and software are used like WEKA, XLSTAT and statistical excel to apply data mining algorithms to the logged data.