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Abstract-The quality of software systems is the most important factor to consider when designing and using these systems. The quality of the database or the database management system is particularly important as it is the backbone for all types of systems that it holds their data. Many researches argued that software with high quality will lead to an effective and secure system. Software quality can be assessed by using software measurements or metrics. Typically, metrics have several problems such as: having no specific standards, sometimes they are hard to measure, while at the same time they are time and resource consuming. Metrics need also to be continuously updated. A possible solution to some of those problems is to automate the process of gathering and assessing those metrics. In this research the metrics that evaluate the complexity of Object Oriented Relational Database (ORDB) are composed of the object oriented metrics and relational database metrics. This research is based on common theoretical calculations and formulations of ORDB metrics proposed by database experts. A tool is developed that takes the ORDB schema as an input and then collects several database structural metrics. Based on those proposed and gathered metrics, a study is conducted and showed that such metrics' assessment can be very useful in assessing the database complexity.

Twins recognition and identification is one of the important challenges in the field of image processing. The strong similarity between identical twins makes it hard to distinguish the twin from his/her sibling. Similarities come from biometric, geometric, and photometric features. In biometric patterns, the fingerprints found to be identical in some cases, geometrically, the twins' faces rarely differ which confuses people. Photometric features are very close to each other even though they rarely success in twins' recognition. We tackle this challenge by a model for twin's face recognition (FR) where our solution is based on deep transfer learning in terms of residual neural networks including two VGG16 trained networks, which are considered to be one of the powerful and deeply learned neural networks. For comparison purposes, we check other approaches to solve the twins' problem including iris, fingerprints, and lip corners. The data used was collected from Google which is a challenge. Data contains 4-pairs of twins with the 17-different position for each one which produces 5×2×17 (170) different images. Collected images were used for comparisons between features. Results show that geometrical features gave 85% of success while photometric features gave 96%. By hybridizing geometrical and photometric features together, the results reach 98% of accuracy. Biometric measures, in this research, prove the superiority of deeply transferring learning over traditional methods. The newly achieved method could be replaced to assist authentication systems that fully depend on biometric features.