Currently, agile methodologies are widely used as a light-weight development process model based on short development cycles, which can satisfy frequent change requests from customers and stakeholders, and reach an efficient product delivery.On the other hand, most suppliers in the automotive industry are currently facing a challenge to comply to automotive SPICE (ASPICE) standard, which is a domainspecific model of ISO/IEC 15504. Moreover, original equipment manufacturers (OEMs) have increasingly requested ASPICE compliance from their suppliers to accurately assess their process quality capability. This paper analyzes to what extent the ASPICE process areas were covered by agile and aims to propose a model based on integrating agile methodologies, as well as customize some agile practices so that it can fit in the Automotive SPICE process model and support automotive suppliers to achieve the required ASPICE rating. MAN.03, which is the process area for project management, was selected from the process reference model (PRM). Then, each base practice (BP) was mapped to the suitable agile methodology. Moreover, MAN.03 consists of 10 BPs, and each BP rating was measured to be N, P, L, or F. For Level 1, we applied Kanban on BP.4 and Scrum on the remaining BPs. For Level 2, we applied Scrum on all generic practices. We presented our results after deploying Kanban, and a restructured version of Scrum, which makes it in compliance with the automotive industry. The approach taken in this study could be applied in other process areas in the PRM afterwards.

(PPDM) privacy preserving data mining is recent advanced research in (DM) data mining field; Many efficient and practical techniques have been proposed for hiding sensitive patterns or information from been discovered by (DM) data mining algorithms. (ARM) Association rule mining is the most important tool in (DM) data mining, that is considered a powerful and interested tool for discovering relationships between items, which are hidden in large database and may provide business competitors with an advantage, thus the hiding of association rules is the most important point in (PPDM) privacy preserving data mining for protecting sensitive and crucial data against unauthorized access; Many Practical techniques and approaches have been proposed for hiding association rules for (PPDM) privacy preserving data mining; In this paper the current existing techniques and algorithms for all approaches for (ARH) association rule hiding have been summarized.

Governments and educational authorities around the world are emphasizing performance evaluation of educational systems. Opinion Mining (OM) has gained acceptance among experts in various regions, including the preparation space. The proposed model involves Two modules: the data preprocessing module and the opinion mining module. The main objective of our article is to enhance educational systems through the analysis of student comments, teacher comments and course comments. Furthermore, the proposed model uses a bundling task to make groups of packs for students from its comments. The datasets were 10,000 instances, 80% of which were for training and 20% for testing. The results showed that K-Means Algorithm had the best accuracy time /Sec of 0.03. The correctly classified 8,000 instances were equal to 96%, and incorrectly classified 2,000 instances were equal to 4%, Accuracy of the model is 95%, Recall is 94.8% and F-Measure is 93.7% between others algorithms. clustering and Association Rule Mining phases Algorithms namely Chi-Square test is good quality than Others Algorithms.