Security in today's world is one of the important challenges that people are facing all over the world in every aspect of their lives. Similarly security in electronic world has a great significance. In this paper, we survey the security of database. This is an area of substantial interest in database because we know that, the use of database is becoming very important in today's enterprise and databases contains information that is major enterprise asset. This survey was conducted to identify the issues and threats in database security, requirements of database security, and how encryption is used at different levels to provide the security.

Recently Intelligent Tutoring Systems (ITS) and Computer-Supported Collaborative Learning (CSCL) have got much attention in the field of computer science, artificial intelligence, cognitive psychology, and educational technologies. An ITS is a technologically intelligent system that provides an adaptive learning paradigm for an individual learner only, while CSCL is also a technology-driven learning paradigm that supports groups of learners in pertaining knowledge by collaboration. In a multidisciplinary research field-the Learning Sciences, both individual and collaborative learning have their own significance. This research aims to extend ITS for collaborative constructivist view of learning using CSCL. Integrating both design architecture of CSCL and ITS, this research model propose a new conceptual framework underpinning "Intelligent Tutoring Supported Collaborative Learning (ITSCL)". ITSCL extend ITS by supporting multiple learners interacting system. ITSCL support three different types of interaction levels. The first level of interaction supports individual learning by learner-tutor interaction. The second and third level of interaction support collaborative learning, by learner-learner interaction and tutor-group of collaborative learners' interactions, respectively. To evaluate ITSCL, a prototype model was implemented to conduct few experiments. The statistical results extrapolate the learning gains, measured from Paired T-Test and frequency analysis, contend a significant learning gain and improvement in the learning process with enhanced learning performance.

Abstract-Data mining has recently emerged as an important field that helps in extracting useful knowledge from the huge amount of unstructured and apparently un-useful data. Data mining in health organization has highest potential in this area for mining the unknown patterns in the datasets and disease prediction. The amount of work done for cardiovascular patients in Pakistan is scarcely very less. In this research study, using classification approach of machine learning, we have proposed a framework to classify unstructured data of cardiac patients of the Armed Forces Institute of Cardiology (AFIC), Pakistan to four important classes. The focus of this study is to structure the unstructured medical data/reports manually, as there was no structured database available for the specific data under study. Multi-nominal Logistic Regression (LR) is used to perform multiclass classification and 10-fold cross validation is used to validate the classification models, in order to analyze the results and the performance of Logistic Regression models. The performancemeasuring criterion that is used includes precision, f-measure, sensitivity, specificity, classification error, area under the curve and accuracy. This study will provide a road map for future research in the field of Bioinformatics in Pakistan.

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