Hybrid based Collaborative Filtering with Temporal Dynamics

Hybrid-based collaborative filters use some part or entire database relating to user preferences for making recommendations for new products and new users. In our time, it is of utmost importance to make recommendations in line with interests and demands of users by making their interest alive. However, although Hybrid-based collaborative filters are used in this area, changing of preferences of users in a time, emergence of new products and new users overshadow success of such systems. Traditional hybrid-based collaborative filtering (CF) technique become insufficient for responding interests and demands changing in a time. For this reason, temporal changes in recommendation systems become an important concept. Together with the study conducted, an appropriate and new method has been developed in line with changing pleasure and demands depending on time. In the recommended system, unlike traditional hybrid technique based CF technique, point given to the products depending on dates scored by users has been attempted to be estimated. In this study, process has been made over netflix data for measuring success of both traditional hybrid based CF technique and the recommended system. Quite successful and rewarding results have been obtained in the issue of accuracy of predicted points. Keywords- Recommendation System;, Data Mining; Temporal Dynamics

Collaborative Filtering with Temporal Dynamics with Using Singular Value Decomposition

Nowadays, Collaborative Filtering (CF) is a widely used recommendation system. However, traditional CF techniques are harder to make fast and accurate suggestions due to changes in user preferences over time, the emergence of new products and the availability of too many users and too many products in the system. Therefore, it becomes more important to make suggestions that are both fast and take the changes in time into consideration. In the presented study, a new method for providing suggestions customized according to the users\u27 preference and taste as they change over time was developed. By combining the time-dependent changes through the SVD (Singular Value Decomposition), a faster suggestion system was developed. Thus, an attempt was made to enhance product prediction success. In the present study all techniques on Netflix data and the results were compared. The results obtained on the accuracy of the predicted ratings were found out to be promising

Classifying Database Users for Intrusion Prediction and Detection in Data Security

The fact that users and applications acquire information using web sites on the internet means that document and information sharing, banking and other operational processes are increasing day by day. Recently however, with the widespread use of the internet, some security problems, such as unauthorized access, data breaches, code infection, malware infections, data leaks and distributed denial of service attacks have emerged. This situation necessitates the protection of the information used in personal and public spaces. In this study, a common model was created to detect user intrusions by taking into account criteria such as the number of transactions performed, their IP addresses, the amount of data they use, the transaction type they perform and the roles they undertake. In this way, the aim was to ensure database security by detecting risky user groups in advance

A Prospective Study of Etiology of Childhood Acute Bacterial Meningitis, Turkey

Vaccines to prevent bacterial meningitis in this region must provide reliable protection against serogroup W-135

A Single-Label Model to Ensure Data Consistency in Information Security

Information security is defined as preventing actions such as unauthorized access and use, modification, and removal of information. It consists of certain basic elements of confidentiality, integrity, and accessibility. There are numerous studies in published literature which have been conducted to ensure information security. However, there is no previous study that covers these three basic elements together. In the present study, a model that includes these three key elements of information security together for big data was proposed and implemented. With this proposed “single-label model,” a more practical and flexible structure was established for all operations (read, write, update, and delete) performed on a database on real data. In previous studies conducted with a label model, separate labels were used for read-only or write-only operations, and there was no structure that could ensure both confidentiality and integrity at the same time. The present study, however, shows what type of authorization and access control could be established between which processes and which users by looking at a single label for all the operations performed on the data. Thus, in contrast to the previous studies seen in published literature, data confidentiality, data integrity, and data consistency were all guaranteed for all transactions. The results of the proposed single-label model were also shown comparatively by conducting an experimental study of its application. The results obtained are promising for further studies

Multi-Level Security Model Developed to Provide Data Privacy in Distributed Database Systems

Information security is related to efforts put in to avoid activities such as unauthorized usage, changing or disseminating of information by having access to these pieces of information. This should not be only thought as capturing of information but also as avoiding the violation of particulars such as integrity, availability, and confidentiality. Vulnerability that occurs in any one of these three basic elements will be evaluated as violation of information security. In this study with the aim to develop a multi-level access control method, Improved Bell-LaPadula security model has been adopted to distributed systems and hence, it was aimed to show how the property of confidentiality, being one of the three basic elements in information security, has been provided. The developed model proposed in the study has been applied on data cluster which has been obtained from real life. Performance of the proposed model has been compared with the performances of Role Based Access Control and Traditional Access Control models. As the obtained results were compared, it was observed that with the proposed model data were provided in a more secure and rapid way to be shared by the users

A New Scalable and Expandable Access Control Model for Distributed Database Systems in Data Security

Access control models are an important tool developed for securing today’s data systems. Institutions use the access control models specifically to define who their employees are, what they can do, which resources they can reach, and which processes they can perform and use them to manage the whole process. This is a very hard and costly process for institutions with distributed database systems. However, access control models cannot be implemented in a qualified way due to the fact that the conditions for defining users’ demands to reach resources distributed on different servers, one of which is consequentially bound to the other, the verification and authorization of those user demands, and being able to monitor the actions of the users cannot be configured in an efficient way all the time. With our model suggested in this study, the aim is to automatically calculate the permissions and access levels of all users defined in the distributed database systems for the objects, and, in this way, we will reach a more efficient decision as to which objects the users can access while preventing their access to the information they do not need. Our proposed model in this study has been applied to real life data clusters from organizations providing health and education services and a public service. With the proposed model, all models have been run on servers sharing resources in a private network. The performance of the proposed model has been compared to that of traditional access models. It was confirmed that the proposed model presented an access control model providing more accurate access level results as well as being scalable to many distributed database systems

Characteristics and outcomes of carbapenemase harbouring carbapenem-resistant Klebsiella spp. bloodstream infections: a multicentre prospective cohort study in an OXA-48 endemic setting

A prospective, multicentre observational cohort study of carbapenem-resistant Klebsiella spp. (CRK) bloodstream infections was conducted in Turkey from June 2018 to June 2019. One hundred eighty-seven patients were recruited. Single OXA-48-like carbapenemases predominated (75%), followed by OXA-48-like/NDM coproducers (16%). OXA-232 constituted 31% of all OXA-48-like carbapenemases and was mainly carried on ST2096. Thirty-day mortality was 44% overall and 51% for ST2096. In the multivariate cox regression analysis, SOFA score and immunosuppression were significant predictors of 30-day mortality and ST2096 had a non-significant effect. All OXA-48-like producers remained susceptible to ceftazidime-avibactam

International Nosocomial Infection Control Consortium (INICC) national report on device-associated infection rates in 19 cities of Turkey, data summary for 2003-2012

Background: Device-associated healthcare-acquired infections (DA-HAI) pose a threat to patient safety, particularly in the intensive care unit (ICU). We report the results of the International Infection Control Consortium (INICC) study conducted in Turkey from August 2003 through October 2012