Detecting Fake Users on Social Media with a Graph Database

Social media has become a major part of people’s daily lives as it provides users with the convenience to connect with people, interact with friends, share personal content with others, and gather information. However, it also creates opportunities for fake users. Fake users on social media may be perceived as popular and influential if not detected. They might spread false information or fake news by making it look real, manipulating real users into making certain decisions. In computer science, a social network can be treated as a graph, which is a data structure consisting of nodes being the social media users, and edges being the connections between users. Graph data can be stored in a graph database for efficient data analysis. In this paper, we propose using a graph database to achieve an increased scalability to accommodate larger graphs. Centrality measures as features were extracted for the random forest classifier to successfully detect fake users with high precision, recall, and accuracy. We have achieved promising results especially when compared with previous studies.

Grid text classification method based on DNN neural network

With the rapid development of network technology, the electric power Internet of Things needs to face a large number of electronic texts and a large number of distributed data access and analysis requirements. If the system wants to complete accurate and efficient data analysis and build an existing data and service standard system covering the entire chain of energy and power business on the existing basis, it must implement massive electronic text retrieval, information extraction and classification in the power grid system. In order to achieve this purpose, a DNN neural network classification model is constructed to classify the text information of the power grid, and the effectiveness of the method is verified by experiments based on data from the substation information system.

SAS Enterprise Guide 6.1 for physicians: getting started

Objective. To develop algorithm of data analysis of prospective non-randomized clinical trial AMIRICABG (ClinicalTrials.gov Identifier: NCT03050489) using SAS Enterprise Guide 6.1. Materials and methods. Data collection was performed according prospective non-randomized clinical trial AMIRICABG in Pavlov First Saint Petersburg State Medical University, Saint Petersburg, Russia between 20162019 years with 336 patients. There is database with clinical, laboratory and instrumental data. Statistical analysis was performed with SAS Enterprise Guide 6.1. Results. There was developed algorithm of data analysis of prospective non-randomized clinical trial AMIRICABG. This algorithm could be useful for physicians and researchers for data analysis. Conclusion. Presented algorithm of data analysis could make easier and improve efficient data analysis. SAS Enterprise Guide 6.1 allows fast and accurate process big data.

The Effect of eHealth Information Systems on Health Information Management in Hospitals in Bulawayo, Zimbabwe

EHealth information systems have brought about a lot of positives which include timeous reporting, efficient data analysis, better decision making, coordination and better work processes. Zimbabwe has also adopted the eHealth information systems and this study sought to establish the effects of eHealth information systems on the management of health information in hospitals in Bulawayo, Zimbabwe. The study applies a qualitative research methodology in which a case study research design and a purposive sampling technique were used. Document analysis and face to face interviews were held with a total of eleven research participants.

Variational algorithms for analyzing noisy multi-state diffusion trajectories

Single particle tracking offers a non-invasive high-resolution probe of biomolecular reactions inside living cells. However, efficient data analysis methods that correctly account for various noise soures are needed to realize the full quantitative potential of the method. We report new algorithms for hidden Markov based analysis of single particle tracking data, which incorporate most sources of experimental noise, including heterogeneuous localization errors and missing positions. Compared to previous implementations, the algorithms offer significant speed-ups, support for a wider range of inference methods, and a simple user interface. This will enable more advanced and exploratory quantitative analysis of single particle tracking data.