Social User Mining

In recent years, the pervasive use of social media has generated huge amounts of data that starts to gain a lot of attentions. Each social media source utilizes different data types such as textual and visual. For example, Twitter is for a short text message, Flickr is for images and videos, and Facebook allows all of these data types. With the use of data mining techniques, the social media data opens a lot of opportunities for researchers. To address these challenges and to discover unknown information about users, we first introducing data assemble module to handle both textual and visual information from different media sources. After that, we Introducing data integration module to integrate textual and visual data. In addition, we proposed two different applications for social user mining

Social Profiling of Flickr: Integrating Multiple Types of Features for Gender Classification

With the pervasive use of social media sites, an extraordinary amount of data has been generated in different data types such as text and image. Combining image features and text information annotated by users reveals interesting properties of social user mining, and serves as a powerful way of discovering unknown information about the users. However, there has been few research work reported about combination of image and text data for social user mining. In this study, we propose a novel idea to classify the gender of user by integrating multiple types of features. We utilize not only text information, i.e., tag or description, but also images posted by a user with semantic based data fusion technique

Social User Mining: User Profiling of Social Media Network Based on Multimedia Data Mining

In recent years, the pervasive use of social media has generated extraordinary amounts of data that has started to gain an increasing amount of attention. Each social media source utilizes different data types such as textual and visual. For example, Twitter is used to transmit short text messages, whereas Flickr is used to convey images and videos. Moreover, Facebook uses all of these data types. From the social media users’ standpoint, it is highly desirable to find patterns from different data formats. The result of the huge amount of data from different sources or types has provided many opportunities for researchers in the fields of data mining and data analytics. Not only the methods and tools to organize and manage such data have become extremely important, but also methods and tools to discover hidden knowledge from such data, which can be used for a variety of applications. For example, the mining of a user's profile on social media could help to discover any missing information, including the user's location or gender information. However, the task of developing such methods and tools is very challenging. Social media data is unstructured and different from traditional data because of its privacy settings, data noise, and large capacity of data. Moreover, combining image features and text information annotated by users reveals interesting properties of social user mining, and serves as a useful tool for discovering unknown information about the users. Minimal research has been conducted on the combination of image and text data for social user mining. To address these challenges and to discover unknown information about users, we proposed a novel mining framework for social user mining that includes: 1) a data assemble module for different media source, 2) a data integration module, and 3) mining applications. First, we introduced a data assemble module in order to process both the textual and the visual information from different media sources, and evaluated the appropriate multimedia features for social user mining. Then, we proposed a new data integration method in order to integrate the textual and the visual data. Unlike the previous approaches that used a content based approach to merge multiple types of features, our main approach is based on image semantics through a semi-automatic image tagging system. Lastly, we presented two different application as an example of social user mining, gender classification and user location

UB Online Courses: An On-Demand Video-Streaming Service for Online Education

Online education in the past few years has become a very convenient and cost-effective alternative to the traditional class-based education offered on college and university campuses. Its popularity can been seen in the increasing number of online courses being offered by many universities around the world; and the University of Bridgeport is no exception. To handle the increasing demand on online education programs, the current methods and technologies need to be improved to meet the student expectations of ease of use, accessibility and availability. In this work we’re addressing issues in the current online education services provided at UB, such as the standardization of video formats, making the lecture videos viewable on smart phone devices, and making all courses available in a central location

Then and Now Map

With the internet technology spreads to everywhere of the world, it is possible to find the photographs for the same location or find the current photograph based on an old photograph. It is not difficult for human beings to do it manually. Our goal is how to do it automatically. The whole automatic process includes extracting address from the website, and using CRF, Lesk Algorithm gets the most possible one. In our proposed approach, we divide all websites into 4 cases firstly as the chart shown below

Molecular Phylogenetic Analysis of 16S rRNA Sequences Identified Two Lineages of Helicobacter pylori Strains Detected from Different Regions in Sudan Suggestive of Differential Evolution

Background. Helicobacter pylori (H. pylori) is ubiquitous among humans and one of the best-studied examples of an intimate association between bacteria and humans. Phylogeny and Phylogeography of H. pylori strains are known to mirror human migration patterns and reflect significant demographic events in human prehistory. In this study, we analyzed the molecular evolution of H. pylori strains detected from different tribes and regions of Sudan using 16S rRNA gene and the phylogenetic approach. Materials and methods. A total of 75 gastric biopsies were taken from patients who had been referred for endoscopy from different regions of Sudan. The DNA extraction was performed by using the guanidine chloride method. Two sets of primers (universal and specific for H. pylori) were used to amplify the 16S ribosomal gene. Sanger sequencing was applied, and the resulted sequences were matched with the sequences of the National Center for Biotechnology Information (NCBI) nucleotide database. The evolutionary aspects were analyzed using MEGA7 software. Results. Molecular detection of H. pylori has shown that 28 (37.33%) of the patients were positive for H. pylori and no significant differences were found in sociodemographic characteristics, endoscopy series, and H. pylori infection. Nucleotide variations were observed at five nucleotide positions (positions 219, 305, 578, 741, and 763–764), and one insertion mutation (750_InsC_751) was present in sixty-seven percent (7/12) of our strains. These six mutations were detected in regions of the 16S rRNA not closely associated with either tetracycline or tRNA binding sites; 66.67% of them were located in the central domain of 16S rRNA. The phylogenetic analysis of 16S rRNA sequences identified two lineages of H. pylori strains detected from different regions in Sudan. The presence of Sudanese H. pylori strains resembling Hungarian H. pylori strains could reflect the migration of Hungarian people to Sudan or vice versa. Conclusion. This finding emphasizes the significance of studying the phylogeny of H. pylori strains as a discriminatory tool to mirror human migration patterns. In addition, the 16S rRNA gene amplification method was found useful for bacterial identification and phylogeny

Age and frailty are independently associated with increased COVID-19 mortality and increased care needs in survivors: results of an international multi-centre study

Introduction: Increased mortality has been demonstrated in older adults with coronavirus disease 2019 (COVID-19), but the effect of frailty has been unclear. Methods: This multi-centre cohort study involved patients aged 18 years and older hospitalised with COVID-19, using routinely collected data. We used Cox regression analysis to assess the impact of age, frailty and delirium on the risk of inpatient mortality, adjusting for sex, illness severity, inflammation and co-morbidities. We used ordinal logistic regression analysis to assess the impact of age, Clinical Frailty Scale (CFS) and delirium on risk of increased care requirements on discharge, adjusting for the same variables. Results: Data from 5,711 patients from 55 hospitals in 12 countries were included (median age 74, interquartile range [IQR] 54–83; 55.2% male). The risk of death increased independently with increasing age (>80 versus 18–49: hazard ratio [HR] 3.57, confidence interval [CI] 2.54–5.02), frailty (CFS 8 versus 1–3: HR 3.03, CI 2.29–4.00) inflammation, renal disease, cardiovascular disease and cancer, but not delirium. Age, frailty (CFS 7 versus 1–3: odds ratio 7.00, CI 5.27–9.32), delirium, dementia and mental health diagnoses were all associated with increased risk of higher care needs on discharge. The likelihood of adverse outcomes increased across all grades of CFS from 4 to 9. Conclusion: Age and frailty are independently associated with adverse outcomes in COVID-19. Risk of increased care needs was also increased in survivors of COVID-19 with frailty or older age.</p