IEEE 2014 ASP.NET with C# Projects
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The document discusses multiple projects focused on leveraging social media data and enhancing web functionalities using ASP.NET with C#. One project develops a workflow to analyze student tweets for insights into educational experiences, while another explores cost-effective viral marketing in social networks, emphasizing the importance of optimal seeding strategies. Additionally, it presents an image re-ranking framework for improving web image search results by aligning visual features with semantic meanings.
IEEE 2014 ASP.NET with C# Projects
- 1. IEEE 2014 ASP.NET with C# Projects Web : www.kasanpro.com Email : sales@kasanpro.com List Link : http://kasanpro.com/projects-list/ieee-2014-asp-net-with-c-projects Title :Mining Social Media Data for Understanding Student's Learning Experiences Language : ASP.NET with C# Project Link : http://kasanpro.com/p/asp-net-with-c-sharp/mining-social-media-data-understanding-students-learning-experien Abstract : Students' informal conversations on social media (e.g. Twitter, Facebook) shed light into their educational experiences - opinions, feelings, and concerns about the learning process. Data from such uninstrumented environment can provide valuable knowledge to inform student learning. Analyzing such data, however, can be challenging. The complexity of student's experiences reflected from social media content requires human interpretation. However, the growing scale of data demands automatic data analysis techniques. In this paper, we developed a workflow to integrate both qualitative analysis and large - scale data mining techniques. We focus on engineering student's Twitter posts to understand issues and problems in their educational experiences. We first conducted a qualitative analysis on samples taken from about 25,000 tweets related to engagement, and sleep deprivation. Based on these results, we implemented a multi - label classification algorithm to classify tweets reflecting student's problems. We then used the algorithm to train a detector of student problems from about 35,000 tweets streamed at the geo - location of Purdue University. This work, for the first time, presents a methodology and results that show how informal social media data can provide insights into students' experiences. Title :Cost-effective Viral Marketing for Time-critical Campaigns in Large-scale Social Networks Language : ASP.NET with C# Project Link : http://kasanpro.com/p/asp-net-with-c-sharp/cost-effective-viral-marketing-time-critical-campaigns-large-scale-so Abstract : Online social networks (OSNs) have become one of the most effective channels for marketing and advertising. Since users are often influenced by their friends, "wordof- mouth" exchanges, so-called viral marketing, in social networks can be used to increase product adoption or widely spread content over the network. The common perception of viral marketing about being cheap, easy, and massively effective makes it an ideal replacement of traditional advertising. However, recent studies have revealed that the propagation often fades quickly within only few hops from the sources, counteracting the assumption on the self-perpetuating of influence considered in literature. With only limited influence propagation, is massively reaching customers via viral marketing still affordable? How to economically spend more resources to increase the spreading speed? We investigate the cost-effective massive viral marketing problem, taking into the consideration the limited influence propagation. Both analytical analysis based on power-law network theory and numerical analysis demonstrate that the viral marketing might involve costly seeding. To minimize the seeding cost, we provide mathematical programming to find optimal seeding for medium-size networks and propose VirAds, an efficient algorithm, to tackle the problem on largescale networks. VirAds guarantees a relative error bound of O(1) from the optimal solutions in power-law networks and outperforms the greedy heuristics which realizes on the degree centrality. Moreover, we also show that, in general, approximating the optimal seeding within a ratio better than O(log n) is unlikely possible. Title :Web Image Re-Ranking UsingQuery-Specific Semantic Signatures Language : ASP.NET with C# Project Link : http://kasanpro.com/p/asp-net-with-c-sharp/web-image-re-ranking-usingquery-specific-semantic-signatures Abstract : Image re-ranking, as an effective way to improve the results of web-based image search, has been adopted by current commercial search engines. Given a query keyword, a pool of images are first retrieved by the search engine based on textual information. By asking the user to select a query image from the pool, the remaining images are re-ranked based on their visual similarities with the query image. A major challenge is that the similarities of visual features do not well correlate with images' semantic meanings which interpret users' search intention. On the other hand, learning a universal visual semantic space to characterize highly diverse images from the web is difficult and inefficient. In this paper, we propose a novel image re-ranking framework, which automatically offline learns
- 2. different visual semantic spaces for different query keywords through keyword expansions. The visual features of images are projected into their related visual semantic spaces to get semantic signatures. At the online stage, images are re-ranked by comparing their semantic signatures obtained from the visual semantic space specified by the query keyword. The new approach significantly improves both the accuracy and efficiency of image re-ranking. The original visual features of thousands of dimensions can be projected to the semantic signatures as short as 25 dimensions. Experimental results show that 20% ? 35% relative improvement has been achieved on re-ranking precisions compared with the stateof- the-art methods.