List of IEEE ASP.NET with C# Projects. It Contains the IEEE Projects in the Language ASP.NET with C# with ieee publication year 2014

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.