CHETAN PURI

The application works as an integrated interactive user interface for teaching academic subjects through the Local Area Network (LAN) medium. The ap plication was kind of which supported virtual class room concept. It was interaction between a remote instructor and group of students. The purpose of this application is enhancing educational equity .While switching from traditional classroom to virtual classroom. We had tried to cover all the concept and working done in a traditional classroom.

Fingerprint recognition with identical twins is a challenging task due to the closest genetics -based relationship existing i n the identical twins. Several pioneers have analyzed the similarity between twins fingerprints. In this work we continue to investigate the topic of the similarity of identical twin fingerprints. Our contributions are summarized as follows: (1) Two state-of-the-art fingerprint identification methods: P071 and VeriFinger 6.1 were used, rather than one fingerprint identification method in previous studies. (2) Six impressions per finger were captured, rather than just one impression, which makes the genuine distribution of matching scores more realistic

Twitter has attracted millions of users to share and disseminate most up-to-date information, resulting in large volumes of data produced every day. However, many applications in Information Retrieval (IR) and Natural Language Processing (NLP) suffer seve rely from the noisy and short nature of tweets. In this experiment, propose a novel framework for tweet segmentation in a batch mode, called HybridSeg. Splitting tweets into meaningful segments, the semantic or context information is well preserved and eas ily extracted by the downstream applications. As an application, show that high accuracy is achieved in named entity recognition by applying segment -based part -of-speech (POS) tagging. Event detection from tweets is an important task to understand the cur rent events/topics attracting a large number of common users. However, the unique characteristics of tweets (e.g., short and noisy content, diverse and fast changing topics, and large data volume) make event detection a challenging task. In this event detection task propose a segment -based event detection system for tweets. After clustering bursty segments into candidate events, Wikipedia is exploited to identify the realistic events and to derive the most newsworthy segments to describe the identified even ts.