Work place: Department of CS&SE, AUCE(A), Andhra Pradesh, Visakhapatnam, India
Research Interests: Pattern Recognition, Image Compression, Image Manipulation, Network Architecture, Network Security, Image Processing
Dr. D.Lalitha Bhaskari is a Professor in Department of computer science & Systems Engineering in Andhra University College of Engineering(A), Andhra Pradesh, Visakhapatnam, India. she has received her Ph.D from JNT University Hyderabad in 2009. 6 Phds were awarded under her guidance and she is presently guiding more than 20 research scholars. Her research interests include Cryptography & Network Security, Stenography & Digital Watermarking, Pattern Recognition, Image Processing, Cyber Crime & Digital Forensics.
DOI: https://doi.org/10.5815/ijitcs.2018.07.06, Pub. Date: 8 Jul. 2018
With the growth in the communication over Internet via short messages, messaging services and chat, still emails are the most preferred communication method. Thousands of emails are been communicated everyday over different service providers. The emails being the most effective communication methods can also attract a lot of spam or irrelevant information. The spam emails are annoying and consumes a lot of time for filtering. Regardless to mention, the spam emails also consumes the main allocated inbox space and at the same time causes huge network traffic. The filtration methods are miles away from perfection as most of these filters depends on the standard rules, thus making the valid emails marked as spam. The first step of any email filtration should be extracting the key phrases from the emails and based on the key phrases or mostly used phrases the filters should be activated. A number of parallel researches have demonstrated the key phrase extraction policies. Nonetheless, the methods are truly focused on domain specific corpuses and have not addressed the email corpuses. Thus this work demonstrates the key phrases extraction process specifically for the email corpuses. The extracted key phrases demonstrate the frequency of the words used in that email. This analysis can make the further analysis easier in terms of sentiment analysis or spam detection. Also, this analysis can cater to the need for text summarization. The proposed component based framework demonstrates a nearly 95% accuracy.[...] Read more.
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