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T-Vigilant: To Unmask Radical Attacks and Halt the Innocents, IEEE Xplore Digital Library
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Forbearance and wisdom should be the key
now. The system to prevent terrorist attacks that will
relay emergency alerts to all phones is set to begin. This
system could warn people of terrorist strikes by text
message. With the popularity of Social Networks, mostly
news providers used to split their news in various social
networking sites and web blogs. In India, many news
groups stake their news on Twitter micro blogging
service provider which provides real-nature to the
system. The system is an early precursor that collects and
analyzes real-time news of events such as terrorist attack,
hijack, bomb blast etc. from Twitter and detects a target
event. Objective behind is to ooze message to all the
phones in a given area, providing them with up-to-date
and accurate guidance on the specific threat and the best
way to escape. Machine learning techniques were used to
train the data. In order to create the instances words
from each short message were consider and bag-of-words
approach was used to create feature vector. The data was
trained using KNN (K – Nearest Neighbor) machine
learning techniques. The KNN is a typical learning
algorithm based on analogy, so if category has a certain
amount of the training samples which helps to guarantee
the accuracy of classification. Large amount of feature
will be collected for current research. The performance
will speak the efficaciousness of the system.
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T-Alert: Terrorist Alert System Using Data Mining Techniques, International Journal of Engineering Science and Computing (IJESC), ISSN: 2250-1371, Vol-6, Issue-5, May 2016
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With the popularity of Social Networks, mostly news providers used to share their news in various social networking sites and web blogs. In India, many news groups share their news on Twitter micro blogging service provider.These data carries valuable information relevant to social research areas. Thus, the idea is to categorize the news into different groups so the news groups in India are identified. News groups are selected on their popularity to extract the short messages from Twitter Micro Blog. Short message extracted from Twitter was classified into 12 major groups. Machine learning techniques were used to train the data. In order to create the instances words from each short message were consider and bag-of-words approach was used to create feature vector. The data was trained using Random Forest machine learning techniques. Random forest is a best ensemble learning method, which is consist of multiple decision trees built on random inputs and separating nodes on a random subset of features. Because of its good classification and generalization ability, random forest is preferred in various domains. Large amount of feature will be collected for current research. The performance will speak the efficacious of the system.
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Twitter News Stratification Using Random Forest, International Journal of Advances in Electronics and Computer Science, ISSN: 2393-2835 Volume-2, Issue-9, Sept-2015
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With the popularity of Social Networks, mostly news providers used to share their news in various social networking sites and web blogs. In India, many news groups share their news on Twitter micro blogging service provider.These data carries valuable information relevant to social research areas. Thus, the idea is to categorize the news into different groups so the news groups in India are identified. News groups are selected on their popularity to extract the short messages from Twitter Micro Blog. Short message extracted from Twitter was classified into 12 major groups. Machine learning techniques were used to train the data. In order to create the instances words from each short message were consider and bag-of-words approach was used to create feature vector. The data was trained using Random Forest machine learning techniques. Random forest is a best ensemble learning method, which is consist of multiple decision trees built on random inputs and separating nodes on a random subset of features. Because of its good classification and generalization ability, random forest is preferred in various domains. Large amount of feature will be collected for current research. The performance will speak the efficacious of the system.
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KNN Based Twitter Newscaster, International Journal of Advanced Computational Engineering and Networking, ISSN: 2320-2106, Vol-3, Issue-8, Aug-2015
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With the popularity of Social Networks, mostly news providers used to share their news in various social networking sites and web blogs. In India, many news groups share their news on Twitter micro blogging service provider.These data carries valuable information relevant to social research areas. Thus, the idea is to categorize the news into different groups so the news groups in India are identified. News groups are selected on their popularity to extract the short
messages from Twitter Micro Blog. Short message extracted from Twitter was classified into 12 major groups. Machine learning techniques were used to train the data. In order to create the instances words from each short message were consider and bag-of-words approach was used to create feature vector. The data was trained using KNN (K – Nearest Neighbor) machine learning techniques. The KNN is a typical learning algorithm based on analogy, so each category has a certain amount of the training samples which helps representatives guarantee the accuracy of classification. Large amount of feature will be collected for current research.The performance will speak the efficacious of the system.
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Comparison of Different Navigation Prediction Techniques, International Journal of Computer Science and Information Technologies (IJCSIT), ISSN: 0975-9646, Vol-6, Issue-2, 2015
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Discovering web navigation patterns plays an important role in web mining which is used for
prediction and management of website efficiently. As we all know that web site structure is always changed, many Navigation Prediction Techniques need not only consider the frequency of click behavior but also web site structure to mine web navigation patterns for navigation prediction, dynamic mining approach is also based on the previous mining results and formed new patterns just from the modifying some part of the web data. We had compared Different Navigation Prediction Techniques based on criteria’s like data structure used for intermediate storage and mining, original database used for mining, re-mining is done to track changes in the web structure, pattern tree construction for improving prediction phase and Technique used for navigation prediction and we found that Mining Web Navigation Patterns with Dynamic Thresholds for Navigation Prediction is the best from that technologies
