In genetic engineering, conventional techniques and algorithms employed by forensic scientists to assist in identification of individuals on the basis of their respective DNA profiles involves more complex computational steps and mathematical formulae, also the identification of location of mutation in a genomic sequence in laboratories is still an exigent task. This novel approach provides ability to solve the problems that do not have an algorithmic solution and the available solutions are also too complex to be found. The perfect blend made of bioinformatics and neural networks technique results in efficient DNA pattern analysis algorithm with utmost prediction accuracy.
The conventional techniques and algorithms employed by forensic scientists to assist in the identification of individuals on the basis of their respective Deoxyribonucleic acid base(DNA) pair profiles involves more computational steps and mathematical formulas that leads to more complexity. DNA identification is not considered by many as a biometric recognition technology, mainly because it is not yet an automated process i.e. it takes more time to analyze the DNA finger prints and samples collected from the crime scene, it will be considered as a future biometric trait if it's suitably automated. Neural networks learn by examples so that it can be trained with known examples of a problem to gain knowledge about it so the neural network can be effective to solve unknown or untrained instances of the problem if it is aptly trained. The perfect blend made of bioinformatics, neural networks and fuzzy logic results in efficient algorithms of pattern analysis techniques that induce automation which is inevitable in DNA profiling that became manually impractical with the growing amount of data.
Nowadays, various social media platforms are available in Internet like Facebook, Twitter and Instagram for uniting the people. Twitter is one among the most famous platform in social media due to its available information among users. Users allows to find new friends and update their latest information and activities. Twitter is using Google Safe-browsing to detect the spam URL and block spam links. Due to the presence of advanced API which enables to read and write the data in Twitter, different kinds of spammers are attracted in the Twitter. There are various existing researches applied various machine learning techniques to determine the twitter spam. However, there is no comprehensive evaluation on their algorithms and lack of accuracy in large dataset. To rectify these issues, this research proposed hybrid method with the combination of Artificial Neural Networks with Fuzzy Decision Tree (ANN-FDT). The proposed classifier classified the span and non-span tweets based on the labels. For experimental analysis, the proposed classifier applied on large dataset of 600 million public tweets. The performance of proposed algorithm is evaluated by means of measures like accuracy, TPR, FPR and F-measure. From the results it can be seen that the proposed technique has improved performance.
Twitter is one of the most popular social media networks and therefore it is prone to misuse. One of the ways in which people misuse Twitter is by spamming. Spam becomes an issue once a communication medium especially one, which enables global communication and handle huge amount of online data. Since Twitter is popular among so many people, it makes it easy for spammers to thrive. Spammers are people who send unwanted messages to people to either advertise a product or lure the victims into clicking malicious links, which may affect their user systems. The main objective of these spammers is usually to make money from their victims. In the last years, several systems has-been made with the aim of determining whether a user is a spammer or not. However, these systems cannot filter each spam message and a different account can be created by a spammer and used to send other messages. This paper proposes a content-based approach, which can be used to filter spam tweets. The approach involves using tweets in machine learning and compression algorithms in order to filter the undesired tweets.
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