Automation of DNA Finger Printing for Precise Pattern Identification using Neural fuzzy Mapping approach

Pushpalatha, A.; Mukunthan, B.

doi:10.5120/1761-2411

Cited by 3 publications

(1 citation statement)

References 9 publications

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“…In other paper, a neural network based multi-classifier system [8] for the identification of Escheri-chia coli promoter sequences in strings of DNA is presented. Pushpalatha and Mukunthan [9] applied a Neural-fuzzy Mapping to identify DNA Finger Printing. A genetic algorithm [10] was used as a preprocessing tool to Identifying significant genes from DNA microarray.…”

Section: Introductionmentioning

confidence: 99%

Pattern Recognition of mtDNA with Associative Models

2016

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Abstract. In this paper we applied an associative memory for the pattern recognition of mtDNA that can be useful to identify bodies and human remains. In particular, we used both morphological hetroassociative memories: max and min. We process the problem of pattern recognition as a classification task. Our proposal showed a correct recall, we obtained the 100% of recalling of all the learned patterns. We simulated a corrupted sample of mtDNA by adding noise of two types: additive and subtractive. The memory showed a correct recall when we applied less or equal than 55% of both types of noise.

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Section: Introductionmentioning

confidence: 99%

Pattern Recognition of mtDNA with Associative Models

2016

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A Multi-Classifier Approach for Twitter Spam Detection Using Innovative Ann-FDT Algorithm

Arunkrishna¹,

Mukunthan²

2020

INDJCSE

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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.

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Developing the Framework Using Deep Neural Network for Detection of Spam and Fake Spam Messages in Twitter

N. Anil Kumar,

Thatha Anusha,

Nagamalla Durga Prasad

et al. 2024

Int. J. Sci. Res. Comput. Sci. Eng. Inf. Technol

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Social media plays vital role among the user communities for social gathering, entertainment, communication, sharing knowledge so on. Twitter is one such network to connect millions of users to share information. Nowadays, there are humpteen numbers of users using social media for social engagements. Due to the fact that wide publicity of individuals and products get viral in social media, everyone wish to use social media as a platform to promote their product. Furthermore, large number of people relies on social media contents to take decisions. Twitter is one of the social media platforms to post the media contents by the user. Spammers are illegal users intrude the twitter account and send the duplicate messages to promote advertisement, phishing, scam and personal blogs etc. In this paper, a novel spam detection mechanism is introduced to detect the suspicious users on twitter. The system has been designed such a way that it initially set with semi-supervised at the tweet level and update into supervised level for learning the input tweets to detect the spammers. The proposed system will also identify the type of spammers and will also remove duplicate tweets. We have applied with multi-classifier algorithms like naïve Bayesian, K-Nearest neighbor and Random forest into twitter data set and the performance is compared. The experimental result shows very promising results.

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Automation of DNA Finger Printing for Precise Pattern Identification using Neural fuzzy Mapping approach

Cited by 3 publications

References 9 publications

Pattern Recognition of mtDNA with Associative Models

Pattern Recognition of mtDNA with Associative Models

A Multi-Classifier Approach for Twitter Spam Detection Using Innovative Ann-FDT Algorithm

Developing the Framework Using Deep Neural Network for Detection of Spam and Fake Spam Messages in Twitter

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