Identification of unique repeated patterns, location of mutation in DNA finger printing using artificial intelligence technique

Mukunthan, B.; Nagaveni, N.

doi:10.1504/ijbra.2014.059516

Cited by 3 publications

(2 citation statements)

References 9 publications

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“…Mukunthan and Nagaveni, 2014, India [47] Identification of unique repeated patterns, location of mutation in DNA finger printing using artificial intelligence technique Artificial neural network techniques were applied to DNA profiling and sequencing.…”

Section: Pereira Et Al 2011 Portugal [46]mentioning

confidence: 99%

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Artificial Intelligence and Forensic Genetics: Current Applications and Future Perspectives

Sessa,

Esposito,

Cocimano

et al. 2024

Applied Sciences

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The term artificial intelligence (AI) was coined in the 1950s and it has successfully made its way into different fields of medicine. Forensic sciences and AI are increasingly intersecting fields that hold tremendous potential for solving complex criminal investigations. Considering the great evolution in the technologies applied to forensic genetics, this literature review aims to explore the existing body of research that investigates the application of AI in the field of forensic genetics. Scopus and Web of Science were searched: after an accurate evaluation, 12 articles were included in the present systematic review. The application of AI in the field of forensic genetics has predominantly focused on two aspects. Firstly, several studies have investigated the use of AI in haplogroup analysis to enhance and expedite the classification process of DNA samples. Secondly, other research groups have utilized AI to analyze short tandem repeat (STR) profiles, thereby minimizing the risk of misinterpretation. While AI has proven to be highly useful in forensic genetics, further improvements are needed before using these applications in real cases. The main challenge lies in the communication gap between forensic experts: as AI continues to advance, the collaboration between forensic sciences and AI presents immense potential for transforming investigative practices, enabling quicker and more precise case resolutions.

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Section: Pereira Et Al 2011 Portugal [46]mentioning

confidence: 99%

“…By comparing an individual's DNA profile to a massive DNA database, AI algorithms can quickly identify potential matches. This process, known as DNA fingerprinting, has been instrumental in solving cold cases and exonerating wrongfully convicted individuals [47,48,50,56].…”

Section: Klosa Et Al 2023 Poland [56]mentioning

confidence: 99%

Artificial Intelligence and Forensic Genetics: Current Applications and Future Perspectives

Sessa,

Esposito,

Cocimano

et al. 2024

Applied Sciences

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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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Identification of unique repeated patterns, location of mutation in DNA finger printing using artificial intelligence technique

Cited by 3 publications

References 9 publications

Artificial Intelligence and Forensic Genetics: Current Applications and Future Perspectives

Artificial Intelligence and Forensic Genetics: Current Applications and Future Perspectives

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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