Credit Card Fraud Detectıon in Retaıl Shopping Using Reinforcement Learning

SaiRamesh, L.; Ashok, E.; Sabena, S.; Ayyasamy, A.

doi:10.1007/978-3-030-41862-5_158

Cited by 2 publications

(3 citation statements)

References 6 publications

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“…Sairamesh et al [6] have examined credit card in online shopping using machine learning algorithms. Carlos et al [7] utilize a technique that includes extracting linkbased and content-based features from private and public datasets and utilizing the Web Topology by exploiting link dependencies between web pages, one limitation is that their accuracy is obtained without regularization methods applied to classifiers.…”

Section: Related Workmentioning

confidence: 99%

“…The output of ݂ ௧ multiplies with the previous cell state, the values between 0 and 1 and closer to zero are dropped. The ܺ ௧ from the embedding layer and ℎ ௧ିଵ is also passed through the input gate which uses the sigmoid and provides output ݅ ௧ and the tanh activation function provides ‫ܥ‬ ௧ as given in the equation ( 5) & (6).…”

Section: Long Short-term Memory (Lstm)mentioning

confidence: 99%

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Classifications of E-MAIL SPAM Using Deep Learning Approaches

Anshumaanmishra

VigneshwaranPandi

2022

Advances in Parallel Computing Algorithms, Tools and Paradigms

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Spamming is an art used to deceive people and, in most cases, send unwanted messages that can be used by cybercriminals to trick victims and get confidential credentials from the victim. Spamming occurs via email, SMS, social networking websites calling the victim’s phone number, etc. Spamming can exist for various reasons, but it can be used for malicious purposes mainly like trying to forge the victim for gathering the personal information, bank details card details, passwords, and other confidential data about the intended user. To overcome the security breach, the spam messages are classified for the understanding of spam has been done using different methods like Machine Learning and Natural Language Processing. We propose a method to identify the email spam messages and also to classify the message using deep learning approaches such as Recurrent Neural Network (RNN), Long-Short Term Memory (LSTM), and Bilateral Long Short-Term Memory (BLSTM). We have used an email spam dataset with over 5000 spam text samples are used to train our deep learning models. The proposed method separated the sentences from the email into words followed by their root words, and each word is assigned an index number before training. We have used regularization and dropout layers in our models to reduce the chances of overfitting. The proposed method is evaluated and compared with other existing models based on the history of the loss curve, Precision-Recall, and accuracy. Based on the results, it is observed that our Bi-LSTM model produced higher accuracy than other existing systems.

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Section: Related Workmentioning

confidence: 99%

Section: Long Short-term Memory (Lstm)mentioning

confidence: 99%

Classifications of E-MAIL SPAM Using Deep Learning Approaches

Anshumaanmishra

VigneshwaranPandi

2022

Advances in Parallel Computing Algorithms, Tools and Paradigms

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“…Aravind G et al [12] gives the knowledge representation model for design an hybrid system in prediction process. As fraud detection in stock market price is identified by filtering the fake reviews given for the firm stock as mentioned in [13]. Also the author form [14] and [15] used the machine learning approach for their application.…”

Section: Related Workmentioning

confidence: 99%

Stock Market Prediction Using Machine Learning Techniques

Reshma¹,

S²,

Sathiyavathi³

et al. 2021

Advances in Parallel Computing Technologies and Applications

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Predicting the future in all the areas using machine learning techniques was the recent research in the current scenario. Stock market is one among them which needs the prediction future market to invest in the new enterprise or to sell their existing shares to get profit. This need the efficient prediction technique which studies the previous exchanges of stock market and gives the future prediction based on that. This article proposed the prediction system of stock market price based on the exchange takes place in previous scenario. The system studies the diversing effect of market price of product in a particular time gap and analyze its future trend whether it’s loss or gain. During the system of thinking about diverse strategies and variables that should be taken into account, we observed out that strategies like random forest, Support vector machine and regression algorithm. Support vector regression is a beneficial and effective gadget gaining knowledge of approach to apprehend sample of time collection dataset. The data collected for the four years duration which was accumulated to get the expecting prices of the share of the firm. It can produce true prediction end result if the fee of essential parameters may be decided properly. It has been located that the guide vector regression version with RBF kernel indicates higher overall performance while in comparison with different models.

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Credit Card Fraud Detectıon in Retaıl Shopping Using Reinforcement Learning

Cited by 2 publications

References 6 publications

Classifications of E-MAIL SPAM Using Deep Learning Approaches

Classifications of E-MAIL SPAM Using Deep Learning Approaches

Stock Market Prediction Using Machine Learning Techniques

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