Analysis of Data Security Prognostic Method Utilizing Cognitive Machine Learning Behavior
Keywords:
Machine Learning, Big Data, Financial Security, Machine Learning, TensorFlowAbstract
Machine Learning and Big Data of today's IT sector. Large volumes of data are reviewed and information extracted using big data storage. Machine learning, on the other hand, refers to a computer's ability to learn and develop without being explicitly taught. Decision trees and neural networks are used in combination with machine learning methods for these reasons. Many sectors have seen amazing development as a result of the dominating mix of Machine Learning and big data. One of these industries is the e-commerce industry. Financial analysts may use predictive analytics to track and exchange critical information about the various economic problems. They automatically retain data on their daily transactions, payments and linked systems, allowing customers to remotely access and manage the financial transactions using the concept of cognitive behaviour of Machine Learning. Along with this we will cover the part of intrusion or any other vulnerabilities. We will use Privacy-preserving techniques using Deep learning models with TensorFlow privacy-preserving method for financial data. we would implement the neural net. Beyond that, we will need to explore various RNNs models to determine appropriate data and context. We will test the trained model and evaluate performance using Accuracy, Precision, recall and F score.
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