(untitled) (International Journal of Engineering and Computer Science, 2023)
Now a day, as a human living in the society is very difficulty with facing many insecure situations in the society. Addressing targeted social security issues using deep learning techniques is a complex task that requires careful planning and implementation. Deep learning can be a powerful tool to analyze and predict various social security-related problems, such as fraud detection, benefit eligibility determination, and resource allocation. Using deep learning for targeted social security issues is a long-term effort, and it’s essential to continuously update and improve the models to stay ahead of evolving challenges. Additionally, always prioritize data security, privacy, and ethical considerations throughout the process. In this emphasize of the entitle work Fraud detection using deep learning is a valuable application of artificial intelligence and machine learning techniques to identify and prevent fraudulent activities in various domains, such as finance, e-commerce, healthcare, and more. Deep learning models, which are a subset of machine learning, can be particularly effective for fraud detection due to their ability to automatically learn complex patterns and features from data.