Computer Science- Unaided

  Boosting Secure Digital Payments with ML Fraud Detection Introduction The rapid proliferation of digital payments in recent years has led to increased fraudulent activities. This is a serious concern for consumers, financial institutions, fintech enterprises, and even the government. Many organizations are working to identify various methods, including machine learning (ML), to detect fraudulent activities. Businesses are discovering that the effective use of ML algorithms combined with traditional digital payment methods can improve fraud detection. Instant payment adoption is on the rise by many industry leaders, including those in gig work like DoorDash and Instacart. Banks are incorporating new real-time transaction and payment protocols. In addition, mobile payments are expanding, with mobile wallets and contactless payments—such as Apple Pay and Google Pay—becoming ubiquitous, allowing secure one-tap payments via biometric authentication, like fingerprint scanning a...

                                                               DIGITAL IMAGE PROCESSING Digital Image Processing means processing digital image by means of a digital computer. We can also say that it is a use of computer algorithms, in order to get enhanced image either to extract some useful information.   Digital image processing is the use of algorithms and mathematical models to process and analyze digital images. The goal of digital image processing is to enhance the quality of images, extract meaningful information from images, and automate image-based tasks. The basic steps involved in digital image processing are: Image acquisition: This involves capturing an image using a digital camera or scanner, or importing an existing image into a computer. Image enhancement: This involves improving the visual quality of a...