Cyber security comes with a combination of various security policies, AI techniques, network technologies that work together to protect various computing resources like computing networks, intelligent programs, and sensitive data from attacks. Nowadays, the shift to digital freedom had led to opened many new challenges for financial services. Cybercriminals have found the ability to leverage e- currency exchanges and other financial transactions to perform their fraudulent activities. The unregulated channel makes it essential for banks and financial institutions to deploy advanced AI & ML (DL) techniques to fight cybercrime. This can be implemented by deploying AI & ML (DL) techniques. Customers are experiencing an increase in the fraud-hit rate in financial banking operations. It is difficult to defend against dynamic cyber-attacks using conventional non- dynamic algorithms. Therefore, AI with machine learning techniques has been set up with cyber security to build intelligent models for malware categorization & intelligently sensing the fraught with danger. This paper introduces the cyber security defense mechanism by using artificial intelligence (AI), machine learning (ML)) techniques with the current Feedzai security model to identifying fraudulent banking transaction. We have given a preface to the popular ML & AI model with random forest algorithm and Feedzai’s Open ML fraud detection software tool, which provides automatic fraud-recognition to the current intelligent framework for solving Financial Fraud Detection.

This paper aims at combining object detection at real time and recognition with suitable deep learning methods in order to detect and recognize objects position as well as the names of multiple objects detected by the camera using an object detector algorithm. This is to aid the visually impaired user without the help of any other person. The image and video processing algorithms were designed to take real-time inputs from the camera, Deep Neural Networks were used to predict the objects and uses Google’s famous Text-To-Speech (GTTS) API module for the anticipated voice output precisely detecting and recognizing the category or class of objects and locations contained. Our best result shows that the system recognizes 91 categories of outdoor objects and produces the output in speech i.e. in an audio format even when a reduced amount of spectral information from the data is available.

No abstract

No abstract

No abstract