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An Enhanced Secure Deep Learning Algorithm for Fraud Detection in Wireless Communication

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In today’s era of technology, especially in the Internet commerce and banking, the transactions done by the Mastercards have been increasing rapidly. The card becomes the highly useable equipment for Internet shopping. Such demanding and inflation rate causes a considerable damage and enhancement in fraud cases also. It is very much necessary to stop the fraud transactions because it impacts on financial conditions over time the anomaly detection is having some important application to detect the fraud detection. A novel framework which integrates Spark with a deep learning approach is proposed in this work. This work also implements different machine learning techniques for detection of fraudulent like random forest, SVM, logistic regression, decision tree, and KNN. Comparative analysis is done by using various parameters. More than 96% accuracy was obtained for both training and testing datasets. The existing system like Cardwatch, web service-based fraud detection, needs labelled data for both genuine and fraudulent transactions. New frauds cannot be found in these existing techniques. The dataset which is used contains transaction made by credit cards in September 2013 by cardholders of Europe. The dataset contains the transactions occurred in 2 days, in which there are 492 fraud transactions out of 284,807 which is 0.172% of all transaction.


Python for Machine Learning

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This book was designed around major building blocks of the Python ecosystem that are useful to machine learning projects. There are a lot of things you could learn about Python, from language mechanics to the various libraries. Our goal is to take you straight to developing an intuition for the elements you can use in Python projects with laser-focused tutorials. We designed the tutorials to focus on how to get things done with Python. They give you the tools to both rapidly understand and apply each technique or operation. Each tutorial is designed to take you about one hour to read through and complete, excluding the extensions and further reading. You can choose to work through the lessons one per day, one per week, or at your own pace. I think momentum is critically important, and this book is intended to be read and used, not to sit idle. I would recommend picking a schedule and sticking to it.


Time series Forecasting: Using a LSTM Neural Network to predict Bitcoin prices

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The cryptocurrency market is an extremely unstable and complex market, due to cryptocurrencies themselves being extremely volatile assets: their value fluctuates immensely in the span of a few hours. As opposed to stocks, cryptocurrencies hold no intrinsic value. The value of a stock is intrinsically correlated with a company's performance & profitability. For example, on the one hand, Amazon ($AMZN) and Netflix ($NFLX) saw their stock prices soar during the pandemic, due to an increase in online shopping and a higher demand for video streaming services. Recently, Netflix dropped significantly because the quarter's objectives were not met, and the platform had lost 200,000 subscribers.


Daily AI Roundup: Biggest Machine Learning, Robotic And Automation Updates

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This is your AI Daily Roundup today. We are covering the top updates from around the world. The updates will feature state-of-the-art capabilities in artificial intelligence (AI), Machine Learning, Robotic Process Automation, Fintech, and human-system interactions. We cover the role of AI Daily Roundup and its application in various industries and daily lives. Software intelligence company Dynatrace announced it has extended its advanced AIOps capabilities for leading database environments, including Oracle and Microsoft SQL.


How AI-Powered tech is transforming the credit risk process

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The global data and intelligence solutions provider, Provenir, is leading the marketplace through its data insights innovation and technologies. The US-based software technology company which supports the international fintech industry, ensures the marketplace is a global data and intelligence ecosystem that makes accessing data fast and easy. Now, Provenir has invited industry professionals to join them in their latest webinar that outline how can AI-powered risk decisioning can play a part in transforming the entire credit risk decisioning process. The session, which is presented by key industry leaders, explores how technology continues to evolve and advances in big data, digital transformation, and AI/ML are creating new opportunities for financial services and fintechs to improve their credit decisioning processes. The webinar panel discussion is being moderated by FinTech Magazine and will provide a spectrum of topics for discussion that outline the importance of using AI/ML to transform credit risk decisioning.


10 startups riding the wave of AI innovation

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We are excited to bring Transform 2022 back in-person July 19 and virtually July 20 - 28. Join AI and data leaders for insightful talks and exciting networking opportunities. Organizations are increasingly adopting AI-enabled technologies to address existing and emerging problems within the enterprise ecosystem, meet changing market demands and deliver business outcomes at scale. Shubhangi Vashisth, senior principal research analyst at Gartner, said that AI innovation is happening at a rapid pace. Vashisth further noted that innovations including edge AI, computer vision, decision intelligence and machine learning will have a transformational impact on the market in coming years. However, while AI-powered technologies are helping to build more agile and effective enterprise systems, they usher in new challenges. For example, Gartner notes that AI-based approaches if left unchecked can perpetuate bias, leading to issues, loss of productivity and revenue.


How Data Science is Reshaping the Future?

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There is no doubt that data science is shaping the future. As businesses become more digitized, the role of data science is becoming more and more important. If you want to stay ahead of the curve, it's important to learn this technology and get into one of the most promising job roles of Data Science. Data science is the process of extracting insights from data. It blends mathematics, statistics, and computer science to analyze large amounts of data and to improve decision making. The aim of data science is to enable organizations to make better decisions about where to allocate their resources, how to optimize their business processes, and how to serve their customers better.


Why we need human-centered AI

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Welcome to AI book reviews, a series of posts that explore the latest literature on artificial intelligence. There are two contrasting but equally disturbing images of artificial intelligence. One warns about a future in which runaway intelligence becomes smarter than humanity, creates mass unemployment, and enslaves humans in a Matrix-like world or destroys them a la Skynet. A more contemporary image is one in which dumb AI algorithms are entrusted with sensitive decisions that can cause severe harm when they do go wrong. What both visions have in common is the absence of human control.


How to Make Artificial Intelligence (AI) and Machine Learning Work for You

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Most data organisations hold is not labeled, and labeled data is the foundation of AI jobs and AI projects. "Labeled data, means marking up or annotating your data for the target model so it can predict. In general, data labeling includes data tagging, annotation, moderation, classification, transcription, and processing." Particular features are highlighted by labeled data and the classification of those attributes maybe be analysed by models for patterns in order to predict the new targets. An example would be labelling images as cancerous and benign or non-cancerous for a set of medical images that a Convolutional Neural Network (CNN) computer vision algorithm may then classify unseen images of the same class of data in the future. Niti Sharma also notes some key points to consider.


Why we need human-centered AI

#artificialintelligence

Welcome to AI book reviews, a series of posts that explore the latest literature on artificial intelligence. There are two contrasting but equally disturbing images of artificial intelligence. One warns about a future in which runaway intelligence becomes smarter than humanity, creates mass unemployment, and enslaves humans in a Matrix-like world or destroys them a la Skynet. A more contemporary image is one in which dumb AI algorithms are entrusted with sensitive decisions that can cause severe harm when they do go wrong. What both visions have in common is the absence of human control.