How Artificial Intelligence and machine learning research impacts payment card fraud detection: A survey and industry benchmark

2018 ◽  
Vol 76 ◽  
pp. 130-157 ◽  
Author(s):  
Nick F. Ryman-Tubb ◽  
Paul Krause ◽  
Wolfgang Garn
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Fraud Detection ◽  
Payment Card ◽  
Learning Research ◽  
Research Impacts

scholarly journals Machine Learning and Artificial Intelligence in Online Fake Transaction Alerting

2015 ◽  
Vol 3 (2) ◽  
pp. 115-126 ◽  
Author(s):  
Naresh Babu Bynagari
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Fraud Detection ◽  
Learning Ability ◽  
The Core ◽  
The Past ◽  
Music Selection ◽  
Intelligent Analysis ◽  
Set Up ◽  
Detection Mechanisms

Artificial Intelligence (AI) is one of the most promising and intriguing innovations of modernity. Its potential is virtually unlimited, from smart music selection in personal gadgets to intelligent analysis of big data and real-time fraud detection and aversion. At the core of the AI philosophy lies an assumption that once a computer system is provided with enough data, it can learn based on that input. The more data is provided, the more sophisticated its learning ability becomes. This feature has acquired the name "machine learning" (ML). The opportunities explored with ML are plentiful today, and one of them is an ability to set up an evolving security system learning from the past cyber-fraud experiences and developing more rigorous fraud detection mechanisms. Read on to learn more about ML, the types and magnitude of fraud evidenced in modern banking, e-commerce, and healthcare, and how ML has become an innovative, timely, and efficient fraud prevention technology.

scholarly journals Energy and Policy Considerations for Modern Deep Learning Research

2020 ◽  
Vol 34 (09) ◽  
pp. 13693-13696
Author(s):  
Emma Strubell ◽  
Ananya Ganesh ◽  
Andrew McCallum
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Network Models ◽  
Intelligence Community ◽  
Environmental Costs ◽  
Neural Network Models ◽  
Learning Research ◽  
Specialized Hardware ◽  
Compute Time

The field of artificial intelligence has experienced a dramatic methodological shift towards large neural networks trained on plentiful data. This shift has been fueled by recent advances in hardware and techniques enabling remarkable levels of computation, resulting in impressive advances in AI across many applications. However, the massive computation required to obtain these exciting results is costly both financially, due to the price of specialized hardware and electricity or cloud compute time, and to the environment, as a result of non-renewable energy used to fuel modern tensor processing hardware. In a paper published this year at ACL, we brought this issue to the attention of NLP researchers by quantifying the approximate financial and environmental costs of training and tuning neural network models for NLP (Strubell, Ganesh, and McCallum 2019). In this extended abstract, we briefly summarize our findings in NLP, incorporating updated estimates and broader information from recent related publications, and provide actionable recommendations to reduce costs and improve equity in the machine learning and artificial intelligence community.

Analysis of Machine Learning Research and Application

2010 ◽  
Vol 171-172 ◽  
pp. 740-743
Author(s):  
Xian Min Wei
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Data Mining ◽  
Computer Technology ◽  
Text Categorization ◽  
Network Intrusion Detection ◽  
Network Intrusion ◽  
Learning Research ◽  
Core Part ◽  
Important Branch

The artificial intelligence is an important branch of computer science, in recent years with the development of computer technology, artificial intelligence has also been in good development. Machine learning is a core part of artificial intelligence, machine learning background, research status, and applications in network intrusion detection, text categorization and data mining were studied in this paper.

Deep Clustering

2019 ◽  
pp. 164-179
Author(s):  
M. Parimala Boobalan
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Image Segmentation ◽  
Deep Learning ◽  
Financial Analysis ◽  
Data Representation ◽  
Deep Network ◽  
Two Factors ◽  
Learning Research

Clustering is an unsupervised technique used in various application, namely machine learning, image segmentation, social network analysis, health analytics, and financial analysis. It is a task of grouping similar objects together and dissimilar objects in different group. The quality of the cluster relies on two factors: distance metrics and data representation. Deep learning is a new field of machine learning research that has been introduced to move machine learning closer to artificial intelligence. Learning using deep network provides multiple layers of representation that helps to understand images, sound, and text. In this chapter, the need for deep network in clustering, various architecture, and algorithms for unsupervised learning is discussed.

scholarly journals Thesis on Machine Learning Methods and Its Applications

2021 ◽  
Vol 9 (10) ◽  
pp. 746-757
Author(s):  
Dharmapriya M S
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Machine Learning Methods ◽  
Processing Data ◽  
History Of ◽  
Learning Research ◽  
Learning Machine

Abstract: In the 1950s, the concept of machine learning was discovered and developed as a subfield of artificial intelligence. However, there were no significant developments or research on it until this decade. Typically, this field of study has developed and expanded since the 1990s. It is a field that will continue to develop in the future due to the difficulty of analysing and processing data as the number of records and documents increases. Due to the increasing data, machine learning focuses on finding the best model for the new data that takes into account all the previous data. Therefore, machine learning research will continue in correlation with this increasing data. This research focuses on the history of machine learning, the methods of machine learning, its applications, and the research that has been conducted on this topic. Our study aims to give researchers a deeper understanding of machine learning, an area of research that is becoming much more popular today, and its applications. Keywords: Machine Learning, Machine Learning Algorithms, Artificial Intelligence, Big Data.

scholarly journals Donkey Kong's Legacy

2021 ◽  
Vol 26 ◽  
pp. 71-84
Author(s):  
Johannes Bruder
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Computational Neuroscience ◽  
Model Organism ◽  
Machine Learning Algorithms ◽  
Human Cognition ◽  
Deep Dive ◽  
Learning Research ◽  
Test Beds ◽  
And Behavior

The article discusses forms of contamination between human and artificial intelligence in computational neuroscience and machine learning research. I begin with a deep dive into an experiment with the legacy microprocessor MOS 6502, conducted by two engineers working in computational neuroscience, to explain why and how machine learning algorithms are increasingly employed to simulate human cognition and behavior. Through the strategic use of the microprocessor as “model organism” and references to biological and psychological lab research, the authors draw attention to speculative research in machine learning, where arcade video games designed in the 1980s provide test beds for artificial intelligences under development. I elaborate on the politics of these test beds and suggest alternative avenues for machine learning research to avoid that artificial intelligence merely reproduces settler-colonialist politics in silico.

Machine Learning in Agriculture

2018 ◽  
Vol 8 (6) ◽  
pp. 26 ◽  
Author(s):  
Matthew N. O. Sadiku ◽  
Chandra M. M Kotteti ◽  
Sarhan M. Musa
Keyword(s):  
Climate Change ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Disease Diagnosis ◽  
Automated Detection ◽  
Disease Prediction ◽  
Paper Briefly ◽  
Agriculture Sector ◽  
Modern Agriculture ◽  
Crop Disease

Machine learning is an emerging field of artificial intelligence which can be applied to the agriculture sector. It refers to the automated detection of meaningful patterns in a given data.  Modern agriculture seeks ways to conserve water, use nutrients and energy more efficiently, and adapt to climate change.  Machine learning in agriculture allows for more accurate disease diagnosis and crop disease prediction. This paper briefly introduces what machine learning can do in the agriculture sector.

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