Nowadays, the craze of sports is increasing lavishly. In the same way, the risk of attempting to play sports in an imprecise manner is also increasing. A small injury may lead to a bad impact. Keeping in mind such consequences, here is a proposed solution in order to analyze joint movements of a sportsperson which will help the athlete to improve the pose.
Human pose estimation is an important problem in the field of Computer Vision. Its algorithms help to locate the parts such as wrists, ankles, knees etc. This is done in order to provide…
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Dance on Human Pose Estimation Using Artificial Intelligence with Complete Tutorial & Source Code Download Free.
A Human Pose Skeleton speaks to the direction of an individual in a graphical organization. Basically, it is a bunch of directions that can be associated with depict the posture of the individual. Every co-ordinate within the skeleton is understood as a neighborhood or a joint, or a keypoint. A substantial association between two sections is known as a couple.
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Real Time Object Detection in Python And OpenCV
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Anomaly and fraud detection is a multi-billion-dollar industry. According to a Nilson Report, the amount of global credit card fraud alone was USD 7.6 billion in 2010. In the UK fraudulent credit card transaction losses were estimated at more than USD 1 billion in 2018. To counter these kinds of financial losses a huge amount of resources are employed to identify frauds and anomalies in every single industry.
In data science, “Outlier”, “Anomaly” and “Fraud” are often synonymously used, but there are subtle differences. An “outliers’ generally refers to a data point that somehow stands out from the rest of the crowd. However, when this outlier is completely unexpected and unexplained, it becomes an anomaly. That is to say, all anomalies are outliers but not necessarily all outliers are anomalies. In this article, however, I am using these terms interchangeably.
There are numerous reasons why understanding and detecting outliers are important. As a data scientist when we make data preparation we take great care in understanding if there is any data point unexplained, which may have entered erroneously. Sometimes we filter completely legitimate outlier data points and remove them to ensure greater model performance.
There is also a huge industrial application of anomaly detection. Credit card fraud detection is the most cited one but in numerous other cases anomaly detection is an essential part of doing business such as detecting network intrusion, identifying instrument failure, detecting tumor cells etc.
A range of tools and techniques are used to detect outliers and anomalies, from simple statistical techniques to complex machine learning algorithms, depending on the complexity of data and sophistication needed. The purpose of this article is to summarise some simple yet powerful statistical techniques that can be readily used for initial screening of outliers. While complex algorithms can be inevitable to use, sometimes simple techniques are more than enough to serve the purpose.
Below is a primer on five statistical techniques.
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Today’s article is my 5th in a series of “bite-size” article I am writing on different techniques used for anomaly detection. If you are interested, the following are the previous four articles:
Today I am going beyond statistical techniques and stepping into machine learning algorithms for anomaly detection.
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According to a recent report financial losses due to fraudulent transactions have reached about $17 billion USD, with as many as 5% of consumers experiencing fraud incidents of some kind.
In light of such a big volume of financial losses, every industry is taking fraud detection seriously. It’s not just the financial industries that are susceptible, anomalies are prevalent in every single industry and can take many different forms — such as network intrusion, disturbances in business performances and abrupt changes in KPIs etc.
Fraud/anomaly/outlier detection has long been the subject of intense research in data science. In the ever-changing landscape of fraud detection, new tools and techniques are being tested and employed every day to screen out abnormalities. In this series of articles, so far I’ve discussed six different techniques for fraud detection:
Today I’m going to introduce another technique called DBSCAN — short for Density-Based Spatial Clustering of Applications with Noise.
As the name suggests, DBSCAN is a density-based and unsupervised machine learning algorithm. It takes multi-dimensional data as inputs and clusters them according to the model parameters — e.g. epsilon and minimum samples. Based on these parameters, the algorithm determines whether certain values in the dataset are outliers or not.
Below is a simple demonstration in Python programming language.
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