A simple machine learning based framework for processing the inline inspection data of subsea pipelines

This paper presents a simple machine learning based framework for diagnosing the inline inspection data (ILI) of subsea pipelines. ILI data are obtained by intelligent pigging devices operating along subsea pipelines. The wall thickness (WT) and standoff distance (SO) are collected by the sensors installed on the pigging, which are normally in the format of 2D arrays. There are many uncertainties for the ILI data collected from the offshore survey. An attempt was made to apply the machine learning method to diagnose the uncertainties. A convolutional neural network (CNN) is used, the ILI data are discretized and processed in 64x64 grid size. Fabricated training datasets were made for training the machine learning model since the ground truth information (actual corroded wall thickness) is hardly known in this case. The trained model was successfully. It is demonstrated that certain corrosion patterns have been recognized by the trained model. Comparisons were performed between the new method and traditional methods with case studies on real ILI data. The validity of the methodology was discussed.

Random forest model identifies serve strength as a key predictor of tennis match outcome

Tennis is a popular sport worldwide, boasting millions of fans and numerous national and international tournaments. Like many sports, tennis has benefitted from the popularity of rigorous record-keeping of game and player information, as well as the growth of machine learning methods for use in sports analytics. Of particular interest to bettors and betting companies alike is potential use of sports records to predict tennis match outcomes prior to match start. We compiled, cleaned, and used the largest database of tennis match information to date to predict match outcome using fairly simple machine learning methods. Using such methods allows for rapid fit and prediction times to readily incorporate new data and make real-time predictions. We were able to predict match outcomes with upwards of 80%accuracy, much greater than predictions using betting odds alone, and identify serve strength as a key predictor of match outcome. By combining prediction accuracies from three models, we were able to nearly recreate a probability distribution based on average betting odds from betting companies, which indicates that betting companies are using similar information to assign odds to matches. These results demonstrate the capability of relatively simple machine learning models to quite accurately predict tennis match outcomes.

Android Image Classifier

Image classification is the task of identifying an image. Android image classification model is trained to recognize various classes of images. For example, we may train a model to recognize photos representing three different types of animals: rabbits, hamsters, and dogs. Optimized pre-trained models are provided byTensor Flow Lite that we can deploy in our mobile applications. Simple Machine Learning (ML) algorithms in Python make relatively easy to start explore datasets and make some first predictions. We can make these trained models useful in the real world by making them available to make predictions on either the Web or Portable devices.

Navigating Bitcoin Panic-Selling using Linear Approach

COVID-19 affects significant human activity around the globe, including Bitcoin prices. The Bitcoin price is well known for its volatility, so it is not a big shocker when the panic-selling occurs during the pandemic. However, the mechanism to cope with these breakouts, especially the bearish one, is contentious. The experts give numerous pieces of advice with different conclusions in the end. It is also the same with Machine Learning. Various kernels show different results regarding how the price will move. It depends on the window size, how the data is being preprocessed, and the algorithm used. This paper inspects the best combination that various machine learning can offer with a linear approach to navigate the price prediction based on its depth interval, window size until the algorithms themselves. This paper also proposed a new approach to seeing the prediction range called s-steps ahead prediction using a linear model. The result shows that simple machine learning can herd 99.715% profit even during the bearish breakout.