Abstract: Novel technical ways of analyzing a blockchain system using machine learning are presented, including structures and techniques that can facilitate blockchain address risk assessment via graph analysis. In various embodiments, a system can access a blockchain. The system can build a transaction graph based on analysis of the blockchain. Nodes of the transaction graph can respectively represent blockchain addresses that are recorded in the blockchain. In various cases, edges in the transaction graph can respectively represent blockchain transactions between different ones of the blockchain addresses that are recorded in the blockchain. The system can calculate risk scores respectively corresponding to the blockchain addresses, based on analyzing the transaction graph via at least one machine learning algorithm. These techniques can improve computer operational efficiency by avoiding the execution of unnecessary blockchain transactions.

Abstract: A reference noise cancellation device for a transient electromagnetic field is provided. The device includes a transmitter, a transmitting loop, a reference coil, a signal coil and a receiver. The transmitter generates a measurement timing sequence to trigger the receiver to synchronously record the induction signal in the signal coil and the electromagnetic noise in the reference coil. A bipolar current waveform is generated and is injected into the transmitting loop through the first connecting line. The signal coil receives an induced magnetic field of an underground conductor and surrounding magnetic field noise. The reference coil receives surrounding electromagnetic noise. The receiver receives and records the induced magnetic field and surrounding electromagnetic noise of the underground conductor in the signal coil and the surrounding electromagnetic noise in the reference coil.

Abstract: A method includes receiving a set of training data and selecting a first machine learning platform based on a first optimization function that metrics past machine learning platforms used for training on the set of training data. The method also includes selecting a first algorithm supported by the first machine learning platform based on a second optimization function that metrics past algorithms used for training on the set of training data. Further, the method includes determining one or more hyperparameters supported by the first algorithm based on a third optimization function that metrics past combinations of hyperparameters from the set of hyperparameters used for training on the set of training data. The method also includes training a machine learning model on the set of training data using the first machine learning platform, the first algorithm, and the one or more hyperparameters.

Abstract: A method includes receiving a set of training data and selecting a first machine learning platform based on a first optimization function that metrics past machine learning platforms used for training on the set of training data. The method also includes selecting a first algorithm supported by the first machine learning platform based on a second optimization function that metrics past algorithms used for training on the set of training data. Further, the method includes determining one or more hyperparameters supported by the first algorithm based on a third optimization function that metrics past combinations of hyperparameters from the set of hyperparameters used for training on the set of training data. The method also includes training a machine learning model on the set of training data using the first machine learning platform, the first algorithm, and the one or more hyperparameters.

Abstract: A method includes training a first machine learning model based on a set of training data and based on the training, determining a first performance metric corresponding to the first machine learning model. The method also includes determining one or more past performance metrics corresponding to one or more machine learning models that were previously trained based on the set of training data. Based on the first performance metric and the one or more past performance metrics, the method includes automatically selecting a second machine learning model to train based on the set of training data.

Abstract: Novel technical ways of analyzing a blockchain system using machine learning are presented, including structures and techniques that can facilitate blockchain address risk assessment via graph analysis. In various embodiments, a system can access a blockchain. The system can build a transaction graph based on analysis of the blockchain. Nodes of the transaction graph can respectively represent blockchain addresses that are recorded in the blockchain. In various cases, edges in the transaction graph can respectively represent blockchain transactions between different ones of the blockchain addresses that are recorded in the blockchain. The system can calculate risk scores respectively corresponding to the blockchain addresses, based on analyzing the transaction graph via at least one machine learning algorithm. These techniques can improve computer operational efficiency by avoiding the execution of unnecessary blockchain transactions.

Abstract: A method includes receiving a set of training data and selecting a first machine learning platform based on a first optimization function that metrics past machine learning platforms used for training on the set of training data. The method also includes selecting a first algorithm supported by the first machine learning platform based on a second optimization function that metrics past algorithms used for training on the set of training data. Further, the method includes determining one or more hyperparameters supported by the first algorithm based on a third optimization function that metrics past combinations of hyperparameters from the set of hyperparameters used for training on the set of training data. The method also includes training a machine learning model on the set of training data using the first machine learning platform, the first algorithm, and the one or more hyperparameters.

Abstract: A method includes training a first machine learning model based on a set of training data and based on the training, determining a first performance metric corresponding to the first machine learning model. The method also includes determining one or more past performance metrics corresponding to one or more machine learning models that were previously trained based on the set of training data. Based on the first performance metric and the one or more past performance metrics, the method includes automatically selecting a second machine learning model to train based on the set of training data.

Abstract: Machine learning often uses ensemble classifiers, such as random forest or gradient boosting tree classifiers to solve problems. One issue with such classifiers is that they may be prone to data overfitting. This can cause the classifier to perform relatively worse when dealing with data outside of a training set. One technique to avoiding overfitting is using random dropout on decision trees in the ensemble classifier (e.g. drop three percent of all decision trees to create a final classifier). However, random dropout can be improved upon. Penalty based dropout can assess the performance of individual trees using a validation data set (which may be separate from the training set). Instead of using random dropout, some of the worst performing trees can be dropped instead, leading to better overall performance.