This research assumes that you are familiar with class imbalance and the ADASYN algorithm. We strongly encourage our readers to review the conference article that launched ADASYN (just type that into Google Scholar or see the References section of this document), and then read any number of articles in Towards Data Science that discuss class imbalance and ADASYN. Because this is neither a guide nor an overview; it is voyage into uncharted waters with startling discoveries. The answers are 1) surprising, 2) fascinating, and 3) extraordinary, in that order. All models in this research were conducted using the RandomForest and LogisticRegression algorithms in the sci-kit learn library to gain information about both tree and linear structures, respectively. All predictive models were 10-fold cross-validated with stratified sampling using "stratify y" in train_test_split and "cv 10" in GridSearchCV.

Nonlinear wave interactions, such as shock refraction at an inclined density interface, in magnetohydrodynamic (MHD) lead to a plethora of wave patterns with myriad wave types. Identification of different types of MHD waves is an important and challenging task in such complex wave patterns. Moreover, owing to the multiplicity of solutions and their admissibility for different systems, especially for intermediate-type MHD shock waves, the identification of MHD wave types is complicated if one solely relies on the Rankine-Hugoniot jump conditions. MHD wave detection is further exacerbated by the unphysical smearing of discontinuous shock waves in numerical simulations. We present two MHD wave detection methods based on a convolutional neural network (CNN) which enables the classification of waves and identification of their locations. The first method separates the output into a regression (location prediction) and a classification problem assuming the number of waves for each training data is fixed. In the second method, the number of waves is not specified a priori and the algorithm, using only regression, predicts the waves' locations and classifies their types. The first fixed output model efficiently provides high precision and recall, the accuracy of the entire neural network achieved is up to 0.99, and the classification accuracy of some waves approaches unity. The second detection model has relatively lower performance, with more sensitivity to the setting of parameters, such as the number of grid cells N_{grid} and the thresholds of confidence score and class probability, etc. The proposed two methods demonstrate very strong potential to be applied for MHD wave detection in some complex wave structures and interactions.

In a worldwide health crisis as exigent as COVID-19, there has become a pressing need for rapid, reliable diagnostics. Currently, popular testing methods such as reverse transcription polymerase chain reaction (RT-PCR) can have high false negative rates. Consequently, COVID-19 patients are not accurately identified nor treated quickly enough to prevent transmission of the virus. However, the recent rise of medical CT data has presented promising avenues, since CT manifestations contain key characteristics indicative of COVID-19. This study aimed to take a novel approach in the machine learning-based detection of COVID-19 from chest CT scans. First, the dataset utilized in this study was derived from three major sources, comprising a total of 17,698 chest CT slices across 923 patient cases. Image preprocessing algorithms were then developed to reduce noise by excluding irrelevant features. Transfer learning was also implemented with the EfficientNetB7 pre-trained model to provide a backbone architecture and save computational resources. Lastly, several explainability techniques were leveraged to qualitatively validate model performance by localizing infected regions and highlighting fine-grained pixel details. The proposed model attained an overall accuracy of 0.927 and a sensitivity of 0.958. Explainability measures showed that the model correctly distinguished between relevant, critical features pertaining to COVID-19 chest CT images and normal controls. Deep learning frameworks provide efficient, human-interpretable COVID-19 diagnostics that could complement radiologist decisions or serve as an alternative screening tool. Future endeavors may provide insight into infection severity, patient risk stratification, and prognosis.

A confusion matrix is a supervised machine learning evaluation tool that provides more insight into the overall effectiveness of a machine learning classifier. Unlike a simple accuracy metric, which is calculated by dividing the number of correctly predicted records by the total number of records, confusion matrices return 4 unique metrics for you to work with. While I am not saying accuracy is always misleading, there are times, especially when working with examples of imbalanced data, that accuracy can be all but useless. Let's consider credit card fraud. It is not uncommon that given a list of credit card transactions, that a fraud event might make up a little as 1 in 10,000 records.

The identification of out-of-distribution content is critical to the successful implementation of neural networks. Watchdog techniques have been developed to support the detection of these inputs, but the performance can be limited by the amount of available data. Generative adversarial networks have displayed numerous capabilities, including the ability to generate facsimiles with excellent accuracy. This paper presents and empirically evaluates a multi-tiered watchdog, which is developed using GAN generated data, for improved out-of-distribution detection. The cascade watchdog uses adversarial training to increase the amount of available data similar to the out-of-distribution elements that are more difficult to detect. Then, a specialized second guard is added in sequential order. The results show a solid and significant improvement on the detection of the most challenging out-of-distribution inputs while preserving an extremely low false positive rate.

The following discusses using cross-validation to evaluate the classifier we built in the previous post, which classifies images from the MNIST dataset as either five or not five. Let's take a brief look at the problem that cross-validation solves. When building a model, we risk overfitting the model on the test set when evaluating different hyperparameters. This is because we can tweak the hyperparameters until the model performs optimally. In overfitting, knowledge about the test set "leaks" into the model, and evaluation metrics no longer report on generalization.

Machine learning methods were applied to reconsider the results of several passive seismic experiments in Finland. We created datasets from different stages of the receiver function technique and processed them with one of basic machine learning algorithms. All the results were obtained uniformly with the $k$-nearest neighbors algorithm. The first result is the Moho depth map of the region. Another result is the delineation of the near-surface low $S$-wave velocity layer. There are three such areas in the Northern, Southern, and central parts of the region. The low $S$-wave velocity in the Northern and Southern areas can be linked to the geological structure. However, we attribute the central low $S$-wave velocity area to a large number of water-saturated cracks in the upper 1-5 km. Analysis of the structure of this area leads us to the conclusion that macrofracturing was caused by the last deglaciation.

If the question'What is sentiment analysis?' popped up in your mind as you clicked on this blog, I think you will find my first blog in this series interesting. Essentially, sentiment analysis is a natural language processing technique used to determine the emotional tone of textual data. It is primarily used to understand customer satisfaction, and gauge brand reputation, call center interactions as well as customer feedback and messages. There are various types of sentiment analysis that are common in the real world. In this part of my blog series, let me walk you through the implementation of sentiment analysis.

Laird Harrison writes about science, health and culture. His work has appeared in national magazines, in newspapers, on public radio and on websites. He is at work on a novel about alternate realities in physics. Harrison teaches writing at the Writers Grotto.

In 1945 the volume of human knowledge doubled every 25 years. Now, that number is 12 hours [1]. With our collective computational power rapidly increasing, vast amounts of data and our ability to assimilate it, has seeded unprecedented fertile ground for innovation. Healthtech companies are rapidly sprouting from data ridden soil at exponential rates. Cell free DNA companies, once a rarity, are becoming ubiquitous. The genomics landscape, once dominated by the few, are being inundated by a slew of competitors. Grandiose claims of being able to diagnose 50 different cancers from a single blood sample, or use AI to best dermatologists, radiologists, pathologists, etc., are being made at alarming rates. Accordingly, it's imperative to know how to assess these claims as fact or fiction, particularly when such claimants may employ "statistical misdirection". In this addition to "The Insider's Guide to Translational Medicine" we disarm perpetrators of statistical warfare of their greatest ...