Innovative Featurization and Modeling for Solar Flare Prediction

Solar flares and the associated coronal mass ejections are spontaneous high energy bursts ofplasma from the Sun. Since the interaction of this high energy plasma with the earth&rsquo;s atmosphere can significantly impact human activity (radiation hazards, economic loss from infrastructure damage, global navigation satellite system interference, etc.), accurately forecasting solar flares is of vital importance. These flares have been known to be associated with sunspots that have complex magnetic field structures called active regions. In recent years, the use of deep learning-based methods for solar flare prediction has gained significant traction. Much of this work is classification based &mdash; every input magnetogram image is labeled as flaring or non-flaring based on whether a solar flare of a certain magnitude or higher occurred in the vicinity of the active region in the next k hours. Since solar eruptions are rare events, a highly imbalanced dataset is produced under such a labeling scheme, that poses problems for designing and evaluating machine learning models. Typically, images of photospheric magnetic field observations (called magnetograms) of active regions are either used as input data for convolutional neural network methods which automatically extract features, or are pre-processed to extract physics-based features that are used to train machine learning models such as support vector machines (SVM), multi-layer perceptrons (MLP), extremely randomized trees (ERT), etc. In this thesis, I explore novel directions in both approaches &mdash; feature engineering-based methods for training traditional machine learning models (e.g. MLP) and CNN-based deep learning prediction methods. First, I propose a new method to extract features from magnetograms using topological data analysis as a substitute for the standard physics-based features used predominantly by existing feature-based models. Through rigorous machine learning methodologies such as hyperparameter tuning, I show that these abstract shape-based features extracted do just as well as the carefully designed physics-based features widely used in this field. Second, as part of the same study, I show that increasing the complexity of the machine learning model does not guarantee an increase in the prediction performance &mdash; a simple logistic regression model in fact performs slightly better than the complex long short-term memory (LSTM) model. I also study the model complexity further through dimensionality reduction using principal component analysis (PCA) and assess the effects upon model performance. Lastly, I propose a hybrid ML architecture that combines the forecasting power of a feature-based model (ERT) with that of an image-based prediction model (CNN), and show that this architecture reduces false positives in prediction &mdash; an important issue in this highly data-imbalanced forecasting problem.</p

Shape-based Feature Engineering for Solar Flare Prediction

Solar flares are caused by magnetic eruptions in active regions (ARs) on the surface of the sun. These events can have significant impacts on human activity, many of which can be mitigated with enough advance warning from good forecasts. To date, machine learning-based flare-prediction methods have employed physics-based attributes of the AR images as features; more recently, there has been some work that uses features deduced automatically by deep learning methods (such as convolutional neural networks). We describe a suite of novel shape-based features extracted from magnetogram images of the Sun using the tools of computational topology and computational geometry. We evaluate these features in the context of a multi-layer perceptron (MLP) neural network and compare their performance against the traditional physics-based attributes. We show that these abstract shape-based features outperform the features chosen by the human experts, and that a combination of the two feature sets improves the forecasting capability even further.Comment: To be published in Proceedings for Innovative Applications of Artificial Intelligence Conference 202

Using scaling-region distributions to select embedding parameters

Reconstructing state-space dynamics from scalar data using time-delay embedding requires choosing values for the delay $\tau$ and the dimension $m$. Both parameters are critical to the success of the procedure and neither is easy to formally validate. While embedding theorems do offer formal guidance for these choices, in practice one has to resort to heuristics, such as the average mutual information (AMI) method of Fraser & Swinney for $\tau$ or the false near neighbor (FNN) method of Kennel et al. for $m$. Best practice suggests an iterative approach: one of these heuristics is used to make a good first guess for the corresponding free parameter and then an "asymptotic invariant" approach is then used to firm up its value by, e.g., computing the correlation dimension or Lyapunov exponent for a range of values and looking for convergence. This process can be subjective, as these computations often involve finding, and fitting a line to, a scaling region in a plot: a process that is generally done by eye and is not immune to confirmation bias. Moreover, most of these heuristics do not provide confidence intervals, making it difficult to say what "convergence" is. Here, we propose an approach that automates the first step, removing the subjectivity, and formalizes the second, offering a statistical test for convergence. Our approach rests upon a recently developed method for automated scaling-region selection that includes confidence intervals on the results. We demonstrate this methodology by selecting values for the embedding dimension for several real and simulated dynamical systems. We compare these results to those produced by FNN and validate them against known results -- e.g., of the correlation dimension -- where these are available. We note that this method extends to any free parameter in the theory or practice of delay reconstruction

Towards automated extraction and characterization of scaling regions in dynamical systems

Scaling regions&mdash;intervals on a graph where the dependent variable depends linearly on the independent variable&mdash;abound in dynamical systems, notably in calculations of invariants like the correlation dimension or a Lyapunov exponent. In these applications, scaling regions are generally selected by hand, a process that is subjective and often challenging due to noise, algorithmic effects, and confirmation bias. In this paper, we propose an automated technique for extracting and characterizing such regions. Starting with a two-dimensional plot&mdash;e.g., the values of the correlation integral, calculated using the Grassberger&ndash;Procaccia algorithm over a range of scales&mdash;we create an ensemble of intervals by considering all possible combinations of end points, generating a distribution of slopes from least squares fits weighted by the length of the fitting line and the inverse square of the fit error. The mode of this distribution gives an estimate of the slope of the scaling region (if it exists). The end points of the intervals that correspond to the mode provide an estimate for the extent of that region. When there is no scaling region, the distributions will be wide and the resulting error estimates for the slope will be large. We demonstrate this method for computations of dimension and Lyapunov exponent for several dynamical systems and show that it can be useful in selecting values for the parameters in time-delay reconstructions. &nbsp;</p

Revolutionizing Healthcare: The Role of AI-Based Medical Expert Systems in Building a Better Future

Modern society has an increasing need for better architecture and medical care. However, this difficulty is not sufficiently addressed by present medical architecture. The Medicinal Expert technique can be used to help persons in need in order to address this issue. A tremendous amount of medical data, including patient medical histories, records, and new medications, can be managed and maintained using this technology. It can help with decision-making and fill in for specialists when they are not present. The Medicinal Expert approach is a complex computer software system that generates forecasts using empirical data and expert knowledge. Based on the available training data and knowledge base, these systems function intelligently. Additionally, there are numerous Medical Expert System tools that support clinicians, help with diagnosis, and are crucial for instructing medical students. In this study, we introduce an AI-based Medical Expert System, its features, and its potential to help patients and medical students. We also go through some key findings from recent and prior research on expert systems, as well as how these systems can make the world a better place

Simulating a motor-free area in Pune, India using A/B Street

This project aims to utilize open-source data and open-source tools like A/B Street to simulate a motor-free area in Pune, India, also reviewing implementations around the world and policy recommendations

Leveraging the mathematics of shape for solar magnetic eruption prediction

Current operational forecasts of solar eruptions are made by human experts using a combination of qualitative shape-based classification systems and historical data about flaring frequencies. In the past decade, there has been a great deal of interest in crafting machine-learning (ML) flare-prediction methods to extract underlying patterns from a training set – e.g. a set of solar magnetogram images, each characterized by features derived from the magnetic field and labeled as to whether it was an eruption precursor. These patterns, captured by various methods (neural nets, support vector machines, etc.), can then be used to classify new images. A major challenge with any ML method is the featurization of the data: pre-processing the raw images to extract higher-level properties, such as characteristics of the magnetic field, that can streamline the training and use of these methods. It is key to choose features that are informative, from the standpoint of the task at hand. To date, the majority of ML-based solar eruption methods have used physics-based magnetic and electric field features such as the total unsigned magnetic flux, the gradients of the fields, the vertical current density, etc. In this paper, we extend the relevant feature set to include characteristics of the magnetic field that are based purely on the geometry and topology of 2D magnetogram images and show that this improves the prediction accuracy of a neural-net based flare-prediction method