scholarly journals Machine Learning Applications to Kronian Magnetospheric Reconnection Classification

2020 ◽  
Author(s):  
Tadhg Garton ◽  
Caitriona Jackman ◽  
Andrew Smith ◽  
Kiley Yeakel ◽  
Shane Maloney
Keyword(s):  
Machine Learning ◽  
Magnetic Reconnection ◽  
Large Time ◽  
Skill Score ◽  
Machine Learning Applications ◽  
Heidke Skill Score ◽  
Learning Machine ◽  
High Level ◽  
Automated Methods ◽  
The Magnetic Field

<p>Signatures of magnetic reconnection in Saturn's magnetotail are identified in magnetometer observations by characteristic deviations in the northward component of the magnetic field. These magnetic deflections are caused by travelling plasma structures created by reconnection rapidly passing over the observing spacecraft. The identification of these reconnection signatures has long been performed by eye, and more recently through semi-automated methods, however these methods are often limited through a required human verification step. Here, we present a fully automated, supervised learning machine learning (ML) model to identify evidence of reconnection in Cassini MAG (magnetometer) observations of the Kronian magnetosphere, constructed using the Smith et al, 2016. reconnection catalogue which contains hundreds of examples of plasmoids, travelling compression regions and dipolarizations. This ML model is capable of rapidly identifying reconnection events in large time-span Cassini datasets, tested against the full year of 2010 with a high level of accuracy (99\%), true skill score (0.97), and Heidke skill score (0.85). From this ML model, a full cataloguing and examination of magnetic reconnection in the Kronian magnetosphere across Cassini's near Saturn lifetime is now possible.</p>

scholarly journals Machine Learning Applications to Kronian Magnetospheric Reconnection Classification

2021 ◽  
Vol 7 ◽  
Author(s):  
Tadhg M. Garton ◽  
Caitriona M. Jackman ◽  
Andrew W. Smith ◽  
Kiley L. Yeakel ◽  
Shane A. Maloney ◽  
...  
Keyword(s):  
Neural Network ◽  
Magnetic Field ◽  
Magnetic Reconnection ◽  
Network Model ◽  
Neural Network Model ◽  
Skill Score ◽  
Feed Forward Neural Network ◽  
The North ◽  
Machine Learning Applications ◽  
High Level

The products of magnetic reconnection in Saturn’s magnetotail are identified in magnetometer observations primarily through characteristic deviations in the north–south component of the magnetic field. These magnetic deflections are caused by traveling plasma structures created during reconnection rapidly passing over the observing spacecraft. Identification of these signatures have long been performed by eye, and more recently through semi-automated methods, however these methods are often limited through a required human verification step. Here, we present a fully automated, supervised learning, feed forward neural network model to identify evidence of reconnection in the Kronian magnetosphere with the three magnetic field components observed by the Cassini spacecraft in Kronocentric radial–theta–phi coordinates as input. This model is constructed from a catalog of reconnection events which covers three years of observations with a total of 2093 classified events, categorized into plasmoids, traveling compression regions and dipolarizations. This neural network model is capable of rapidly identifying reconnection events in large time-span Cassini datasets, tested against the full year 2010 with a high level of accuracy (87%), true skill score (0.76), and Heidke skill score (0.73). From this model, a full cataloging and examination of magnetic reconnection events in the Kronian magnetosphere across Cassini's near Saturn lifetime is now possible.

Down the deep rabbit hole: Untangling deep learning from machine learning and artificial intelligence

First Monday ◽  
2019 ◽  
Author(s):  
Niel Chah
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Historical Context ◽  
Historical Background ◽  
Deep Roots ◽  
Technical Guide ◽  
Learning Machine ◽  
High Level

Interest in deep learning, machine learning, and artificial intelligence from industry and the general public has reached a fever pitch recently. However, these terms are frequently misused, confused, and conflated. This paper serves as a non-technical guide for those interested in a high-level understanding of these increasingly influential notions by exploring briefly the historical context of deep learning, its public presence, and growing concerns over the limitations of these techniques. As a first step, artificial intelligence and machine learning are defined. Next, an overview of the historical background of deep learning reveals its wide scope and deep roots. A case study of a major deep learning implementation is presented in order to analyze public perceptions shaped by companies focused on technology. Finally, a review of deep learning limitations illustrates systemic vulnerabilities and a growing sense of concern over these systems.

scholarly journals Deep Learning to Detect Skin Cancer using Google Colab

2019 ◽  
Vol 8 (6) ◽  
pp. 2176-2183 ◽  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Skin Cancer ◽  
Domain Knowledge ◽  
Error Rates ◽  
Data Set ◽  
Training Models ◽  
Learning Machine ◽  
High Level

Different mathematical models, Artificial Intelligence approach and Past recorded data set is combined to formulate Machine Learning. Machine Learning uses different learning algorithms for different types of data and has been classified into three types. The advantage of this learning is that it uses Artificial Neural Network and based on the error rates, it adjusts the weights to improve itself in further epochs. But, Machine Learning works well only when the features are defined accurately. Deciding which feature to select needs good domain knowledge which makes Machine Learning developer dependable. The lack of domain knowledge affects the performance. This dependency inspired the invention of Deep Learning. Deep Learning can detect features through self-training models and is able to give better results compared to using Artificial Intelligence or Machine Learning. It uses different functions like ReLU, Gradient Descend and Optimizers, which makes it the best thing available so far. To efficiently apply such optimizers, one should have the knowledge of mathematical computations and convolutions running behind the layers. It also uses different pooling layers to get the features. But these Modern Approaches need high level of computation which requires CPU and GPUs. In case, if, such high computational power, if hardware is not available then one can use Google Colaboratory framework. The Deep Learning Approach is proven to improve the skin cancer detection as demonstrated in this paper. The paper also aims to provide the circumstantial knowledge to the reader of various practices mentioned above.

scholarly journals Calibrated Forecasts of Extreme Windstorms Using the Extreme Forecast Index (EFI) and Shift of Tails (SOT)

2016 ◽  
Vol 31 (5) ◽  
pp. 1573-1589 ◽  
Author(s):  
Marie Boisserie ◽  
Laurent Descamps ◽  
Philippe Arbogast
Keyword(s):  
Extreme Events ◽  
Skill Score ◽  
Decision Makers ◽  
False Alarms ◽  
Conditional Probabilities ◽  
Severe Event ◽  
Heidke Skill Score ◽  
High Level ◽  
Different Levels ◽  
Selection Of

Abstract This study presents a method that improves extreme windstorm early warning in regards to past events that hit France during the last 30 years. From a 21-member ensemble forecast, the extreme forecast index (EFI) and the shift of tails (SOT) are used to produce calibrated forecasts for a selection of 59 windstorm cases. The EFI and SOT forecasts are evaluated for windstorms of different levels of severity and for various forecast index thresholds using the Heidke skill score (HSS), hit rate (HR), and false alarm rate (FA). The HR and FA show that a “zero misses” level always goes conjointly with a high level of false alarms. The HSS shows maxima that are associated with EFI (or SOT) thresholds that could be used as a rationale for decision-makers to issue warnings. For most extreme events, it is found that a higher level of HR can be achieved using the SOT rather than the EFI. Overall, most of the windstorms are well anticipated 3–4 days ahead. To facilitate the use of EFI or SOT forecasts, it is suggested that extra information in the form of conditional probabilities be added, hence linking the EFI (or SOT) values to a risk of occurrence of a severe event. Finally, this anticipation of extreme events is illustrated by maps of EFI and SOT for four historical windstorms.

scholarly journals Cloud Detection from FY-4A’s Geostationary Interferometric Infrared Sounder Using Machine Learning Approaches

2019 ◽  
Vol 11 (24) ◽  
pp. 3035 ◽  
Author(s):  
Qi Zhang ◽  
Yi Yu ◽  
Weimin Zhang ◽  
Tengling Luo ◽  
Xiang Wang
Keyword(s):  
Machine Learning ◽  
Detection Method ◽  
Spectral Characteristics ◽  
Skill Score ◽  
Hyperspectral Data ◽  
Learning Approaches ◽  
Cloud Detection ◽  
Good Ability ◽  
Heidke Skill Score ◽  
Meteorological Elements

FengYun-4A (FY-4A)’s Geostationary Interferometric Infrared Sounder (GIIRS) is the first hyperspectral infrared sounder on board a geostationary satellite, enabling the collection of infrared detection data with high temporal and spectral resolution. As clouds have complex spectral characteristics, and the retrieval of atmospheric profiles incorporating clouds is a significant problem, it is often necessary to undertake cloud detection before further processing procedures for cloud pixels when infrared hyperspectral data is entered into assimilation system. In this study, we proposed machine-learning-based cloud detection models using two kinds of GIIRS channel observation sets (689 channels and 38 channels) as features. Due to differences in surface cover and meteorological elements between land and sea, we chose logistic regression (lr) model for the land and extremely randomized tree (et) model for the sea respectively. Six hundred and eighty-nine channels models produced slightly higher performance (Heidke skill score (HSS) of 0.780 and false alarm rate (FAR) of 16.6% on land, HSS of 0.945 and FAR of 4.7% at sea) than 38 channels models (HSSof 0.741 and FAR of 17.7% on land, HSS of 0.912 and FAR of 7.1% at sea). By comparing visualized cloud detection results with the Himawari-8 Advanced Himawari Imager (AHI) cloud images, the proposed method has a good ability to identify clouds under circumstances such as typhoons, snow covered land, and bright broken clouds. In addition, compared with the collocated Advanced Geosynchronous Radiation Imager (AGRI)-GIIRS cloud detection method, the machine learning cloud detection method has a significant advantage in time cost. This method is not effective for the detection of partially cloudy GIIRS’s field of views, and there are limitations in the scope of spatial application.

scholarly journals Machine Learning and Security Applications in Digital Library

2019 ◽  
Vol 9 (1) ◽  
pp. 2165-2168
Keyword(s):  
Machine Learning ◽  
Digital Library ◽  
Web Of Science ◽  
Citation Network ◽  
Review Of Literature ◽  
Security Applications ◽  
Citation Network Analysis ◽  
Machine Learning Applications ◽  
Learning Machine ◽  
Applications Of Machine Learning

This paper presents the applications of machine learning machine learning applications in the digital library. Using machine learning it is possible to search and retrieve non-textual information. The paper also discusses the machine learning applications in security aspects. A systematic review of literature is also done and with the help of citation mapping in Web of Science citation network analysis is presented

scholarly journals A Brief Review on Spiking Neural Network - A Biological Inspiration

2021 ◽  
Author(s):  
Taki Hasan Rafi
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Neural Networks ◽  
Deep Learning ◽  
Mathematical Structure ◽  
Spiking Neural Networks ◽  
Spiking Neural Network ◽  
Current Flows ◽  
Learning Machine ◽  
High Level

Recent advancement of deep learning has been elevated the multifaceted nature in various applications of this field. Artificial neural networks are now turning into a genuinely old procedure in the vast area of computer science; the principal thoughts and models are more than fifty years of age. However, in this modern computing era, 3rd generation intelligent models are introduced by scientists. In the biological neuron, actual film channels control the progression of particles over the layer by opening and shutting in light of voltage changes because of inborn current flows and remotely led to signals. A comprehensive 3rd generation, Spiking Neural Network (SNN) is diminishing the distance between deep learning, machine learning, and neuroscience in a biologically-inspired manner. It also connects neuroscience and machine learning to establish high-level efficient computing. Spiking Neural Networks initiate utilizing spikes, which are discrete functions that happen at focuses as expected, as opposed to constant values. This paper is a review of the biological-inspired spiking neural network and its applications in different areas. The author aims to present a brief introduction to SNN, which incorporates the mathematical structure, applications, and implementation of SNN. This paper also represents an overview of machine learning, deep learning, and reinforcement learning. This review paper can help advanced artificial intelligence researchers to get a compact brief intuition of spiking neural networks.

scholarly journals Volcanic SO<sub>2</sub> Effective Layer Height Retrieval for OMI Using a Machine Learning Approach

2020 ◽  
Author(s):  
Nikita M. Fedkin ◽  
Can Li ◽  
Nickolay A. Krotkov ◽  
Pascal Hedelt ◽  
Diego G. Loyola ◽  
...  
Keyword(s):  
Machine Learning ◽  
Volcanic Eruptions ◽  
Volcanic Plume ◽  
Aerosol Layer ◽  
Ozone Monitoring Instrument ◽  
Layer Height ◽  
Machine Learning Applications ◽  
Satellite Sensors ◽  
Computationally Intensive ◽  
Learning Machine

Abstract. Information about the height and loading of sulfur dioxide (SO2) plumes from volcanic eruptions is crucial for aviation safety and for assessing the effect of sulfate aerosols on climate. While SO2 layer height has been successfully retrieved from backscattered Earthshine ultraviolet (UV) radiances measured by the Ozone Monitoring Instrument (OMI), previously demonstrated techniques are computationally intensive and not suitable for near real-time applications. In this study, we introduce a new OMI algorithm for fast retrievals of effective volcanic SO2 layer height. We apply the Full Physics Inverse Learning Machine (FP_ILM) algorithm to OMI radiances in the spectral range of 310–330 nm. This approach consists of a training phase that utilizes extensive radiative transfer calculations to generate a large dataset of synthetic radiance spectra for geophysical parameters representing the OMI measurement conditions. The principal components of the spectra from this dataset in addition to a few geophysical parameters are used to train a neural network to solve the inverse problem and predict the SO2 layer height. This is followed by applying the trained inverse model to real OMI measurements to retrieve the effective SO2 plume heights. The algorithm has been tested on several major eruptions during the OMI data record. The results for the 2008 Kasatochi, 2014 Kelud, 2015 Calbuco, and 2019 Raikoke eruption cases are presented here and compared with volcanic plume heights estimated with other satellite sensors. For the most part, OMI-retrieved effective SO2 heights agree well with the lidar measurements of aerosol layer height from Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) and thermal infrared retrievals of SO2 heights from the infrared atmospheric sounding interferometer (IASI). The errors in OMI retrieved SO2 heights are estimated to be 1–1.5 km for plumes with relatively large SO2 signals (> 40 DU). The algorithm is very fast and retrieves plume height in less than 10 min for an entire OMI orbit. This approach offers a promising prospect of using physics-based machine learning applications to other instruments.

BAND NN: A Deep Learning Framework For Energy Prediction and Geometry Optimization of Organic Small Molecules

2019 ◽  
Author(s):  
Siddhartha Laghuvarapu ◽  
Yashaswi Pathak ◽  
U. Deva Priyakumar
Keyword(s):  
Machine Learning ◽  
Density Functional ◽  
Computational Cost ◽  
Geometry Optimization ◽  
Dft Methods ◽  
Energy Prediction ◽  
Machine Learning Model ◽  
Equilibrium Structures ◽  
High Level ◽  
Non Equilibrium

Recent advances in artificial intelligence along with development of large datasets of energies calculated using quantum mechanical (QM)/density functional theory (DFT) methods have enabled prediction of accurate molecular energies at reasonably low computational cost. However, machine learning models that have been reported so far requires the atomic positions obtained from geometry optimizations using high level QM/DFT methods as input in order to predict the energies, and do not allow for geometry optimization. In this paper, a transferable and molecule-size independent machine learning model (BAND NN) based on a chemically intuitive representation inspired by molecular mechanics force fields is presented. The model predicts the atomization energies of equilibrium and non-equilibrium structures as sum of energy contributions from bonds (B), angles (A), nonbonds (N) and dihedrals (D) at remarkable accuracy. The robustness of the proposed model is further validated by calculations that span over the conformational, configurational and reaction space. The transferability of this model on systems larger than the ones in the dataset is demonstrated by performing calculations on select large molecules. Importantly, employing the BAND NN model, it is possible to perform geometry optimizations starting from non-equilibrium structures along with predicting their energies.

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