Machine learning in solar physics

Solar physics has entered the era of big data, and machine learning has gained more and more recognition as a good tool for big data research. This paper reviews the application results of machine learning in solar physics since 2007. Our studies have shown that research in this field has increased significantly during the last four years. Massive solar observation data obtained from various instruments on the ground and in space have been applied, and the topics have covered major aspects of solar physics, such as solar flares, coronal mass ejections, sunspots. Although some good results have emerged and proved that machine learning is suitable for data analysis of solar physics, there has not been a breakthrough yet. The machines learning methods that used in this field involve classification, regression, clustering, dimensionality reduction, and deep learning. However, classical algorithms, especially classical classification method is more popular. This means that the application of machine learning in solar physics is still in its infancy, but it also means that there is still a lot of work in this field that can be studied in the future.

Plants meet machines: Prospects in machine learning for plant biology

Machine learning in medical applications is one of the focus areas of the researchers these days. Machine Learning with the application of Artificial Intelligence is not only giving solutions to the complex problems but also revolutionised the medical field. The main motive of machine learning is to improve its learning process over time by taking all the relevant data and information in the form of different inputs and observations. This study reviews different medical disease prediction and detection techniques with the help of distinct deep learning & machine learning models. The problems related to medical diseases, like cancer related diseases, heart, lung, thyroid and kidney diseases are being discussed in this article. Detection and analysing of medical diseases is one of the prominent applications of machine and deep learning. Deep learning as a technology offers a huge set of different and innovative tools which are relevant to different issues faced in the field of medical image processing. This study will discuss about the applications of Machine Learning, and then discuss some of the advancements done in different diseases like breast cancer, heart disease, skin disease, kidney disease etc.

Solar flares are outbursts in the solar atmosphere resulting from sudden release of magnetic energy. The associated high energy particles and radiation threaten the safety of astronauts, reduce the lifetime of satellites, disturb the radio communications and degrade the precision of Global Positioning System. The radiation reaches the Earth about 8 min, and high energy particles take about 30 min to reach the earth after a solar flare. So solar flare forecasting is critical for providing enough time to respond to the space weather effects. Up to now, many statistical and machine learning methods are used to build a solar flare forecasting model. A machine learning based solar flare forecasting model normally requires solar physicists to design a feature extractor which can transform the observational images of active regions into physical features, and then the relationships between the features and the solar flares are discovered by the machine leaning algorithm. The priori knowledge of the solar physicists is added into the solar flare forecasting model by designing the feature extractor. For most of the machine learning methods, the hard part is what kinds of features should be extracted from the raw data. Considerable solar physicists spend a lot of time extracting the physical parameters from observational data of active regions. Deep learning method, which removes this manual step, can automatically discover useful patterns from the raw data and build a forecasting model. Instead of designing the feature extractor by solar physicists, we learn a solar flare forecasting model from magnetogram pixels by using deep learning method. We use Caffe, which is a deep learning framework developed by the Berkeley Vision and Learning Center, to build a convolutional neural network for solar flare forecasting. In order to compare the performance of proposed forecasting model with that of the forecasting model built by using traditional machine learning method, we build the other solar flare forecasting model based on the same dataset. In the traditional forecasting model, physical parameters designed by the solar physicist are extracted from the magnetogram of active regions, and then these parameters are fed to the forecasting model. We build a traditional forecasting model by multilayer neural networks. For convenience, the solar flare forecasting model built by using deep learning method is called deep model, and the solar flare forecasting model built by using the traditional multilayer neutral networks is called traditional model. Using the same testing data, the performances of the deep model and the traditional model are evaluated and compared. We find that the performance of the deep model is little better than that of the traditional model. The results confirm that the deep model can automatically learn solar flare forecasting features from magnetograms of active regions. This is our first time to automatically learn the forecasting patterns for solar flares from raw data instead of designing the physical patterns by solar physicists. The effectiveness of the deep learning method for the solar flare forecasting is validated. In the future, the deep learning method can be used to automatically discover the solar flare forecasting patterns from the vector magnetograms or the extreme ultraviolet images of active regions.

Machine learning (ML) applications face many new, hardly predictable aspects in their production environments. Detecting new aspects in an ML production environment and understanding their impacts on the ML application is crucial if organizations are to ensure ML applications functionality. A monitoring entity is essential if one is to monitor ML applications in their production environments, to both continually minimize risks and improve ML application’s performance. But existing monitoring approaches are struggling to deal with specifics that arise from ML applications. We aim at deriving monitoring practices and providing a holistic view over required steps in successful ML applications monitoring. Since there has been little research on this topic, we followed a qualitative research approach, i.e., we conducted an interview study combined with a multivocal literature review. Thus, we provide a theoretical framework of an ML-enabled agent in its production environment, five characteristics of ML applications’ production environments and 17 monitoring practices – 14 practices arranged sequentially on a typical quality management cycle and three cross-sectional practices. To outline the ML specifics that arise in monitoring ML applications, we investigate the five ML production environment characteristics’ influences on the ML monitoring practices.

With the rapid development of the Internet and the rise of e-commerce, commercial enterprises are faced with a large amount of data and a complex market environment. In this situation, machine learning, as a powerful tool, is widely used in the field of business analysis. In this dissertation, we take Amazon and eBay as examples to study the application of machine learning in the company's business analytics, focusing on its role in market prediction, customer behavior analysis and operation optimization. By analyzing the relevant cases, we find that machine learning technology plays an important role in helping companies make more accurate decisions and improve efficiency. Studying the application of Amazon machine learning in business analytics can promote in-depth research on the application of machine learning in business in academia, and promote the application and development of machine learning technology in other business scenarios. Overall, the application of machine learning in business analytics can help companies understand customer behavior, optimize operations, and improve sales results. However, there are still some challenges, such as data quality, algorithm selection and privacy protection. Therefore, further research and innovation are necessary to advance the development of machine learning applications in business analytics.

Machine learning (ML) techniques discover knowledge from large amounts of data. Modeling in ML is becoming essential to software systems in practice. The accuracy and efficiency of ML models have been focused on ML research communities, while there is less attention on validating the qualities of ML models. Validating ML applications is a challenging and time-consuming process for developers since prediction accuracy heavily relies on generated models. ML applications are written by relatively more data-driven programming based on the black box of ML frameworks. All of the datasets and the ML application need to be individually investigated. Thus, the ML validation tasks take a lot of time and effort. To address this limitation, we present a novel quality validation technique that increases the reliability for ML models and applications, called MLVal. Our approach helps developers inspect the training data and the generated features for the ML model. A data validation technique is important and beneficial to software quality since the quality of the input data affects speed and accuracy for training and inference. Inspired by software debugging/validation for reproducing the potential reported bugs, MLVal takes as input an ML application and its training datasets to build the ML models, helping ML application developers easily reproduce and understand anomalies in the ML application. We have implemented an Eclipse plugin for MLVal that allows developers to validate the prediction behavior of their ML applications, the ML model, and the training data on the Eclipse IDE. In our evaluation, we used 23,500 documents in the bioengineering research domain. We assessed the ability of the MLVal validation technique to effectively help ML application developers: (1) investigate the connection between the produced features and the labels in the training model, and (2) detect errors early to secure the quality of models from better data. Our approach reduces the cost of engineering efforts to validate problems, improving data-centric workflows of the ML application development.

Machine-Learning Implementation in Clinical Anesthesia: Opportunities and Challenges.

Identification of genomic signals as indicators for functional genomic elements is one of the areas that received early and widespread application of machine learning methods. With time, the methods applied grew in variety and generally exhibited a tendency to improve their ability to identify some major genomic and transcriptomics signals. The evolution of machine learning in genomics followed a similar path to applications of machine learning in other fields. These were impacted in a major way by three dominant developments, namely an enormous increase in availability and quality of data, a significant increase in computational power available to machine learning applications, and finally, new machine learning paradigms, of which deep learning is the most well-known example. It is not easy in general to distinguish factors leading to improvements in results of applications of machine learning. This is even more so in the field of genomics, where the advent of next-generation sequencing and the increased ability to perform functional analysis of raw data have had a major effect on the applicability of machine learning in OMICS fields. In this paper, we survey the results from a subset of published work in application of machine learning in the recognition of genomic signals and regions in human genome and summarize some lessons learnt from this endeavor. There is no doubt that a significant progress has been made both in terms of accuracy and reliability of models. Questions remain however whether the progress has been sufficient and what these developments bring to the field of genomics in general and human genomics in particular. Improving usability, interpretability and accuracy of models remains an important open challenge for current and future research in application of machine learning and more generally of artificial intelligence methods in genomics.

EDITORIAL article Front. Astron. Space Sci., 16 March 2023Sec. Astrostatistics Volume 10 - 2023 | https://doi.org/10.3389/fspas.2023.1163530

Advances and applications of machine learning and deep learning in environmental ecology and health

Accurate prediction of the fatigue behaviour of materials is crucial for ensuring the reliability and durability of structural components in various engineering applications. Machine learning (ML) techniques have demonstrated significant potential in predicting fatigue behaviour by analysing complex datasets. This research paper explores the application of deep learning, a subset of ML, for predicting the fatigue behaviour of materials. The study focuses on the development and optimization of deep learning models to accurately predict fatigue life and failure modes based on material properties, loading conditions, and other relevant factors. The research aims to improve the understanding and prediction of fatigue behaviour, leading to enhanced design and optimization of materials and structures.
 The prediction of fatigue behaviour in materials is a critical aspect in engineering design and structural integrity assessment. Traditional approaches rely on empirical models and physical testing, which can be time-consuming and resource-intensive. In recent years, the application of machine learning, particularly deep learning techniques, has shown promising results in predicting the fatigue behaviour of materials. This paper presents an analysis of the application of machine learning, specifically deep learning, in predicting the fatigue behaviour of materials. The study focuses on the use of deep learning models, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), to analyse the complex relationships between material properties, loading conditions, and fatigue life. The paper discusses the methodology for training and validating the deep learning models using available fatigue data sets. Furthermore, it examines the performance and accuracy of the models in predicting fatigue life compared to traditional approaches. The findings suggest that deep learning models can effectively capture the nonlinear and intricate patterns in fatigue data, leading to accurate predictions of fatigue life. The practical implications of integrating machine learning into fatigue prediction are discussed, including the potential for accelerated design optimization, reduced testing requirements, and enhanced structural reliability. The contribution of this study lies in the exploration and evaluation of deep learning techniques for predicting the fatigue behaviour of materials, providing insights into the capabilities and limitations of machine learning approaches in this domain. Machine learning, particularly deep learning, as a valuable tool in predicting the fatigue behaviour of materials, enabling more efficient and reliable engineering design processes.

The purpose of the study was to provide a comprehensive review of recent machine learning (ML) and deep learning (DL) applications in urological practice. Numerous studies have reported their use in the medical care of various urological disorders; however, no critical analysis has been made to date. A detailed search of original articles was performed using the PubMed MEDLINE database to identify recent English literature relevant to ML and DL applications in the fields of urolithiasis, renal cell carcinoma (RCC), bladder cancer (BCa), and prostate cancer (PCa). In total, 43 articles were included addressing these four subfields. The most common ML and DL application in urolithiasis is in the prediction of endourologic surgical outcomes. The main area of research involving ML and DL in RCC concerns the differentiation between benign and malignant small renal masses, Fuhrman nuclear grade prediction, and gene expression-based molecular signatures. BCa studies employ radiomics and texture feature analysis for the distinction between low- and high-grade tumors, address accurate image-based cytology, and use algorithms to predict treatment response, tumor recurrence, and patient survival. PCa studies aim at developing algorithms for Gleason score prediction, MRI computer-aided diagnosis, and surgical outcomes and biochemical recurrence prediction. Studies consistently found the superiority of these methods over traditional statistical methods. The continuous incorporation of clinical data, further ML and DL algorithm retraining, and generalizability of models will augment the prediction accuracy and enhance individualized medicine.

Recent breakthroughs in Machine Learning (ML) applications, and especially in Deep Learning (DL), have made DL models a key component in almost every modern computing system. The increased popularity of DL applications deployed on a wide-spectrum of platforms (from mobile devices to datacenters) have resulted in a plethora of design challenges related to the constraints introduced by the hardware itself. "What is the latency or energy cost for an inference made by a Deep Neural Network (DNN)?" "Is it possible to predict this latency or energy consumption before a model is even trained?" "If yes, how can machine learners take advantage of these models to design the hardware-optimal DNN for deployment?" From lengthening battery life of mobile devices to reducing the runtime requirements of DL models executing in the cloud, the answers to these questions have drawn significant attention. One cannot optimize what isn't properly modeled. Therefore, it is important to understand the hardware efficiency of DL models during serving for making an inference, before even training the model. This key observation has motivated the use of predictive models to capture the hardware performance or energy efficiency of ML applications. Furthermore, ML practitioners are currently challenged with the task of designing the DNN model, i.e., of tuning the hyper-parameters of the DNN architecture, while optimizing for both accuracy of the DL model and its hardware efficiency. Therefore, state-of-the-art methodologies have proposed hardware-aware hyper-parameter optimization techniques. In this paper, we provide a comprehensive assessment of state-of-the-art work and selected results on the hardware-aware modeling and optimization for ML applications. We also highlight several open questions that are poised to give rise to novel hardware-aware designs in the next few years, as DL applications continue to significantly impact associated hardware systems and platforms.

The domain of space weather (SW) encompasses various processes from the Sun's interior, through the active Sun, to planetary and Earth systems, and can also impact the Earth's surface and interior.The most energetic explosive events on the solar surface are solar flares, which are characterized by intense electromagnetic emission and often followed by particle emissions, termed coronal mass ejections (CME) during which solar material, such as electrons and solar plasma, is ejected throughout the solar corona into interplanetary space.Solar flare forecasting is an important task in the context of space weather research, as it faces open problems in solar physics and in space weather forecasting [5].Although it is well-established that solar flares are a consequence of reconnection and reconfiguration of magnetic field lines high in the solar corona, yet there is still no agreement about the physical model that better explains the sudden magnetic energy release and the resulting acceleration mechanisms [6].Further, solar flares are the main trigger of the whole space weather connections and it is still a challenging forecasting issue predicting them.In the last decades, machine and deep learning reached a key role in solar flare forecasting due to both the availability of a plethora of data acquired by many telescopes and the numerous recent developments in machine/deep learning.Flare forecasting may rely on features extracted from magnetogram images of Active Regions (ARs) e.g. the ones recorded by the Helioseismic and Magnetic Imager (HMI) on-board the Solar Dynamics Observatory (SDO).Such features, representing physical parameters, can be previously extracted from HMI images, or the most recent trend is to automatically apply deep learning techniques as Convolutional-type Neural Networks on HMI images without the need of a priori feature extraction.However, when machine and deep learning methods were adopted there are mainly three technical aspects that may have significant impacts on the forecasting effectiveness.First, a reliable validation strategy have to be defined to appropriately train and test the machine and deep learning method; second the optimization of the network parameters depends on an appropriate choice of the loss function in the training phase; finally, the assessment of the prediction power of the algorithm should account for the dynamical nature of the flare forecasting problem.This talk shows a validation strategy based on the construction of uniformly distributed training, validation and test sets [1]; then it shows that score-oriented loss (SOL) functions allow an optimization of the network that automatically accounts for the kind of skill score used for the prediction assessment [4]; finally, the talk introduces value-weighted skill scores for flare forecasting that take into account the temporal distribution of the predictions with respect to the actual occurrences [2].The results are shown when a video-based deep learning method is adopted [1,3].