Efficient non-ergodic ground-motion prediction for large datasets

Simulation of a non-Gaussian stochastic process based on a combined distribution of the UHPM and the GBD

Pressure gradient (PG) in liquid-liquid flow is one of the key components to design an energy-efficient transportation system for wellbores. This study aims to develop five robust machine learning (ML) algorithms and their fusions for a wide range of flow patterns (FP) regimes. The MLs include Support Vector Machine (SVM), Gaussian Process (GP), Random Forest (RF), Artificial Neural Network (ANN), k-Nearest Neighbor (kNN), and fusions of these five MLs. A total of eleven hundred experimental data points for nine FPs (two stratified and seven dispersed patterns) in horizontal wellbores are used to develop the MLs. The MLs' performance is evaluated using the metrics including mean absolute percentage error (MAPE), median absolute percentage error (MdAPE), coefficient of variation of root mean squared error (CV-RMSE), and adjusted coefficient of determination. The evaluation metrics are cross-validated using a repeated train-test split strategy. Seven important predictor variables are identified using a supervised feature selection approach: oil and water velocities, FP, input diameter, oil and water density, and oil viscosity. The results show that the high PG prediction accuracy can be achieved using GP compared to other MLs except for the ML-fusions (p < 0.05). A Friedman's test and Wilcoxon Sign-Rank post hoc analysis with Bonferroni correction show that PG prediction errors using GP are significantly lower than using the ANN model (p < 0.05). The values are 18.44 % and 23.9 % for CV-RMSE, 11.6 % and 10.06 % for MAPE, and 7.5 % and 6.75 % for MdAPE, using ANN and GP, respectively. While the previous studies mostly used ANN to demonstrate the capability of MLs to predict PG over the mechanistic or correlation-based models, the present research has shown that GP is even better than ANN using a wide range of FPs and a large data set. • Machine-learning algorithms are developed to predict the pressure gradient. • The Gaussian Process model produces significantly lower prediction error. • The median absolute percentage error using Gaussian Process is 6.75 %. • More than 95 % variation of pressure gradient can be explained using seven variables.

Physics-constrained machine learning is emerging as an important topic in the field of machine learning for physics. One of the most significant advantages of incorporating physics constraints into machine learning methods is that the resulting model requires significantly less data to train. By incorporating physical rules into the machine learning formulation itself, the predictions are expected to be physically plausible. Gaussian process (GP) is perhaps one of the most common methods in machine learning for small datasets. In this paper, we investigate the possibility of constraining a GP formulation with monotonicity on three different material datasets, where one experimental and two computational datasets are used. The monotonic GP is compared against the regular GP, where a significant reduction in the posterior variance is observed. The monotonic GP is strictly monotonic in the interpolation regime, but in the extrapolation regime, the monotonic effect starts fading away as one goes beyond the training dataset. Imposing monotonicity on the GP comes at a small accuracy cost, compared to the regular GP. The monotonic GP is perhaps most useful in applications where data are scarce and noisy, and monotonicity is supported by strong physical evidence.

Continual learning in medical devices: FDA's action plan and beyond

Comprehensive evaluation and systematic comparison of Gaussian process (GP) modelling applications in peptide quantitative structure-activity relationship

Communication is a crucial element of every health care profession, rendering communication skills training in all health care professions as being of great importance. Technological advances such as artificial intelligence (AI) and particularly machine learning (ML) may support this cause: it may provide students with an opportunity for easily accessible and readily available communication training. This scoping review aimed to summarize the status quo regarding the use of AI or ML in the acquisition of communication skills in academic health care professions. We conducted a comprehensive literature search across the PubMed, Scopus, Cochrane Library, Web of Science Core Collection, and CINAHL databases to identify articles that covered the use of AI or ML in communication skills training of undergraduate students pursuing health care profession education. Using an inductive approach, the included studies were organized into distinct categories. The specific characteristics of the studies, methods and techniques used by AI or ML applications, and main outcomes of the studies were evaluated. Furthermore, supporting and hindering factors in the use of AI and ML for communication skills training of health care professionals were outlined. The titles and abstracts of 385 studies were identified, of which 29 (7.5%) underwent full-text review. Of the 29 studies, based on the inclusion and exclusion criteria, 12 (3.1%) were included. The studies were organized into 3 distinct categories: studies using AI and ML for text analysis and information extraction, studies using AI and ML and virtual reality, and studies using AI and ML and the simulation of virtual patients, each within the academic training of the communication skills of health care professionals. Within these thematic domains, AI was also used for the provision of feedback. The motivation of the involved agents played a major role in the implementation process. Reported barriers to the use of AI and ML in communication skills training revolved around the lack of authenticity and limited natural flow of language exhibited by the AI- and ML-based virtual patient systems. Furthermore, the use of educational AI- and ML-based systems in communication skills training for health care professionals is currently limited to only a few cases, topics, and clinical domains. The use of AI and ML in communication skills training for health care professionals is clearly a growing and promising field with a potential to render training more cost-effective and less time-consuming. Furthermore, it may serve learners as an individualized and readily available exercise method. However, in most cases, the outlined applications and technical solutions are limited in terms of access, possible scenarios, the natural flow of a conversation, and authenticity. These issues still stand in the way of any widespread implementation ambitions.

Simulation-based optimization of large-scale dedicated bus lanes allocation: Using efficient machine learning models as surrogates

Ground-motion prediction equations (GMPEs) are essential tools in seismic hazard studies to estimate ground motions generated by potential seismic sources. Global GMPEs which are based on well-compiled global strong-motion databanks, have certain advantages over local GMPEs, including more sophisticated parameters in terms of distance, faulting style, and site classification but cannot guarantee the local/region-specific propagation characteristics of shear wave (e.g., geometric spreading behavior, quality factor) for different seismic regions at larger distances (beyond about 80 km). Here, strong-motion records of northern Iran have been used to estimate the propagation characteristics of shear wave and determine the region-specific adjustment parameters for three of the NGA-West2 GMPEs to be applicable in northern Iran. The dataset consists of 260 three-component records from 28 earthquakes, recorded at 139 stations, with moment magnitudes between 4.9 and 7.4, horizontal distance to the surface projection of the rupture (R JB) less than 200 km, and average shear-wave velocity over the top 30 m of the subsurface (V S30) between 155 and 1500 m/s. The paper also presents the ranking results for three of the NGA-West2 GMPEs against strong motions recorded in northern Iran, before and after adjustment for region-dependent attenuation characteristics. The ranking is based on the likelihood and log-likelihood methods (LH and LLH) proposed by Scherbaum et al. (Bull Seismol Soc Am 94: 2164–2185, 2004, Bull Seismol Soc Am 99, 3234–3247, 2009, respectively), the Nash–Sutcliffe model efficiency coefficient (Nash and Sutcliffe, J Hydrol 10:282–290, 1970), and the EDR method of Kale and Akkar (Bull Seismol Soc Am 103:1069–1084, 2012). The best-fitting models over the whole frequency range are the ASK14 and BSSA14 models. Taking into account that the models’ performances were boosted after applying the adjustment factors, at least moderate regional variation of ground motions is highlighted. The regional adjustment based on the Iranian database reveals an upward trend (indicated as high Q factor) for the selected database. Further investigation to determine adjustment factors based on a much richer database of the Iranian strong-motion records is of utmost important for seismic hazard and risk analysis studies in northern Iran, containing major cities including the capital city of Tehran.

In recent years, artificial intelligence has been widely used in the field of meteorology. However, it is perhaps less known that AI-based tools have been used for over 30 years in the modeling of pollutant dispersion in the atmosphere.In the early 1990s, Slovenia and other parts of Europe faced significant air pollution problems caused by SO₂ emissions from coal-fired power plants without desulfurization systems. Due to the highly complex terrain, simple physical Gaussian dispersion models were not suitable for reconstructing the dispersion of SO₂ in the atmosphere, while complex numerical Lagrangian particle dispersion models were still in development.Therefore, as early as 1992, we modeled air pollution using a Multilayer Perceptron Artificial Neural Network (MLPANN). We developed the world's first comprehensive method for selecting training samples, features, and the topology of the neural network, enabling the model to learn from measured meteorological variables and pollutant concentrations around the power plant to predict SO₂ concentrations at a selected location in advance.This type of artificial neural network remains the foundation for various derived structures used for machine learning from data even today.In the following decades, we expanded the use of MLPANN to ozone prediction. Researchers from other Slovenian research groups also worked on this topic. Meanwhile, the use of these and similar tools in pollutant dispersion modelling began to grow on a global scale.Together with Brazilian researchers, we successfully applied MLPANN for predicting diffuse solar radiation. In this field, we also managed to develop a spatially transferable model, which is not a common capability for artificial neural network-based models.For the classification of wind fields based on measurements from ground-based meteorological stations, we used a Kohonen neural network.Later, we added another tool called "Gaussian processes" (which, like MLPANN, is a universal approximator) and the tool "decision trees." We expanded the use to point-based forecasting of basic meteorological variables. In recent years, with high computational capabilities available even without supercomputers, we have been using these tools for surrogate models that can represent or predict pollutant concentration fields in the atmosphere, not only for individual points but for entire areas around pollution sources.Other Slovenian groups from Slovenian Meteorological Agency, the Department of Meteorology, and the Faculty of Computer Science have, in recent years, used machine learning for global medium-range forecasts of daily averages of meteorological variables and for post-processing weather forecasts from physical models.In the presentation, we will explain the basic principles of building models based on artificial neural networks in a way that is understandable to laypeople. For experts in the field, we will showcase numerous examples of their application. For the latter group, we hope these examples will inspire others to expand the use of these modern tools even further.

Artificial intelligence and Machine Learning approaches in sports: Concepts, applications, challenges, and future perspectives

Artificial intelligence for patient scheduling in the real-world health care setting: A metanarrative review

Manufacturing industries are constantly forced by market demands to produce low-cost, high-quality products. Market demands are complex and variable, especially in fast-paced industrial environments. Traditional manufacturing procedures are being compelled to create and adapt innovative practices and methods in order to meet increasing market demands. However, as manufacturing technology advances, so does the need to put it into practice in a timely and cost-effective manner. A combination of old production tools and modern technologies, either hardware or software, that transforms traditional manufacturing into intelligent manufacturing is the result of this urgent demand. Intelligent manufacturing systems combine optimization approaches with sensor-based control systems to create complex systems. These intelligent and optimized systems can produce high-quality items at a lesser cost and at a faster rate. New advancements in machining have been possible by latest developments in information and communications technologies, particularly artificial intelligence. Modern machining systems are very sophisticated and automated, but their performance is mostly dependent on the operators’ knowledge, which is based on a combination of theory and experience. With the integration of simulation, sensing, modelling, control, and monitoring of the process, intelligent machining attempts to enable intelligent behavior in the machining system. Machine learning and intelligent prediction of machining system behavior are possible due to the recording of process input and output data. This chapter provides an introduction to intelligent machining. The various components of intelligent machining like Sensors, Machine learning and knowledge discovery, Database knowledge discovery, Programmable Logical controller (PLC), Information integration via knowledge graphs are presented. In starting, requirements for the developments of intelligent machining and subdivision of intelligent machining with details of various types of sensors with their features and function are presented; afterwards the role of machine learning and how it is important in intelligent machining, the role and importance of knowledge database are briefly mentioned. Finally, history and use and advantages of PLC, role of intelligent machining for implementation of green manufacturing in manufacturing industries is described.

PurposeThe first transmission of coronavirus to humans started in Wuhan city of China, took the shape of a pandemic called Corona Virus Disease 2019 (COVID-19), and posed a principal threat to the entire world. The researchers are trying to inculcate artificial intelligence (Machine learning or deep learning models) for the efficient detection of COVID-19. This research explores all the existing machine learning (ML) or deep learning (DL) models, used for COVID-19 detection which may help the researcher to explore in different directions. The main purpose of this review article is to present a compact overview of the application of artificial intelligence to the research experts, helping them to explore the future scopes of improvement.MethodsThe researchers have used various machine learning, deep learning, and a combination of machine and deep learning models for extracting significant features and classifying various health conditions in COVID-19 patients. For this purpose, the researchers have utilized different image modalities such as CT-Scan, X-Ray, etc. This study has collected over 200 research papers from various repositories like Google Scholar, PubMed, Web of Science, etc. These research papers were passed through various levels of scrutiny and finally, 50 research articles were selected.ResultsIn those listed articles, the ML / DL models showed an accuracy of 99% and above while performing the classification of COVID-19. This study has also presented various clinical applications of various research. This study specifies the importance of various machine and deep learning models in the field of medical diagnosis and research.ConclusionIn conclusion, it is evident that ML/DL models have made significant progress in recent years, but there are still limitations that need to be addressed. Overfitting is one such limitation that can lead to incorrect predictions and overburdening of the models. The research community must continue to work towards finding ways to overcome these limitations and make machine and deep learning models even more effective and efficient. Through this ongoing research and development, we can expect even greater advances in the future.

Earth observation from satellite sensory data pose challenging problems, where machine learning is currently a key player. In recent years, Gaussian Process (GP) regression and other kernel methods have excelled in biophysical parameter estimation tasks from space. GP regression is based on solid Bayesian statistics, and generally yield efficient and accurate parameter estimates. However, GPs are typically used for inverse modeling based on concurrent observations and in situ measurements only. Very often a forward model encoding the well-understood physical relations is available though. In this work, we review three GP models that respect and learn the physics of the underlying processes in the context of inverse modeling. First, we will introduce a Joint GP (JGP) model that combines in situ measurements and simulated data in a single GP model. Second, we present a latent force model (LFM) for GP modeling that encodes ordinary differential equations to blend data-driven modeling and physical models of the system. The LFM performs multi-output regression, adapts to the signal characteristics, is able to cope with missing data in the time series, and provides explicit latent functions that allow system analysis and evaluation. Finally, we present an Automatic Gaussian Process Emulator (AGAPE) that approximates the forward physical model via interpolation, reducing the number of necessary nodes. Empirical evidence of the performance of these models will be presented through illustrative examples of vegetation monitoring and atmospheric modeling.

The global healthcare ecosystem is being changed thanks to substantial advancements in the fields of machine learning (ML) and artificial intelligence (AI). The potential of ML and AI to improve patient care, increase diagnostic accuracy, optimize treatment regimens, and reduce administrative procedures is covered in this chapter as we investigate the many methods and uses of ML and AI within the healthcare industry. ML and AI have the potential to change healthcare delivery, resource allocation, and disease prevention through the use of large-scale data analysis, predictive modeling, and intelligent decision-making systems. This chapter presents a thorough review of the existing ML and AI methods used in healthcare, emphasizing their advantages, difficulties, and potential future applications. The global healthcare ecosystem might be changed by the introduction of ML and AI, resulting in improved patient outcomes. ML and AI can help expand access to healthcare by enabling remote diagnosis and telemedicine, especially in underserved areas. This aligns with the goal of ensuring healthy lives and promoting well-being for all ages.